Energy efficiency aware load distribution and electricity cost volatility control for cloud service providers

Paul, Debdeep; Zhong, Wen‐De; Bose, Sanjay K.

doi:10.1016/j.jnca.2015.08.012

Cited by 28 publications

(24 citation statements)

References 46 publications

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“…[19]. We assume that the data center is being operated in the real time electricity market, which is quite common in practice, and that the data centers are quite big consumers of electricity [19,25,36]. Hence, the energy cost incurred by IaaS provide i in the timeslot t is given by:…”

Section: E[x]mentioning

confidence: 99%

“…In literature, the adverse effects of server switching has been modeled as both linear [18] and non-linear functions [19]. For the purposes of the present paper, we assume a linear cost as our focus is energy aware pricing, and we are not interested in analyzing the structure of the switching cost function itself and its effects as described in [19,25]:…”

Section: E[x]mentioning

confidence: 99%

“…However, the model proposed in the paper can handle an arbitrary number of data centers operated by an IaaS. In that case, the respective IaaS needs to perform some GLB strategy as presented in [25]. The electricity price data is obtained from [43].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…This is also effective in reducing electricity cost incurred by the operator of a data center. Typically, the price of electricity varies across geographical locations, and this strategy can make use of this spatial variation in electricity price to reduce the cost [25]. The inherent idea behind these strategies is to direct the traffic (workload) to the places where more renewable energy is available and thereby decrease the brown energy consumption, which results in reduction in the emission of greenhouse gases.…”

Section: Introductionmentioning

confidence: 99%

“…Given these research endeavors in academia and many successful implementation adopted by the industries, we are quite convinced that energy efficiency practices in cloud computing system have become quite mature over the past few decades. There have also been efforts to design algorithms for GLB considering the deferrable jobs so as to reap even more benefits [25,26]. A strategy for implementing energy aware scheduling of batched jobs under GLB based framework may be found in [27].…”

Section: Introductionmentioning

confidence: 99%

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Energy Aware Pricing in a Three-Tiered Cloud Service Market

2016

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Abstract:We consider a three-tiered cloud service market and propose an energy efficient pricing strategy in this market. Here, the end customers are served by the Software-as-a-Service (SaaS) providers, who implement customized services for their customers. To host these services, these SaaS providers, in turn, lease the infrastructure related resources from the Infrastructure-as-a-Service (IaaS) or Platform-as-a-Service (PaaS) providers. In this paper, we propose and evaluate a mechanism for pricing between SaaS providers and Iaas/PaaS providers and between SaaS providers and the end customers. The pricing scheme is designed in a way such that the integration of renewable energy is promoted, which is a very crucial aspect of energy efficiency. Thereafter, we propose a technique to strategically provide an improved Quality of Service (QoS) by deploying more resources than what is computed by the optimization procedure. This technique is based on the square root staffing law in queueing theory. We carry out numerical evaluations with real data traces on electricity price, renewable energy generation, workload, etc., in order to emulate the real dynamics of the cloud service market. We demonstrate that, under practical assumptions, the proposed technique can generate more profit for the service providers operating in the cloud service market.

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Section: E[x]mentioning

confidence: 99%

Section: E[x]mentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Aware Pricing in a Three-Tiered Cloud Service Market

2016

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Cost‐minimizing online algorithm for internet green data centers on multi‐source energy

Shen

Liang

2018

Concurrency and Computation

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Huge energy consumption of large-scale cloud data centers damages environments with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy. However, the intermittent availability of renewable energy sources makes it quite challenging to cooperate with dynamically arriving workload.Meanwhile, the different natures (eg, price and carbon emission) of multiple energy sources also bring more challenges to achieve an optimal trade-off among carbon emission, power cost, and service level agreement (SLA). In this paper, we study the problem of reducing the long-term energy cost for geo-distributed cloud centers, where multiple sources of renewable energy are considered and SLA requirement and carbon budget are satisfied. To tackle the randomness of workload arrival, varying electricity price, and intermittent supply of renewable energy, we first formulate the cost minimization problem as a constraint stochastic optimization problem. Second, based on Lyapunov optimization technique, we propose an online control algorithm to solve it and provide the rigorous theory analysis to demonstrate its performance. By converting the long-term optimization problem to a mixed integer linear programming problem in each time slot, we analyze its inherent structure and propose an efficient algorithm to solve it based on Brenner's method.Our proposed algorithm makes online decisions rely only on the current system state and achieve [O( 1 ∕V), O(V)] cost emission trade-off. Finally, the effectiveness of our algorithm is evaluated by extensive simulations based on real-world data traces. KEYWORDS geo-distributed cloud center, green-aware, Lyapunov optimization, renewable energy INTRODUCTIONDriven by the exploding demand of enterprises and personal computation, the scale of cloud data centers has become increasingly large. As stated in the latest report, 1 some leading IT enterprises (eg, Facebook, Amazon, and Google) own their inter-continental data centers in different regions that contain thousands of servers. Thus, accompanied with the corresponding electricity cost is up to millions dollars per year. Meanwhile, large electricity consumption from "brown energy" (produce from oil or coal) brings a large amount of greenhouse gases (GHG) emissions which cause great damages to the fragile environment. Recent studies have shown that Information and Communications Technology (ICT) sector is responsible for 2% of global carbon emission, which places it on par with the aviation industry, and this is set to be doubled by 2020. 2,3 Therefore, it is significant for the large-scale data centers to reduce the net carbon footprint and power cost.Recently, reducing power consumption to make data centers green with renewable energy has received a lot of attention both in industry. 4 and academia. 5,6 Compared to brown energy, renewable energy (such as wind energy and solar energy) is more environmentally friendly with low carbon footprint. Google, Microsoft, and Faceboo...

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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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Energy efficiency aware load distribution and electricity cost volatility control for cloud service providers

Cited by 28 publications

References 46 publications

Energy Aware Pricing in a Three-Tiered Cloud Service Market

Energy Aware Pricing in a Three-Tiered Cloud Service Market

Cost‐minimizing online algorithm for internet green data centers on multi‐source energy

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

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