P. Akilandeswari ¹, H. Srimathi ¹, H. Srimathi ¹

Affiliations
1 Survey and Analysis on Task Scheduling in Cloud Environment, IN

Abstract

Objectives: In cloud environment many scheduling algorithms are available to achieve the quality of service, but the scalability of task execution increases, scheduling becomes more complex. So there is a need for better scheduling. This paper deals with the survey of dynamic scheduling, different classification and scheduling algorithms currently used in cloud providers. Analysis: This paper focuses on a comparative study of static and dynamic task scheduling currently used and various researchers work on dynamic task scheduling for many applications. Findings: Scheduling methods/techniques used by various cloud service providers, advantages and limitations of each method are discussed. Scheduling algorithms in cloud setup in each paper is analyzed. Application: Analyzing task scheduling in cloud environment, uniform standard can be applied for all cloud providers. Online scheduling and dynamic scheduling can be further improved

Keywords

Cloud Computing, Cloud Setup, Static and Dynamic Scheduling, Task Scheduling Algorithms, Various Metrics.

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P. Akilandeswari ¹, H. Srimathi ¹, Deepali ¹

Affiliations
1 Computer Science and Engineering, SRM University, Chennai 603203, Tamil Nadu, IN

Abstract

Objectives: Dynamic assignment of tasks to different VM in cloud datacenter and Load-Balancing by migration of tasks from an Overloaded VM to Candidate VM. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) algorithm is used finding each iteration the P_BEST (Personal Best Solution) i.e. current solution is compared with the G_BEST (Global Best Solution) i.e. previous best solution and the G_BEST value is updated at each iteration for calculating the minimum execution time. The parameters being calculated are Global_BEST Solution Function, Personal_BEST Solution Function and Average Utilization for each processor. Findings: A Dynamic Approach for Task-Scheduling using the load-balancing technique is implemented in this paper. Two algorithms namely - Particle Swarm Optimization (PSO) and Improved PSO are used and a comparison is made between them based on number of performance parameters like Scheduling Length (Make Span), Total Execution Time and Total Transfer or Migration Time. A Utilization Graph is plotted to show this comparison which compares these algorithms based on their Cloudlet Length (Scheduling Length) and Total Execution Time. Improved PSO algorithm has lesser or minimum execution time as compared to the PSO algorithm because in the Improved PSO Algorithm two parameters are being considered namely- Cloudlet Length and MIPS (Million Instructions Per Second) which leads to maximum utilization of available resources by all VM. Application/Improvements: This approach is used for dynamically assigning tasks to VM and checking maximum utilization of available resources through load balancing and minimizing the overall execution time and migration time.

Keywords

Candidate VM, Cloud Computing, Cloudlet, PSO, Task Migration, Transfer Time, Virtual Machine.

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