Author_Institution :
Sci. & Technol. on Parallel & Distrib. Process. Lab. Nat. Univ. of Defense Technol., Changsha, China
Abstract :
User request trace-oriented monitoring is an effective method to improve the reliability of cloud systems. However, there are some difficulties in getting traces in practice, which hinder the development of trace-oriented monitoring research. In this paper, we release a fine-grained user request-centric open trace data set, called Trace Bench, collected on a real world cloud storage system deployed in a real environment. During collecting, many aspects are considered to simulate different scenarios, including cluster size, request type, workload speed, etc. Besides recording the traces when the monitored system is running normally, we also collect the traces under the situation with faults injected. With a mature injection tool, 14 faults are introduced, including function faults and performance faults. The traces in Trace Bench are clustered in different files, where each file corresponds to a certain scenario. The whole collection work lasted for more than half a year, resulting in more than 360, 000 traces in 361 files. In addition, we also employ several applications based on Trace Bench, which validate the helpfulness of Trace Bench for the field of trace-oriented monitoring.
Keywords :
cloud computing; fault diagnosis; fault tolerant computing; open systems; reliability; storage management; TraceBench; cloud storage system; cloud system reliability; cluster size; fine-grained user request-centric open trace data set; function faults; open data set; performance faults; request type; trace-oriented monitoring; user request trace-oriented monitoring; workload speed; Cloud computing; Context; Educational institutions; Fault diagnosis; Instruments; Monitoring; Servers; cloud computing; data set; fault injection; trace-oriented monitoring; workload generation;