Energy policies for data-center monolithic schedulers

Fernández-Cerero, Damián; Fernández-Montes, Alejandro; Ramírez, Juan Antonio Ortega

doi:10.1016/j.eswa.2018.06.007

Cited by 23 publications

(14 citation statements)

References 26 publications

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“…Bansal et al [10] introduced the concept of arbitrary power function and proved that an online clairvoyant algorithm (OCA) is (2 + )-competitive with an objective of minimizing the fractional weighted flow time plus energy. Authors presented [28] an expert and intelligent system that applies various energy policies to maximize the energy-efficiency of data-center resources. Authors claimed that around 20% of energy consumption can be saved in without exerting any noticeable impact on data-center performance.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

et al. 2019

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“…There are also some researchers who improve energy efficiency by adjusting datacenter energy usage strategies. Dami'an et al [26] proposed an intelligent system for various energy policies in response to high energy consumption in datacenters. In the case of a high utilization rate, if appropriate policies are adopted, energy consumption of about 20% can be saved without the performance of the datacenter experiencing a significant impact.…”

Section: Approaches To Improve the Energy Efficiencymentioning

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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“…One of the most popular approaches to increase efficiency is to switch off idle servers [75,78], which is usually tested in dedicated simulation tools [74]. Several models, including games theory models [72,73], are used to balance opposite requirements in data centers, such as performance and energy consumption.…”

Section: Software Solutions For Infrastructure Efficiencymentioning

confidence: 99%

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Kilanioti

Fernández-Montes

Fernández-Cerero

et al. 2019

Lecture Notes in Computer Science

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This chapter presents the authors' work for the Case Study entitled "Delivering Social Media with Scalability" within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with

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Energy policies for data-center monolithic schedulers

Cited by 23 publications

References 26 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

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

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

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