TY - GEN
T1 - Adaptive software cache management
AU - Einziger, Gil
AU - Eytan, Ohad
AU - Friedman, Roy
AU - Manes, Ben
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/11/26
Y1 - 2018/11/26
N2 - Developing a silver bullet software cache management policy is a daunting task due to the variety of potential workloads. In this paper, we investigate an adaptivity mechanism for software cache management schemes which offer tuning parameters targeted at the frequency vs. recency bias in the workload. The goal is automatic tuning of the parameters for best performance based on the workload without any manual intervention. We study two approaches for this problem, a hill climbing solution and an indicator based solution. In hill climbing, we repeatedly reconfigure the system hoping to find its best setting. In the indicator approach, we estimate the workloads’ frequency vs. recency bias and adjust the parameters accordingly in a single swoop. We apply these adaptive mechanisms to two recent software management schemes. We perform an extensive evaluation of the schemes and adaptation mechanisms over a large selection of workloads with varying characteristics. With these, we derive a parame-terless software cache management policy that is competitive for all tested workloads.
AB - Developing a silver bullet software cache management policy is a daunting task due to the variety of potential workloads. In this paper, we investigate an adaptivity mechanism for software cache management schemes which offer tuning parameters targeted at the frequency vs. recency bias in the workload. The goal is automatic tuning of the parameters for best performance based on the workload without any manual intervention. We study two approaches for this problem, a hill climbing solution and an indicator based solution. In hill climbing, we repeatedly reconfigure the system hoping to find its best setting. In the indicator approach, we estimate the workloads’ frequency vs. recency bias and adjust the parameters accordingly in a single swoop. We apply these adaptive mechanisms to two recent software management schemes. We perform an extensive evaluation of the schemes and adaptation mechanisms over a large selection of workloads with varying characteristics. With these, we derive a parame-terless software cache management policy that is competitive for all tested workloads.
UR - https://www.scopus.com/pages/publications/85060372292
U2 - 10.1145/3274808.3274816
DO - 10.1145/3274808.3274816
M3 - Conference contribution
AN - SCOPUS:85060372292
T3 - Proceedings of the 19th International Middleware Conference, Middleware 2018
SP - 94
EP - 106
BT - Proceedings of the 19th International Middleware Conference, Middleware 2018
PB - Association for Computing Machinery, Inc
T2 - 19th ACM/IFIP/USENIX International Middleware Conference, Middleware 2018
Y2 - 10 December 2018 through 14 December 2018
ER -