Sl.No.
CODE
TITLE
AUTHOR
1.
SP-309
PRIVACY-PRESERVING MULTI KEYWORD RANKEDSEARCH OVER ENCRYPTED CLOUD DATA.
RAVIKUMAR.P S.MARAGATHAM
2.
SP-397
RESEARCH AND DEVELOPMENT TREND OF CLOUDCOMPUTING.
NIKITHASHREE.N.S MANJUNATH.M SAMARA MUBEEN
3.
SP-398
ANDROID APPLICATION FRAMEWORK FOR CLOUDCOMPUTING ENVIRONMENT USING VPN.
VINUTHA.S C.K.RAJU
DR.M.SIDDAPPA
4.
SP-329
CLOUD COMPUTING BASED RESOURCEMANAGEMENT IN ENERGY EFFICIENT MANNER
SHIVAKUMAR.S.SOBANI, DR.A.SREENIVASAN
5.
FP-122
CLOUD COMPUTING AND EMERGING IT TRENDS. MRS.J.SRIMATHIMRS.D.KALAIVANI
6.
INCREASING DATA PRIVACY AND COMPUTATIONEFFICIENCY THROUGH LINEAR PROGRAMMING OUTSOURCING IN CLOUD COMPUTING
HARSHA N DR M SIDDAPPA
7.
DYNAMIC RESOURCE ALLOCATION IN CLOUD FORPARALLEL DATA PROCESSING
K.B.MANASA N.L.UDAYAKUMAR DR.M.SIDDAPPA
8.
SP-440
DATA SECURE AND DEPENDABLE STORAGE SER VICESIN CLOUD COMPUTING.
AJAY KUMARA M A, MR. SHARAVANA .K
9.
SP-341
INCREASING DATA PRIVACY AND COMPUTATION EFFICIENCY THROUGH LINEAR PROGRAMMING OUTSOURCING IN CLOUD COMPUTINGHARSHA N, DR M SIDDAPPA
10
SP-347
DYNAMIC RESOURCE ALLOCATION IN CLOUD FOR PARALLEL DATA PROCESSINGK.B.MANASA N.L.UDAYAKUMAR DR.M.SIDDAPPA
11
SP-348
DYNAMIC LOAD SHARING MULTICAST ALGORITHMSON CLOUD FOR DATA INTENSIVE APPLICATIONS
SUHASINI N.L UDAYAKUMAR DR. M. SIDDAPPA
12
SP-438
HORNS: A HOMOMORPHIC ENCRYPTION SCHEME FOR CLOUD COMPUTING USING RESIDUE NUMBER SYSTEMARUN KUMBI ANASUYA PRAKASH
13
SP-451
PROVIDING SECURITY IN CLOUD COMPUTING USING PROTECTION RINGSMAHESH SHEELVANT DAYANANDA R B
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
1
Research and Development Trend of
Cloud Computing
Nikithashree N S¹, Manjunath M², Samara Mubeen
3¹IV SEM MTech, JNN College of Engineering, Shimoga,
² IV SEM MTech Dept of CSE, SJBIT, Bangalore
3
Assistant Professor, JNN College of Engineering, Shimoga,
¹[email protected], ²[email protected],[email protected]
Abstract - With the development of parallel
computing, distributed computing, grid computing, and a new computing model appeared. The basic principles of cloud computing is to make the computing be assigned in great number of distributed computers, rather than the local computer. The Cloud computing is new paradigm for computing in Which all required resources are available as a service With the help of rich set of features it is getting more and More popular and well accepted by many computing communities Cloud computing provides people the way to share distributed resources and services that belong to different organizations/site. Since cloud computing share distributed resources via the network in the open environment many believe that Cloud will reshape the entire ICT industry as a revolution. The running o f the enterprise’s data center is just like Internet. This makes the enterprise use the resource in the application that is needed, and access computer and storage according to the requirement.
1. Introduction
Cloud computing, a new kind of computing model, is Coming With the rapid development of the Internet, user requirement is realized through the Internet, different from Changing with the need. Cloud computing is an extend of grid computing, distributed computing, and parallel com putting. Its foreground is to provide secure, quick, convenient data storage and netcomputing service cantered by internet. The factors that impel the occurring and development of cloud computing include: the development of grid computing, the appearance of high quality technology in storage and data transportation, and the appearance of Web2.0, especially the development of Virtualization. Virtualization is the main character. Most software and hardware have provided support to virtualization. We can virtualizes many factors such as IT resource, software, hardware, operating system and net storage, and manage them in the cloud com putting platform. Cloud computing is a novice approach in which every required resource is providing from one end to another. There was a huge
expectation from users’ community to get the
resources whenever they need. People were expecting
the computing Facilities and resources on demand. The birth of cloud computing gave the light on it and finally it has become possible to address such expectations. Since distributed systems and network computing were used wildly, security has become an urgent problem and will be more important in the future. In order to improve the work efficiency, the different services are distributed in different servers that are distributed in different places. In contrast to the fast developing of distributed computing technologies, people have remained insufficient in the field of information security and safety. In recently, a new trend Attracts people’s attention. Users from multiple environment hope use the distributed computing more efficient, just like using the electric power. Security is therefore a major element in any cloud computing infrastructure, because it is necessary to ensure that only authorized access is permitted and secure behavior is accepted. Trust is the major concern of the consumers and provider of services that participate in a cloud computing environment.
2. Cloud computing
A. The background of cloud computing
In recent 10 years, Internet has been developing very quickly. The cost of storage, the power consumed by computer and hardware is increasing. The storage space in data center can’t meet our needs and the system and service of original internet can’t solve above questions, so we need new solutions. At the same time, large enterprises have to study data source fully to support its business. The collection and analysis must be built on a new platform. Why we need cloud computing? It is to utilize the vacant resources of computer, increase the economic efficiency through improving utilization rate, and decrease the equipment energy consumption.
