Dynamic and Static Characteristics Based Algorithm to Allocate VMs to Jobs in the Cloud

In present scenario, many organizations are trying to minimize the capital expenditure by using the cloud. In the cloud, IT infrastructure and applications are provided as services based on pay-as-you-use model. Cloud providers try to achieve maximum profits in short time, while the cloud users want their work to be done with minimum cost. However, utilization of cloud resources efficiently is an NP-hard optimization problem. Many algorithms have been proposed in the literature for scheduling VMs in cloud environment. The disadvantage with these algorithms is the high time complexity. In this we have proposed a new algorithm which analyses logs, where details of the jobs are stored, for predicting the execution time of the new job. The proposed algorithm schedules the job to a particular VM, based on the average turn around time and other dynamic properties of the load existing in the VMs, present in the cloud. Our preliminary study indicates that our algorithm is able to provide good performance by reducing the time complexity.

[1]  T. Ragunathan,et al.  Efficient Scheduling Algorithm for Cloud , 2015 .

[2]  Richard Gibbons,et al.  A Historical Application Profiler for Use by Parallel Schedulers , 1997, JSSPP.

[3]  Medhat A. Tawfeek,et al.  Cloud task scheduling based on ant colony optimization , 2013, 2013 8th International Conference on Computer Engineering & Systems (ICCES).

[4]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[5]  Warren Smith,et al.  Predicting Application Run Times Using Historical Information , 1998, JSSPP.

[6]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[7]  S. M. Johnson,et al.  Optimal two- and three-stage production schedules with setup times included , 1954 .

[8]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[9]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  D. Dutta,et al.  A genetic: algorithm approach to cost-based multi-QoS job scheduling in cloud computing environment , 2011, ICWET.

[12]  Joel J. P. C. Rodrigues,et al.  Metaheuristic Scheduling for Cloud: A Survey , 2014, IEEE Systems Journal.

[13]  Huankai Chen,et al.  User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing , 2013, 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH).

[14]  Abhinav Hans,et al.  Towards the various cloud computing scheduling concerns: A review , 2014, 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH).

[15]  Warren Smith,et al.  Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance , 1999, JSSPP.