In this paper, we design an analytically and experimentally better online energy and job scheduling algorithm with the objective of maximizing net profit for a service provider in green data centers. We first study the previously known algorithms and conclude that these online algorithms have provable poor performance against their worst-case scenarios. To guarantee an online algorithm's performance in hindsight, we design a randomized algorithm to schedule energy and jobs in the data centers and prove the algorithm's expected competitive ratio in various settings. Our algorithm is theoretical-sound and it outperforms the previously known algorithms in many settings using both real traces and simulated data. An optimal offline algorithm is also implemented as an empirical benchmark.
Most of prior work focuses on either maximizing green energy consumption or minimizing brown energy consumption cost except @cite_16 which studied the net profit maximization problem for centralized data center service providers. Actually there is a trade-off between the minimization of energy expenditure and the maximization of net profit. @cite_16 proposed a systematic approach to maximize green data center's profit with a stochastic assumption on 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 make no particular assumptions over the workload's stochastic property and we allow the workloads to include a batch job which requests to be simultaneously executed on multiple nodes. In addition, we incorporate varying brown energy prices in our model.
@cite_16
2095546124
An increasing number of data centers today start to incorporate renewable energy solutions to cap their carbon footprint. However, the impact of renewable energy on large-scale data center design is still not well understood. In this paper, we model and evaluate data centers driven by intermittent renewable energy. Using real-world data center and renewable energy source traces, we show that renewable power utilization and load tuning frequency are two critical metrics for designing sustainable high-performance data centers. Our characterization reveals that load power fluctuation together with the intermittent renewable power supply introduce unnecessary tuning activities, which can increase the management overhead and degrade the performance of renewable energy driven data centers.