Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

Armenta-Cano, Fermin A.; Tchernykh, Andrei; Cortés-Mendoza, Jorge M.; Yahyapour, Ramin; Drozdov, Alexander Yu.; Bouvry, Pascal; Kliazovich, Dzmitry; Avetisyan, Arutyun; Nesmachnow, Sergio

doi:10.1134/s0361768817030021

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…Similar task lengths are considered in the related literature. 3,35 The average utilization of resources owned by tenants is about 80% in the considered planning period.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Muraña

Nesmachnow

2021

SIMULATION

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This article presents the evaluation of multicriteria planning heuristics for demand response in datacenters and supercomputing facilities. This is a relevant problem for science nowadays, when the growing application of cutting-edge technologies (numerical methods, big data processing, artificial intelligence, smart systems, etc.) has raised the energy demands in datacenters. The proposed approach involves a negotiation mechanism for colocation datacenters, where the datacenter operator agrees prices and quality of service with a group of tenants. Twelve different multicriteria heuristics are proposed for planning using both local and global information at tenants and datacenter operator levels. The proposed approach is evaluated applying simulations over realistic scenarios considering different tenant sizes and heterogeneity levels that model different business models for datacenters. Several metrics are computed and Pareto analysis is provided. The main results indicate that accurate trade-off values between the problem objectives are obtained, offering different options for decision making. The proposed approach provides a useful and applicable method for demand response planning in modern datacenters.

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“…Similar task lengths are considered in the related literature. 3,35 The average utilization of resources owned by tenants is about 80% in the considered planning period.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Muraña

Nesmachnow

2021

SIMULATION

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“…Workloads are specified in a format that extends the Standard Workload Format (swf). The extended format, introduced by [11], adds two new fields to the format: codec utilization and job type. Fields of the extended swf format used in this work include:…”

Section: Workloadsmentioning

confidence: 99%

“…This tool allows modeling and simulating cloud computing infrastructures, provisioning environments, and applications [10]. In this work, a custom version of CloudSim [11] is extended by including new energy models, built considering the empirical power consumption evaluation, and different allocation policies.…”

Section: Introductionmentioning

confidence: 99%

Empirical power consumption characterization and energy aware scheduling in data centers

Muraña

2021

CLEIej

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Energy-efficient management is key to reduce operational cost and environmental contamination in modern data centers. Energy management and renewable energy utilization are strategies to optimize energy consumption in high-performance computing. In any case, understanding the power consumption behavior of physical servers in datacenter is fundamental to implement energy-aware policies effectively. These policies should deal with possible performance degradation of applications to ensure quality of service. This manuscript presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating models to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency.

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“…Some works study the contention of all resources by dividing tasks into CPU-intensive, disk-intensive, I/O-intensive, memory-intensive and network-intensive [22]. For eample, Zhu et al [23] propose a scheduling strategy called green scheduling that makes the tasks, whose resource requirements are complementary, execute in parallel.…”

Section: Related Workmentioning

confidence: 99%

Energy efficiency optimization in big data processing platform by improving resources utilization

Song

Thomas

et al. 2019

Sustainable Computing: Informatics and Systems

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Big data creates tremendous value for humanity, but also places a heavy burden on the environment. Energy consumption of computing platforms, especially big data processing platforms, cannot be ignored, and optimization of energy usage is imperative. To the out-of-core data processing, this paper proposes that the energy efficiency of a big data processing platform is closely related to the utilization of its computing resources; and an efficient resource allocation strategy for data processing tasks improves the platform's resources utilization. To improve the resources utilization, different resources are allocated according to a taskrelated Best Resource Ratio (BRR for short), such as "cpu, disk, network in the ratio of 1:2:4", rather than the resource's quantity, such as "cpu=1GHz, network=20MB/s". We deduce the BRR of data processing tasks, and design a resource ratio based approach (R 2 ), which includes a task scheduling algorithm and resource allocation algorithm, for energy efficiency optimization. Experiments show that the R 2 approach can improve energy efficiency by 10%, 10% and 6% compared to FIFO, Capacity and Fair schedulers respectively, by maximizing resource utilization of a 12-node MapReduce platform.

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Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

Cited by 12 publications

References 12 publications

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Empirical power consumption characterization and energy aware scheduling in data centers

Energy efficiency optimization in big data processing platform by improving resources utilization

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