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Opportunistic Virtual Probing Technique for Data Servers in Multi-Cloud
A. SELVAKUMAR, KAVITHAYENI K.
Department Computer Science and Engineering Christ College of Engineering and Technology
Abstract
Cloud has become one of the variant access storage and retrieval network these days. Using a basic credential and user information a user can process/request multiple data at a time. Due to increased server traffic concentration, the possibility of serving a user in-time and constant traffic patterns in a cloud are being affected that are considered to be the drawbacks in cloud concept. Though various optimization algorithms have been proposed for LB and to preserve lossy connections recent approaches in HBLB have been admired in resource allocation and user in-time-service metrics. Yet the fulfillment is restricted with one-time-resources fetch and refetching process of a vm after utilizing resources. To minimize the re-use of resources and to avoid multiple agent access of a vm-resource, we propose a “Probe based definite vm resource sharing method” to avoid complexity and time variant method of a cloud server in optimization. Besides LB, a probabilistic resource best after fetch and release helps in preserving the re- allocation of the resource before it is actually being dismissed.
Keywords: load balancing, virtual machine, traffic concentration, resources allocation, fetching, optimization.
I. INTRODUCTION
Cloud computing is a new technology. It provides all the data at a lower cost. In cloud computing users can access resources all the time through internet. They need to pay only for those resources as much they use .In Cloud computing cloud provider outsourced all the resources to their client. There are many existing issues in cloud computing. The main problem is load balancing in cloud computing. Load balancing helps to distribute all loads between all the nodes. It also ensures that every computing resource is distributed efficiently and fairly. It helps in preventing bottlenecks of the system which may occur due to load imbalance. It provides high satisfaction to the users. Load balancing is a relatively new technique that provides high resource utilization and better response time[1].
Cloud computing and storage solution provide users and enterprises with various capabilities to store and process their data in third party data centers. It relies on sharing of resources to achieve coherence and economics of scale. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. The cloud model of computing promotes availability.
Cloud computing definition in various aspects: i) cloud computing is defined as a type of computing that relies on sharing computing resources rather than having local server or personal device to handle application [2] ii) Cloud computing is a model for enabling ubiquitous network access to a shared pool of configurable computing resources [3].
A. CLOUD COMPUTING MODEL
B. TYPES OF CLOUD SERVICES (i). Software as a Service (SaaS)
SaaS provided all the application to the consumer which is provided by the providers. Applications are running on a cloud infrastructure. Interfaces (web browser) are used access the applications. The consumer does not control the internal function. The capability provided to the consumer in this highest level is to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a Web browser (e.g., Web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure, including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. That Customers who are not able to developed software, but they need high level applications can also be take advantages from SaaS. There are some of applications of software of services. Customer resource management (CRM) Video conferencing, IT service management, Accounting, Web analytics, Web content management Advantages
1. SaaS Cloud Providers often take into account multiple platforms: mobile, browser, and so on. If you or your organization wants software that can be accessed from multiple platforms, this might be an easy way to make that happen. As part of this, SaaS Cloud Providers may also provide apps for mobile devices.
2. The SaaS Cloud Provider may provide better security than your existing software (security—or inadequate security—can also be a disadvantage). Better security may come in part because it is critical for the SaaS Cloud Provider and is part of their main business. In-house security, on the other hand, is not usually an individual's or a organization's main business and, therefore, may not be as good as that offered by the SaaS Cloud Provider.[4]
3. Platform as a Service (PaaS)
PaaS provides all the resources to the customers that are required for building applications. It provides all the services on the internet .User not need to download and install the software. Consumers deploy all the application onto the cloud infrastructure. There are different tools and programming languages are provided to the uses to develop the applications. The consumer does not control network, servers, operating systems, or storage. Consumer controls all applications which they deploy.
The capability provided to the consumer in this intermediate level is to deploy onto the cloud infrastructure consumer created or acquired applications developed using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure, including network, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Advantages
1. The maintenance and upgrades of tools, database systems, etc. and the underlying infrastructure are the responsibility of the PaaS Cloud Provider.