B. Cloud computing principle
It is difficult to define the cloud computing. Computing is a virtual pool of computing resources. It provides computing resources in the pool for users through internet. Integrated cloud computing is a whole dynamic computing system. It provides a mandatory application program environment. It can deploy, allocate or
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
2
reallocate computing resource dynamically and monitor the usage of resources at all times. Generally speaking cloud computing has a distributed foundation establishment, and monitor the distributed system, to achieve the purpose of efficient use of the system.
C. Cloud computing style
Though people have different views on the cloud computing, they have already reached an agreement on the basic style on it. Its style is as follows:
1. SAAS (Software as a service)
This kind of cloud computing transfer programs to millions of users through browser. In the user’s views, this can save some cost on servers and software. In the provider’s views, they only need to maintain one program, this can also save cost. SAAS is commonly used in human resource management system and ERP (Enterprise Resource Planning). Google Apps and Zoho Office is also providing this kind of service.
2 Utility Computing Recently Amazon.com,
Sun, IBM and other companies that provide storage services and virtual services are appearing. Cloud computing is creating virtual data center for IT industry to make it can provide service for the whole net through collecting memory, IO equipment, storage and computing power to a virtual resource pool.
C. Network service
Net service has a close relation with SAAS.
The service providers can help programmers develop applications based on internet instead of providing single machine procedure through providing API (Application Programming Interface)
D. PAAS (Platform as a service)
Platform as a service, another SAAS, this kind of cloud computing providing development environment as a service. You can use the middleman’s equipment to develop your own program and transfer it to the users through internet and servers.
E. MSP (management service provider)
This is one of the ancient applications of cloud computing. This application mostly serves the IT industry instead of end users. It is often used in mail virus scanning and program monitoring.
F. Commercial service platform
The commercial service platform is the mixture of SAAS and MSP (Mixed signal Processor), this kind of computing provides a platform for the interaction between users and service provider. For instance, the user individual expense management system can
manage user’s expense according user’s setting and coordinate all the services that users purchased.
G. Integrating internet
It can integrate all the companies that provide similar services, so that users can compare and select their service provider.
3. Features of cloud computing
1. Ultra large-scale
The scale of cloud is large. The cloud of Google has
owned more than one million servers. Even in Amazon, IBM, Microsoft, Yahoo, they have more than hundreds of thousands servers.
2. Virtualization
Cloud computing makes user get service anywhere,
through any kind of terminal. The resources it required come from cloud instead of visible entity.
3. High reliability
Cloud uses data multi-transcript fault tolerant, the
computation node isomorphism exchangeable and so on to ensure the high reliability of the service. Using cloud computing is more reliable than local computer.
4. Versatility
\Cloud computing doesn’t aim at certain special Application. It can produce various applications supported by cloud, and one cloud can support different applications running it at the same time.
5. High extendibility
The scale of cloud can extend dynamically to meet the increasingly requirement.
6. On demand service
Cloud is a large resource pool that you can buy according to your need; cloud is just like running water, electric, and gas that can be charged by the amount that you used.
4. Need of cloud computing
1. Technology Trends:
The trend of technology is moving very fast and also the environment is changing very rapidly. This is one of the big challenges for the government to maintain the pace with fast growing technology and changing environment. The cloud computing takes the responsibility of this and free the government or user from this.
2. Automation of multiple Management tasks:
The management tasks like assigning the resources to the processes have become very tedious work because of the volume and complexity of the tasks the cloud computing is designed to automate the multiple tasks with security.
3. High Availability and No downtime:
Availability is always desirable features of the
computation system. The services should be always available for the citizens.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
3
5. System architecture
System Architecture is the collection of independent components that are connected or interacted through the well defined connectors. One component can interact with another with the set of defined agreements.
Existing system
Problem of Existing System
In existing system, even intra-departments interaction is also not reliable and smooth within the ministry. Among them the incompatibility among the different data is very big issues at present scenario because they do not consider or follow the standardization while developing the system or any application. These problems have made a big frustration to the citizens. This is one of the reasons why citizen is not happy or satisfy with the performances of ministry. They Have lot of complains against the ministry.
Proposed architecture
The proposed following architecture as a solution. We try to address the problems with Enterprise Architecture [EA]. In EA, the core part is middleware so we start the making the architecture with the basic concept of middleware. Service Oriented Architecture [SOA] is one of the software architectures. This architecture is also based upon two
different actors i.e. service provider and
service receiver this architecture is very
popular and matured in providing the best services to the authorized users.
Enterprise Architecture
Figure 2 How does it work?
We have identified three types of services for above architecture. These are Full sharing
services, Partial sharing services and No sharing services. In full sharing services, all
services are sharable among the ministries, and in partial sharing, some services are sharable and some are not where as no sharing services are not sharable at all. As per the types of services, we propose private and public cloud in our architecture. No sharing services of a ministry are kept in the private cloud and full sharing services are kept in public cloud. The ministry has the entire control over the services in the private cloud that is why the confidential services for internal uses are kept on it. We can See manager between private and public cloud. This manager controls and protects the private cloud from external users.
6. Cloud Adoption challenges
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
4
It is clear that the security issue has played the most important role in hindering Cloud computing. Without doubt, putting your data, running your software at someone else's hard disk using someone else's CPU appears daunting to many. Well-known security issues such as data loss, phishing, botnet (running remotely on a collection of machines) pose serious threats to organization's data and software. Moreover, the multi-tenancy model and the pooled computing resources in cloud computing has introduced new security challenges that require novel techniques to tackle with. For example, hackers are planning to use Cloud to organize botnet as Cloud often provides more reliable infrastructure services at a relatively cheaper price for them to start an attack.
2. Costing Model
Cloud consumers must consider the tradeoffs amongst computation, communication, and integration. While migrating to the Cloud can significantly reduce the infrastructure cost, it does raise the cost of data communication, i.e. the cost of transferring an organization's data to and from the public and community Cloud [6] andthe cost per unit (e.g. a VM) of computing resource used is likely to be higher.
