Green data centers have become more and more popular recently due to their sustainability. The resource management module within a green data center, which is in charge of dispatching jobs and scheduling energy, becomes especially critical as it directly affects a center's profit and sustainability. The thrust of managing a green data center's machine and energy resources lies at the uncertainty of incoming job requests and future showing-up green energy supplies. Thus, the decision of scheduling resources has to be made in an online manner. Some heuristic deterministic online algorithms have been proposed in recent literature. In this paper, we consider online algorithms for green data centers and introduce a randomized solution with the objective of maximizing net profit. Competitive analysis is employed to measure online algorithms' theoretical performance. Our algorithm is theoretical-sound and it outperforms the previously known deterministic algorithms in many settings using real traces. To complement our study, optimal offline algorithms are also designed.
Among the work on centralized data centers, @cite_38 @cite_24 studied a model which is the same as ours presented in this paper. @cite_6 aimed to improve green energy usage and @cite_36 had the goal of reducing brown energy costs. The algorithmic idea underlying the above-mentioned solutions is greedy and they employed algorithms known as and . All prior work focuses on either maximizing green energy consumption or minimizing brown energy consumption cost except @cite_17 which studied the net profit maximization problem for centralized data center service providers. @cite_17 proposed a systematic approach to maximize green data center's profit with a stochastic assumption over the workload --- the workload that they studied is restricted to online service requests with variable arrival rates. In this paper, we study the profit maximization problem in a more general setting. In particular, we do not make particular assumptions over the workload's stochastic property. In addition, we incorporate dynamic brown energy price in our model which is a widely used energy charging scheme in data centers.
@cite_38 @cite_36 @cite_6 @cite_24 @cite_17
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While a large body of work has recently focused on reducing data center's energy expenses, there exists no prior work on investigating the trade-off between minimizing data center's energy expenditure and maximizing their revenue for various Internet and cloud computing services that they may offer. In this paper, we seek to tackle this shortcoming by proposing a systematic approach to maximize green data center's profit, i.e., revenue minus cost. In this regard, we explicitly take into account practical service-level agreements (SLAs) that currently exist between data centers and their customers. Our model also incorporates various other factors such as availability of local renewable power generation at data centers and the stochastic nature of data centers' workload. Furthermore, we propose a novel optimization-based profit maximization strategy for data centers for two different cases, without and with behind-the-meter renewable generators. We show that the formulated optimization problems in both cases are convex programs; therefore, they are tractable and appropriate for practical implementation. Using various experimental data and via computer simulations, we assess the performance of the proposed optimization-based profit maximization strategy and show that it significantly outperforms two comparable energy and performance management algorithms that are recently proposed in the literature. Renewable (or green) energy, such as solar or wind, has at least partially powered data centers to reduce the environmental impact of traditional energy sources (brown energy with high carbon footprint). In this paper, we propose a holistic workload scheduling algorithm to minimize the brown energy consumption across multiple geographically distributed data centers with renewable energy sources. While green energy supply for a single data center is intermittent due to daily seasonal effects, our workload scheduling algorithm is aware of different amounts of green energy supply and dynamically schedules the workload across data centers. The scheduling decision adapts to workload and data center cooling dynamics. Our experiments with real workload traces demonstrate that our scheduling algorithm greatly reduces brown energy consumption by up to 40 in comparison with other scheduling policies. It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective. To reduce the negative environmental implications (e.g., CO 2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets. In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with real-world weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.