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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In this paper, we propose GreenSlot, a parallel batch job scheduler for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meeting the jobs' deadlines. If grid energy must be used to avoid deadline violations, the scheduler selects times when it is cheap. Our results for production scientific workloads demonstrate that Green-Slot can increase green energy consumption by up to 117 and decrease energy cost by up to 39 , compared to a conventional scheduler. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem. Recently, the demand for data center computing has surged, increasing the total energy footprint of data centers worldwide. Data centers typically comprise three subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes heat generated by these subsystems. This work presents a novel approach to model the energy flows in a data center and optimize its operation. Traditionally, supply-side constraints such as energy or cooling availability were treated independently from IT workload management. This work reduces electricity cost and environmental impact using a holistic approach that integrates renewable supply, dynamic pricing, and cooling supply including chiller and outside air cooling, with IT workload planning to improve the overall sustainability of data center operations. Specifically, we first predict renewable energy as well as IT demand. Then we use these predictions to generate an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce both the recurring power costs and the use of non-renewable energy by as much as 60 compared to existing techniques, while still meeting the Service Level Agreements. As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy. 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.