Despite computation becomes much complex on data with unprecedented large-scale , we argue computers or smart devices should and will consistently provide information and knowledge to human being in the order of a few tens milliseconds. We coin a new term 10-millisecond computing to call attention to this class of workloads. Public reports indicate that internet service users are sensitive to the service or job-level response time outliers, so we propose a very simple but powerful metric-outlier proportion to characterize the system behaviors.The outlier proportion is defined as follows: for N completed requests or jobs, if M jobs or requests' latencies exceed the outlier limit t, e.g. 10 milliseconds, the outlier proportion is M/N. 10-millisecond computing raises many challenges for both software and hardware stacks. In this paper, as a case study we investigate the challenges raised for conventional operating systems. For typical latency-critical services running with Linux on a 40-core server - a main-stream server hardware system in near future  , we found, when the outlier limit decreases, the outlier proportion of a single server will significantly deteriorate. Meanwhile, the outlier proportion is further amplified by the system scale, including the system core number . For a 1K-scale system , we surprisingly find that to reduce the service or job-level outlier proportion to 10\%, running Linux (version 2.6.32) or LXC (version 0.7.5) or XEN (version 4.0.0), respectively, the outlier proportion of a single server needs to be reduced by 871X, 2372X, 2372X accordingly. We also conducted a list of experiments to reveal the current Linux systems still suffer from poor outlier performance, including Linux kernel version 3.17.4, Linux kernel version 2.6.35M, a modified version of 2.6.35 integrated with sloppy counters and two representative real time schedulers .
Despite computation becomes much complex on data with an unprecedented scale , we argue computers or smart devices should and will consistently provide information and knowledge to human being in the order of a few tens milliseconds. We coin a new term 10-millisecond computing to call attention to this class of workloads. 10-millisecond computing raises many challenges for both software and hardware stacks. In this paper, using a typical workload-memcached on a 40-core server ( a main-stream server in near future ), we quantitatively measure 10-ms computing's challenges to conventional operating systems. For better communication, we propose a simple metric-outlier proportion to measure quality of service: for N completed requests or jobs, if M jobs or requests' latencies exceed the outlier threshold t , the outlier proportion is M/N . For a 1K-scale system running Linux (version 2.6.32) , LXC (version 0.7.5) or XEN (version 4.0.0), respectively, we surprisingly find that so as to reduce the service outlier proportion to 10\% (10\% users will feel QoS degradation), the outlier proportion of a single server has to be reduced by 871X, 2372X, 2372X accordingly. Also, we discuss the possible design spaces of 10-ms computing systems from perspectives of datacenter architectures, networking, OS and scheduling, and benchmarking .
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Public reports indicate that internet service users are sensitive to the service or job-level response time outliers, so we propose a very simple but powerful metric-outlier proportion to characterize the system behaviors.The outlier proportion is defined as follows: for N completed requests or jobs, if M jobs or requests' latencies exceed the outlier limit t, e.g. 10 milliseconds, the outlier proportion is M/N.
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as a case study we investigate the challenges raised for conventional operating systems. For typical latency-critical services running with Linux
using a typical workload-memcached
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), we quantitatively measure 10-ms computing's challenges to conventional operating systems. For better communication, we propose a simple metric-outlier proportion to measure quality of service: for N completed requests or jobs, if M jobs or requests' latencies exceed the outlier threshold t
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we found, when the outlier limit decreases,
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proportion of a single server will significantly deteriorate. Meanwhile, the outlier
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further amplified by the system scale, including the system core number
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, we surprisingly find that to reduce the service or job-level outlier proportion to 10\%,
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we surprisingly find that so as to reduce the service outlier proportion to 10\% (10\% users will feel QoS degradation), the outlier proportion
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We also conducted a list of experiments to reveal the current Linux systems still suffer from poor outlier performance, including Linux kernel version 3.17.4, Linux kernel version 2.6.35M, a modified version of 2.6.35 integrated with sloppy counters and two representative real time schedulers
Also, we discuss the possible design spaces of 10-ms computing systems from perspectives of datacenter architectures, networking, OS and scheduling, and benchmarking