2. Various pricing models may allow paying only for what you use. This, for example, can allow an individual or a small organization to use sophisticated development software that they could not afford if it was installed on an internal, dedicated server.[4]
3. Infrastructure as a Service (IaaS)
In this service consumer does not manage or control the underlying cloud infrastructure. In infrastructure as a service consumer able to control operating systems, storage, and all applications which they deployed. There is a limited control of customer on the networking components. Infrastructure Providers control storing and processing capacity. Virtualization is used assign and dynamically resizes these resources to build systems as demanded by customers. Consumers deploy the software stacks that run their services. Provider provide network, services as on demand services. User use these services directly .It can be used to avoid buying, housing, and managing the basic hardware and software infrastructure components, scales up and down quickly to meet demand. The capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems; storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Advantages
1. Various pricing models may allow paying only fir what we use. This, for example, can allow individual or a small organization to use sophisticated development software that they could not afford if it was installed on an internal, dedicated server.
2. Some IaaS Providers provide development options for multiple platforms: mobile, browser, and so on. If you or your organization wants to develop software that can be accessed from multiple platforms, this might be an easy way to make that happen [4].
C. TYPES OF CLOUD
1) Public Cloud: The cloud infrastructure is made available to the general public or a large industry group and is owned by an organization .Anyone can use public cloud as they want without restriction[5].
2) Private Cloud: The cloud infrastructure is used by a single organization. Private cloud is only managed by the organization or a third party[5].
4) Hybrid Cloud: Hybrid cloud is a combination of two or more clouds (private, community, or public). That remains unique entities but is bound together by standardized technology that enables data and application portability. Ex: cloud bursting for load-balancing between clouds [5].
II. LOADBALANCINGTECHNIQUE
A. HONEY – BEE TECHNIQUE
Honey-bee based resource allocation has two stages. One is dancing phase and other is LB phase. In a dancing phase, the agent map request handlers to each of the resources to fetch appropriate data to serve the request. The agents make a traversal in the time lesser than the time out, to service the request. The LB phase is active when a server is flooded due to unexpected fragments approaching the server.
The response handler intimates the agent mapping unit to initiate a virtual agent to share the requested resource handling from the server. After load has been distributed or shared between the agents (mapped and temporary agent), the resources is delivered to the user. Once the user port is closed after reception, the temporary agent is diminished by the mapping unit and an RSI (request status information) is acknowledged back to the resource handler unit.
An algorithm for load balancing in nimble peer-to-peer system and adding together hybrid environments. In most peer-to-peer system the non uniform of objects in the feel and with the load of the node can be distorted continuously due to the insertion, taking away and subsidiary various operations. This will leads to fall the produce a consequences of the system. So the concept of virtual server can be introduced. The load hint of the peer nodes is stored in inconsistent directories in this proposed load balancing algorithm. These directories in the back to schedule reassignment of the virtual servers to generate an improved relation. Greedy heuristic algorithm used to locate out a bigger resolved for the proper utilization of the nodes.
The huge number of virtual servers in the system helps to gathering the utilization. The various load counsel in to the corresponding pool and with the virtual server assignments are to be finished. This proposed algorithm should be applied to every second types of resources associated to storage, bandwidth etc, It was intended to handle the various situations in imitation of changing load of the node, node adroitness, entering and leaving of nodes and in addition to insertion and elimination of the nodes. Advantages are high node utilization and increasing scalability [6].
III. SYSTEMCONCEPT
Other than LB, in order to minimize the traffic concentration at one particular server, the virtual agents are held at each port of the request. The probe information and traffic concentration are updated to the Agent Mapping Unit. When the traffic concentration of a server exceeds its saturated handling capacity, the ported virtual agent is initiated to receive the information based on sharing. The virtual machine after completing the resource allocation, usually releases the connection. Here, we make an
opportunistic broadcast with a Probe Timer (PT) before which the resources can be shared on request.
