Heuristic-Based Request Scheduling Subject to a Percentile Response Time SLA in a Distributed Cloud

Boloor, Keerthana; Chirkova, Rada; Salo, Timo J.; Viniotis, Yannis

doi:10.1109/glocom.2010.5683946

Cited by 24 publications

(11 citation statements)

References 7 publications

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“…So by using this scheduling algorithm SLA can be achieved easily and satisfy the customer in the cloud business. The performance of the LAS Algorithm which is proposed in Hierarchical Scheduling works much better than the existing FCFS and SJF Algorithm by completing all the jobs within the stipulated time [9]. Hence from the above performance evaluation it is clear that the proposed Hierarchical Scheduling will increases the performance in the cloud environment and increases the customer satisfaction by quickly responding to the user jobs.…”

Section: Experimental Results and Performance Evaluationmentioning

confidence: 87%

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Rajavel

Mala

2012

Advances in Intelligent and Soft Computing

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Abstract.One of the challenging issues in Cloud computing Environment is meeting the Service Level Agreement (SLA). The SLA is an agreement signed between the service provider and the service consumer for accessing the service provided by the service provider over the internet. We can investigate the negotiation strategy between the service provider and the service consumer through the third party called a Broker. In many approaches SLA is designed and trusted through the measurement of various non-functional requirements such as response time of job, CPU usage, memory usage and the storage used by the consumer. Main focus of the business process is satisfying the customer need by quick response. In our proposed approach for satisfying the service consumer (customer), parameter such as response time of deadline based job is considered. The response time of the job is affected due to improper scheduling of the job. Therefore a novel hierarchical scheduling with job prioritization is used to give more priority for deadline based jobs. This approach will satisfy the service consumer and meet the SLA by increasing the performance of the scheduling algorithm.

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Section: Experimental Results and Performance Evaluationmentioning

confidence: 87%

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Rajavel

Mala

2012

Advances in Intelligent and Soft Computing

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“…The system level scheduling handles resource management within datacenters [2,9,10]. A novel approach of heuristicbased request scheduling at each server, in each of the geographically distributed datacenters, to globally minimize the penalty charged to the cloud computing system is proposed in [21]. A new fault tolerant scheduling algorithm MaxRe is proposed in [22].…”

Section: Related Workmentioning

confidence: 99%

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Keshk¹,

El-Sisi²,

Tawfeek³

2014

IJISA

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Abstract-Cloud computing is a type of parallel and distributed system consisting of a co llect ion of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different co mputing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorith ms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimizat ion algorith m for load balancing compared with different scheduling algorith ms has been proposed. Ant Co lony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual mach ines. The main contribution of our work is to balance the system load while t rying to min imizing the make span of a given tasks set. The load balancing factor, related to the job fin ishing rate, is proposed to make the job fin ishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simu lated using Cloudsim toolkit package. Experimental results showed that, the proposed algorith m outperformed scheduling algorith ms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).

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“…The system level scheduling handles resource management [2,12]. A novel approach of heuristic-based request scheduling at each server, in each of the geographically distributed datacenters, to globally minimize the penalty charged to the cloud computing system is proposed in [14]. Scheduling based genetic algorithm is proposed in [15,16].…”

Section: Cloud Computing and Task Schedulingmentioning

confidence: 99%

Cloud Computing Online Scheduling

Keshk¹

2014

IOSRJEN

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-Cloud computing has gained a lot of attention to be used as a computing model for a variety of application domains. Task scheduling is the fundamental issue in this environment. To utilize cloud efficiently, a good task scheduling algorithm is needed to assign tasks to resources in cloud. Cloud task can be divided into two categories such as on-line mode service and the batch mode service. In this paper, online cloud task scheduling based on virtual machine adaptive fault tolerance and load balancing using ant colony algorithm is proposed. The main contribution of this work is that load balancing factor is added and the system tolerates the faults by tacking the decision on the basis of reliability of the virtual machines in scheduling process. The proposed scheduling strategy was simulated using the Cloudsim toolkit package. Experimental results show that the proposed algorithm achieved the better load balance than Join-shortest-queue (JSQ) and Modified Ant Colony Optimization (MACO) algorithms.

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Heuristic-Based Request Scheduling Subject to a Percentile Response Time SLA in a Distributed Cloud

Cited by 24 publications

References 7 publications

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Cloud Computing Online Scheduling

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