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SHA:QoS-Aware Software and Hardware Auto-Tuning for Database Systems

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While databases are widely-used in commercial user-facing services that have stringent quality-of-service(QoS)requirement,it is crucial to ensure their good performance and minimize the hardware usage at the same time.Our investigation shows that the optimal DBMS(database management system)software configuration varies for different us-er request patterns(i.e.,workloads)and hardware configurations.It is challenging to identify the optimal software and hardware configurations for a database workload,because DBMSs have hundreds of tunable knobs,the effect of tuning a knob depends on other knobs,and the dependency relationship changes under different hardware configurations.In this paper,we propose SHA,a software and hardware auto-tuning system for DBMSs.SHA is comprised of a scaling-based per-formance predictor,a reinforcement learning(RL)based software tuner,and a QoS-aware resource reallocator.The perfor-mance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance requirement.The software tuner fine-tunes the DBMS software knobs to optimize the performance of the workload.The resource reallocator assigns the saved resources to other applications to im-prove resource utilization without incurring QoS violation of the database workload.Experimental results show that SHA improves the performance of database workloads by 9.9％on average compared with a state-of-the-art solution when the hardware configuration is fixed,and improves 43.2％of resource utilization while ensuring the QoS.

auto-tuningdatabase configurationjoint tuningutilizationquality-of-service(QoS)

李进、陈全、唐晓新、过敏意

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Department of Computer Science,Shanghai Jiao Tong University,Shanghai 200240,China

Department of Computer Science and Technology,Shanghai University of Finance and Economics,Shanghai 200433,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

62022057618320066163201761872240

2024

10.1007/s11390-022-1751-3

计算机科学技术学报(英文版)
中国计算机学会

计算机科学技术学报(英文版)

CSTPCD
影响因子：0.432
ISSN：1000-9000
年,卷(期)：2024.39(2)