International Journal of Emerging Technology & Research

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1 International Journal of Emerging Technology & Research A Study Based on the Survey of Optimized Dynamic Resource Allocation Techniques in Cloud Computing Sharvari J N 1, Jyothi S 2, Neetha Natesh 3 1, 2, 3 Department of ISE, Dr.AIT, Bangalore, Karnataka, India Abstract The business benefits of cloud and virtualization have spurred many organizations to consider updating their IT infrastructures from accelerated service delivery costs and resource optimization. Cloud computing offers a variety of resources to cloud users. It is an on demand service as it offers flexible and dynamic resource allocation and guarantees services the pay as you use manner to the public. Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the gains in the cloud model come from resource multiplexing through virtualization technology. This paper provides a detailed description of the various dynamic resource allocation techniques in cloud for cloud users. Keywords: Cloud computing, virtualization, dynamic resource allocation, cloud users. 1. Introduction Cloud computing is a computing technology, where a pool of resources are connected in private and public networks and to provide these dynamically scalable infrastructure for application. The elasticity and the lack of upfront capital investment offered by cloud computing is appealing to many businesses. There is a lot of discussion on the benefits and costs of the cloud model and on how to move legacy applications onto the cloud platform. It offers the virtualized resources to the cloud users. Cloud computing provide dynamic provisioning and thus can allocate machines to store data and add or remove the machines according to the workload demands. Cloud computing platforms such as, those provided by Microsoft, Amazon, Google, IBM. Cloud computing is an environment for sharing resources without the knowledge of the infrastructure and can makes it possible to access the applications and its associated data from anywhere at any time. The cloud deployment models are: Public cloud Private cloud Hybrid cloud Community cloud The cloud service models are: Software as a Service Platform as a Service Infrastructure as a Service Cloud computing is based on virtualization technology. Virtualization technology is used to allocate the data center resources dynamically based on the application demands. Virtual machine live migration technology makes it possible to mapping between the virtual machines (VMs) and the physical machines (PMs) while applications are running. Live migration increase the resource utilization and provide the better performance result. 2. Resource Allocation A process of assigning available resources to the needed cloud applications in cloud computing is known as resource allocation. Cloud resources can be provisioned on demand in a fine-grained, multiplexed manner. In cloud the resource allocation is based on the infrastructure as a service (IaaS). In cloud platforms, resource allocation takes place at two levels: Copyright reserved by IJETR (Impact Factor: 0.997) 674

2 The load balancer assigns the requested instances to physical computers, to balance the computational load of multiple applications across physical computers whenever an application is uploaded to the cloud. Requests should be assigned to a specific application instance to balance the computational load across a set of instances of the same application whenever an application receives multiple incoming requests. The criteria to satisfy resource allocation techniques are: Resource contention: When two applications try to access the same resource at the same time. Resource fragmentation: When the resources are isolated and they cannot be allocated to the needed application due to fragmentation. Scarcity of resources: When the demand for the resources is high and the resources are limited. Over provisioning of resources: When the applications get surplus resources than the demanded one. 2.1 Issues of Research in Dynamic Resource Allocation Some of the issues in dynamic resource allocation techniques in cloud environment have been analysed Optimization of Multi-Attribute Resource Allocation in Self-Organizing Clouds dynamically It uses Self-Organizing Clouds (SOC) achieves maximum resource utilization and also delivers optimal execution efficacy. SOC connect a large number of desktop computers on the internet by P2P network. Each participating computer acts as a resource provider and a resource consumer. Self-Organizing Clouds consist of two main issues: Locating a qualified node to satisfy a user s task resource demand with bounded delay Optimizing a task s execution time by determining the optimal shares of the multi-attribute resources to allocate to the tasks with various QoS constraints, such as the expected time for execution. It mainly uses two algorithms: Dynamic optimal proportional share Multi Range Query Protocol Dynamic Resource Allocation for Spot Markets in Clouds For each virtual machine the demand can fluctuate independently at run time and hence it becomes a problem to dynamically allocate data center resources to each spot market to maximize the revenue of the cloud provider. The solutions to the above problems may be presented as follows: A market analysis for forecasting the demand of each spot market can be done. A scheduling and consolidation mechanism that allocates resources dynamically to each spot market to maximize the total revenue can be implemented. Dynamic resource allocation framework consists of the following components: Market Analyzer: Analyzes the market situation and forecasts the future demand and supply level. Predict the future demands use AR(auto regressive model) Capacity planner: Capacity planner decide the expected price of each VM Different pricing schemes: In the fixed pricing scheme, price of a VM type does not vary with the current supply and demand. In the uniform pricing scheme, the price of a VM type is adjustable at run-time. VM scheduler: Make online scheduling decision for revenue maximization Dynamic resource allocation policy has the multiple machine configurations and Copyright reserved by IJETR (Impact Factor: 0.997) 675