Probe time is the maximum active time after a resource is being released from one request and is awaiting a released time. If a request needs the balanced resource, but its Probe Time exceeds the actual release time, the virtual machine moves the resource to the local cloud storage. The local storage sets a timed out condition for the started data within which it has to be served. Once the data is server, the local manager re-imbuses the stored data to the virtual machine from which the release phase is continued. If number of request has approached a virtual machine within the Probe Time, then the virtual machine releases the resources.
The request handler on receiving the request from the end user. The request handler creates fetching agent (bees) for collecting data from the available resources. If the requested resources is available with the local storage, then the resources handler flow the resources to the request handler and then to the end user. For accessing foreign resources, the request handler on creating bees, requests the agent unit for accessing foreign- resources. The agent unit creates a resolver to handle the foreign resource access requests. The foreign resource handler mapping unit allocates the vm to the forwarded bees. The vm broadcasts its active time (relieving time span) to the requesting bees.
Before the expiry time, the vm broadcasts its state to the other agent in the network. If any agent wants to access the same resource as the first request then the vm extends its expiry time in order to service the further requests. When the numbers of bees are maximum, then the vm checks for the capacity of handling the bees and accepts the following requests. The same condition is checked for the inferring servers to handle the load ubiquitously without creating congestion. This result in seamless connectivity keeping the link uptime constant. The vm resources after further services can extent/shrink its expiry time based on the availability of request. Through this, the bandwidth wastage is minimized by constantly maintaining the utilization of the links.
A. SYSTEM ARCHITECTURE
is handled by service agent. Allocation takes place and then the task agent specifies the task that agent has to do base on the pdf value and then it is received by request handler from task agent. After the process are over then status of the agent is displayed which is used for the fetching or using this agent without the time delay. Once it reaches the request handler then it is send to the task object.
Once the request received to the task object, it automatically fetches the resources that are asked for the resource. Then the loads are balanced by means of creation of n number of agents to work with is based on the input request. With respect to pdf value the agent is allocated to the end user. In the task object the agent allocation process is done along with the load balancing.
B. FLOWCHART
The flowchart for the proposed system is based on the work flow of the process. The first step is to receive the request from the end user then the agent is allocated if the agent is busy it will wait for the acknowledgement from the data center otherwise the process handler assign the request to the resources handler. When the resources handler is available then it will map to the resource then
the service request is made available to continue the process. If the resource handler is not available then it should request for a vm the data center fetch it to the local vm handler. Then it will check whether the resource is sufficient is yes then vm is shared to the global resource and get the time slot for the best time of execution request then it is mapped to its corresponding agent. On the other hand after the request is mapped to the resources along with the service handler. If the request is serviced then it will release the process and also the resource of that agent.
IV. CONCLUSION
The tasks are to be sending to the under loaded machine and like foraging bee the next tasks are also sent to that virtual machine till the machine gets overloaded as flower patches exploitation is done by scout bees. Honey bee behavior inspired load balancing improves the overall throughput of processing and priority based balancing focuses on reducing the amount of time a task has to wait on a queue of the VM. Thus, it reduces the response of time of VMs. We have compared our proposed algorithm with other existing techniques. Results show that our algorithm stands good without increasing additional overheads.
A. PARAMETER EVALUATION
(i) Resource Mapping in Load Balancing
(ii) Throughput
(iii) Bandwidth Utilization
(iv). Maximum Transfer Unit
(v) Buffer overhead
(vi) Resource mapping
REFERENCES
[1] Won Kim, Department of Software Design & Management, Gachon University,Gyeonggi- do, South Korea, “International Journal of Web and Grid Services”,volume 9, issue 3, August 2013, pp 287-303.
[2] http://www.webopedia.com/TERM/C/cl oud_computing.html [3] Peter Mell, Timothy Grance, Recommendations of the National
Institute of Standards and Technology , September 2011.
[4] Grosu, D., Das, A., “Auction-Based Resource Allocation Protocols In Grids”, 16th Iasted International Conference On Parallel And Distributed Computing And Systems, 2004
[5] http://www.servicearchitecture.com/artic
les/cloudcomputing/cloud_computing_defi nition.html
