{"id":814942,"date":"2022-02-21T02:33:06","date_gmt":"2022-02-21T10:33:06","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=814942"},"modified":"2024-10-17T12:51:18","modified_gmt":"2024-10-17T19:51:18","slug":"jarvis-large-scale-server-monitoring-with-adaptive-near-data-processing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/jarvis-large-scale-server-monitoring-with-adaptive-near-data-processing\/","title":{"rendered":"Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing"},"content":{"rendered":"

Rapid detection and mitigation of issues that impact <\/span>performance<\/span> and<\/span> reliability<\/span> is<\/span> paramount<\/span> for<\/span> large-scale<\/span> online <\/span>services.<\/span> For<\/span> real-time<\/span> detection<\/span> of<\/span> such<\/span> issues,<\/span> datacenter<\/span> oper<\/span>ators<\/span> use<\/span> a<\/span> stream<\/span> processor<\/span> and<\/span> analyze<\/span> streams<\/span> of<\/span> monitoring <\/span>data collected from servers (referred to as data source nodes) and <\/span>their<\/span> hosted<\/span> services.<\/span> The<\/span> timely<\/span> processing<\/span> of<\/span> incoming<\/span> streams <\/span>requires<\/span> the<\/span> network<\/span> to<\/span> transfer<\/span> massive<\/span> amounts<\/span> of<\/span> data,<\/span> and <\/span>significant<\/span> compute<\/span> resources<\/span> to<\/span> process<\/span> it.<\/span> These<\/span> factors<\/span> often <\/span>create<\/span> bottlenecks<\/span> for<\/span> stream<\/span> analytics.<\/span><\/p>\n

To<\/span> help<\/span> overcome<\/span> these<\/span> bottlenecks,<\/span> current<\/span> monitoring<\/span> sys<\/span>tems<\/span> employ<\/span> near-data<\/span> processing<\/span> by<\/span> either<\/span> computing<\/span> an<\/span> op<\/span>timal<\/span> query<\/span> partition<\/span> based<\/span> on<\/span> a<\/span> cost<\/span> model<\/span> or<\/span> using<\/span> model-<\/span>agnostic<\/span> heuristics.<\/span> Optimal<\/span> partitioning<\/span> is<\/span> computationally<\/span> ex<\/span>pensive,<\/span> while<\/span> model-agnostic<\/span> heuristics<\/span> are<\/span> iterative<\/span> and<\/span> search <\/span>over<\/span> a<\/span> large<\/span> solution<\/span> space.<\/span> We<\/span> combine<\/span> these<\/span> approaches <\/span>by<\/span> using<\/span> model-agnostic<\/span> heuristics<\/span> to<\/span> improve<\/span> the<\/span> partitioning <\/span>solution from a model-based heuristic. Moreover, current systems <\/span>use<\/span> operator-level<\/span> partitioning:<\/span> if<\/span> a<\/span> data<\/span> source<\/span> does<\/span> not<\/span> have <\/span>sufficient<\/span> resources<\/span> to<\/span> execute<\/span> an<\/span> operator<\/span> on<\/span> all<\/span> records,<\/span> the <\/span>operator<\/span> is<\/span> executed<\/span> only<\/span> on<\/span> the<\/span> stream<\/span> processor.<\/span> Instead,<\/span> we <\/span>perform<\/span> data-level<\/span> partitioning\u2014i.e.,<\/span> we<\/span> allow<\/span> an<\/span> operator<\/span> to<\/span> be <\/span>executed<\/span> both<\/span> on<\/span> a<\/span> stream<\/span> processor<\/span> and<\/span> data<\/span> sources.<\/span><\/p>\n

We<\/span> implement<\/span> our<\/span> algorithm<\/span> in<\/span> a<\/span> system<\/span> called<\/span> Jarvis,<\/span> which <\/span>enables<\/span> quick<\/span> adaptation<\/span> to<\/span> dynamic<\/span> resource<\/span> conditions.<\/span> Our <\/span>evaluation<\/span> on<\/span> a<\/span> diverse<\/span> set<\/span> of<\/span> monitoring<\/span> workloads<\/span> suggests <\/span>that<\/span> Jarvis<\/span> converges<\/span> to<\/span> a<\/span> stable<\/span> query<\/span> partition<\/span> within<\/span> seconds <\/span>of<\/span> a<\/span> change<\/span> in<\/span> node<\/span> resource<\/span> conditions.<\/span> Compared<\/span> to<\/span> current <\/span>partitioning<\/span> strategies,<\/span> Jarvis<\/span> handles<\/span> up<\/span> to<\/span> 75%<\/span> more<\/span> data <\/span>sources<\/span> while<\/span> improving<\/span> throughput<\/span> in<\/span> resource-constrained <\/span>scenarios<\/span> by<\/span> 1.2-4.4<\/span>\u00d7<\/span>.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Rapid detection and mitigation of issues that impact performance and reliability is paramount for large-scale online services. For real-time detection of such issues, datacenter operators use a stream processor and analyze streams of monitoring data collected from servers (referred to as data source nodes) and their hosted services. The timely processing of incoming streams requires 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