PL-DVFS: combining Power-aware List-based scheduling algorithm with DVFS technique for real-time tasks in Cloud Computing

Safari, Monireh; Khorsand, Reihaneh

doi:10.1007/s11227-018-2498-z

Cited by 41 publications

(11 citation statements)

References 33 publications

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“…In the energy‐efficient load balancing algorithms, Safari and Khorsand 13 proposed a new approach for the problem of minimizing energy consumption in task allocation of VMs on heterogeneous cloud system. They have combined power‐aware list‐based scheduling algorithm with DVFS technique under deadline constraint to maintain the quality of service.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the proposed approach and to have a better view of its advantages, the following algorithms are described which are then compared in the following subsections: This algorithm work toward minimizing energy of directed acyclic graph‐structured applications on heterogeneous cloud system. It also combines power‐aware list‐based scheduling algorithm with dynamic voltage and frequency scaling (DVFS) technique for real‐time tasks to maintain the quality of service while considering tasks deadlines 13 Energy aware for tasks scheduling with deadline constraint (EATSD): This approach is based on considering different users' priorities and deadlines constraints that can allocate the resources on the basis of their class as well as the class of received tasks so that the energy consumption is reduced 34 …”

Section: Performance Evaluationmentioning

confidence: 99%

“…Minimizing the energy usage of data centers is an important challenge and complex issue for data centers in the cloud 13, 14 . To address this problem, many techniques have been applied in order to reduce energy usage such as load balancing.…”

Section: Introductionmentioning

confidence: 99%

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An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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“…This results in increased resource utilization as well as helps to reduce tasks rejection. In the work by Safari et al [34], a power-aware and list based scheduling method is combined with DVFS (PL-DVFS) for real time tasks is proposed. PL-DVFS maintains QoS using tasks deadlines.…”

Section: Related Workmentioning

confidence: 99%

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Ali

Qureshi

et al. 2020

IEEE Access

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Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadlinebased tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5%-29.3%), on minimum resources (3.9%-25.2% less) while majority were meeting their deadlines (93.21%-94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

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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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PL-DVFS: combining Power-aware List-based scheduling algorithm with DVFS technique for real-time tasks in Cloud Computing

Cited by 41 publications

References 33 publications

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

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

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