Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment

Safari, Monire; Khorsand, Reihaneh

doi:10.1016/j.simpat.2018.07.006

Cited by 87 publications

(26 citation statements)

References 24 publications

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“…Proper scheduling can decrease data center energy consumption, service-level agreement (SLA) violations, and increase resource utilization [33]. For data center energy reduction, one of the most efficient methods is dynamic voltage and frequency scaling (DVFS) which changes component voltage and frequency to decrease energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…They proposed a soft error-aware energy-efficient task scheduling approach for workflow applications. Safari and Khorsand [33] presented a new energy-aware scheduling algorithm that arranges the workflow tasks based on their deadlines, and the execution time of the tasks is extended by the use of DVFS. However, further research is required to take account of deadlines, costs, or other SLA parameters.…”

Section: Related Workmentioning

confidence: 99%

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Aggregation Measure Factor-Based Workflow Application Scheduling in Heterogeneous Environments

et al. 2020

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With the development of heterogeneous distributed computing environment, workflow application scheduling has become an important and challenging problem while Quality of Service (QoS) guarantees are ensured for science workflows. In this paper, we first introduce an aggregation measure factor to balance the execution time and cost of workflow applications. Then, we propose an aggregation measure factor-based scheduling algorithm (AFSA) for workflow applications in a heterogeneous distributed environment. The proposed algorithm through allocating the sub-budget and sub-deadline for each task to choose available processors takes into account of the budget and deadline aggregation to select the processor for the science workflows. Furthermore, we introduce both a planning success rate and normalized deadline (ND) as performance metrics to evaluate workflow application scheduling algorithms. Furthermore, we use both a randomly generated data set and a real-world workflow data set in our experiments for the performance evaluation. Moreover, our experimental results demonstrate that the proposed AFSA has a higher balance factor and an almost equal or higher planning success rate under different workflow application structures compared to the existing algorithms, BHEFT, HBCS, WMFCO, and DBCS.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Aggregation Measure Factor-Based Workflow Application Scheduling in Heterogeneous Environments

et al. 2020

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“…A number of classes can be devised in the RUBiS setting, and for each class, the average response time, and average cost can be determined as a SLA. Each VM then can be allocated to one of these classes . The configuration of the experiment environment is shown in Figure .…”

Section: Experimental Evaluationmentioning

confidence: 99%

A self‐learning fuzzy approach for proactive resource provisioning in cloud environment

2019

Self Cite

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The development of a communication infrastructure has made possible the expansion of the popular massively multiplayer online games. In these games, players all over the world can interact with one another in a virtual environment.The arrival rate of new players to the game environment causes fluctuations and players always expect services to be available and offer an acceptable service-level agreement (SLA), especially in terms of response time and cost.Cloud computing emerged in the recent years as a scalable alternative to respond to the dynamic changes of the workload. In massively multiplayer online games applications, players are allowed to lease resources from a cloud provider in an on-demand basis model. Proactive management of cloud resources in the face of workload fluctuations and dynamism upon the arrival of players are challenging issues. This paper presents a self-learning fuzzy approach for proactive resource provisioning in cloud environment, where key is to predict parameters of the probability distribution of the incoming players in each period. In addition, we propose a self-learning fuzzy autoscaling decision-maker algorithm to compute the proper number of resources to be allocated to each tier in the massively multiplayer online games by applying the predicted workload and user SLA. We evaluate the effectiveness of the proposed approach under real and synthetic workloads. The experimental results indicate that the proposed approach is able to allocate resources more efficiently than other approaches.

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“…Cost: The sum of the cost of allocating a VM and the cost of penalties for SLA violation is calculated as

italicTotalCost = V M . italicCost + italicPenaltyCost

V M . italicCost = \sum_{i = 1}^{M} V M {Price}_{i} \times ((), V M_{Time}_{i} + V M_italicInit_{Cost}_{i})

italicPenaltyCost = \sum_{i = 1}^{M} T_{i} . italicαPenalty \times T_{i} . italicPenalty \times R e q . {SLAV}_{i}

{SLAV}_{i} = {Response}_{i} - {Deadline}_{i} .

For the calculation of the cost of the allocated VMs (as VM.Cost ), VMPrice i is the cost of the VM for the i th request, VM _ Time i is the length of time each VM has been rented from the cloud provider to service to the user request, VM _ Init _ Cost i is the cost of the initial configuration of the VM for the i th request, and PenaltyCost is the penalty the provider pays to the user for a SLA violation . When calculating the PenaltyCost , the user requests ( M ), T i .…”

Section: Performance Evaluationmentioning

confidence: 99%

FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments

2018

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Recent advancements in web-based application, especially in cloud computing environment, allows cloud service providers to deploy and provide web-based services in the form of multitier applications. One of the most suitable infrastructure for the running of multitier applications is a cloud computing infrastructure. Since the arrival rate of users to the multitier applications varies over the time, deciding about the right amount of resources required to handle the each tier of multitier applications is not trivial, and it depends on the current workload of its each tier. Therefore, it is necessary to automatically provision resources to deal with fluctuating demands of the multitier applications. In this paper, we propose a hybrid resource provisioning approach for multitier applications based on a combination of the concept of the autonomic computing and the fuzzy analytical hierarchy process approach. Moreover, we present a framework based on MAPE-k control loop for autonomous resource provisioning of multitier applications in cloud computing environments. The effectiveness of the proposed approach under real and synthetic workloads was evaluated. The experimental results indicate that the proposed solution outperforms in terms of allocated virtual machines, response time, and cost compared with the other approaches. KEYWORDS autonomic computing, cloud computing, FAHP, multitier applications, resource provisioning, workload prediction INTRODUCTIONCloud computing is one of the most popular technologies in the businesses, educational institutions, governments, and the research community that has become an integral part of many users are heavily dependent on cloud-based applications for their day-to-day activities in both professional and personal lives. 1 Recent advancements in the web-based application, especially in cloud computing environment, allows cloud service providers to deploy and provide web-based services in the form of multitier applications. As the cloud is elastic, it can be used in places where the varying load is one of the main characteristics. [2][3][4] It can also be used where on-demand access is required. Similarly, because of its property of multitenancy, it can be used in places where several applications are to be operated. 5,6 Therefore, one of the most suitable infrastructures for the running of multitier applications is cloud computing infrastructures. On the other hand, since the arrival rate of users to the multitier applications varies over the time, deciding about the right amount of resources Softw Pract Exper. 2018;48:2147-2173.wileyonlinelibrary.com/journal/spe

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Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment

Cited by 87 publications

References 24 publications

Aggregation Measure Factor-Based Workflow Application Scheduling in Heterogeneous Environments

Aggregation Measure Factor-Based Workflow Application Scheduling in Heterogeneous Environments

A self‐learning fuzzy approach for proactive resource provisioning in cloud environment

FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments

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