TimeTrader：利用延迟尾部为在线数据密集型应用节省数据中心能源
TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications
摘要：
运行在线数据密集型应用程序（OLDI）的数据中心消耗大量能源。然而，由于其严格的响应时间要求，降低其能量是具有挑战性的。OLDI的一个关键方面是，每个用户查询都会到达集群中的所有或许多节点，因此总体时间预算由回复延迟分布的尾部决定;回复在网络和计算中都会看到延迟变化。先前的工作提出通过直接基于响应时间（即，在较低负载时，该建议利用为峰值负载提供的时间预算中的平均松弛）。相比之下，我们提出TimeTrader通过利用在截止日期之前到达的次关键回复中的延迟松弛来减少能量（例如，80%的回复比尾部快3- 4倍）。此松弛存在于所有负载中，并包含前一个工作的负载相关松弛。虽然以前的工作改变了叶子的响应时间分布，以消耗较低负载时的松弛时间，但TimeTrader通过减慢单个次关键节点的速度而不增加错过的最后期限来重塑所有负载时的分布。TimeTrader利用了网络和计算预算的松弛。此外，TimeTrader利用最早截止日期优先调度，在很大程度上将关键请求与次关键请求的排队延迟解耦，然后可以在不伤害关键请求的情况下减慢次关键请求的排队延迟。实际系统测量和大规模模拟的结合表明，在不增加错过截止日期的情况下，TimeTrader在512个节点的数据中心中分别在90%和30%的负载下节省了15-19%和41-49%的能源，而以前的工作节省了0%和31- 37%。
英文摘要：
Datacenters running on-line, data-intensive applications (OLDIs) consume significant amounts of energy. However, reducing their energy is challenging due to their tight response time requirements. A key aspect of OLDIs is that each user query goes to all or many of the nodes in the cluster, so that the overall time budget is dictated by the tail of the replies' latency distribution; replies see latency variations both in the network and compute. Previous work proposes to achieve load-proportional energy by slowing down the computation at lower datacenter loads based directly on response times (i.e., at lower loads, the proposal exploits the average slack in the time budget provisioned for the peak load). In contrast, we propose TimeTrader to reduce energy by exploiting the latency slack in the sub- critical replies which arrive before the deadline (e.g., 80% of replies are 3-4x faster than the tail). This slack is present at all loads and subsumes the previous work's load-related slack. While the previous work shifts the leaves' response time distribution to consume the slack at lower loads, TimeTrader reshapes the distribution at all loads by slowing down individual sub-critical nodes without increasing missed deadlines. TimeTrader exploits slack in both the network and compute budgets. Further, TimeTrader leverages Earliest Deadline First scheduling to largely decouple critical requests from the queuing delays of sub- critical requests which can then be slowed down without hurting critical requests. A combination of real-system measurements and at-scale simulations shows that without adding to missed deadlines, TimeTrader saves 15-19% and 41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512 nodes, whereas previous work saves 0% and 31-37%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
