In a cloud data center, servers are always over-provisioned in an active state to meet the peak demand of requests, wasting a large amount of energy as a result. One of the options to reduce the power consumption of data centers is to reduce the number of idle servers, or to switch idle servers into low-power sleep states. However, the servers cannot process the requests immediately when transiting to an active state. There are delays and extra power consumption during the transition. In this paper, we consider using state-of-the-art servers with multi-sleep modes. The sleep modes with smaller transition delays usually consume more power when sleeping. Given the arrival of incoming requests, our goal is to minimize the energy consumption of a cloud data center by the scheduling of servers with multi-sleep modes. We formulate this problem as an integer linear programming (ILP) problem during the whole period of time with millions of decision variables. To solve this problem, we divide it into sub-problems with smaller periods while ensuring the feasibility and transition continuity for each sub-problem through a Backtrack-and-Update technique. We also consider using DVFS to adjust the frequency of active servers, so that the requests can be processed with the least power. Our simulations are based on traces from real world. Experiments show that our method can significantly reduce the power consumption for a cloud data center.

中文翻译：

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云数据中心多睡眠模式服务器的节能调度

在云数据中心，服务器总是在活跃状态下过度配置以满足请求的高峰需求，从而浪费了大量的能源。降低数据中心功耗的选择之一是减少空闲服务器的数量，或者将空闲服务器切换到低功耗睡眠状态。但是，当转换到活动状态时，服务器无法立即处理请求。过渡期间存在延迟和额外功耗。在本文中，我们考虑使用具有多睡眠模式的最先进的服务器。具有较小转换延迟的睡眠模式通常在睡眠时消耗更多功率。鉴于传入请求的到来，我们的目标是通过调度具有多睡眠模式的服务器来最小化云数据中心的能耗。我们将此问题表述为整数线性规划 (ILP) 问题，在整个时间段内有数百万个决策变量。为了解决这个问题，我们将其划分为周期更短的子问题，同时通过回溯和更新技术确保每个子问题的可行性和过渡连续性。我们还考虑使用 DVFS 来调整活动服务器的频率，以便以最少的功率处理请求。我们的模拟基于来自现实世界的痕迹。实验表明，我们的方法可以显着降低云数据中心的功耗。我们将其划分为周期更短的子问题，同时通过回溯和更新技术确保每个子问题的可行性和过渡连续性。我们还考虑使用 DVFS 来调整活动服务器的频率，以便以最少的功率处理请求。我们的模拟基于来自现实世界的痕迹。实验表明，我们的方法可以显着降低云数据中心的功耗。我们将其划分为周期更短的子问题，同时通过回溯和更新技术确保每个子问题的可行性和过渡连续性。我们还考虑使用 DVFS 来调整活动服务器的频率，以便以最少的功率处理请求。我们的模拟基于来自现实世界的痕迹。实验表明，我们的方法可以显着降低云数据中心的功耗。