3. Charging Model
From a cloud provider's perspective, the elastic resource pool (through either virtualization or multi-tenancy) has made the cost analysis a lot more complicated than regular data centres, which often calculates their cost based on consumptions of static computing. Moreover, an instantiated virtual machine has become the unit of cost analysis rather than the underlying physical server. A sound charging model needs to incorporate all the above as well as VM associated items such as software licenses, virtual network usage, node and hypervisor management overhead, and so on.
4. Service Level Agreement
Although cloud consumers do not have control over the underlying computing resources, they do need to ensure the quality, availability, reliability, and performance of these resources when consumers have migrated their core business functions onto their entrusted cloud. In other words, it is vital for consumers to obtain guarantees from providers on service delivery. Typically, these are provided through Service Level Agreements (SLAs) negotiated between the providers and consumers.
7. Geographic Information Service
A good GIS platform can not only provides various functions, but also need to be conveniently and transparently accessed at anytime, anywhere by anyone. Some aspects that
GIS platform should take into account are listed as following
1. Hierarchical and distributed data storage.
The geographical data should be stored on different levels with different detail and accuracy. Each level of these data can be stored at different places, nodes, or servers in a distribution way.
2. Time-series data. Sometimes geographical
information changes quickly or slowly, so we must take into account the temporal nature of data and add time dimension while storing data.
3. GIS workflow. GIS applications often
operate on large-scale datasets, so how to partition and how many chunks to split become a challenge problem. The GIS platform should have the capability to organize many specific GIS operations, tasks in a GIS workflow.
4. Rich GIS APIs. The GIS platform should
provide rich APIs for developers and users to deal with complex and changing business needs.
Figure 3 1.Applications layer
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
5
Applications layer is on the top of this architecture. In this layer, the users access various GIS services. As you know,
everything (Data, Software, Platform) in cloud computing platform is regarded as a service and stored in the clouds.
2.Geographic Information Services Layer
A lot of geographic information services are available on the internet by some GIS software and services providers. We had also developed some GIS data services such as Web Map Service (WMS), Web Coverage Service (WCS), and Web Feature Service (WFS) according to the Open Geospatial Consortium (OGC) specifications or ISO standards. Some non standard services such as image tile service, data processing service, data transformation service, and special professional services are provided in our GeoGlobe Services Platform (GSP). However, most of these services are based on Service Oriented Architecture (SOA), Web Services (WS) or Grid Computing (GC) technologies, some features of cloud computing are not considered in early development. In the new architecture of GIS platform, the geographic information and services are stored in the cloud environment which refers to virtualization, distributed file system, and parallel computing etc. So the older services should transform to suit the needs of cloud computing.
3. Cloud Computing Layer
The cloud computing Operation System (OS) acts as an entrance for the user. Registered users can search the current services or data they need. At the mean time, cloud computing OS acts as a repository of services deployed by different developers. It provides flexible and scalable super computing environment for the users or applications, and the underlying software and hardware details are hidden. In this layer, all the cloud computing technologies such as parallel computing, distributed file system, computing and storing virtualization and so on are encapsulated for the developers of services.
Conclusion
Among the many IT giants driven by trends in cloud computing has not doubtful. It gives almost everyone has brought good news. For enterprises, cloud computing is worthy of consideration and try to build business systems as a way for businesses in this way can undoubtedly bring about lower costs, higher profits and more choice; for large scale industry. There is the advent of cloud
computing is bound to birth a number of new jobs. cloud computing will bring a revolutionary change in the Internet. Since cloud computing is based upon the virtual machine for virtualizing the physical resources, the reliability, availability and others non functional properties are very good.
References
[1]http://en.wikipedia.og/wiki/Cloud_computing [2] inition/0,,sid201_gci1287881,00.html [3](U.S.) Nicholas. Carr, fresh Yan Yu, "IT is no longer important: the Internet great change of the high ground cloud computing," The Big Switch:Rewining the World,from Edison to Google,CITIC Publishing House, October 2008
[4] Tal Garfinkel, Mendel Rosenblum, and Dan Boneh, "Flexible OS Support and Applications for Trusted Computing", the 9th Workshop on Hot Topics in Operating Systems (HotOS IX), USENIX, 2003.
[5]Y. Chen, V. Paxton, and R. Katz, "What’s New About Cloud Computing Security?," 2010.
[6]A. Leinwand, "The Hidden Cost of the Cloud: Bandwidth Charges," 09/07/17/the-hidden-cost-of-the-cloud-bandwidth.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
6
Android Application Framework for Cloud
Computing Environment using VPN.
Vinutha.S C.K.Raju Dr.M.Siddappa
PGstudent, Department of CSE, Asst.Professor, Department of CSE, HOD Department of CSE ,SSIT, Tumkur SSIT,Tumkur SSIT, Tumkur. [email protected] [email protected] [email protected]
Abstract: Android smart phone users and mobile
applications are growing rapidly. Cloud Computing helps to manage the data in a distributed environment which supports several platforms, systems and applications. A VPN can provide secure information transport by authenticating users. The possible Android applications are - Electronic Health Record Client based on the Android Platform [4], A Distributed Urban Sensing Platform[6], Android Smart Phone Surveillance System.
Keywords: Android, Cloud Computing, VPN, C2DM.
I. Introduction
Android is a software stack for mobile devices that includes an operating system, middleware and key applications. It allows developers to write managed code in Java language, controlling the device via Google-developed Java libraries. Applications written in C and other languages can be compiled to ARM native code and run, but this development path is not officially supported by Google.
Cloud Computing is an on-demand network access model which helps us to share resources such as networks, servers, storage, applications, and services. This cloud model promotes availability and is composed of essential characteristics, deployment models, and various service models.
A VPN is a private network that uses a public network to connect remote sites or users together. By using a VPN, businesses ensure security anyone intercepting the encrypted data can't read it.
An Android Application will be developed utilizing Cloud to Device Messaging for Cloud
Server within the Virtual Private Network of public network.
II. Android Platform Architecture
Android has built-in tool which make it easy for application development. Android provides an open development platform and offers developers the capability to build greatly rich and innovative applications. Fig 1. Shows the Android operating system architecture.
Fig 1. Android System Architecture.
