An Efficient Multi Queue Job Scheduling for Cloud Computing

Karthick, A. V.; Ramaraj, E.; Subramanian, Rengakrishnan

doi:10.1109/wccct.2014.8

Cited by 82 publications

(27 citation statements)

References 5 publications

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“…The evaluation of the MOGAMCC is carried with the other existing optimization methods such as Min-Min [18] and MOSA [17]. The three objective functions are considered for evaluation of proposed MOGAMCC i.e.…”

Section: Results Evaluationmentioning

confidence: 99%

An Evolutionary Multi-Objective Approach for Resource Scheduling in Mobile Cloud Computing

Dasari¹,

Saritha²

2017

IJIES

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Abstract:Mobile cloud computing (MCC) is one of the evolving fields in recent years. The complexity of MCC made researchers to concentrate on efficient application development. In MCC, resource scheduling is treated as one of the major issues. Genetic Algorithms (GAs) are efficient search techniques to find the optimal solution for the scheduling problem. GAs has the ability to optimize the resource scheduling in both homogeneous and heterogeneous environments. This paper presents the multi objective genetic algorithm for MCC (MOGAMCC) environment. To implement the MOGAMCC, the cloudsim toolkit was extended with the MOGA and its task scheduling approach determines the optimal scheduling policy. A single point crossover model is employed for the generation of new population. Mutation process is carried by randomly changing the bit positions in the chromosomes. The experimental results show that the proposed model finds the optimal trade-off between the defined objectives and which ultimately reduces the makespan.

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Section: Results Evaluationmentioning

confidence: 99%

An Evolutionary Multi-Objective Approach for Resource Scheduling in Mobile Cloud Computing

Dasari¹,

Saritha²

2017

IJIES

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“…FCFS, SJF, Combinational Backfill and Improved Backfill. The Multi-Queue Scheduling gave more importance to select job dynamically with respect to achieving the optimum cloud scheduling problem and using free space in an economic way [20].…”

Section: E Choudhary Monika Et Al (2012)mentioning

confidence: 99%

A Review on CPU Scheduling Algorithms in Cloud Environment

Rohil¹

2018

IJRASET

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Cloud computing is a technique in which computing is delivered as a service rather than a product, whereby shared resources and software, and information is provided to consumers as a utility over networks. One of the main advantages and motivations behind Cloud Computing is reducing the CAPEX (capital expenditures) of systems from the perspective of cloud users and providers. To get the favour of cloud client needs to just interface with the web and after that client can perform operations without much using the intense computing and storage. Cloud computing administration is given by CSP (cloud service provider) according to client necessities. Keeping in mind the end goal to satisfy the request of diverse clients, they give distinctive nature of services. In order to conclude the term cloud, it is an executable environment having a dynamic behaviour of resources as well as clients giving various services [2][3] [4].

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“…It maintains the list of all the available system that are in busy in processing the jobs by proper load balancing as well maintains list of systems who are idle and are ready to process jobs. It also indicates the scheduler to schedule the output of various jobs which is collected back by the queue manager [7]. Three queues are formed as small, medium and long which is based on ascending order measured in terms of burst time of the submitted client job.…”

Section: Figure 1: Architecture Of Mqs For Cloud Computingmentioning

confidence: 99%

“…The execution of the jobs is carried out in the dynamic manner. The optimal allocation decreases time and availability of space in a productive manner without compensating the quality of the system [7]. (B) Ant Colony Optimization (ACO) Ant colony optimization (ACO) is a meta-heuristic algorithm inspired from models of cooperative food search in ants [13].…”

Section: Figure 1: Architecture Of Mqs For Cloud Computingmentioning

confidence: 99%