Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers

Khalil, Muhammad Imran Khan; Ahmad, Iftikhar; Almazroi, Abdulwahab Ali

doi:10.1109/access.2019.2924085

Cited by 21 publications

(12 citation statements)

References 36 publications

(102 reference statements)

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“…Since renewable energy sources are infrequent, infrastructure load control solutions must be customised to the intermittent nature of the sources. Furthermore, the authors in [ 143 ] have addressed the problem of reducing energy costs for geographically distant data centres while maintaining assured service quality (i.e., service latency) under time-varying system dynamics. They proposed a green geographical load balancing (GreenGLB) online solution for interactive and indivisible work distribution based on the greedy algorithm design approach.…”

Section: Environmentmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

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Section: Environmentmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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“…Researchers, academics, and businesses have focused heavily over the past decade on energy management approaches to reduce the overall expenses of the data center [13,14,[28][29][30][31][32][33][34]. However, prediction of data centre load [35][36][37][38][39] is mandatory before power management.…”

Section: Glb and Power Managementmentioning

confidence: 99%

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

et al. 2022

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It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.

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“…However, DDS relies on the QoS policy guarantee to provide high reliability and flexibility. At present, the data communication service established by using DDS middleware has been widely used in many real-time multi-party communication application scenarios such as multi-party video conference, multi-party voice intercom, unified command and operation [11][12]. DDS is mainly used in three design and application environments, including:…”

Section: Concept and Composition Of Data Distribution Servicementioning

confidence: 99%

Distributed System Simulation Application Considering Data Distribution Service

2020

DPS

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Data distribution service (DDS) is a distributed real-time communication middleware specification published by object management group (OMG). This paper mainly studies the application analysis of distributed system simulation considering data distribution service. This paper first introduces the concept and composition of data distribution service, and then puts forward a distributed system data distribution model, and constructs a "bus type" data distribution model. And the performance of the data distribution model is tested. Through the performance test results, we can know that the data distribution model built in this paper can use DDS to achieve efficient remote procedure call development and operation.

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Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers

Cited by 21 publications

References 36 publications

Energy efficiency in cloud computing data centers: a survey on software technologies

Energy efficiency in cloud computing data centers: a survey on software technologies

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

Distributed System Simulation Application Considering Data Distribution Service

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