A. The application layer
The Android software platform will come with a set of basic applications like browser, e-mail client, SMS program, maps, calendar, contacts and many more. All these applications are written using the Java programming language. It should be mentioned that applications can be run simultaneously, it is possible to hear music and read an e-mail at the same time. This layer will mostly be used by commonly cell phone users.
B. The application framework
An application framework is a software framework that is used to implement a standard structure of an application for a specific operating system. With the help of managers, content providers and other services programmers it can reassemble functions used by other existing applications.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
7
C. The librariesThe available libraries are all written in C/C++. They will be called through a Java interface. These includes the Surface Manager, 2D and 3D graphics, Media Codec’s like MPEG-4 and MP3, the SQL database SQLite and the web browser engine WebKit. Libc--c standard lib, SSL--Secure Socket Layer, SGL--2D image engine, OpenGL|ES--3D image engine, Media Framework--Core part of Android multi-media, SQLite--Embedded database, WebKit--Kernel of web browser, FreeType--Bitmap and Vector & Surface Manager--Manage difference windows for different applications.
D. The runtime
The Android runtime consists of two components. One is a set of core libraries which provide most of the functionality available in the core libraries of the Java programming language. The second one is the virtual machine Dalvik which operates like translator between the application and the operating system.
E. The kernel
Linux provides the hardware abstraction layer for Android, allowing Android to be ported to a wide variety of platforms in the future. Internally, Android uses Linux for its memory management, process management, networking, and other operating system services.
III. Cloud Computing Platform
Cloud computing is the storage, management, processing, and accessing information and other data stored in a specific server. The “cloud” pertains to all these necessary information, whether these are account details of customers, sale documentations, or simply records of any kind of business; the cloud is of utmost interest in making use of the cloud computing technology.
Services offered by cloud:
The term services in cloud computing is the concept of being able to use reusable, fine-grained components across a vendor’s network. This is widely known as “as-a-service”.
Offerings with as a service include traits like following:
• Low barriers to entry, making them available to small businesses.
• Large scalability.
• Multitenancy, which allows resources to be shared by many users.
• Device independence, which allows users to access the systems on different hardware.
Software as a Service (SaaS) is where
application services are delivered over the network on a subscription and on-demand basis. Cisco, Sales force, Microsoft, and Google are a few providers in this layer.
Platform as a Service (PaaS) consists of
run-time environments and software development frameworks and components delivered over the network on a pay-as-you-go basis. PaaS offerings are typically presented as API to consumers. Examples of this are: Google Apps Engine, Amazon Web Services, force.com, and Cisco® WebEx Connect.
Infrastructure as a Service (IaaS) is where
compute, network, and storage are delivered over the network on a pay-as-you-go basis. Amazon pioneered this with AWS (Amazon Web Service), and now IBM and HP are entrants here also.
IV. Virtual Private Network
The VPN protect data while it's traveling on the public network. If intruders attempt to capture the data, they should be unable to read or use it. The VPN provide the same quality of connection for each user even when it is handling its maximum number of simultaneous connections. It is able to extend its VPN services to handle that growth without replacing the VPN technology altogether.
The purpose of the tunneling protocol is to add a layer of security that protects each packet on its journey over the Internet. The packet is traveling with the same transport protocol it would have used without the tunnel; this protocol defines how each computer sends and receives data over its ISP. Each inner packet still maintains the passenger protocol, such as Internet protocol (IP) or AppleTalk, which defines how it travels on the LANs at each end of the tunnel. The tunneling protocol used for encapsulation adds a layer of security to protect the packet on its journey over the Internet.
V. Cloud to Device Messaging
C2DM is a Service which enables us to send
data from cloud servers to their android application on devices. The main components of C2DM are device which runs android application, third party Application Server and C2DM servers.
Life cycle of C2DM involve three steps;
1] Enabling C2DM which helps application register to receive messages.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
8
2] Sending message in which the third party application server sends messages to device. 3] Receiving message in which an application receives a message from C2DM server.
VI. Project Goal
The degradation of mobile performance is due to the network traffic, number of devices, data traffic etc.
Fig 2. Android Cloud with VPN Therefore establishment of “Android Mobile Cloud Computing environment using VPN connection” solve these problems. The overall project goal is represented with the help of Fig 2.
VII. Methodology
Today mobile devices are involved in better speeds and application than before. The upcoming application performance is degrading because of Internet traffic, number of devices, connection speeds etc. Hence load needs to be balanced which can be done with the help of Cloud.
• Android application [2] will be developed which utilizes C2DM for transmission of messages from server to applications on android devices.
• To establish a Virtual private Network, OpenVPN Software [5] is used. OpenVPN helps to create routed/Ethernet by generating a RSA certificate with different keys for each client. OpenVPN helps to connect separate remote networks together into one large network that is fully routed. This virtual private network helps to send data securely through the tunnel.
• To establish a mobile cloud computing environment [3] there are some cloud offerings, which are Android specific while others are more general purpose. There is a tradeoff among the cloud vendors in terms of
customizability and of automation as shown below in Fig 3.:
Fig 3. Customization & Automation Spectrum. Amazon, for example, is very customizable but is not as automated, since you are still dealing with configuring virtual machines and other infrastructure oriented activities. Google’s AppEngine is on the other end of the spectrum, not very customizable but much automated. Any of the best cloud offerings to establish cloud environment can be chosen. Finally Cloud environment helps in balancing the data traffic from different sources.
VIII. Conclusion
In this paper, we proposed a mobile cloud execution framework to execute android applications which utilizes C2DM in cloud virtualized private cloud environment. Encryption and isolation is used to protect data, against the eavesdropper from users and the cloud providers using Virtual private network. Our approach offers opportunity for end users to migrate their android applications from one mobile to another quickly and efficiently. Our framework is still a work in progress. We believe that more applications and systems can benefit from our approach. It is our hope that our framework will provide users and developers a versatile environment to carry out their applications on a range of systems, from mobile devices to cloud servers, in a convenient, efficient and secure fashion.
