Modeling and optimization of energy-efficient and delay-constrained video sharing servers

2015-09-14With the continually growing popularity of online video sharing, energy consumption in video sharing servers has become a pivotal issue. Energy saving in large‐scale video sharing data centers can be achieved by utilizing low power modes in disks, yet this could lead to excessive delay and affect the quality‐of‐service. In this thesis, we present techniques that jointly optimize energy and delay for video sharing servers. Specifically, we present a general energy‐delay optimization framework that can be applied to a variety of issues related to energy management in video‐sharing services. Furthermore, the framework is generally applicable to disks with multiple low‐power modes, including currently available disks and future ones. ❧ This thesis features a comprehensive survey followed by careful examination of three major problems in energy management for video‐sharing services: power mode selection, caching and data placement. For the first topic, we propose a novel model that exploits the unique workload characteristics of video‐sharing services. Based on the model, we formulate the power mode decision problem as a constrained optimization task. By solving the optimization problem, the proposed prediction‐based mode decision (PMD) algorithm selects the optimal power modes for disks with various delay constraints. ❧ For the second topic, we investigate the effects of caching on energy efficiency and study how cache can be better utilized in the context of energy‐delay optimization. We extend the original framework and propose two new techniques along this direction to improve energy efficiency. Firstly, we adopt a energy‐delay‐optimized caching (EDOC) utility for cache replacement. Then, we propose the prediction‐based energy‐efficient prefetching (PEEP) algorithm that effectively reduces mode transition overheads for the video storage server. Experiments show that our schemes achieve significantly more energy savings under the same delay level compared to the traditional threshold‐based energy management scheme. ❧ Finally, we present a learning‐based optimization scheme for the placement of video data. Optimization of data placement has been known to be an NP‐hard problem even when the objective function is explicitly given, and it becomes even more difficult in the context of energy efficiency due to lack of analytical models that can accurately predict energy consumption and service delays. Instead of resorting to heuristic approaches like previous work, we approach the mathematical problem by applying machine learning techniques. The solution we provide can create data-disk allocations that are energy efficient under a wide array of conditions, including different levels of service load, delay requirement and capacity constraints