As cloud computing is moving forward rapidly, cloud providers have been encountering great challenges: long tail latency, low utilization, and high interference. They intend to co-locate multiple workloads on a singl...As cloud computing is moving forward rapidly, cloud providers have been encountering great challenges: long tail latency, low utilization, and high interference. They intend to co-locate multiple workloads on a single server to improve the resource utilization. But the co-located applications suffer from severe performance interference and long tail latency, which lead to unpredictable user experience. To meet these challenges, software-defined cloud has been proposed to facilitate tighter coordination among application, operating system and hardware. Users' quality of service (QoS) requirements could be propagated all the way down to the hardware with differential management mechanisms. However, there is little hardware support to maintain and guarantee users' QoS requirements. To this end, this paper proposes Labeled von Neumann Architecture (LvNA), which introduces a labelling mechanism to convey more software's semantic information such as QoS and security to the underlying hardware. LvNA is able to correlate labels with various entities, e.g., virtual machine, process and thread, and propagate labels in the whole machine and program differentiated services based on rules. We consider LvNA to be a fundamental hardware support to the software-defined cloud.展开更多
Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be irreconcilable.High resource utilization increases the risk of resource contention between c...Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be irreconcilable.High resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable performance.Plenty of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource efficiency.In this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC applications.MAGI is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural networks.The experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.展开更多
基金This work was supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000200 and the National Natural Science Foundation of China under Grant No. 61420106013.
文摘As cloud computing is moving forward rapidly, cloud providers have been encountering great challenges: long tail latency, low utilization, and high interference. They intend to co-locate multiple workloads on a single server to improve the resource utilization. But the co-located applications suffer from severe performance interference and long tail latency, which lead to unpredictable user experience. To meet these challenges, software-defined cloud has been proposed to facilitate tighter coordination among application, operating system and hardware. Users' quality of service (QoS) requirements could be propagated all the way down to the hardware with differential management mechanisms. However, there is little hardware support to maintain and guarantee users' QoS requirements. To this end, this paper proposes Labeled von Neumann Architecture (LvNA), which introduces a labelling mechanism to convey more software's semantic information such as QoS and security to the underlying hardware. LvNA is able to correlate labels with various entities, e.g., virtual machine, process and thread, and propagate labels in the whole machine and program differentiated services based on rules. We consider LvNA to be a fundamental hardware support to the software-defined cloud.
基金This work is supported in part by the National Key Research and Development Program of China under Grant No.2016YFB1000201the National Natural Science Foundation of China under Grant Nos.61420106013 and 61702480the Youth Innovation Promotion Association of Chinese Academy of Sciences and Alibaba Innovative Research(AIR)Program.
文摘Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be irreconcilable.High resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable performance.Plenty of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource efficiency.In this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC applications.MAGI is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural networks.The experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.
基金Project supported by the National Key R&D Program of China(No.2016YFB1000201)the National Natural Science Foundation of China(No.61420106013)the Youth Innovation Promotion Association of Chinese Academy of Sciences