An Architecture for Distributed Content Delivery Network
A minor thesis submitted in partial fulfilment of the requirements for the degree of Masters of Applied Science (Information Technology)
Jaison Paul Mulerikkal
School of Computer Science and Information Technology Science, Engineering, and Technology Portfolio,
Royal Melbourne Institute of Technology Melbourne, Victoria, Australia
Declaration
This thesis contains work that has not been submitted previously, in whole or in part, for any other academic award and is solely my original research, except where acknowledged.
This work has been carried out since January 2007, under the supervision of Dr.Ibrahim Khalil.
Jaison Paul Mulerikkal
School of Computer Science and Information Technology Royal Melbourne Institute of Technology
Acknowledgment
I would like to thank Dr. Ibrahim Khalil, for his continuous support and guidance throughout the course of this minor thesis. It is his constant inspiration and encouragement that helped me to complete this task, successfully. I specially thank him for his painstaking efforts in proof reading the drafts of this work.
Contents
1 Introduction 3
2 Background 9
2.1 CDN Main Concepts . . . 9
2.1.1 Surrogate Servers . . . 9
2.1.2 DNS Lookup and Redirection . . . 9
2.1.3 DNS Load Balancing . . . 10
2.1.4 Replication . . . 11
2.1.5 Selection of Content . . . 11
2.1.6 Cached Delivery . . . 11
2.1.7 Outsourcing Content . . . 12
2.1.8 Accounting and Billing Mechanism . . . 12
2.2 Conventional CDN Architectures . . . 13
2.2.1 Commercial (Client-Server) Architecture . . . 13
2.2.2 Academic (Peer-to-Peer) Architecture . . . 14
2.2.3 Limitations of Existing CDN Architectures . . . 15
2.3 Distributed Content Delivery Network - An Effective Alternative . . . 16
3 Architecture - Distributed Content Delivery Network 17 3.1 DCDN Framework . . . 17
3.1.1 Distribution of Content - The Process . . . 19
3.1.2 Content Delivery to a User . . . 21
3.2 DCDN Design Challenges . . . 25
3.2.1 Security . . . 25
3.2.2 Effective Redirection and Load-balancing Algorithm . . . 26
3.2.3 Billing and SLA (Service Level Agreement) Verification Software . . . 27
3.3 Business Model . . . 27
3.3.1 Network Marketing (NM)/ Multi Level Marketing (MLM) . . . 28
3.3.2 Special Scenarios of DCDN Advantage . . . 30
4 Performance Analysis and Load Balancing Algorithm 31 4.1 Performance Parameters and Assumptions . . . 31
4.2 Queuing Metrics . . . 32
4.3 Queuing Theory Modeling for Different Scenarios . . . 33
4.4 Load Balancing Algorithm for DCDN Servers . . . 34
5 Simulations and Results 38 5.1 Goals . . . 38
5.2 Assumptions . . . 39
5.3 Overview of Simulation Setup . . . 39
5.4 Simulation Results . . . 41
5.4.1 Page Response Time . . . 41
5.4.2 DCDN Surrogate - CPU Utilization vs. CDN Server - CPU Utilization 43 5.4.3 DCDN Server - CPU Utilization vs. CDN Load Balancer - CPU Uti-lization . . . 43
6 Conclusion and Future work 46 6.1 Future Work . . . 47 A Softwares Used 48 B Abbreviations 49 C Symbols 50 D Simulation Snapshots 51
List of Figures
1.1 CDNs and Web Content Distribution . . . 4
3.1 DCDN Content Distribution Architecture . . . 18
3.2 DCDN Content Delivery . . . 20
3.3 DCDN Basic Transition Diagram . . . 22
3.4 DCDN Transition Diagram - Including Contingency Plans . . . 23
3.5 Local DCDN Server Zones - Contingency Plan . . . 24
3.6 DCDN Transition Diagram - Including Security Solutions . . . 26
3.7 Pyramid Scheme . . . 28
3.8 MLM Architecture . . . 29
4.1 Utilization v/s Total System Delay . . . 34
4.2 Utilization v/s Rejection Rate . . . 35
5.1 Page Response Time . . . 42
5.2 DCDN Surrogate(Server) Utilization . . . 43
5.3 DCDN Server (Load Balancer) Utilization . . . 44
D.1 Simulation Snapshot - CDN . . . 52
D.2 Simulation Snapshot - DCDN . . . 53
D.3 Application Configuration . . . 54
List of Tables
1.1 Commercial Content Delivery Networks . . . 5 1.2 Academic Content Delivery Networks . . . 6 5.1 Simulation Setup . . . 40
Abstract
Commercial Content Delivery Networks create their own network of servers around the globe to effectively deliver Web content to the end-users. The peering of Content Delivery Networks (CDN) increase the efficiency of commercial CDNs. But still the high rental rates resulting from huge infrastructure cost make it inaccessible to medium and low profile clients . Aca-demic models of peer-to-peer CDNs aim to reduce the financial cost of content distribution by forming volunteer group of servers around the globe. But their efficiency is at the mercy of the volunteer peers whose commitment is not ensured in their design. We propose a new architecture that will make use of the existing resources of common Internet users in terms of storage space, bandwidth and Internet connectivity to create a Distributed Content Deliv-ery Network (DCDN). The profit pool generated by the infrastructure savings will be shared among the participating nodes (DCDN surrogates) which will function as an incentive for them to support DCDN. Since the system uses the limited computing resources of common Internet users, we also propose a suitable load balancing (LB) algorithm so that DCDN surro-gates are not burdened with heavy load and requests are fairly assigned to them. Simulations have been carried out and the results show that the proposed architecture (with LB) can offer same or even better performance as that of commercial CDN.
Chapter 1
Introduction
The growth of the World Wide Web and new modes of Web services have triggered an exponential increase in Web content and Internet traffic [Molina et al., 2004; Vakali and Pallis, 2003; Presti et al., 2005]. The Web content consists of static content (e.g. Static HTML pages, images, documents, software patches), streaming media (e.g. audio, real time video) and varying content services (e.g. directory service, e-commerce service, file transfer service) [R. Buyya and Tari, 2001]. As the Web content and the Internet traffic increases, individual Web servers find it difficult to cater to the needs of end-users. In order to store and serve huge quantities of Web content, Web server farms - a cluster of Web servers functioning as a single unit - are introduced [Burns et al., 2001].
Even those Web server farms find it difficult to deal with flash crowds - large number of simul-taneous requests for a popular content - that are frequently experienced in Web traffic [Pan et al., 2004]. Moreover, those server farms are geographically distant from the end-users in most of the cases. The non-proximity of the Web servers to the end-users badly affect the response time of the Web requests, resulting in undesirable delays [Pan et al., 2004].
Replication of same Web content around the globe in a net of Web servers is a solution to the above issue. However, it is not financially viable for individual content providers to set up their own server networks. An answer to this challenge is the concept of Content Delivery Network (CDN) that was initiated in 1998 [Douglis and Kaashoek, 2001; Vakali and Pallis, 2003].
The basic idea is to improve the performance and scalability of content retrieval by geograph-ically distributing a network of Web servers around the globe and allowing several content providers to host their content in those servers. . It allows a number of content providers to
Public Internet ISP ISP ISP Internet Backbone CDN Node CDN Node CDN Node Web Server (Content Provider)
Figure 1.1: CDNs and Web Content Distribution
upload their Web content into the same network of Web servers (also called, CDN servers) and thereby to reduce the cost of content replication and distribution.
