TY - GEN
T1 - EXTRA
T2 - 29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
AU - Li, Teng
AU - Xu, Zhiyuan
AU - Tang, Jian
AU - Wu, Kun
AU - Wang, Yanzhi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/25
Y1 - 2021/6/25
N2 - In this paper, we present design, implementation and evaluation of a control framework, EXTRA (Experience-driven conTRol frAmework), for scheduling in general-purpose Distributed Stream Data Processing Systems (DSDPSs). Our design is novel due to the following reasons. First, EXTRA enables a DSDPS to dynamically change the number of threads on the fly according to system states and demands. Most existing methods, however, use a fixed number of threads to carry workload (for each processing unit of an application), which is specified by a user in advance and does not change during runtime. So our design introduces a whole new dimension for control in DSDPSs, which has a great potential to significantly improve system flexibility and efficiency, but makes the scheduling problem much harder. Second, EXTRA leverages an experience/data driven model-free approach for dynamic control using the emerging Deep Reinforcement Learning (DRL), which enables a DSDPS to learn the best way to control itself from its own experience just as a human learns a skill (such as driving and swimming) without any accurate and mathematically solvable model. We implemented it based on a widely-used DSDPS, Apache Storm, and evaluated its performance with three representative Stream Data Processing (SDP) applications: continuous queries, word count (stream version) and log stream processing. Particularly, we performed experiments under realistic settings (where multiple application instances are mixed up together), rather than a simplified setting (where experiments are conducted only on a single application instance) used in most related works. Extensive experimental results show: 1) Compared to Storm's default scheduler and the state-of-the-art model-based method, EXTRA substantially reduces average end-to-end tuple processing time by 39.6% and 21.6% respectively on average. 2) EXTRA does lead to more flexible and efficient stream data processing by enabling the use of a variable number of threads. 3) EXTRA is robust in a highly dynamic environment with significant workload change.
AB - In this paper, we present design, implementation and evaluation of a control framework, EXTRA (Experience-driven conTRol frAmework), for scheduling in general-purpose Distributed Stream Data Processing Systems (DSDPSs). Our design is novel due to the following reasons. First, EXTRA enables a DSDPS to dynamically change the number of threads on the fly according to system states and demands. Most existing methods, however, use a fixed number of threads to carry workload (for each processing unit of an application), which is specified by a user in advance and does not change during runtime. So our design introduces a whole new dimension for control in DSDPSs, which has a great potential to significantly improve system flexibility and efficiency, but makes the scheduling problem much harder. Second, EXTRA leverages an experience/data driven model-free approach for dynamic control using the emerging Deep Reinforcement Learning (DRL), which enables a DSDPS to learn the best way to control itself from its own experience just as a human learns a skill (such as driving and swimming) without any accurate and mathematically solvable model. We implemented it based on a widely-used DSDPS, Apache Storm, and evaluated its performance with three representative Stream Data Processing (SDP) applications: continuous queries, word count (stream version) and log stream processing. Particularly, we performed experiments under realistic settings (where multiple application instances are mixed up together), rather than a simplified setting (where experiments are conducted only on a single application instance) used in most related works. Extensive experimental results show: 1) Compared to Storm's default scheduler and the state-of-the-art model-based method, EXTRA substantially reduces average end-to-end tuple processing time by 39.6% and 21.6% respectively on average. 2) EXTRA does lead to more flexible and efficient stream data processing by enabling the use of a variable number of threads. 3) EXTRA is robust in a highly dynamic environment with significant workload change.
UR - http://www.scopus.com/inward/record.url?scp=85115428393&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115428393&partnerID=8YFLogxK
U2 - 10.1109/IWQOS52092.2021.9521325
DO - 10.1109/IWQOS52092.2021.9521325
M3 - Conference contribution
AN - SCOPUS:85115428393
T3 - 2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021
BT - 2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 June 2021 through 28 June 2021
ER -