3 this will amplified when the demand pattern changes over the time Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment Virtualization technology is used to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. The techniques used are: Virtualization Technology: This is used to allocate data center resources based on the application demands. Skewness: This is used to measure the unevenness in the multidimensional resource utilization of a server. Skewness can be measured on: Hot spot: If the utilization of any of its resources is above a hot threshold it indicates that the server is overloaded and hence some virtual machines running on it should be migrated away. Cold spot: If the utilizations of all its resources are below a cold threshold it indicates that the server is mostly idle and is a potential candidate to be turned off to save energy. The goals to be achieved are: Overload avoidance: The capacity of the physical machine should be to satisfy the resource needs of all virtual machines running on it. Otherwise the physical machine is overloaded and hence the performance of its virtual machines is degraded. Green Computing: The number of physical machines should be minimized as long as they can satisfy the needs of all the virtual machines. Idle physical machines can be turned off to save energy Resource Allocation Model for cloud computing based on priority A model which enables on demand network access to a shared pool computing resources is known as cloud computing. Allocation of resources with minimum wastage and maximum profit is the main intention. The developed resource allocation algorithm is based on different parameters like time, cost, number of processor request etc. The developed priority algorithm is used for a better resource allocation of jobs in the cloud environment used for the simulation of different models or jobs in an efficient way. Resource Allocation Model In this model client sends the request to the cloud server. The cloud service provider runs the task submitted by the client. The cloud administrator decides the priority among the different users request. Each request consists of different tasks and they have the different parameters such as, Time: It is the computation time required to complete the particular task Processor request: refers to number of processors needed to run the task. More the number of processors, faster will be the completion of task. Importance: refers to how important the user is to a cloud administrator that is whether the user is an old already existing customer to the cloud or a new customer. Price: refers to cost charged by the cloud administrator to cloud users Parallel Data Processing Method for Dynamic Resource Allocation A data processing framework for cloud environment called Nephele is designed. Nephele takes up many ideas of previously already existing processing frameworks but refines them to better match the dynamic and opaque nature of a cloud. The proposed system increases the efficacy of the scheduling algorithm for the real time cloud computing services. The algorithm utilizes the turnaround time utility efficiently by differentiating it into a gain function for a single task. The algorithm assigns high priority for early completion task and less priority for abortions/deadlines. The performance of the cloud can be improved by associating each task with the time utility function. Copyright reserved by IJETR (Impact Factor: 0.997) 676

4 It is not only important to measure the profit when a job is completed in time but also to keep an account of the penalty when a job is aborted or discarded. In the Nephele architecture the client submits the task to the job manager and the job manager allocates and de-allocates the virtual machines A cloud computing perspective based on the Heuristic Resource Allocation using Virtual Machine Migration Virtualization and VM migration capabilities enable the data center to consolidate their computing services and use minimal number of physical servers. An algorithm that keeps the migration time minimum as well as minimizes the number of migrations is designed. The heuristic based VM migration scenario can be partitioned as follows: Determining when a physical server is considered to be overloaded requiring live migration of one or more virtual machines from the physical server under consideration Determining when a physical server is considered as being under loaded hence it becomes a good candidate for hosting VMs that are being migrated from overloaded physical servers. Selection of VMs that should be migrated from an overloaded physical server. VM selection policy (algorithm) has to be applied to carry out the selection process Other techniques based on the survey of Dynamic Resource Allocation in cloud computing The main concern in cloud computing is to manage the Quality of Service and maintain the Service License Agreement for cloud users. The other techniques through which resources can be dynamically allocated are based on: Topology Aware Resource Allocation (TARA) Linear Scheduling Strategy Topology Aware Resource Allocation: It is a resource allocation scheme for allocation of virtual machines in the IaaS. IaaS systems are usually unaware of the host application s requirements and therefore allocate resources independently of its needs. This problem can be overcome by using the prediction algorithm and the genetic algorithm based search. The prediction engine uses a light weight simulator to estimate the performance of the resource allocation. The genetic algorithm search technique allows TARA to guide the prediction engine. Line Scheduling Strategy In this strategy, a server node is used to establish the IaaS cloud environment and KVM/Xen virtualization with LSTR scheduling to allocate resources which maximize the system throughput and resource utilization. 3. Figure This empirical study seeks to achieve the following goals: Carrying out the live migration of VMs in a manner that preserves free resources in order to prevent SLA violations Optimal utilization of resources Performing minimal number of migrations to the extent possible Efficient server consolidation through VM migrations Fig. 1 Overload of the server Copyright reserved by IJETR (Impact Factor: 0.997) 677

5 4. Conclusion This paper presents an overview of the different techniques used in cloud computing for dynamic resource allocation. This system multiplexes virtual to physical resources adaptively based on the changing demand. It also summarizes the advantages with parameters of the various techniques in cloud computing environment. REFERENCES [1]. K C Gouda, Radhika T V, Akshatha M, Priority based resource allocation model for Cloud computing International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 1, January [2]. Sheng Di and Cho-Li Wang, Dynamic Optimization of Multi-Attribute Resource Allocation in Self-Organizing Clouds, IEEE Transactions on parallel and distributed systems, [3]. G. Sivakumar and N Krishnaveni, Survey on Resource Allocation Strategy in Cloud Computing Environment, International Journal of Computer Applications Technology and Research Volume 2, Issue 6, ,2013. [4]. Gunho Leey, Byung-Gon Chunz, Randy H. Katzy, Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud, IJERT [5]. Daniel Warneke and Odej Kao Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud, IEEE Transactions on parallel and distributed systems, January [6]. Venkatesa Kumar, V. And S. Palaniswami, A Dynamic Resource Alllocation Method for parallel data processing in Cloud Computing Resource Allocation Method for Parallel data processing in Cloud Computing, Journal of Computer Science 8 (5): , [7]. V.Vinothina, Dr.R.Sridaran, Dr.Padmavathi Ganapathi, A Survey on Resource Allocation Strategies in Cloud Computing International Journal of Advanced Computer Science and Applications, Vol. 3, No.6, Copyright reserved by IJETR (Impact Factor: 0.997) 678