Enhanced Weighted Round Robin (EWRR) with DVFS Technology in Cloud Energy-Aware

Alnowiser, Abdulaziz; Aldhahri, Eman; Alahmadi, Abdulrahman; Zhu, Mengxia

doi:10.1109/csci.2014.62

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…Wu et al present a DVFS based method which improves the overall utilization of resources resulting in improved energy efficiency [27]. Alnowiser et al provide the solution using concept of weighted round robin algorithm [28]. This algorithm monitors, consolidate, and migrate the overloaded and underloaded VMs that are hosted on PM.…”

Section: Literature Reviewmentioning

confidence: 99%

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

et al. 2020

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Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as ''pay as you go'' provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server's CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server's capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).

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Section: Literature Reviewmentioning

confidence: 99%

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

et al. 2020

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“…Further, energy efficiency is enhanced by increasing utilization of servers and consolidating workloads. Authors in [20] propose enhanced weighted round robin (EWRR) scheduling for resource management. The proposed technique monitors the resources used by the VMs and consolidates the VMs to achieve higher energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed techniques are further evaluated on the basis of SLA violations. SLAV is a metric proposed by authors in [20] to keep the check on the SLA violations that are encountered due to non-availability of resources. Equation 13 is used to calculate the SLAV .…”

Section: ) Sla Violation (Slav)mentioning

confidence: 99%

SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds

et al. 2019

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Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

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“…It is better than SJF and FIFO. A. Alnowiser, E. Aldhahri, and A. Alahmad [3] have suggested Round Robin scheduling algorithm based on time. So, each job is executed only for a particular amount of time.…”

Section: Related Workmentioning

confidence: 99%

“…The traditional scheduling [2] algorithms are not suitable for a large-scale cloud environment. The scheduling process [3] sends the job to the less loaded VMs. Meanwhile monitoring process predict the idle virtual machines based on RAM size, CPU utilization, Speed of VMs, Power consumption, Distance from the users, Bandwidth, MIPS (Million instruction per second).…”

Section: Introductionmentioning

confidence: 99%

A Preference-Based Supervising of Virtual Machines in Cloud Environment

Ezhilarasi¹,

Padmakumari²,

Umamakeswari³

2018

IJET

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Cloud Computing is a well-known technology in today's world. A large number of users are benefited from the cloud services. The cloud computing must provide efficient service on time for customer satisfaction. So, prominent resource monitoring and scheduling techniques are needed. To achieve the customer satisfaction and to reduce the communication overhead, a method called Resource Supervisor (RS) is proposed. The proposed algorithm assigns the preference for the tasks having highest length, monitor the status of the resource and schedule the tasks to various resources quickly. The proposed method is implemented using Cloud Simulator, and experimental results are validated by comparing RS method with existing algorithms, which provides better outcomes and reduces the communication overhead.

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Enhanced Weighted Round Robin (EWRR) with DVFS Technology in Cloud Energy-Aware

Cited by 19 publications

References 15 publications

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds

A Preference-Based Supervising of Virtual Machines in Cloud Environment

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