Implementation of ETPM for Resource Allocation in Cloud System

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Mr.Rajendra Prasad A

, IJRIT-317 International Journal of Research in Information Technology

(IJRIT)

www.ijrit.com ISSN 2001-5569

Implementation of ETPM for Resource Allocation in Cloud System

Mr.Rajendra Prasad A¹, Prof. Jagadish R M²

1M. Tech in Computer Science, Department of CSE, BITM (Affiliated to VTU) Bellary, Karnataka, India

[email protected]

2Professor, Department of CSE, BITM (Affiliated to VTU) Bellary, Karnataka, India

[email protected]

Abstract

Cloud computing builds advantages upon VM technologies and distributed computing, which provides usage of computing resources, allocating resources on demand services. Cloud computing is able to provide illusions of multiplex computing environment to the users on a same physical infrastructure with unlimited computing resources.

Hence, resource utilization choices are made by users on their demands. But still to accomplish a good resource allocation that minimizes the cost associated with it and to meet customer demands with application requirements is a greater challenge. In this paper with the resource allocation algorithm used to formulating a resource allocation problem under deadline driven approach by minimizing user payment based on the cloud environment. The bound value of task execution length depend upon the possible inaccurate workload prediction can be guaranteed to complete tasks by analyzing within the users expected time.

Keywords: Cloud Computing, Resource Allocation, Virtualization.

1. Introduction

Cloud computing has started with a new implementation technologies that gives a greater use of virtual environment on the Internet. Accessibility of Virtualized resources is easy indeed can be dynamically make the cloud system to reconfigure to go with the variable loads. Users over demand of their resources against their true needs in cloud system so in order to avoid this resources provided by cloud system are intended to within the payment structure. For each task’s workload is likely of multiple dimensions. Multidimensional execution results from the computing resources. Even though a task resource type like CPU, can be partitioned into multiple ordered execution phases each calling for a different computing capacity and different prices on demand. This may also lead to a potentially high-dimensional execution scenario. In order to gain high prediction error tolerance ability a resource allocation algorithm is adopted and also minimizing users’ payments subject to their expected deadlines.

Idle physical resources can be partitioned and allocated to new tasks. This makes easy of finding the optimal solution through convex optimization strategies. But it is in viable to directly solve the necessary and sufficient condition to find the optimal solution with their conditions. However, by further analyzing resource allocation algorithm’s optimality approximation ratio given the possibly wrong

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, IJRIT-318

predictions of task’s execution properties. Through the fact, by setting a relatively stricter deadline properly based on our approximation ratio, each task can be guaranteed to be finished within its original deadline even though task properties cannot be predicted accurately.

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2. Literature Survey

Cloud system with virtualization and computing technologies has the ability to change and move large part of the IT industries. Cloud computing has the ability to making software more attractive with its service and having the flexibility to move according to the design of IT hardware. Each and every level should aim at horizontal scalability of virtual machines over the efficiency on a single VM [1]

• Software applications need both scale down and scale up rapidly, which is a needful requirement.

Cloud Computing offers pay-for-use licensing to match the needs of software used in it.

• Infrastructure Software with VMs needs to be alert that it is no longer worked on core metal.

• Performance and cost of purchase is importance as cost of operation has to match the rewarding energy proportionality such as by using disk, unused memory and network into less manageable power option.

Concept of the Virtual Machine technology applies of virtualization to an entire machine, hardware resource and real machine constraints with flexibility and software portability [2]. Day by day Virtual machines are developing as required elements designed in computer system. Major computer components problems are designed and solved by VMs. Multiple operating systems support same hardware platforms and servers at system level.

Convex optimization is also evolving as an important tool for hardware, non-convex problems, optimal value are generated with lower bounds and as a innovative method for developing suboptimal values [3].

3. Methodology 3.1 Existing System

In cloud computing resource allocation is complex as compared to other distributed systems such as grid computing systems. It is inappropriate in a grid system to share the compute resources among the different applications simultaneously running on them due to the expected mutual performance interference among them. Cloud systems do not allow physical hosts directly connect to the users, but influence virtual resources isolated by VM technology. Amazon EC2 and Open Nebula use Cloud management tools that are leveraged by VM resource isolation technology. Scientific research with faster growth make, users to request complicated demands indeed guaranteed to reach time goals from resource allocation with minimized payment that is rarely known. However, inevitable faults in predicting task workloads will make the problem complicate.

3.1.1 Drawbacks of the Existing system

1. There are no predictors for identification of high load in the existing systems.

2. Available predictor in the existing system takes wrong decision in resource allocation and also generates some errors.

3. Cost utilization of existing system is high.

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, IJRIT-319 3.2 Proposed System

The goal of resource allocation algorithm for virtual machine multiplexing technology in cloud system is to minimize the user’s payment of task within the specified deadline. Expected output of the algorithm can be optimized based on KKT condition, which describes that any other solutions can cause larger payment cost.

