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

Mustafa, Saad; Sattar, Kinza; Shuja, Junaid; Sarwar, S.; Maqsood, Tahir; Madani, Sajjad A.; Guizani, Sghaier

doi:10.1109/access.2019.2941145

Cited by 34 publications

(24 citation statements)

References 40 publications

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“…This section presents and analyzes results produced by simulation experiments to show the performance evaluation of the proposed EEH framework. The performance of the proposed EEH framework is compared with the Proactive and Reactive Scheduling (PRS) algorithm [31], the enhanced-Conscious Task Consolidation (ECTC) technique [8], the Maximum Utilization (MaxUtil) technique [8] and the Energy-Performance Trade-off Multi-Resource Cloud Task Scheduling Algorithm (ETMCTSA) [18]. The PRS algorithm developed a scheduling algorithm, which builds the schedule and the schedule is dynamically repaired during the execution time.…”

Section: Resultsmentioning

confidence: 99%

“…Their approach contains two passes of VMs allocation considering VMs of new requests and VMs of overloaded servers. In [8], the authors have proposed two consolidation-based techniques. The two techniques are based on the best fit decreasing approach.…”

Section: B Power-efficiency Based On Consolidationmentioning

confidence: 99%

“…In addition, she stated that each year data centers exhaust electrical power of 200 terawatt-hours and they contribute to the overall CO 2 emissions by around 0.3%. Also, it is expected by 2020 that the industry of information and communication will generate about 12% of the total carbon dioxide emissions [8].…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Hybrid Framework for Green Cloud Computing

et al. 2020

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The increasing growth in the demand for cloud computing services, due to the increasingly digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the requests' scheduling and servers' consolidation approaches rather than depending on only one approach as in existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework over approaches that depend on using only one approach to reduce power consumption in terms of Power Usage Effectiveness (PUE), Data Center Energy Productivity (DCEP), average execution time, throughput and cost-saving. Index terms-green computing, scheduling, consolidation, power consumption.

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Section: Resultsmentioning

confidence: 99%

Section: B Power-efficiency Based On Consolidationmentioning

confidence: 99%

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Energy-Efficient Hybrid Framework for Green Cloud Computing

et al. 2020

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“…Mustafa et al [30] proposed two consolidation based techniques to reduce energy consumption along with resultant SLA violations. They also enhanced two existing techniques that attempt to reduce energy consumption and SLA violations.…”

Section: Ac (Vm) Migrationmentioning

confidence: 99%

DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

Ahvar

Mann

et al. 2021

IEEE Access

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As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emissionefficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a trade off and optimizing energy cost and carbon emission at the same time.INDEX TERMS Edge cloud, Energy consumption, Energy costs, Green computing, Carbon emission, Application placement.

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“…In [31] the authors have proposed two consolidation-based techniques to reduce power consumption. The two methods are based on the Best Fit Decreasing (BFD) approach.…”

Section: Related Workmentioning

confidence: 99%

TMaR: a two-stage MapReduce scheduler for heterogeneous environments

Maleki

Faragardi

Rahmani

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.

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

Cited by 34 publications

References 40 publications

Energy-Efficient Hybrid Framework for Green Cloud Computing

Energy-Efficient Hybrid Framework for Green Cloud Computing

DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

TMaR: a two-stage MapReduce scheduler for heterogeneous environments

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