Security supportive energy-aware scheduling and energy policies for cloud environments

Fernández-Cerero, Damián; Jakóbik, Agnieszka; Grzonka, Daniel; Kołodziej, Joanna; Fernández-Montes, Alejandro

doi:10.1016/j.jpdc.2018.04.015

Cited by 50 publications

(28 citation statements)

References 50 publications

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“…Energy consumption can be reduced by scheduling jobs in an appropriate order. In the last few years, a lot of job scheduling algorithms are proposed with dual objectives [5,6]. The objectives considered response time.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

et al. 2019

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Efficient job scheduling reduces energy consumption and enhances the performance of machines in data centers and battery-based computing devices. Practically important online non-clairvoyant job scheduling is studied less extensively than other algorithms. In this paper, an online non-clairvoyant scheduling algorithm Highest Scaled Importance First (HSIF) is proposed, where HSIF selects an active job with the highest scaled importance. The objective considered is to minimize the scaled importance based flow time plus energy. The processor’s speed is proportional to the total scaled importance of all active jobs. The performance of HSIF is evaluated by using the potential analysis against an optimal offline adversary and simulating the execution of a set of jobs by using traditional power function. HSIF is 2-competitive under the arbitrary power function and dynamic speed scaling. The competitive ratio obtained by HSIF is the least to date among non-clairvoyant scheduling. The simulation analysis reflects that the performance of HSIF is best among the online non-clairvoyant job scheduling algorithms.

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Section: Introductionmentioning

confidence: 99%

Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

et al. 2019

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“…The objective is to minimize the energy consumption by maintaining QoS. Fernández‐Cerero et al 25 proposed a set of energy‐aware strategies for task scheduling in clouds with the ability to hibernate those VMs that operate in idle state. It simultaneously minimizes makespan and power consumption.…”

Section: Related Workmentioning

confidence: 99%

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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“…This method is also a way to adjust the load of the datacenter to achieve energy saving goals. Prior work [25] defined and developed a set of performance and energy-aware strategies for resource allocation, task scheduling, and for the hibernation of virtual machines. They combined energy and performance-aware scheduling policies in order to transfer virtual machines into an idle state and the efficiency achieved by applying the proposed models has been tested using a realistic large-scale cloud computing system simulator.…”

Section: Energy Saving Approaches In Datacenters By Virtual Machine Cmentioning

confidence: 99%

Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization

Wang

Luo

et al. 2019

Energies

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The increase in massive data processing and computing in datacenters in recent years has resulted in the problem of severe energy consumption, which also leads to a significant carbon footprint and a negative impact on the environment. A growing number of IT companies with operating datacenters are adopting renewable energy as part of their energy supply to offset the consumption of brown energy. In this paper, we focused on a green datacenter using hybrid energy supply, leveraged the time flexibility of workloads in the datacenter, and proposed a thermal-aware workload management method to maximize the utilization of renewable energy sources, considering the power consumption of both computing devices and cooling devices at the same time. The critical knob of our approach was workload shifting, which scheduled more delay-tolerant workloads and allocated resources in the datacenter according to the availability of renewable energy supply and the variation of cooling temperature. In order to evaluate the performance of the proposed method, we conducted simulation experiments using the Cloudsim-plus tool. The results demonstrated that the proposed method could effectively reduce the consumption of brown energy while maximizing the utilization of green energy.

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Security supportive energy-aware scheduling and energy policies for cloud environments

Cited by 50 publications

References 50 publications

Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

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

Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization

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