At present the establishment of Virtual Private network is established using the above mentioned method. An Android application is under development using cloud to device messaging. Finally this android application will be deployed on the cloud.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
9
References[1]. Android Developers website. http://developer.android.com/.
[2]. Shih-Hao Hung, Chi-Sheng Shih, Jeng-Peng Shieh, Chen-Pang Lee, and Yi-Hsiang Huang: “An Online Migration Environment for Executing Mobile Applications on the Cloud” in the Proceedings of 2011 Fifth International
Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.
[3].Charalampos Doukas, Thomas Pliakas, and Ilias Maglogiannis: “Mobile Healthcare Information Management utilizing Cloud Computing and Android OS” in the Proceedings
of 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010.
[4]. Dimitris Tychalas & Athanasios Kakarountas: “Planning and Development of an Electronic Health Record Client based on the Android Platform” in the Proceedings of 2010
14th Panhellenic Conference on Informatics.
[5]. Abdullah Alshalan & Garrett Drown: “Cloud VPN”
[6]. Jong Hoon , Ahnn Uichin Lee & Hyun Jin Moon: “GeoServ: A Distributed Urban Sensing Platform” in the Proceedings of 2011 11th
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
10
Cloud Computing Based Resource Management in
Energy Efficient Manner
Shivakumar.S.Sobani,
Dr.A.Sreenivasan
DSCE, Bangalore Professor and Director of PG Studies
Email:
[email protected]
DSCE, Bangalore
ABSTRACT
Virtualization Technology has been employed increasingly widely in modern data centers in order to improve its energy efficiency. In particular, the capability of virtual machine (VM) migration brings multiple benefits for such as resources (CPU, memory, et al.) distribution, energy aware consolidation. However, the migration of virtual machines itself brings extra power consumption. For this reason, a better understanding of its effect on system power consumption is highly desirable. In this paper, we present a power consumption evaluation on the effects of live migration of VMs. Results show that the power overhead of migration is much less in the scenario of employing the strategy of consolidation than the regular deployment without using consolidation. Our results are based on the typical physical server, the power of which is linear model of CPU utilization percentage.
Keywords -
Energy efficiency; Cloudcomputing; Virtualization; Load balancing; live or off-line migration of virtual machines.
1 INTRODUCTION
Virtualization Technology has been employed increasingly widely in modern data centers in order to improve its energy
efficiency. In particular, the capability of virtual machine (VM) migration brings
Multiple benefits for resources(CPU, memory, et al.) distribution, energy aware consolidation. In recent years, more and more data centers start to employ server virtualization strategies for resource sharing to reduce hardware and operating costs. Virtualization technologies (such as Xen, VMware, and Microsoft Virtual Servers) can consolidate applications previously running on multiple physical servers onto a
single physical server, via this way, the energy consumption of data center can be effectively reduced. Consequently, virtualized infrastructures are considered as a key solution to the power management of data center. And using VMs migration technology enables the consolidation of servers spread across many locations. If QoS performance can be maintained in the consolidation, a system can be configured with a fewer number of servers and less power consumption.
Cloud computing has become a very promising paradigm for both consumers and providers in various fields of endeavor, such as science, engineering and business. A cloud typically consists of multiple resources possibly distributed and heterogeneous. Although the notion of a cloud existed in one form or another for some time now (its roots can be traced back to the mainframe era [1]), however, recent advances in virtualization technologies in particular have made it much more compelling compared to the time when it was first introduced. A number of practices can be applied to achieve energy efficiency, such as improvement of applications, algorithms, energy efficient hardware, Dynamic Voltage and Frequency Scaling (DVFS) [2], terminal servers and thin clients, and virtualization of computer resources [3].
Cloud computing naturally leads to energy-efficiency by providing the following characteristics:
• Economy of scale due to elimination of redundancies.
• Improved utilization of the resources.
• Location independence – VMs can be moved to a place where energy is cheaper.
• Scaling up and down – resource usage can be adjusted to current requirements.
• Efficient resource management by the Cloud provider.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
11
One of the important requirements for a Cloud computing environment is providing reliable QoS. It can be defined in terms of Service Level Agreements (SLA) that describes such characteristics as minimal throughput, maximal response time or latency delivered by the deployed system. VMs may not get the required amount of resource when requested. This leads to performance loss in terms of increased response time, time outs or failures in the worst case. Therefore, Cloud providers have to deal with energy-performance trade-off – minimization of energy consumption, while meeting QoS requirements.
A.
Research scope
The focus of this work is on energy-efficient resource management strategies that can be applied on a virtualized data center by a Cloud provider (e.g. Amazon EC2). The main instrument that we leverage is live migration of VMs. The ability to migrate VMs between physical hosts with low overhead gives flexibility to a resource provider as VMs can be dynamically reallocated according to current resource requirements and the allocation policy. Idle physical nodes can be switched off to minimize energy consumption. In this paper we present a decentralized architecture of the resource management system for Cloud data centres and propose the development of the following policies for continuous optimization of VM placement:
• Optimization over multiple system resources – at each time frame VMs are reallocated according to current CPU, RAM and network bandwidth utilization.
• Network optimization – optimization of virtual network topologies created by intercommunicating VMs. Network communication between VMs should be observed and considered in reallocation decisions in order to reduce data transfer overhead and network devices load.
• Thermal optimization – current temperature of physical nodes is considered in reallocation decisions. The aim is to avoid “hot spots” by reducing workload of the overheated nodes and thus decrease error proneness and cooling system load.
B. Research challenges
The key challenges that have to be addressed are:
• How to optimally solve the trade-off between energy savings and delivered performance?
• How to determine when, which VMs, and where to migrate in order to minimize energy consumption by the system, while minimizing migration overhead and ensuring SLA?
• How to develop efficient decentralized and scalable algorithms for resource allocation?
• How to develop comprehensive solution by combining several allocation policies with different objectives?