In a typical CDN environment(Figure 1.1), the replicated Web server clusters are located at the edge of the network to which the end-users are connected. The end-users interact with the CDN specifying the content-service request through cell phone, PDA, laptop, desktop etc. The Web content based on user requests are fetched from the origin server and a user is served with the content from the nearby replicated Web server. Thus the users end up communicating with a replicated CDN server close to them and retrieve files from that server. From the very inception of the concept, CDN has gone through dramatic evolution. There are a number of CDNs available around the globe Douglis and Kaashoek [2001]; Vakali and Pallis [2003]; Pathan [2007] and are collectively called as Conventional CDN architectures in this minor thesis. They can be mainly classified into two:
1. Commercial CDNs 2. Academic CDNs
The Commercial networks are owned by corporate companies and generally follow central-ized client-server architecture. Some of them have more than 20,000 servers around the globe
Name Description
Akamai Founded in 1998 at Massachusetts, USA, Akamai is considered to be the pioneer in CDN business. It has reported a net income of 283.115 million USD in 2005.
Mirror Image Web, Inc Founded in 1999 at Massachusetts, USA. Besides con-tent distribution, streaming and concon-tent access ser-vices are provided.
Local Mirror It is a U.S.-based privately held corporation that of-fers Content Delivery Network service incorporated in 2005. It is a provider for static content, audio, video streaming and distribution.
Limelight Networks Founded in 2001 in Tempe, Arizona, USA Limelight Network provides a network for bandwidth-intensive rich media applications over Web.
Table 1.1: Commercial Content Delivery Networks
to support their network. A list of prominent commercial CDN providers are given in Ta-ble 1.1 [Pathan, 2007].
The academic CDNs are non-profitable in nature and generally follow peer-to-peer archi-tecture. These peer-to-peer Content Delivery Network models allow content providers to organize themselves together and to operate within their own hosting platforms. Some of the important academic CDN providers are given in Table 1.2 [Pathan, 2007].
Conventional CDN architectures - Commercial CDN and Academic CDN - have got their own advantages. But their marjor pitfalls are:
• High rental rates of commercial CDN services resulting from huge infrastructural cost.
• Efficiency of academic CDNs is at the mercy of the volunteer peers whose commitment is not ensured in their design.
The huge financial cost involved in setting up a commercial CDN compels the commercial CDN providers to charge high remuneration for their service from their clients (the content providers). Usually this cost is so high that only large firms can afford it. On the other hand, the academic CDNs do not provide a built-in network of independent servers around
Name Description
Coral It is a free peer-to-peer CDN designed to mirror Web content. It uses architecture very similar to a distributed Web proxy. To access a Website through the Coral cache, we need to simply add .nyud.net:8080 to the hostname in the sites URL.
Globule It is an open-source CDN developed at Vrije University in Amsterdam. It is introduced as a third party module for Apache HTTP server. FCAN Flash Crowd Alleviation Network is an adaptive CDN that dynamically
optimises between peer-to-peer and client-server architectures to allevi-ate flash crowds.
Table 1.2: Academic Content Delivery Networks
the globe. That means, the risk and responsibility of running content distribution network ultimately goes back to the content providers themselves. The content providers, who are generally not interested in taking such big risks and responsibility, do not find academic CDNs as attractive alternatives to commercial CDNs.
Objectives
The above brief discussion (which will be further explained in 2.3.1) suggests that there is a need for more reliable and scalable CDN architecture without fresh infra-structural invest-ment. A unique CDN architecture is required to address these issues.
A lot of work has been done in this area aimed at these ends. An academic CDN, Glob-ule, which is envisaged as Collaborative Content Delivery Network (CCDN) [Pierre and van Steen, 2006a] aims to provide performance and availability through Web servers that cooper-ate across a wide area network. Coppens et al. [2006] proposes the idea of a self-organizing Adaptive Content Distribution Network (ACDN), where they introduce a less centralized replica placement algorithm - (COCOA - Cooperative Cost Optimization Algorithm) which will push the content more to the clients. Though most of these works seem to be theoretically sound, they never challenged the efficiency and reliability of commercial client-server architec-ture for they were purely peer-to-peer architecarchitec-ture which will be effective only at the mercy of participating peers, whose performance is not under the control of suggested architecture. A successful alternative to Commercial CDNs with comparable performance and reliability can be assured only by ensuring proportionate incentives to the participating nodes which will function as a driving force for those peers to stay alive with minimum service rates.
Involving Web users with comparatively high bandwidth of Internet connection (broadband or higher) to form a Distributed Content Delivery Network (DCDN) for proportionate re-muneration evoke curiosity and challenges. Clusters of DCDN surrogates (participating Web users) will be replacing the conventional CDN servers in this architecture pushing the content very much near to the end-users.
The objectives of this thesis can be summed up as follows:
• Suggest a practical and viable architecture for DCDN and discuss its possible challenges.
• Suggest a load balancing algorithm for DCDN servers based on queuing theory analysis of DCDN surrogate.
• Compare the performance of DCDN architecture against commercial CDN architecture using simulation techniques.
Contribution
This work aims to propose a new architecture for CDN that will make use of the limited but readily available resources of common Internet users. To achieve this objective, the thesis makes the following contributions.
• Suggests a unique DCDN architecture and proposes a workable business model to suc-cessfully implement it in the real-time scenario.
• Suggests an appropriate load balancing algorithm for DCDN Local servers by analyzing the performance of DCDN surrogate in terms of average system delay and rejection rate.
• Discusses the performance of DCDN architecture in comparison with commercial CDN using simulation results.
Organization
The origin of CDN and the need and scope of DCDN are given in Chapter 1. The main concepts and the evolution of conventional CDN architectures in the light of previous work are discussed in Chapter 2. Chapter 3 discusses the proposed DCDN architecture in detail. It will be followed by an analysis of major performance parameters of DCDN surrogates - average system delay and rejection rate - in Chapter 4. On the light of those results, a probable load
balancing algorithm for DCDN servers is suggested in the same chapter. Simulations and its results to compare the DCDN architecture against commercial CDN architecture constitute Chapter 5. Finally the thesis is concluded with a discussion about future work.
Chapter 2
Background
In this chapter we discuss the different entities that constitute the technical backbone of a Content Delivery Network (CDN) in the light of previous works. Further the conventional architectures of CDN - commercial (client-server) and academic (peer-to-peer) - are evaluated and the need of a new architecture is discussed.
2.1
CDN Main Concepts
2.1.1 Surrogate Servers
These are the collection of (non-origin) servers that attempt to offload work from origin servers by delivering content on their behalf. Surrogate Servers are to be placed all around the globe, according to various needs and business considerations. Since location of surrogate servers is closely related to the content delivery process, it puts extra emphasis on the issue of choosing the best location for each surrogate. Many approaches (e.g: theoretical, heuristic) have been developed to model the surrogate server placement problem [Telematica Institute, 2007].
