Priority Task Scheduling Strategy for Heterogeneous Multi-Datacenters in Cloud Computing

Er-raji, Naoufal; Benabbou, Faouzia

doi:10.14569/ijacsa.2017.080236

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…The generalized priority algorithm [38] prioritizes the tasks with rules on task dimensions. The task with the largest size gets the best priority within the system and executes early.…”

Section: ) Static Load Balancing Algorithmsmentioning

confidence: 99%

Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

et al. 2022

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The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet. Cloud security represents an important research area. Confidentiality, integrity, and availability are the main cloud security characteristics addressed. Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability. Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system's state. Reactive cloud fault tolerance is activated for system process failures after failure effectively happens. Reactive cloud fault tolerance handles failure after the fault has occurred. Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approaches in a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques. This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types, frameworks, and future directions.

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“…The generalized priority algorithm [38] prioritizes the tasks with rules on task dimensions. The task with the largest size gets the best priority within the system and executes early.…”

Section: ) Static Load Balancing Algorithmsmentioning

confidence: 99%

Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

et al. 2022

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“…The techniques in Table. 2 are examples on this scheduling. The algorithm uses the weighted random strategy, overload assessment and feedback to submit tasks first to the resources with the greatest performance but insure not to overload it [21].…”

Section: Centralized Vs(distributed)decentralized Schedulingmentioning

confidence: 99%

Comparative Study On Cloud Computing Simulation Platforms

Alshathri

2020

International Journal of Systems Applications, Engineering &Amp; Development

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Experimentation in a real environment is quite problem due to the high financial cost and the time required to accomplish it. Above all that, the tests are not repeatable, because many variables cannot be controlled with in the test, which may affect the results. Therefore, using simulation frameworks to evaluate cloud applications is preferred. It is extremely difficult to use real infrastructures for benchmarking the application performance (throughput, cost benefits) under inconstant conditions. Therefore, we cannot execute benchmarking experiments using real-world Cloud environments. To overcome this challenge, the use of simulation tool is the best applicable choice to the developers with substantial resources and parallelized execution. These simulation tools offer the researchers the chance to evaluate the hypothesis in a measured environment and simply emulate the output results. This paper have reviewed and classified the most updates and extends simulation platforms in cloud computing and gave examples on each.

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“…Naoufal Er-raji et. al [14] have addressed the priority task scheduling problem. It considered the parameters such as tasks deadline, tasks age and task length over distributed data-centre in cloud.…”

Section: Related Workmentioning

confidence: 99%

Priority Based Resource Allocation For Meta-Task Scheduling In Cloud Computing

Kavitha¹,

Amalarethinam²

2019

IJRAT

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Cloud computing environment assures to provide on demand computing services through internet on pay-as-you-use basis. Even though there are several issues existing, scheduling is the main issue in cloud. Scheduling of tasks is difficult because of sole elements in cloud computing such as resource heterogeneity, task heterogeneity and user priority. This paper proposes a new Priority based Resource Allocation (PRA) for metatask scheduling in cloud. This algorithm thinks about priority given to meta-task by the users. The instances of high speed resource will be used for scheduling the meta-task set with user priority and heterogeneous resources are allocated for non-priority meta-task set. The PRA algorithm minimizes the makespan and cost, and increases the utilization of resources over the existing algorithm.

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Priority Task Scheduling Strategy for Heterogeneous Multi-Datacenters in Cloud Computing

Cited by 10 publications

References 4 publications

Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

Comparative Study On Cloud Computing Simulation Platforms

Priority Based Resource Allocation For Meta-Task Scheduling In Cloud Computing

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