Energy consumption and resource utilization in clouds are highly coupled. Specifically, resources with a low utilization rate still consume an unacceptable amount of energy compared with their energy consumption when they are fully utilized or sufficiently loaded. According to recent studies in [4–7], average resource utilization in most data centers can be as low as 20%; and the energy consumption of idle resources can be as much as 60% or peak power. In response to this poor resource utilization, task consolidation is an effective technique to increase resource utilization and in turn reduces energy consumption. This technique is greatly enabled by virtualization technologies that facilitate the running of several tasks on a single physical resource concurrently. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization [6, 8]. This encouraging fact further advocates the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power.
Kusic et al. [10] have stated the problem of continuous consolidation as a sequential optimization and addressed it using Limited Look ahead Control (LLC). The proposed model requires simulation-based learning for the application specific adjustments. Due to complexity of the model the optimization controller’s execution time reaches 30 minutes even for a small number of nodes (e.g. 15), that is not suitable for large-scale real-world systems. On the contrary, our approach is heuristic-based
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
12
allowing the achievement of reasonable performance even for large-scale as shown in our experimental studies.
Srikantaiah et al. [11] have studied the problem of requests scheduling for multi-tiered web-applications in virtualized heterogeneous systems in order to minimize energy consumption, while meeting performance requirements. To handle the optimization over multiple resources, the authors have proposed a heuristic for multidimensional bin packing problem as an algorithm for workload consolidation. However, the proposed approach is workload type and application dependent, whereas our algorithms are independent of the workload type and thus are suitable for a generic Cloud environment.
Song et al. [12] have proposed resource allocation to applications according to their priorities in multi-application virtualized cluster. The approach requires machine-learning to obtain utility functions for the applications and defined application priorities. Unlike our work, it does not apply migration of VMs to optimize allocation continuously (the allocation is static). Cardosa et al. [13] have explored the problem of power efficient allocation of VMs in virtualized heterogeneous computing environments. They have leveraged “min”, “max” and “shares” parameters of VMM that represent minimum, maximum and proportion of CPU allocated to VMs sharing the same resource. The approach suits only enterprise environments or private Clouds as it does not support strict SLA and requires knowledge of applications priorities to define shares parameter.
2 MODELS
In this section, we describe the cloud, application and energy models, and define the task consolidation problem targeted in this work. The details of the model presented in this section focus on resource management characteristics and issues from a cloud provider’s perspective.
2.1 Cloud model
The target system used in this work consists of a set R of r resources/processors that are fully interconnected in the sense that a route exists between any two individual resources (Fig. 1).
We assume that resources are homogeneous in terms of their computing capability and capacity; this can be justified by using virtualization technologies. Nowadays, as many-core processors and virtualization tools (e.g., Linux KVM, VMware Workstation & VMware Fusion, Xen, Parallels Desktop for Mac, VirtualBox) are commonplace, the number of concurrent tasks on a single physical resource is loosely bounded. Although a cloud can span across multiple geographical locations (i.e., distributed), the cloud model in our study is assumed to be confined to a particular physical location. The inter-processor communications are assumed to perform with the same speed on all links without substantial contentions. It is also assumed that a message can be transmitted from one resource to another while a task is being executed on the recipient resource, which is possible in many systems.
Fig. (1) Cloud model
2.2 Application model
Services offered by cloud providers can be classified into software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS). Note that, when instances of these services are running, they can be regarded as computational tasks or simply tasks. While IaaS requests are typically tied with predetermined time frames (e.g., pay-per-hour), requests of SaaS and PaaS are often not strongly tied with a fixed amount of time (e.g.,
pay-per-Proceedings of National Conference on Advanced Computer Applications NCACA 2012
13
use). However, it can be possible to have estimates for service requests for SaaS and PaaS based on historical data and/or consumer supplied service information. Service requests in our study arrive in a Poisson process and the requested processing time follows exponential distribution. We assume that the processor/CPU usage (utilization) of each service request can be identifiable. It is also assumed that disk and memory use correlates with processor utilization [6]. Hereafter, application, task and service are used interchangeably.
3. SYSTEM ARCHITECTURE
In this work the underlying infrastructure is represented by a large-scale Cloud data center comprising n heterogeneous physical nodes. Each node has a CPU, which can be multicore, with performance defined in Millions Instructions Per Second (MIPS). Besides that, a node is characterized by the amount of RAM and network bandwidth. Users submit requests for provisioning of m heterogeneous VMs with resource requirements defined in MIPS, amount of RAM and network bandwidth. SLA violation occurs when a VM cannot get the requested amount of resource, which may happen due to VM consolidation. The software system architecture is tiered comprising a dispatcher, global and local managers. The local managers reside on each physical node as a part of a Virtual Machine Monitor (VMM). They are responsible for observing current utilization of the node’s resources and its thermal state. The local managers choose VMs that have to be migrated to another node in the following cases:
• The utilization of some resource is close to 100% that creates a risk of SLA violation.
• The utilization of resources is low, therefore, all the VMs should be reallocated to another node and the idle node should be turned off.
• A VM has intensive network communication with another VM allocated to a different physical host.
• The temperature exceeds some limit and VMs have to be migrated in order to reduce load on the cooling system and allow the node to cool down naturally. The local managers send to the global managers the information about the utilization of resources and VMs chosen to migrate. Besides that, they issue commands for VM resizing, application of DVFS and turning on / off idle nodes. Each global manager is attached to a set of nodes and
processes data obtained from their local managers. The global managers continuously apply distributed version of a heuristic for semi-online multidimensional bin-packing, where bins represent physical nodes and items are VMs that have to be allocated. The decentralization removes a Single Point of Failure (SPF) and improves scalability. Each dimension of an item represents the utilization of a particular resource. After obtaining allocation decision, the global managers issue commands for live migration of VMs.
As shown in Figure 2, the system operation consists of the following steps:
New requests for VM provisioning.
Users submit requests for provisioning of VMs.
Dispatching requests for VM provisioning. The dispatcher distributes
requests among global managers.
Intercommunication between global managers. The global managers exchange information about utilization of resources and VMs that have to be allocated.
Data about utilization of resources and VMs chosen to migrate. The local
managers propagate information about resource utilization and VMs chosen to migrate to the global managers.
Migration commands. The global
managers issue VM migration commands in order to optimize current allocation.