2.1.2 DNS Lookup and Redirection
The first step taken by a client to retrieve the content for a URL from Web is to resolve the server name portion of the URL to the IP address of a machine containing the URL content. The client does this resolution with a Domain Name System (DNS) lookup. The resolution causes a DNS request to be sent to a local DNS server. If the local DNS server does not have the address mapping already in its cache, the local DNS server sends a query
to the authoritative DNS server for the given server name. Servers in a CDN are located at different locations in the Web. A primary issue for a CDN is how to direct client requests for an object served by the CDN to a particular server within the network. DNS redirection and URL rewriting are two of the commonly used techniques for directing client requests to a particular server in a distributed network of content servers [Krishnamurthy et al., 2001; Vakali and Pallis, 2003].
For the DNS redirection technique, the authoritative DNS name server is controlled by the CDN. The technique is termed DNS redirection because when this authoritative DNS server receives the DNS request from the client the DNS server redirects the request by resolving the CDN server name to the IP address of one content server. This resolution is done based on factors such as the availability of resources and network conditions [Molina et al., 2004].
2.1.3 DNS Load Balancing
In order to achieve Web server scalability, the DNS redirection has to be distributed evenly among the surrogate servers in a CDN. When multiple Web servers the surrogate servers -are present in the server group of CDN, these servers appear as one Web server to the Web client. So, the Web traffic needs to be evenly distributed among these servers. Such a load distribution among these servers is known as load balancing. The load balancing mechanism used for spreading Web requests is known asIP Spraying [Dilley et al., 2002]. The equipment used for IP spraying is also called the load dispatcher or the load balancer. In this case, the IP sprayer intercepts each Web request, and redirects them to a server in the server cluster of CDN. Depending on the type of sprayer involved, the architecture can provide scalability, load balancing and fail over requirements [Dilley et al., 2002].
For example, Akamai’s DNS-based load balancing system continuously monitors the state of services and their servers and networks. To monitor the entire system’s health end-to-end, Akamai uses agents that simulate end-user behaviour by downloading Web objects and measuring their failure rates and download times [aka, 2007]. Akamai uses this information to monitor overall system performance and to automatically detect and suspend problematic data centres or servers. Each of the content servers frequently reports its load to a monitoring application, which aggregates and publishes load reports to the local DNS server. That DNS server then determines which IP addresses to return when resolving DNS names. If a certain server load exceeds a pre-defined threshold, the DNS server simultaneously assigns some of the server’s allocated content to additional servers. If the servers load exceeds another threshold, the servers IP address is no longer available to clients. The server can thus shed a fraction of
its load when it experiences moderate to high load. The monitoring system in Akamai also transmits data centre load to the top-level DNS resolver to direct traffic away from overloaded data centres. In addition to load balancing, Akamai’s monitoring system provides centralized reporting on content service for each customer and content server. This information is useful for network operational and diagnostic purposes [Wikipedia, 2007].
2.1.4 Replication
Commercial CDNs (e.g. Akamai) replicate content across the globe for large organizations like CNN or Apple, that needs to deliver large volumes of data in a timely manner.
Using replication techniques, one or more copies of a single Web content (e.g: streaming media asset) can be maintained on one or more surrogate servers. Context-aware heuristics are proposed by Thomas Buchholz and Linnhoff-Popien [2005] for content replication to increase the monetary value of replicated content where a replica’s profit is dependent on the number of requests it receives from time interval. The clients discover an optimal replica origin server for clients to communicate with. Here, optimality is a policy based decision which is based upon proximity or other criteria such as load [Telematica Institute, 2007].
2.1.5 Selection of Content
The choice of content to be delivered to the end-users is important for content selection. Content can be delivered to the customers in full or in partial. In full-site content delivery the surrogate servers perform entire replication in order to deliver the total content site to the end-users. In contrast, partial content delivery provides only embedded objects such as Web page images from the corresponding CDN.
2.1.6 Cached Delivery
A surrogate server may be equipped with a streaming media cache. This enables on-demand content to be dynamically replicated locally, perhaps in an encrypted format. The surrogate may attempt to store all cacheable media files upon first request. When a surrogate receives a client request for on-demand media, it determines whether the content is cacheable. Then it checks to see whether the requested media already resides in its local cache. If the media is not already in the cache, the surrogate acquires the media file from the source server and simultaneously delivers it to the requesting client. Subsequent requests for the same media
clip can be served without repeatedly pulling the clip across the network from the source server [Telematica Institute, 2007].
2.1.7 Outsourcing Content
Given a set of properly placed surrogate servers in a CDN infrastructure and a chosen content for delivery, it is crucial to decide which content outsourcing practice is to follow. There are basically three content outsourcing schemes and they are enumerated below.
1. Cooperative push-based approach: In this appraoch, content is pushed to the surrogate
servers from the origin and each request is directed to the closest surrogate server or otherwise the request is directed to the origin server [Zhiyong Xu and Bhuyan, 2006].
2. Non-cooperative pull-based approach:, Here, client requests are directed (DNS
redirec-tion) to their closest surrogate servers. If there is a cache miss, surrogate servers pull content from the origin server [Dilley et al., 2002].
3. Cooprative pull-based approach: It differs from the non-cooperative approach in the
sense that surrogate servers cooperates each other to get the requested content in case of cache miss. Using a distributed index, the surrogate servers find nearby copies of requested content and store in the cache [Zhiyong Xu and Bhuyan, 2006].
2.1.8 Accounting and Billing Mechanism
CDN providers charge their customers according to the content delivered by their surrogate servers to the clients. There are technical and business challenges in pricing CDN services. The average cost of charging of CDN services is quite high. The most influencing factors af-fecting the price of CDN services include: bandwidth cost, variation of traffic distribution, size of content replicated over surrogate servers, number of surrogate servers, reliability and sta-bility of the whole system and security issues of outsourcing content delivery [Krishnamurthy et al., 2001]. CDNs support an accounting mechanism that collects and tracks information related to request routing, distribution and delivery. This mechanism gathers information in real time and collects it in for each CDN component. This information can be used in CDNs for accounting, billing and maintaining purposes.
2.2
Conventional CDN Architectures
2.2.1 Commercial (Client-Server) Architecture
The classical example is of Akamai. Akamai offers content delivery services to content providers by offering worldwide distributed platform to host their content. It is done by installing a worldwide network of more than twenty thousand Akamai Surrogate Servers [Dil-ley et al., 2002].
Akamai represents the centralized approach of CDN where the customers (the content providers) hire their share of space in Akamai servers to support the distribution and easy download of their Web content (Web pages or dynamic streaming content). A typical approach by which Akamai provides this service is as follows:
1. The client’s browser requests the default Web page at the Content Provider’s site. The site returns the Web page index.html.
2. The HTML code contains link to some content (eg: images) hosted on the Akamai owned server.
3. As the Web browser parses the HTML code, it pull the content from Akamai server [Wikipedia, 2007].
Akamai uses a simple tool called Free Flow Launcher for its customers that they use to Akamaize their pages [Mahajan, 2004]. The users will specify what content they want to be served through Akamai and the tool will go ahead and Akamaize the URLs. This way the customers still have complete control of what gets served through Akamai and what they still are in charge of. Now the customer is responsible only for the content he chooses to server himself and first few hits of other content till the Akamai caches warm up [Reitz, 2000].
Peering of Commercial CDNs
The commercial CDNs are owned and operated by individual companies. Although there are many commercial CDN providers, they do not cooperate in delivering content to end-users in a scalable manner. In addition, content providers are typically subscribed to one of the CDN providers and are unable to utilize services of multiple CDN providers at the same time. Such a closed, non-cooperative model results in creation of islands of CDNs.
