TY - GEN
T1 - Governor
T2 - 2nd International Conference on Autonomic Computing, ICAC 2005
AU - Strickland, Jonathan W.
AU - Freeh, Vincent W.
AU - Ma, Xiaosong
AU - Vazhkudai, Sudharshan S.
PY - 2005
Y1 - 2005
N2 - Scavenging (or resource borrowing) is a common approach used to harness unused resources to perform useful calculations. Since these are volunteer contributions from resource owners, it is vital to reduce the impact of scavenging activities on their native workload to a minimum. To this end, existing impact control systems are either overly conservative in stopping scavenging altogether or inflexible and lack user autonomy to regulate resource usage as in some priority-based techniques. In this paper, we propose a systematic impact control framework for resource scavenging, by quantifying the performance impact a scavenging application incurs on a set of tasks stressing different system resources. For a user-configurable impact threshold, the framework monitors the native workload, determines the dominating native task, and autonomically and adoptively throttles the scavenging application, to bring the impact below target levels. This novel approach has unique benefits of 1) making impact control explicit to resource owners and an easy-to-tune "knob," and 2) adapting to different scavenging applications and native workloads. Our experiments with two scavenging applications, which use resources in very different ways, demonstrate that this framework allows both more aggressive resource scavenging and less impact on native workloads at the same time, compared to a priority-based method. Finally, the framework itself is a lightweight user-level process whose monitoring overhead on native workloads averages as low as 1%.
AB - Scavenging (or resource borrowing) is a common approach used to harness unused resources to perform useful calculations. Since these are volunteer contributions from resource owners, it is vital to reduce the impact of scavenging activities on their native workload to a minimum. To this end, existing impact control systems are either overly conservative in stopping scavenging altogether or inflexible and lack user autonomy to regulate resource usage as in some priority-based techniques. In this paper, we propose a systematic impact control framework for resource scavenging, by quantifying the performance impact a scavenging application incurs on a set of tasks stressing different system resources. For a user-configurable impact threshold, the framework monitors the native workload, determines the dominating native task, and autonomically and adoptively throttles the scavenging application, to bring the impact below target levels. This novel approach has unique benefits of 1) making impact control explicit to resource owners and an easy-to-tune "knob," and 2) adapting to different scavenging applications and native workloads. Our experiments with two scavenging applications, which use resources in very different ways, demonstrate that this framework allows both more aggressive resource scavenging and less impact on native workloads at the same time, compared to a priority-based method. Finally, the framework itself is a lightweight user-level process whose monitoring overhead on native workloads averages as low as 1%.
UR - http://www.scopus.com/inward/record.url?scp=33745508736&partnerID=8YFLogxK
U2 - 10.1109/ICAC.2005.31
DO - 10.1109/ICAC.2005.31
M3 - Conference contribution
AN - SCOPUS:33745508736
SN - 0769522769
SN - 9780769522760
T3 - Proceedings - Second International Conference on Autonomic Computing, ICAC 2005
SP - 64
EP - 75
BT - Proceedings - Second International Conference on Autonomic Computing, ICAC 2005
Y2 - 13 June 2005 through 16 June 2005
ER -