Commands for VM resizing and adjusting of power states. The local
managers monitor their host nodes and issue commands for VM resizing and changes in power states of nodes.
VM resizing, scheduling and migration actions. According to the
received commands, VMM performs actual resizing and migration of VMs as well as resource scheduling.
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
14
Fig. (2) System architecture
Fig. (3) Block diagram
4. ALLOCATION POLICIES
We propose three stages of VM placement optimization: reallocation according to current utilization of multiple system resources, optimization of virtual network topologies established between VMs and VM reallocation considering thermal state of the resources. Each of these stages is planned to be investigated separately and then combined in an overall solution. The developed algorithms have to meet the following requirements:
• Decentralization and parallelism – to eliminate SPF and provide scalability.
• High performance – the system has to be able to quickly respond to changes in the workload.
• Guaranteed QoS – the algorithms have to provide reliable QoS by meeting SLA.
• Independence of the workload type – the algorithms have to be able to perform efficiently in mixed application environments.
The VM reallocation problem can be divided in two: selection of VMs to migrate and determining new placement of these VMs on physical hosts. The first part has to be considered separately for each optimization stage. The second part is solved by application of a heuristic for semi online multidimensional bin-packing problem. At the first optimization stage, the utilization of resources is monitored and VMs are reallocated to minimize the number of physical nodes in use and thus minimize energy consumption by the system. However, aggressive consolidation of VMs may lead to violation of performance requirements. We have proposed several heuristics for selection of VMs to migrate and investigated the trade-off between performance and energy savings. To simplify the problem for the first step we considered only utilization of CPU. The main idea of the policies is to set upper and lower utilization thresholds and keep total utilization of CPU created by VMs sharing the same node between these thresholds. If the utilization exceeds the upper thresholds, some VMs have to be migrated from the node to reduce the risk of SLA violation. If the utilization goes below the lower thresholds, all VMs have to be migrated and the node has to be switched off to save the energy consumed be the idle node. Another problem is to determine particular values of the utilization thresholds. The results of the proposed algorithms evaluation are presented in Section 5.
Due to continuous reallocation, some intensively communicating VMs can be placed inefficiently leading to excessive load on the network facilities. Therefore, it is crucial to consider network communication behavior of VMs in reallocation decisions. The aim of the second proposed optimization stage is to place communicating VMs in a way minimizing the overhead of data transfer over network.
A cooling system of a data center consumes a significant amount of energy. Therefore, the third proposed optimization stage is aimed at optimization of cooling system operation. Due to consolidation, some computing nodes experience high load leading to overheating and thus require
Proceedings of National Conference on Advanced Computer Applications NCACA 2012
15
extensive cooling. Monitoring of the nodes’ thermal state using sensors gives an opportunity to recognize overheating and reallocate workload from the overheated node to allow the natural cooling. The network and temperature optimizations are subjects for the ongoing research work.
5. EVALUATION
As the proposed system is targeted on a large-scale Cloud data center, it is necessary to conduct large-scale experiments to evaluate the algorithms. However, it is difficult to run large-scale experiments on a real-world infrastructure, especially when the experiments have to be repeated for different policies with the same conditions [14]. Therefore, simulation has been chosen as a way to evaluate the proposed heuristics. We have chosen CloudSim toolkit [14] as a simulation framework, as it is built for simulation of Cloud computing environments. In comparison to alternative simulation toolkits (e.g.SimGrid, GangSim), CloudSim supports modelling of on-demand virtualization enabled resource and application management. We have extended the framework in order to enable energy aware simulations as the core framework does not provide this capability. In addition, we have incorporated the abilities to account SLA violations and to simulate dynamic workloads that correspond to web applications and online services.
6. REFEERENCES
[
1] Parkhill D (1966) the challenge of the computer utility. Addison-Wesley Educational, Reading[2] G.Semeraro, G. Magklis, R. Balasubramonian, D. H. Albonesi, S. Dwarkadas, and M. L. Scott, “Energy-efficient processor design using multiple clock domains with dynamic voltage and frequency scaling,” in
Proceedings of the 8th International Symposium
on High-Performance Computer Architecture,
2002, pp. 29–42.
[3] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, “Xen and the art of virtualization,” in
Proceedings of the 19th ACM symposium on Operating systems principles, 2003, p. 177.
[4] Barroso L, Holzle U (2007) The case for energy-proportional computing. IEEE Comput [5] Bohrer P, Elnozahy E, Keller T, Kistler M, Lefurgy C, Rajamony R (2002) The case for
power management in web servers. Power Aware Comput 261–289
[6] Fan X, Weber X-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proc 34th annual international symposium on computer architecture (ISCA ’07), 2007, pp 13–23
[7] Lefurgy C, Wang X, Ware M (2007) Server-level power control. In: Proc IEEE international conference on autonomic computing, Jan 2007 [8] Meisner D, Gold BT, Wenisch TF (2009) PowerNap: eliminating server idle power. In: Proc 14th international conference on architectural support for programming languages and operating systems (ASPLOS ’09), 2009, pp 205–216
[9] R. Nathuji and K. Schwan, “Virtualpower: Coordinated power management in virtualized enterprise systems,” ACM SIGOPS Operating
Systems Review, vol. 41, no. 6, pp. 265–278,
2007
[10] D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, “Power and performance management of virtualized computing environments via lookahead control,”
Cluster Computing, vol. 12, no. 1, pp. 1–15,
2009
[11] S. Srikantaiah, A. Kansal, and F. Zhao, “Energy aware consolidation for cloud computing,” Cluster Computing, vol. 12, pp. 1– 15, 2009
[12] Y. Song, H. Wang, Y. Li, B. Feng, and Y. Sun, “Multi-Tiered On-Demand resource scheduling for VM-Based data center,” in
Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid-Volume 00, 2009, pp. 148–155
[13] M. Cardosa, M. Korupolu, and A. Singh, “Shares and utilities based power consolidation in virtualized server environments,” in
Proceedings of IFIP/IEEE Integrated Network Management (IM), 2009
[14] R. Buyya, R. Ranjan, and R. N. Calheiros, “Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: Challenges and opportunities,” in
Proceedings of the 7th High Performance Computing and Simulation Conference (HPCS’09). IEEE Press, NY, USA, 2009
16
Cloud Computing and Emerging IT Trends
Mrs.J.Srimathi
Assistant Professor / MCA,
Vivekananda Business School for Women,
Thiruchengode, Tamilnadu, India.