To compromise expense and to ensure better service to the clients, CDN providers need to partner together so that each can supply and receive services in a cooperative and collaborative manner that one CDN cannot provide to content providers otherwise. The objective of a CDN is to satisfy its customers with competitive services. If a particular CDN provider is unable to provide quality service to the end-user requests, it may result in Service Level Agreement (SLA) violation and adverse business impact. In such scenarios, one CDN provider partner with other CDN provider(s), which has caching servers located near to the end-user and serve the users request, meeting the Quality of Service (QoS) requirements [Lazar and Terrill, 2001]. This is called peering of CDNs.
A Virtual Organization (VO) model for forming Content and Service Delivery Networks (CSDN) and a policy framework within the VO model is suggested for the peering of CDNs by R. Buyya and Tari [2001]. Delivery of content in such an environment will meet QoS requirements of end-users according to the negotiated SLA.
2.2.2 Academic (Peer-to-Peer) Architecture
Distributed computer architectures labelledpeer-to-peer are designed for the sharing of com-puter resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and accommodate transient populations of nodes while maintaining acceptable connectivity and performance [Androutsellis-Theotokis and Spinellis, 2004].
The same technique has been proposed and adopted for creating reliable CDN for the propa-gation of Web content. A peer-to-peer (P2P) CDN is a system in which the users get together to forward contents so that the load at a server is reduced.
In its most basic form, a peer-to-peer content distribution system creates a distributed storage medium that allows for the publishing, searching, and retrieval of files by members of its network. So, instead of delegating content delivery to an external company (like Akamai), content providers can organize together to trade their (relatively cheap) local resources against (valuable) remote resources.
A classical example would be the academic peer-to-peer CDN - Globule, developed by Vrije University in Amsterdam. It is implemented as a third-party module for the Apache HTTP server that allows any given server to replicate its documents to other Globule servers. This improves the site’s performance; maintain the site available to its clients even if some servers
are down, and to a certain extent help to resist flash-crowds [Pierre and van Steen, 2003]. A user participating in the Globule network is offered a distributed set of servers in which his/her Web content can be replicated. Globule is designed in the form of an add-on module for the Apache Web server. To replicate their content, content providers only need to com-pile an extra module into their Apache server and edit a simple configuration file. Globule automatically replicates the site’s content and redirects clients to a nearby replica. Servers also monitor each other’s availability, so that client requests are not redirected to a failing replica [Halderen and Pierre, 2006; Guillaume Pierre, 2006; Pierre and van Steen, 2006b]. S. Sivasubramanian, B Halderen and G. Pierre rightly observe that ‘a peer-to-peer CDN aims to allow Web content providers to together and operate their own worldwide hosting platform S. Sivasubramanian and Pierre [2004] .’
2.2.3 Limitations of Existing CDN Architectures
Despite the many advantages of commercial CDNs, they suffer from some major limitations. Commercial CDN providers compete each other and forced to set up costly infrastructure around the globe. Since they want to meet the QoS standards agreed with the clients they are constantly in a process of installing and updating new infrastructure. This process gives rise to the following issues:
1. Network cost : Increase in total network cost in terms of new set of servers and
corre-sponding increase in network traffic.
2. Economic cost: Increase in cost per service rate for the distribution of Web content,
resulting from increase in initial investment and running cost of each commercial CDN.
3. Social cost: Content distribution is been centralized to a couple of CDN providers and
the possible issues of monopolization of revenue in this area.
The huge financial cost involved in setting up a commercial CDN compels the commercial CDN providers to charge high remuneration from their clients (the content providers). Usually this cost is so high that only large firms can afford it. As a result, Web content providers of medium and small sizes are not in a position to rent the services of commercial CDN providers.
Moreover, the revenue from content distribution is monopolized. Only large CDN providers with huge infrastructure around the world are destined to amass revenue from this big
busi-ness. At the same time, the resources in terms of processing power, storage capacity and the network availability of large number of common Internet users are ignored who would support a content delivery network for proportionate remunerations.
On the other hand, the academic CDNs are non-profitable initiatives in a peer-to-peer fashion. But they serve only the content providers who owntheir own network of servers around the globe. Or they have to become a part of a voluntary net of servers. However, the academic CDNs do not provide a built-in network of independent servers around the globe. That means, the risk and responsibility of running content distribution network ultimately goes back to the content providers themselves. The content providers, who are generally not interested in taking such big risks and responsibility, do not find academic CDNs as attractive alternatives.
2.3
Distributed Content Delivery Network - An Effective
Al-ternative
The above discussion proves that there is a need for much reliable, responsible and scalable CDN architecture, which can make use of the resources of a large number of general Web users. A unique architecture of Distributed Content Delivery Network (DCDN) is proposed in this thesis to meet these ends.
DCDN aims at involving general Web users with comparatively high bandwidth of Web connection (broadband or higher) to form a highly distributed content delivery network. Those who become the part of DCDN network are called DCDN surrogates. A cluster of those DCDN surrogates that are distributed very much to the local levels around the globe, will replace the conventional CDN server pushing the content very much near to the end-users. Since the content is pushed very much into the local levels, the efficiency of the content retrieval in terms of response time is expected to increase considerably. It will also reduce network traffic, since clients can access the content from locally placed surrogates. A local DCDN server, which is mainly a redirector and load balancer, is designed to redirect the client requests to the appropriate DCDN surrogate servers.
Since DCDN is aimed at using the available storage space and Web connectivity of existing Web users, it will not demand the installation of fresh new infrastructure. This approach is supposed to reduce the economic cost, considerably. This acquired new value (profit pool) could be shared between the DCDN surrogates through proper accounting and billing mech-anism and through highly attractive business models. It will serve as an incentive for the DCDN surrogates to share their resources to support DCDN network.
Chapter 3
Architecture - Distributed Content
Delivery Network
In order to provide a highly distributed network of DCDN surrogates a basic structure of commercial client-server CDN is adopted with novel peer to peer concepts. Therefore the DCDN architecture will be a hybrid architecture which integrates some of the major features of conventional client-server CDN and an academic peer-to-peer CDN.
A single surrogate server in the conventional client-server CDN model is replaced with lightweight DCDN servers (which are basically redirectors) and a number of DCDN surrogates associ-ated with it. However, the content is distributed among the DCDN surrogate servers in a peer-to-peer fashion and retrieved at a client request with the help of DCDN Local servers.
3.1
DCDN Framework
A collection of Local DCDN Servers and innumerable DCDN Surrogates are networked to-gether to deliver requested Web content to the clients. The main elements of DCDN architec-ture Content providers, DCDN servers and DCDN surrogates are arranged in a hierarchical order as depicted in Figure 3.1
Content Provider: It is that entity that request to distribute its Web content through DCDN.
DCDN Administrators: Rather than a technical entity, it is a managerial/business entity. The
entire DCDN network is managed, supported and run by a team of administrators. They do it by controlling and franchising the Master DCDN servers.
Surrogate DCDN Surrogate DCDN Surrogate DCDN Surrogate DCDN Surrogate DCDN Surrogate DCDN Surrogate DCDN Server Local DCDN Server Local DCDN Server Master DCDN Server Local DCDN Content Provider
Figure 3.1: DCDN Content Distribution Architecture
DCDN Servers: DCDN servers are basically redirectors that will only have the knowledge
about the location of the content. They do not store any content as such. It may function as a buffer system, which help to push the content provided by the content providers to DCDN surrogates. They monitor, keep log of and regulate the content flow from providers to the surrogates.
