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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Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired by our empirical study on a trace from a large-scale production data-processing cluster; it allows a set of effective query optimizations that are not possible in a traditional batch processing model. We have developed a query processing system called Comet that embraces batched stream processing and integrates with DryadLINQ. We used two complementary methods to evaluate the effectiveness of optimizations that Comet enables. First, a prototype system deployed on a 40-node cluster shows an I O reduction of over 40 using our benchmark. Second, when applied to a real production trace covering over 19 million machine-hours, our simulator shows an estimated I O saving of over 50 . Interest has been growing in powering data centers (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) energy drawn from the electrical grid, they are not always available. In this keynote talk, I will first discuss the tradeoffs involved in leveraging green energy today and the prospects for the future. I will then discuss the main research challenges and questions involved in managing the use of green energy in data centers. Next, I will describe some of the software and hardware that researchers are building to explore these challenges and questions. Specifically, I will overview systems that match a data center's computational workload to the green energy supply. I will also describe Parasol, the solar-powered micro-data center we have just built at Rutgers University. Finally, I will discuss some potential avenues for future research on this topic.