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.
How to schedule green energy in an efficient and effective manner has been investigated extensively. Although green energy has the advantages of being cost-effective and environmental-friendly, there is a challenge in using it due to its daily seasonal variability. Another challenge comes from customers' workload fluctuations @cite_33 . There could lead to a temporal mismatch between the green energy supply and the workload's energy demand in the time axis --- a heavy workload arrives when the green energy supply is low. One solution is to bank'' green energy in batteries for later possible use. However, this approach incurs huge energy lost and high additional maintenance cost @cite_3 . Thus, a run-time online algorithm for a matching of workload and energy is highly demanded for green data centers.
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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. 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. 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. The variable and intermittent nature of many renewable energy sources makes integrating them into the electric grid challenging and limits their penetration. The current grid requires expensive, largescale energy storage and peaker plants to match such supplies to conventional loads. We present an alternative solution, in which supply-following loads adjust their power consumption to match the available renewable energy supply. We show Internet data centers running batched, data analytic workloads are well suited to be such supply-following loads. They are large energy consumers, highly instrumented, agile, and contain much scheduling slack in their workloads. We explore the problem of scheduling the workload to align with the time-varying available wind power. Using simulations driven by real life batch workloads and wind power traces, we demonstrate that simple, supply-following job schedulers yield 40-60 better renewable energy penetration than supply-oblivious schedulers.