In the proposed architecture, DCDN servers are of two types: Master and Local.
1. DCDN Master Servers: Master DCDN servers are the first point of contact of a content
provider. A global network of Master DCDN servers are set up in such a way that every network region will have at least one Master DCDN server. Network region can be geographical regions like, the Americas, Europe, Asia and Asia Pacific, and Africa or network regions identified on the basis of a number of other criteria like, network traffic and network volume. Content providers deal with administrators through Master DCDN servers and reach terms and conditions with DCDN administrators for the service provided by DCDN. They monitor, regulate and control the content flow into DCDN servers and surrogates.
2. DCDN Local Servers: They are placed very near to the end-users (virtually they reside among the end-users). A number of Local servers can come under the service of a single Master server. They have got two major functions.
Firstly, they decide where to place the content (among the surrogates) and keep log of it. So, Local DCDN servers will have more local and specific knowledge about a particular Web content. Secondly, they find out and return the IP address of the best available surrogate a client on request for a particular content under the care of DCDN. In doing so, they also function as a load balancer that will protect the surrogates in the network from being overloaded.
These Local DCDN servers are networked together to form a globally distributed mas-sive DCDN architecture.
The distinction between Master and Local servers refer only to the role a given server plays within DCDN. The same server can act both as a Master as well as a Local server, if it is assigned to do so.
DCDN Surrogates: As explained before, DCDN surrogates are the large number of Web users
who offers resources in terms of storage capacity, bandwidth and processing power to store and make available DCDN Web content. A requested client Web content is ultimately fetched from DCDN surrogates.
DCDN Client: The client refers to an end user, who makes a request for a particular Web
content using a Web browser. The assumption is that the client uses a standard Web browser, without the use of any special component such as plugins or daemons.
3.1.1 Distribution of Content - The Process
The aim is the place the replica of the content as close as possible to the clients. In this process, firstly, the content providers approach DCDN administrators. Once the Service Level Agreement is reached, content providers can upload their content to DCDN net. This can be done either through the Master DCDN servers or through the Local DCDN servers assigned by the Master DCDN servers. If they are uploading the content through the Master servers, they will push it to the Local servers. The Local servers push replicas to the surrogates in their own region and keep a track of these records. The Master servers will have more universal knowledge about it (Like, what are the network areas in which a particular content is distributed) and the Local servers will have more local knowledge of the location of the content (That is, which are the surrogates that actually holding a particular content).
2 3 1 5 4 Content Provider Master DCDN Server Local DCDN Server Server DNS Surrogate DCDN Suggogate DCDN DCDN Surrogate Client
Figure 3.2: DCDN Content Delivery
On request from a Local server, a surrogate may share the replicas with other surrogates in a peer-to-peer fashion. This will offload the Local severs from additional workload. The process will make sure that the Local server still has the knowledge about the replicated content in the new surrogate/s.
However, the content providers need not choose to distribute their content in a true global manner. If they want DCDN to support only for some region(s), they can request for regional support too. In that case, the administrators (with the help of Master servers) choose only those Local DCDN servers, which are set by the parameters given in the QoS (Quality of Service) agreement between the content provider and DCDN administration. (For example, if the content is to be distributed in the Asia and Asia Pacific region, it is sent to the Local DCDN servers at those regions only).
In order to keep sync with the updates and modifications, or in the event of termination of
serviceto a specific content provider, Master DCDN through the Local DCDN servers request
the DCDN surrogates to update/delete the content.
only general Web users with low storage capacity. Moreover, they may not be connected to the Web for all the time but makes themselves online for a considerable period of time, everyday. DCDN relay on the magnitude of storage space and bandwidth expected from the innumerably large number of surrogates participating in the DCDN net and their absolute proximity to the clients.
Partial Replication
Because of the unlikelihood of being online at the time of request of a specific content in a specific surrogate, the same content is replicated in large number of surrogates. It not suggested that the whole content of a Website should be stored in an individual surrogate. Partial replication of a Website is allowed because the storage space of surrogates are expected not to be very big. In case of partial replication, the knowledge about the remaining content is kept in the respective surrogate to facilitate HTTP redirection in case of query for the rest of the content. The content is updated, deleted or added dynamically in a regular manner, in sync with the Local server updates.
The Local DCDN server assesses the demand for a particular content in a particular local-ity. Local DCDN server increases or decreases the number of replications within its locality according to this assessment. That is, if there is higher demand for a particular content in a particular locality, the number of replicas in that locality is increased or vice versa. This will allow efficient content delivery service with optimum use of resources.
3.1.2 Content Delivery to a User
The DCDN Local server, which is envisaged as a redirector, will follow the DNS protocol. It will take care of the queries related to the Websites under the care of DCDN. This information is shared with other DNS servers too. So, when there is a request for a Website under the care of DCDN, the DNS redirectors will redirect it to the nearest available Local DCDN server. The DCDN Local server searches the log of active surrogates holding those files using a suitable technique (Eg. Distributed Hash Table (DHT) algorithm). It will then make a decision based on the other relevant parameters (availability of full or partial replica content, bandwidth, physical/online nearness, etc) and will return the IP addresses of the best suitable surrogate to the client.
Now the client fetches the content from the respective surrogate. The participating surrogates will have a client daemon program running on their machines, which will handle the requests
Client Server DNS Surrogate DCDN Server Local Master Server Provider Content
Figure 3.3: DCDN Basic Transition Diagram
from the clients and the parent DCDN server. If the surrogate is having only a partial content of the Website under request, it has to get the rest from other surrogates. The surrogate may use HTTP redirection to fetch the content from other surrogates.
Diagrammatical representation of the above process is given in Figure 3.2 and the following interactions between different entities of DCDN are identified.
1. Local DCDN Server - DNS Server Interaction: The Local DCDN server updates the
DNS server with the list of content providers under DCDN care and request DNS server to map corresponding URL requests to the IP address of the Local DCDN server. DNS Server queries the Local DCDN server from time to time to update its library.
2. Client - DNS Server Interaction: Client requests for a particular content (Website)
under DCDN care. The DNS server directs the request to the Local DCDN server, using DNS protocol.
3. Client - Local DCDN Server Interaction: Local DCDN server finds out the best
pos-sible surrogate to cater to the request of the client and returns the IP address of that particular surrogate.
be-Client Server DNS Surrogate DCDN Server Local Master Server Provider Content
Figure 3.4: DCDN Transition Diagram - Including Contingency Plans
tween the Local server and the surrogates. The content from the content providers are stored in the surrogates through the Local DCDN servers. The surrogates inform their availability and non-availability to the Local server as and when they become online or offline in terms of connectivity. Local servers keep a track of it. Local DCDN servers direct the surrogates to add, delete, update or modify the content according to the decisions made from time to time.
5. DCDN Surrogate - Client Interaction: Once the Local DCDN server returns the IP
address of the most suitable surrogate, the client contacts that surrogate to fetch the requested content. On request from the client, surrogate delivers the content to the client.
