David Lo
Rama Govindaraju
Christos Kozyrakis
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Abstract
User-facing, latency-sensitive services, such as websearch, underutilize their computing resources during daily periods of low traffic. Reusing those resources for other tasks is rarely done in production services since the contention for shared resources can cause latency spikes that violate the service-level objectives of latency-sensitive tasks. The resulting under-utilization hurts both the affordability and energy efficiency of large-scale datacenters. With the slowdown in technology scaling caused by the sunsetting of Moore’s law, it becomes important to address this opportunity.
We present Heracles, a feedback-based controller that enables the safe colocation of best-effort tasks alongside a latency-critical service. Heracles dynamically manages multiple hardware and software isolation mechanisms, such as CPU, memory, and network isolation, to ensure that the latency-sensitive job meets latency targets while maximizing the resources given to best-effort tasks. We evaluate Heracles using production latency-critical and batch workloads from Google and demonstrate average server utilizations of 90% without latency violations across all the load and colocation scenarios that we evaluated.
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- Improving Resource Efficiency at Scale with Heracles
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Reviews
Bayard Kohlhepp
Most of the paper's authors are connected to Google, and their work centers on Google workload performance improvement. They've developed runtime controller software, Heracles, that uses real-time feedback and static modeling rules to adjust resource allocation within servers in order to meet service-level objectives (SLOs). The paper's closing section demonstrates that Heracles improved performance in test systems. It's great that they've made Google faster, but what use is this Google performance tool to the rest of us Unless and until they make Heracles freely downloadable (and we have server applications that can make use of it), the tool itself is of no general interest. The value of this paper, though, lies not in the end product, but in the journey that led to the end product. The first nine or ten pages describe the authors' analysis of resource contention, specifically the interplay between latency critical (LC) tasks and noncritical, best-effort (BE) tasks. All applications, from Internet of Things (IoT) to the cloud, on smartphones and in data center servers, face the problem of guaranteeing quick response from critical services despite the unpredictable activity of background tasks. At present, we solve the problem by over allocating resources. We throw money at the problem, paying for peak usage scenarios, while day in and day out we tolerate idle central processing units (CPUs) and underutilized storage. The analysis that led to Heracles, summarized in this paper, brings us a step closer to building efficient systems. The authors have created a template we can all use to analyze resource contention. They also identify specific tools and techniques used to address contention issues, quantify performance improvements achieved by using those tools, and survey numerous research contributors for further investigation. The rest of us will probably never use Heracles, but we can all use this advice to improve our own little corner of the universe. Online Computing Reviews Service
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