Mrs.D.Kalaivani,
Assistant Professor / MCA,
Vivekanandha Institute of Information and
Management Studies,
Thiruchengode, Tamilnadu, India
Abstract
India as a country with a huge population base has never been given adequate resources to embrace innovation. The case is even worse if you are an individual who wants to gain access to the latest technologies for your research and development needs. Cloud Computing addresses this challenge to a great extent and provides access to the necessary IT resources to satisfy your IT needs in an affordable way. Cloud computing is raising major disruptive force for both IT vendors and users as companies globally attempt to reduce cost of ownership for IT infrastructure. The different layers of services in cloud computing are different technological concepts like Grid computing, Virtual computing, etc. Could computing and Virtualization have opened up opportunities for organizations to offer a Virtual Desktop
environment to
companies and individuals. This paper provides a comprehensive analysis of cloud computing services, market segmentation, technology basics, trends, key players and challenges for cloud deployment in enterprise IT with virtualization.
Keywords
VMWare is a program is owned by ENC Corporation. It
allows you to create and use virtual operating systems
VIM is a Vendor Independent Messaging. Iaas is a Infrastructure as a service.
CAGR means Compound reaching Annual Growth Rage.
Virtualization and Cloud Computing
Cloud technology allows to manage and increase available system resources on the cloud automatically and this sets cloud hosting apart from traditional hosting providers who are confined to the physical limitations of a server.
Virtualization is the creation of a virtual (rather than actual) version of something, such as an operating system, a server, a storage device or network resources.
1.1 Virtualization
The ability to run multiple operating systems on a single physical system and share the underlying hardware resources is known as Virtualization. Virtualization deals with the heterogeneity of the infrastructure and it will allow
partitioning and isolating of physical resources with application execution. In virtualization, VIM provides a
uniform view of the resource pool and life-cycle
management. Virtualization is the creation of a virtual (rather than actual) version of something such as an operating system, a server, a storage device or network resources.
a)
Hardware virtualization:
It refers to the creation of a virtual machine that acts like a real computer with an operating system. Software executed on these virtual machines is separated from the underlying hardware resources.
b)
Operating system virtualization:
It is commonly used in virtual hosting environments, where it is useful for securely allocating finite hardware resources amongst a large number of mutually-distrusting users.
c)
Memory virtualization:
It allows networked and distributed servers to share a pool of memory to overcome physical memory limitation which is a common bottleneck in software performance. With this capability integrated into the network, applications can take advantage of a very large amount of memory to improve overall performance, system utilization, increase
memory
usage efficiency, and enable new use cases.
d)
Application virtualization:
It describes software technologies that improve portability, manageability and compatibility of applications by encapsulating them from the underlying operating system on which they are executed.
e)
Desktop virtualization:
It is the concept of isolating a logical operating system (OS ) instance from the client that is used to access it.
17
1.2 Cloud Computing
Cloud Computing is internet-based computing whereby shared resources, software, and information are provided to computers and devices on demand. Cloud Computing is a paradigm shift from mainframe to client-server in the early 1980s. “The provisioning of services in a timely on-demand manner to allow the scaling up and down of resources.” Cloud computing overlaps some of the concepts of distributed, grid and utility computing.
•
Grid Computing:
The combination of computer resources from multiple administrative domains applied to a common task.•
Utility Computing:
The packaging of computing resources (computation, storage etc.) as a metered service similar to a traditional public utility.Cloud Computing IaaS is the combination of those old concepts of utility and grid.
2. Basic Requirements of Cloud Computing
2.1
Transparency
One of the premises of Cloud Computing is that services are delivered transparently regardless of the physical implementation within the "cloud”. This fundamental concept is another version of virtualization where multiple resources appear to the user as a single resource. For example, when a service is provisioned to a user or an organization, it may need only a single server (real or virtual) to handle demand. But as more users access that service it may require the addition of more servers (real or virtual).
2.2
Scalability
Cloud Computing service providers are in a need to scale up and build out "mega data centers". Making things even more difficult will be the need to scale on-demand in real-time in order to make the most efficient use of application infrastructure resources. Many Claims that this will require a virtualized infrastructure such that resources can be provisioned and de-provisioned quickly, easily and, one hopes, automatically. The "control node" often depicted in high-level diagrams of the "cloud computing mega data center" will need to provide on-demand dynamic application scalability.
2.3
Intelligent Monitoring
In order to achieve the on-demand scalability and transparency required of a mega data center in the cloud, the control node, i.e. application delivery solution will need to have intelligent monitoring capabilities. It needs to know the applications and services being served from the cloud and understand when behavior is outside accepted norms. The application delivery mechanism is not only to provide the information about when an application or service is in trouble, but also take action based on that information.
If an application is responding slowly and is detected by the monitoring mechanism, then the delivery solution should adjust application requests accordingly. If the number of concurrent users accessing a service is reaching capacity, then the application delivery solution should be able to not only detect that through intelligent monitoring but participate in the provisioning of another instance of the service in order to ensure service to all clients.
2.4
Security
In cloud computing the mega data center must be architected with security in mind, and it must be considered a priority for every application, service, and network infrastructure solution that is deployed. The application delivery solution, as the "control node" in the mega data center, is necessarily one of the first entry points into the cloud data center and must be secure. This Cloud computing provides network security, protocol security, transport layer security, and application security should be prime candidates for implementation at the edge of the cloud, in the control node.
3. Deployment Model of Cloud Computing
Figure-1 Deployment Model of Cloud Computing
Cloud Computing IaaS = Grid Computing + (Utility Computing * N)
…or…
Cloud Computing IaaS is a Grid of Compute Utilities
