{"id":160425,"date":"2011-04-01T00:00:00","date_gmt":"2011-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/accurate-latency-estimation-in-a-distributed-event-processing-system-2\/"},"modified":"2018-10-16T20:17:36","modified_gmt":"2018-10-17T03:17:36","slug":"accurate-latency-estimation-in-a-distributed-event-processing-system-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/accurate-latency-estimation-in-a-distributed-event-processing-system-2\/","title":{"rendered":"Accurate Latency Estimation in a Distributed Event Processing System"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A distributed event processing system consists of one or more nodes (machines), and can execute a directed acyclic graph (DAG) of operators called a dataflow (or query), over long-running high-event-rate data sources. An important component of such a system is cost estimation, which predicts or estimates the &#8220;goodness&#8221; of a given input, i.e., operator graph and\/or assignment of individual operators to nodes. Cost estimation is the foundation for solving many problems: optimization (plan selection and distributed operator placement), provisioning, admission control, and user reporting of system misbehavior.<\/p>\n<p>Latency is a significant user metric in many commercial real-time applications. Users are usually interested in quantiles of latency, such as worst-case or 99th percentile. However, existing cost estimation techniques for event-based dataflows use metrics that, while they may have the side-effect of being correlated with latency, do not directly or provably estimate latency. In this paper, we propose a new cost estimation technique using a metric called Mace (Maximum cumulative excess). Mace is provably equivalent to maximum system latency in a (potentially complex, multi-node) distributed event-based system. The close relationship to latency makes Mace ideal for addressing the problems described earlier. Experiments with real-world datasets on Microsoft StreamInsight deployed over 1-13 nodes in a data center validate our ability to closely estimate latency (within 4%), and the use of Mace for plan selection and distributed operator placement.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A distributed event processing system consists of one or more nodes (machines), and can execute a directed acyclic graph (DAG) of operators called a dataflow (or query), over long-running high-event-rate data sources. An important component of such a system is cost estimation, which predicts or estimates the &#8220;goodness&#8221; of a given input, i.e., operator graph [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160425","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"IEEE","msr_edition":"27th International Conference on Data Engineering (ICDE 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