The transition diagram (Figure 3.3) clubs the major two flows of interactions in DCDN, namely, content distribution and the content delivery. The sequence of interactions are already discussed in 3.1.1 and 3.1.2.
zone 1 zone 2
to the wider dcdn net
Figure 3.5: Local DCDN Server Zones - Contingency Plan
Contigency Plans
The special design of DCDN suggests the possibility of a number of unavailable surrogates at any instance. So, it becomes a high priority to assess the availability of surrogates at every moment. Asking the surrogates to notify the Local server as and when they become online and offline, DCDN achieve this end. At the same time the Local servers issue ping commands at regular intervals to make sure the availability of surrogates, if at all they fail without notifying the Local server. So, the sequence diagram is modified as in Figure 3.4 Another scenario is, when specific Web content is not available within a local DCDN Network. In order to cope up with this scenario, each DCDN local surrogate will be classifying the nearby DCDN Local servers into zones in the representative order of network proximity (Figure 3.5). That is, the nearby Local DCDN servers with least cost accessibility will fall in
zone 1, and so on. When a specific content is not found in a Local DCDN net, the DCDN
server will first search its availability in the nearby zone 1 DCDN servers. If its found, the request is redirected to the specific Local DCDN server. If its not found in the lower zones the search is extended to the higher zones, till the specific content is found.
3.2
DCDN Design Challenges
In spite of all its advantages, DCDN architecture arouses its own unique set of challenges. The major challenges would be:
• Security
• Efficient algorithm for the effective load balancing and DNS redirection.
• Development of efficient software for quantifying the service of DCDN servers and peers.
3.2.1 Security
The security requirements for a DCDN service environment will be driven by the general security issues such as:
1. Authentication of a content provider (who is recognized by the administrators to use the service of DCDN) while uploading its content to DCDN through Master/Local servers. 2. Authentication of Master and Local DCDN server when they contact each other (for
sharing/updating content information and so on).
3. Authentication of Local Servers by the surrogates to authenticate pushed content. In addition to the above issues, maintaining integrity of the content provided by the content provider throughout the DCDN surrogate replicas become a crucial criteria in the business success of DCDN. This is because, the large number of surrogates suggest possible vulnera-bility of the content being manipulated by vicious surrogates or hackers. On the other hand, content providers will be keen to see that their original content is not tampered within the DCDN network.
The DCDN daemon running on the surrogates are supposed to ensure security of the content stored in it. The DCDN surrogate daemon authenticates the injected content from the Local DCDN server and make sure that they receive original replicas. Different security measures can be employed to block any attack from the hackers or even from surrogate owner itself to access of tamper the content within the DCDN daemon. One of the solutions is to make sure that we track down the anomalies when the content is tampered and delivered to the end-users. If that can be identified, the respective surrogate can be put on alert, corrected or even eliminated from DCDN.
Client Server DNS Surrogate DCDN Server Local Master Server Provider Content
Figure 3.6: DCDN Transition Diagram - Including Security Solutions
This can be achieved by stamping all content injected to the surrogate with a digital stamp like md5 or the like. The Local server will keep a record of these digital stamps. On each delivery of content, the surrogate daemon shall calculate the digital stamp of the delivered content and send it back to the Local server. The Local server compares it with its database and makes sure that there is no anomaly. If there an anomaly is found, content manipulation is identified and the Local server takes appropriate action. Verification of digital stamp for each and every transaction can create a huge volume of traffic between surrogates and the Local server. In order to moderate this traffic, this security measure can be done in some random basis.
The final transition diagram incorporating the contingency and security issues is shown in Figure 3.6. Furhter discussions about the security of DCDN architecture are out of the scope of this minor thesis.
3.2.2 Effective Redirection and Load-balancing Algorithm
The key to the success of DCDN would rely on the success of an effective redirection algorithm. The DCDN will be having multiple replications of the same content within a local DCDN set up to ensure scalability of the system. This replication may exponentially increase as
the number of local DCDN networks increase throughout the globe. A combination of NDS HTTP address redirection system as mentioned in 3.1.2 has to be a developed as a possible solution in this regard.
The DCDN server has to distribute the load within a local system. It should also take care of the availability or non-availability of peer nodes. If the requested content is not within the local DCDN system, DCDN server should be able to make the right decision to get it from the other local DCDN systems without causing network congestion. Effective load-balancing algorithms have to be developed in this regard. Based on the results of queuing delay analysis, a basic algorithm for DCDN servers is proposed in the next chapter.
3.2.3 Billing and SLA (Service Level Agreement) Verification Software
DCDN has to provide content providers with accounting and access-related information. This information has to be provided in the form of aggregate or detailed log files. In addition, DCDN should collect accounting information to aid in operation, billing and SLA verification. The DCDN Master surrogates deal with these content provider related issues.
At the same time, DCDN has to quantify proper remuneration for surrogates according to their availability, performance, storage space, etc. There is a need for generalized systems or protocols in the calculation of the contributions of surrogates and a local DCDN servers, on the basis of the business model adopted for DCDN.
3.3
Business Model
The success of DCDN architecture depends upon building up a global DCDN tree consists of Major/Local DCDN servers and considerably large number of DCDN surrogates. There should be strong incentive for individuals to become a part of DCDN tree. The incentive is the shared monetary benefit from the bonus pot, which is filled with the money saved by not paying to the middlemen, that is, the commercial CDNs. According to their share of service -the online availability, storage, bandwidth, processing power and o-ther relevant factors - -the surrogates are to be offered proportionate remuneration.
A possible business model for DCDN could be that of Network Marketing/ Multilevel mar-keting which is based on pyramid scheme.
1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000
Figure 3.7: Pyramid Scheme
3.3.1 Network Marketing (NM)/ Multi Level Marketing (MLM)
Wikipedia defines it as follows: ‘Multi-level marketing (MLM) (also called network marketing or NM) is a business model that combines direct marketing with franchising [Wikipedia, b].’ In a typical multi-level marketing or network marketing arrangement, individuals associate with a parent company as an independent contractor or franchisee and are compensated based on their sales of products or service, as well as the sales achieved by those they bring into the business. This is like many franchise companies where royalties are paid from the sales of individual franchise operations to the franchisor as well as to an area or region manager. MLM is inspired by the mathematical model ofPyramid scheme. If a pyramid were started by a human being at the top with just 10 people beneath him, and 100 beneath them, and 1000 beneath them, etc., the pyramid would involve everyone on earth in just ten layers of people with a single man on top. The human pyramid would be about 60 feet high and the bottom layer would have more than 4.5 billion people [Skeptic Dictonary, 2007]. Figure 3.7 will help us to see this:
This scheme is effectively used by MLM giants such as Amway, Big Planet, Excel communi-cations, Mary Kay, etc [Wikipedia, a]. A general business model of NM/MLM Distributor hierarchy (Figure 3.81), which resembles the DCDN hierarchy, shows the scope of adopting NM/MLM model for the effective creation of DCDN tree of surrogates.
In the DCDN model, the Distributor will be replaced with the content provider, the first level will be the net of Master DCDN servers and the second level will be of Local DCDN servers.
Figure 3.8: MLM Architecture
In other words, the DCDN administers will be franchising the concept to the Master/Local server levels. They in turn recruit the final level of hierarchy - the surrogates - to store the content in local levels. According to the expansion needs, more and more levels could be envisaged in the long run. This can be achieved by adding different layers of Master DCDN servers in different hierarchical levels.
