Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience （QoE）, where response latency is a critical factor. A lo...Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience （QoE）, where response latency is a critical factor. A lot of research efforts are dedicated to reducing high tail-latency and improving user QoE, such as software-defined cloud computing （SDC）. However, the traditional availability analysis of cloud computing captures the pure failure-repair behavior with user QoE ignored. In this paper, we propose a conceptual framework, experience availability, to properly assess the effectiveness of SDC while taking into account both availability and response latency simultaneously. We review the related work on availability models and methods of cloud systems, and discuss open problems for evaluating experience availability in SDC. We also show some of our preliminary results to demonstrate the feasibility of our ideas.展开更多
The tail latency of end-user requests,which directly impacts the user experience and the revenue,is highly related to its corresponding numerous accesses in key-value stores.The replica selection algorithm is crucial ...The tail latency of end-user requests,which directly impacts the user experience and the revenue,is highly related to its corresponding numerous accesses in key-value stores.The replica selection algorithm is crucial to cut the tail latency of these key-value accesses.Recently,the C3 algorithm,which creatively piggybacks the queue-size of waiting keys from replica servers for the replica selection at clients,is proposed in NSDI 2015.Although C3 improves the tail latency a lot,it suffers from the timeliness issue on the feedback information,which directly influences the replica selection.In this paper,we analysis the evaluation of queuesize of waiting keys of C3,and some findings of queue-size variation were made.It motivate us to propose the Prediction-Based Replica Selection(PRS)algorithm,which predicts the queue-size at replica servers under the poor timeliness condition,instead of utilizing the exponentially weighted moving average of the state piggybacked queue-size as in C3.Consequently,PRS can obtain more accurate queue-size at clients than C3,and thus outperforms C3 in terms of cutting the tail latency.Simulation results confirm the advantage of PRS over C3.展开更多
基金This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000205, the National Natural Science Foundation of China under Grant No. 61402325, and the Tianjin City Application Foundation and Cutting- Edge Technology Research Program under Grant No. 14JCQNJC00500.
文摘Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience （QoE）, where response latency is a critical factor. A lot of research efforts are dedicated to reducing high tail-latency and improving user QoE, such as software-defined cloud computing （SDC）. However, the traditional availability analysis of cloud computing captures the pure failure-repair behavior with user QoE ignored. In this paper, we propose a conceptual framework, experience availability, to properly assess the effectiveness of SDC while taking into account both availability and response latency simultaneously. We review the related work on availability models and methods of cloud systems, and discuss open problems for evaluating experience availability in SDC. We also show some of our preliminary results to demonstrate the feasibility of our ideas.
文摘The tail latency of end-user requests,which directly impacts the user experience and the revenue,is highly related to its corresponding numerous accesses in key-value stores.The replica selection algorithm is crucial to cut the tail latency of these key-value accesses.Recently,the C3 algorithm,which creatively piggybacks the queue-size of waiting keys from replica servers for the replica selection at clients,is proposed in NSDI 2015.Although C3 improves the tail latency a lot,it suffers from the timeliness issue on the feedback information,which directly influences the replica selection.In this paper,we analysis the evaluation of queuesize of waiting keys of C3,and some findings of queue-size variation were made.It motivate us to propose the Prediction-Based Replica Selection(PRS)algorithm,which predicts the queue-size at replica servers under the poor timeliness condition,instead of utilizing the exponentially weighted moving average of the state piggybacked queue-size as in C3.Consequently,PRS can obtain more accurate queue-size at clients than C3,and thus outperforms C3 in terms of cutting the tail latency.Simulation results confirm the advantage of PRS over C3.
