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

With 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.

随着在线视频分享的热度持续攀升，视频分享服务器的能耗已成为关键议题。大规模视频分享数据中心的节能可通过利用磁盘的低功耗模式实现，但这可能引发过高延迟，影响服务质量（Quality-of-Service, QoS）。在本论文中，我们提出了可联合优化视频分享服务器能耗与延迟的技术方案。具体而言，我们构建了一个通用的能耗-延迟优化框架，可应用于视频分享服务中各类能耗管理相关问题。此外，该框架普遍适用于具备多种低功耗模式的磁盘，包括当前已商用的磁盘及未来新型磁盘。
本论文首先开展了全面的调研，随后针对视频分享服务能耗管理中的三大核心问题展开深入研究：功耗模式选择、缓存管理与数据放置。针对第一个问题，我们提出了一种新颖的模型，该模型充分利用了视频分享服务独有的工作负载特性。基于此模型，我们将功耗模式决策问题建模为带约束的优化任务。通过求解该优化问题，所提出的基于预测的模式决策（Prediction-based Mode Decision, PMD）算法可为具备不同延迟约束的磁盘选择最优功耗模式。
针对第二个问题，我们研究了缓存对能效的影响，并探讨了在能耗-延迟优化场景下如何更高效地利用缓存。我们扩展了原有的优化框架，并在此方向上提出了两种新技术以提升能效。其一，我们采用了能耗-延迟优化缓存（Energy-Delay Optimized Caching, EDOC）效用函数用于缓存替换策略；其二，我们提出了基于预测的节能预取（Prediction-based Energy-Efficient Prefetching, PEEP）算法，可有效降低视频存储服务器的模式切换开销。实验结果表明，相较于传统的基于阈值的能耗管理方案，我们的方案在相同延迟水平下可实现显著更高的节能效果。
最后，我们针对视频数据的放置问题提出了一种基于学习的优化方案。众所周知，即便目标函数明确给出，数据放置优化也属于NP难问题；而在能效优化场景下，由于缺乏可准确预测能耗与服务延迟的解析模型，该问题的难度进一步提升。不同于此前工作依赖启发式方法的思路，我们通过引入机器学习技术来求解该数学问题。我们所提出的解决方案可生成在多种场景下均具备能效的数据-磁盘分配方案，这些场景包括不同的服务负载水平、延迟要求及容量约束。