Eventually, an active DCDN server develops a hierarchical substructure known as a down-line, that looks like an organization chart in a company with a lot of employees. Each DCDN server gets commission/remuneration on the service of surrogates in their down-line. There are also likely to be performance bonuses available for reaching certain service levels. The profit earned from the commission over its surrogates become the driving force for the DCDN servers (Master/Local) to maintain their technological infrastructure (both hardware and software) and to add more and more surrogates to their hierarchical structure. This will finally improve the scalability and the efficiency of DCDN network.With this kind of business model, there are no big capital requirements, no geographical limitations and no special education or skills needed for its participants. Since, the revenue collected from the content providers are proportionately shared among the surrogates it can become a low-overhead, home-based business for the participating surrogates. Network marketing is a people-to-people business, which goes very well with the idea of near peer-to-peer architecture of DCDN.
3.3.2 Special Scenarios of DCDN Advantage
The new architectural model suggested for DCDN and the corresponding MLM business model will open up whole new possibilities in content distribution. DCDN architecture is supposed to be more effective in distributing static content than that of dynamic content. The most important beneficiaries of DCDN will be the popular streaming media sharing websites like
youtube.com and photo-sharing websites (e.g. Picasa Web Albums, Orkutpictures) who has
to support millions of media files uploaded throughout the world and to effectively deliver to the end-users in a more distributive manner. The popular music sharing services will also find DCDN as an effective and cheaper means of delivering their services to the customers.
Chapter 4
Performance Analysis and Load
Balancing Algorithm
The performance of DCDN architecture can be expressed in terms of total delay in retrieving a Web content. Here, the DCDN surrogates are expected to be the bottlenecks for they are the common Internet users with limited resources. So, we can say that the success of DCDN architecture will depend upon the performance of DCDN surrogates.
This chapter analyzes the performance of a DCDN surrogate using queuing theory techniques. On the basis of this analysis a load balancing algorithm for DCDN server is suggested.
4.1
Performance Parameters and Assumptions
Total delay in retrieving a content is the sum of propagation delay, processing delay at DCDN surrogates and the transmission delay.
Transmission delay is the time required by a DCDN surrogate to transmit all data packets of the requested content onto the transmission link. In our case, It is directly proportional to the available bandwidth of DCDN surrogate. Once the packets are pushed onto the link, they need to be propagated to the client. The time taken for propagation is called propagation delay. Propagation delay is taken out of consideration in analyzing the performance of DCDN architecture. This is because, it assumes replication of content much near to the clients than that of conventional architectures. The processing speed of surrogates is assumed to be so high that processing delay is negligible as compared to transmission delay.
In nutshell, the efficiency of DCDN network can be expressed in terms of transmission delay at DCDN surrogate. This is termed as total system delay at the surrogates. In order to ensure better performance a truncated buffer system is suggested at DCDN surrogates. Also we assume a poisson process of randomly spaced requests in time and an exponential distribution of service-time. It will result in M/M/c/k model of queuing analysis, where c is number of servers or server daemon programs engaged and k is the total buffer size.
4.2
Queuing Metrics
Total system delay in a DCDN surrogate for different M/M/c/k models and corresponding rejection rates are found out using the following formulas as explained by D. Gross and C. M. Harris [Gross and Harris, 1998]. We assume to replicate the effect of multiple servers in a single surrogate by running more than one DCDN surrogate daemons (as multiple processes or threads) within the same machine.
• Service Time (S): In our case, it is the transmission time, which is equal to;
S =U pstreamCapacityF ilesize
Therefore, Service rate of the DCDN surrogate µ= (S1)
• Server Utilization (ρ): ρ= (λ
µ) for M/M/1/k and
ρ= (cµλ) for M/M/c/k queuing model
where λis the arrival rate of requests to DCDN surrogate.
• Effective arrival rate λe: λe=λ(1−Pk) wherePkis the probability ofk requests in the
system.
• Probablity of zero requests in the system P0:
P0 = c−1 X i=0 (λn/µ)i i! + (λ/µ)c c! 1−ρk−c+1 1−ρ −1
– Probablity of n customers in system for 0≤n≤c Pn =
(λ µ) n n! P0 – Probablity of n customers in system for c≤n≤k Pn =
(λ µ) n c!cn−c P0
• Average number of requests in the queue Lq:
• Average number of requests in the system L:
L=Lq+λµ(1−Pk)
• Average System Delay W: Average System Delay is the time duration a request has to
wait from the moment it enters a server queue, till it is served by any of the servers available to take a request.
for M/M/c/k, W = λL e
• Rejection Rate: The number of requests that will be lost due to congestion per unit
time is given by: λPk
If the mean arrival rate of requests in greater than the service rate of surrogates, it will choke the surrogates. In order to avoid this scenario, the mean arrival rate of requests (λ) is to be kept less than the service rate of surrogates. In other words, server utilization (ρ) is kept below one in all queuing models.
At the same time, we have to be cautious about the probability of blocking (loss) of requests. Since we cannot afford the loss of requests beyond a very minimum level, rejection rate of requests for different models is to be taken into account in the design of a load balancing algorithm for DCDN server.
4.3
Queuing Theory Modeling for Different Scenarios
Different queuing theory models are analyzed for different cases to find out average system delay and rejection rate as described in the previous section. The following assumptions are made to analyze the queuing parameters.
1. The surrogates are supposed to have a minimum of DLS/Cable Web access.
2. Minimum capacity of DLS/Cable line is rated at 768 Kbps downstream and 128 Kbps upstream.
Using a Web Page Analyzer1 it is found that the average size of Web pages of medium size content providers (example: www.rajagiritech.ac.in) is about 30 KB. However, the upstream capacity of surrogates will not be same for all surrogates in real-time scenario. We can reasonably assume that there will also be some surrogates with higher level of connectivity
20 40 60 80
Utilization in Percentage(Access Rate/Service Rate)
0 2 4 6
Average Delay in DCDN Surrogate (sec)
M/M/1/1 M/M/1/2 M/M/1/3 M/M/2/2 M/M/2/3 M/M/3/3 20 40 60 80
Utilization in Percentage(Access Rate/Service Rate)
0 2 4 6
Average Delay in DCDN Surrogate (sec)
M/M/1/1 M/M/1/2 M/M/1/3 M/M/2/2 M/M/2/3 M/M/3/3
(with 128 Kbps capacity) (with 256 Kbps capacity )
Figure 4.1: Utilization v/s Total System Delay
who would be able to give a better performance. In order to reflect this scenario, we have also analyzed the queuing delay for a doubled service rate of DCDN surrogates. That means, the upstream capacity of surrogates is raised from 128 Kbps to 256 Kbps.
A surrogate originally intending to serve a single request at a time may actually end up serving 2 or 3 in a real-time scenario. This may happen if the multiple requests for a particular content is only available with a single surrogate. In that case, the surrogate is supposed to serve those requests with reduced service rates, i.e., for M/M/1/1 the service rate is µ; for M/M/2/2 it isµ/2, and for M/M/3/3 the rate is µ/3.
The values are found using QtsPlus, a queuing theory analysis software provided by D. Gross and C. M. Harris2 [Gross and Harris, 1998]. The results of these analysis are compiled in Figure 4.1 for 128 Kbps and 256 Kbps upstream capacity. The Rejection rate of different queuing models are presented in Figure 4.2.