The approximation ratio generated by algorithm can be analysed for expanded execution time for user predicted deadline for inaccurate tasks.

The characteristics under incorrect prediction that guarantee tasks execution time within its deadline are:

• Based on the cloud environment facilitated with VM resource isolation technology the proposed system formulates deadline driven resource allocation issues, and propose a solution with polynomial time that minimises user payment in their expected terms for deadlines.

• The execution of upper bound task length based on the inaccurate workload is analysed by proposing the error- tolerant method with assurance of task completion within deadline

• Effectiveness over real VM is validated in cluster environment under different levels of competition.

3.2.2 Advantages in the Proposed System

1. The tasks can be executed within the specified deadline bound.

2. Significant allocation of resources to the tasks can be provided with Optimal allocation algorithm 3. The cost for resource allocation to the cloud users can be reduced

3.3 System Architecture

Formulating a deadline driven resource allocation problem in cloud system based on the cloud environment is facilitated with VM resource isolation technology. A new solution that could complete the given tasks by achieving error tolerance and minimize user’s payment in terms of their expected deadline.

Fig. 1 System Architecture

In the above specified architecture some of the tasks are assigned to user. The computation and disk processing tasks are predicted based on the execution time i.e 3 to 4 hours respectively. The user, scheduler checks the pre-collected availability states of all the nodes, along with estimation of minimal payment of running the task within its deadline on each of them upon receiving the request.

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The host that requires less payment will run the task through a customized instance of VM with isolated resources. In particular, VM will be customized with such a CPU that the task can be completed within specified deadline and its payment can also be minimized. The computation results will be provided to the users.

4. Experimental Results

Fig. 2 Payment Minimization Graph

As from the above figure.2 Payment Minimization Graph shows how the new Optimal Allocation Algorithm helps in resource allocation in cloud system. As per the experimental results, X-Axis indicates number of tasks and Y-Axis indicates the time to complete the given tasks. The overall tasks are accomplished to complete within the given deadline, which is being predicted by optimal resource allocation algorithm with KKT condition. The algorithm not only predicts but also manages to make VMs to be balanced according to the resource allocation in cloud system. This results from the optimal allocation algorithm is managed to increase the upper bound values to achieve guaranteed execution of all task as per users deadline by minimizing the resource utilization cost as well as handling the faults while resource allocation.

5. Conclusion

Cloud system with resource allocation algorithm supporting VM-multiplexing technology, minimize the payment of users based on their task and made an effort to guarantee its execution within deadline. The output of the proposed algorithm is optimal based on KKT condition which describes that any other solutions would cause larger costs in payment. Analyzing ratio of approximation for execution time generated by this algorithm to the deadline of user expectations under incorrect task prediction. The resources are provisioned approximately sufficient, this paper guarantee for task execution time within deadline even during incorrect prediction. In the upcoming eras, it can be adopted with stricter/original deadlines into still some more excellent management tools like Open Nebula and Amazon EC2, for maximizing the system-wide performance.

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, IJRIT-321 References

[1] Sheng Di, Member, IEEE, and Cho-Li Wang, Member, IEEE “Error-Tolerant Resource Allocation and Payment Minimization for Cloud System” JUNE 2013

[2] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A.

Rabkin, I. Stoica, and M. Zaharia, “Above the Clouds: A Berkeley View of Cloud Computing,” Technical Report UCB/EECS-2009-28, EECS Dept., Univ. California, Berkeley, Feb. 2009.

[3] L.M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner, “A Break in the Clouds: Towards a Cloud Definition,” SIGCOMM Computer Comm. Rev., vol. 39, no. 1, pp. 50-55, 2009.

[4] I. Foster and C. Kesselman, The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, Nov. 2003.

[5] J.E. Smith and R. Nair, Virtual Machines: Versatile Platforms For Systems and Processes. Morgan Kaufmann, 2005.

[7] D. Milojicic, I.M. Llorente, and R.S. Montero, “Open nebula: A Cloud Management Tool,” IEEE Internet Computing, vol. 15, no. 2, pp. 11-14, Mar./Apr. 2011.

[8] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2009

[9] K. Ramamritham, J.A. Stankovic, and W. Zhao, “Distributed Scheduling of Tasks with Deadlines and Resource Requirements,” IEEE Trans. Computers, vol. 38, no. 8, pp. 1110-1123, Aug. 1989.

[10] L. Zhao, Y. Ren, and K. Sakurai, “A Resource Minimizing Scheduling Algorithm with Ensuring the Deadline and Reliability in Heterogeneous Systems,” Proc. 25th IEEE Int’l Conf. Advanced Information Networking and Applications (AINA ’11), pp. 275-282, 2011.

[11] F. Chang, J. Ren, and R. Viswanathan, “Optimal Resource Allocation in Clouds,” Proc. IEEE Int’l Conf. Cloud Computing, pp. 418-425, 2010.