Minimizing Electricity Bills for Geographically Distributed Data Centers with Renewable and Cooling Aware Load Balancing

Dou, Hui; Qi, Yong; Wei, Wei; Song, Houbing

doi:10.1109/iiki.2015.52

Cited by 13 publications

(5 citation statements)

References 11 publications

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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%

“…Allocating W(t) at each time t to the single data center is guaranteed by constraints ( 10) and (11); having no overprovisioned servers in data center i is indicated by constraint (12). The average response time is tracked by constraint (13), and the total power taken from the energy market is not negative and does not exceed a particular threshold value indicated by constraint (14). Finally, the constraints (15) ensure that renewable energy cannot be negative and does not exceed the upper limit.…”

Section: Problem-ii: Minimization Of Energy Costmentioning

confidence: 99%

“…From 18 September 2018, to 17 October 2018, we utilize the hourly energy prices q i (t)-in dollar MWh for three geo-dispersed data centers. The wholesale energy market provides these hourly rates, which are available online [14]. Dynamic hourly energy prices are depicted in Figure 3.…”

Section: Description Of Energy Pricesmentioning

confidence: 99%

“…However, due to battery capacity limitations, completely powering data centers is problematic [13]. To solve this problem, CSPs primarily rely on brown energy and batteries as a backup during power failure [14].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Glb and Power Managementmentioning

confidence: 99%

Section: Problem-ii: Minimization Of Energy Costmentioning

confidence: 99%

Section: Description Of Energy Pricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…The seminal research work [6], [13], [22], [23], [36], [37] proposed the idea of renewable aware GLB to minimize the cost of the DC. Later on, research studies [8], [14], [15], [17], [18], [33], [38]- [42] highlighted the contemporary issues of minimizing the cost of DCs. They investigated how GLB should be operated in the presence of dynamic electricity prices, user demand uncertainty, and/or sources of renewable energy.…”

Section: A Geographical Load Balancingmentioning

confidence: 99%

Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers

2019

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In this paper, we investigate the problem of energy cost minimization for geographically distributed data centers with the guaranteed quality of service (i.e., service delay) under time-varying system dynamics. In order to satisfy the user demands, these data centers (DCs) consume a large amount of energy. The increasing energy cost of the DCs is a contemporary problem for the online service providers. To reduce the energy cost of the DCs, recent research studies suggest the workload distribution techniques among geo-distributed data centers by exploiting the dynamic electricity prices and an increased use of the renewable energy. In this paper, we propose a green geographical load balancing (GreenGLB) online algorithm based on the greedy algorithm design technique for the interactive and indivisible workload distribution. An indivisible workload is a sequential task, which cannot be further divided and must be assigned to a single data center. The basic idea of our algorithm is to assign the incoming workload at each time considering the current offered prices of electricity, the renewable energy levels, and respecting the given set of constraints. The experimental results based on the real-world traces illustrate the effectiveness of GreenGLB over the existing workload distribution techniques and attain a significant reduction in the energy cost of the geo-distributed DCs.INDEX TERMS Energy efficiency in cloud computing, geographical load balancing, geographically distributed data centers, green computing, dynamic electricity prices.

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Optimization‐based workload distribution in geographically distributed data centers: A survey

Ahmad

Khalil

Shah

2020

Int J Communication

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SummaryEnergy efficiency is a contemporary and challenging issue in geographically distributed data centers. These data centers consume significantly high energy and cast a negative impact on the energy resources and environment. To minimize the energy cost and the environmental impacts, Internet service providers use different approaches such as geographical load balancing (GLB). GLB refers to the placement of data centers in diverse geolocations to exploit variations in electricity prices with the objective to minimize the total energy cost. GLB helps to minimize the overall energy cost, achieve quality of service, and maximize resource utilization in geo‐distributed data centers by employing optimal workload distribution and resource utilization in the real time. In this paper, we summarize various optimization‐based workload distribution strategies and optimization techniques proposed in recent research works based on commonly used optimization factors such as workload type, load balancer, availability of renewable energy, energy storage, and data center server specification in geographically distributed data centers. The survey presents a systemized and a novel taxonomy of workload distribution in data centers. Moreover, we also debate various challenges and open research issues along with their possible solutions.

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Minimizing Electricity Bills for Geographically Distributed Data Centers with Renewable and Cooling Aware Load Balancing

Cited by 13 publications

References 11 publications

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

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

Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers

Optimization‐based workload distribution in geographically distributed data centers: A survey

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