4.4
Load Balancing Algorithm for DCDN Servers
Many load-balancing algorithms have been proposed in the past to ensure scalable Web servers [Bryhni et al., 2000; Godfrey et al., 2004; Aweya et al., 2002; Wolf and Yu, 2001; Chen et al., 2005]. The stateless property of HTTP protocol by which requests can be routed
20 40 60 80
Utilization in Percentage(Access Rate/Service Rate)
0 1 2 3 4
Loss of requests per unit time
M/M/1/1 M/M/1/2 M/M/1/3 M/M/2/2 M/M/2/3 M/M/3/3
Figure 4.2: Utilization v/s Rejection Rate
separately to different servers is widely used to achieve load sharing in a cluster of Web servers [Bryhni et al., 2000]. The canonical name (CNAME) associated with a Web link can be mapped to the IP addresses of a number of replicated servers, who hold the same content. Bryhni et al. [2000] suggest that this mapping can be done at the network to achieve best performance. Same techniques can be adopted for DCDN but by customizing it for its highly distributed nature.
An algorithm for load-balancing in highly heterogeneous and dynamic P2P environment is suggested by Godfrey et al. [2004]. They uses the concept ofvirtual server where a physical node hosts one or more virtual servers. The load balancing is done by moving virtual servers from heavily loaded physical nodes to lightly loaded physical servers. But it is proposed on the assumption that load balancer has got very little control over where the objects are stored. But in DCDN environment, DCDN server has got more control over the content within its surrogates. Moreover, the load balancing algorithms in P2P systems, generally do not consider the difference in capacity of its peers. In DCDN we can not discard this difference as we want to offer as efficient service as that of a commercial CDN. The formulation of a simple but efficient load balancing algorithm to ensure almost equal server load to the surrogates by making use of the information and control residing with the DCDN server becomes an inevitability.
Uti-lization v/s Rejection Rate graph (Figure 4.2) in the previous section, the following inferences can be made.
1. The best performance is expected from DCDN, when surrogates follow M/M/c/c queu-ing models.
2. The reduction in average delay time is almost directly proportional to increase in upload capacity, in all the scenarios.
3. Loss of requests can be reduced by increasing the number of requests in the whole system by increasing the value of kin M/M/c/k queuing model.
Based on the above inferences we can suggest a load balancing algorithm for the DCDN severs. An algorithm based on M/M/c/c model is expected to be more scalable and comparatively higher efficient than other models. This reflection is made by considering an optimum balance between total system delay and rejection rate. The real time scenario also suggests that there may be cases where multiple content have to be served to different clients simultaneously from a single surrogate. In the light of above discussion, we make the assumption that the surrogates will be designed to support M/M/c/c queuing model of request streams where the value of c will be proportional to the processing capacity of surrogates. The following
optimum server load algorithm for effective load balancing is proposed to ensure reasonable
load sharing between the surrogates.
Load Balancing Algorithm for DCDN Server
1: let, DCDN server has the knowledge of the service rate (µ) of its surrogates;
2: let, DCDN server is aware of the requests send to (λ) its surrogates;
3: let, DCDN surrogates support only M/M/c/c queuing models;
4: c is the maximum number of requests allowed in a particular surrogate;
5: Web requests arrive at the Local DCDN Server;
6: if requested content is available in the Local DCDN surrogate networkthen
7: if there are surrogates withP0 (Probability of NO requests) equal to 100 (i.e., idle surrogates)
then
8: send request to the surrogate with highest c value ( i.e., to surrogate with highest service
capacity);
9: else
10: whilesearch do not exceed the Max Trial Numberdo
11: find the surrogate with lowest Server Utilization
(ρ= (λ
cµ));
12: end while
14: end if
15: else
16: redirect request to other Local DCDN server who has the requested content;
17: end if
We expect that this algorithm will distribute the workload reasonably well between the surro-gates. However, this can only be validated by conducting extensive simulations which repro-duce the highly distributed DCDN environment. The next chapter provides those simulations and its results.
Chapter 5
Simulations and Results
In the previous chapter, major two matrices of interest, namely queuing delay and rejection rate at the DCDN surrogates were discussed. The results were compiled in the form of graphs. On the basis of those results, a probable load balancing algorithm for DCDN servers was suggested.
Various scenarios are created using simulation tool to replicate the DCDN as well as the commercial client-server CDN architecture. Simulations are conducted to compare the per-formance of DCDN architecture with the client-server CDN architecture usingoptimum server load - load balancing algorithm.
The simulations are conducted using Opnet IT Guru network simulator. The main reason to use Opnet IT Guru is its user-friendliness in picking the predefined models and objects using drag and drop functionality. The Opnet predefined model and objects are validated and hence require no further validation. The devices, links and nodes in Opnet IT Guru are using reasonable assumptions and enable us to have a strong data analysis
This chapter presents the goals, assumptions and the setup of simulations. The performance comparison between different scenarios of DCDN and commercial CDN architectures using optimum server load - load balancing algorithm is further discussed.
5.1
Goals
The objective of the simulations is to evaluate the feasibility of DCDN architecture. That is to check the performance of DCDN architecture in comparison with that of commercial client-server CDN architecture in terms of page response time, utilization of DCDN server
(load balancer, in case of conventional CDN) and utilization of DCDN surrogate (CDN server, in case of conventional CDN). The simulation scenarios were designed to achieve the following goals:
• The simulations should be able to provide some reasonable data to show that the DCDN architecture will be able to give better or at least the same performance of the commer-cial client-server CDN architecture.
• The technologies and the protocols used in the simulation environment should reproduce the standard protocols used in the industry.
• The simulation should allow the addition, deletion and modification of the clients, DCDN servers (load balancers) and the surrogates (servers) for easy comparison of different parameters used for the evaluation.
5.2
Assumptions
The simulations are designed to simulate a commercial client-server CDN environment in the first place and then to simulate the DCDN setup. The following assumptions are made to create those environments:
• DCDN server lies within the IP cloud unlike in the case of commercial CDN (where it is the load balancer of CDN server farm). The use of an additional IP cloud be-tween DCDN server and DCDN surrogates is assumed to represent this environment (Figure D.2).
• The simulations are conducted in a standard PC and the results are expected to be only suggestive. However, we assume that similar scenarios of commercial CDN and DCDN setup are comparable since both are conducted at similar environments.
• The data obtained from the simulations can be scaled with an appropriate value so as to have a reasonable approximation of the parameters assessed.
5.3
Overview of Simulation Setup
The experiment was conducted by choosing a standard commercial CDN setup serving 150 clients. Performance of the setup was found in terms of page response time, load balancer
Commercial CDN DCDN: Scenario 1 DCDN: Scenario 2 DCDN: Scenario 3 Number of Clients 150 150 75 30 Number of Surro-gates (or Servers)
3 6 6 6
Link Capacity
(Mbps)
100 10 10 10
Load Balancing Algo-rithm
round robin
server load server load server load
Table 5.1: Simulation Setup
utilization and server utilization. The environment was reset to represent DCDN architecture and the above performance parameters were found again. The experiment was repeated until the DCDN setup could replicate the performace of commercial CDN setup, by altering the critical parameters of simulation enviornment . The critical parameters that defined the different simulation scenarios were:
• Number of Clients: The clients were all HTTP clients with requests of medium file size.
The number of requests in the s