Impatient Task Mapping in Elastic Cloud using Genetic Algorithm

Abstract

Problem statement: Task scheduling is the main factor that determines the performance of any distributed system. Cloud computing comes with a paradigm of distributed datacenters. Each datacenter consists of physical machines that host virtual machines to execute customers' tasks. Resources allocation on the cloud is different from other paradigms and the mapping algorithms need to be adapted to the new characteristics. This study takes the problem of immediate task scheduling under an intercloud infrastructure using a genetic algorithm. An impatient task needs to be scheduled as soon as it enters the system taking into account the input and output files location and its QoS requirements. Approach: This study proposes an algorithm that can find a fast mapping using genetic algorithms with "exist if satisfy" condition to speed up the mapping process and ensures the respecting of all task deadlines. Cloudsim simulator was used to test the proposed algorithm with real datasets collected as a cloud benchmark. Mapping time and makespan are the performance metrics that are used to evaluate the proposed system. Results: The results show an improvement in the proposed system compared to MCT algorithm as illustrated throughout the study. Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources.

A Review on Secured Cloud Computing Environment

“…Wu and Huang (2011) implemented quasi-experimental method was applied to the study of 110 fifth grade students of Tunglo Elementary School, Taiwan. Mehdi et al (2011) proposes Impatient Task Mapping in Elastic Cloud using Genetic Algorithm. This algorithm finds a fast mapping using genetic algorithms.…”

“…Implementation of cloud become more rapid in the recent years, such as Wu and Huang (2011), Mehdi et al (2011), Kumar and Balasubramanie (2012) and Ponnuramu and Tamilselvan (2012). Wu and Huang (2011) implemented quasi-experimental method was applied to the study of 110 fifth grade students of Tunglo Elementary School, Taiwan.…”

Heuristics for Efficient Resource Allocation in Cloud Computing

“…2) System performance objectives such as the system throughput measured by the number of jobs executed by a system [6,15,20,22,33,35,36], and 3) Application performance objectives such as response time (e.g., execution time and makespan), quality of service (QoS), etc. [6,11,12,14,15,19,22,24,26,29,30,35,37,[38][39][40].…”

Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

“…Constraints considered in simulation are completion time and bandwidth utilization. Cloudsim simulator is used to map tasks to resources; mapping time and makespan time are the performance metrics in [25]. They have included time taken to stage in the input files and stage out the output files to desired location.…”