TY - GEN
T1 - Predictive and dynamic resource allocation for enterprise applications
AU - Al-Ghamdi, M.
AU - Chester, A. P.
AU - Jarvis, S. A.
PY - 2010
Y1 - 2010
N2 - Dynamic resource allocation has the potential to provide significant increases in total revenue in enterprise systems through the reallocation of available resources as the demands on hosted applications change over time. This paper investigates the combination of workload prediction algorithms and switching policies: the former aim to forecast the workload associated with Internet services, the latter switch resources between applications according to certain system criteria. An evaluation of two well known switching policies - the proportional switching policy (PSP) and the bottleneck aware switching policy (BSP) - is conducted in the context of seven workload prediction algorithms. This study uses real-world workload traces consisting of approximately 3.5M requests, and models a multi-tiered, cluster-based, multi-server solution. The results show that a combination of the bottleneck aware switching policy and workload predictions based on an autoregressive, integrated, moving-average model can improve system revenue by as much as 43%.
AB - Dynamic resource allocation has the potential to provide significant increases in total revenue in enterprise systems through the reallocation of available resources as the demands on hosted applications change over time. This paper investigates the combination of workload prediction algorithms and switching policies: the former aim to forecast the workload associated with Internet services, the latter switch resources between applications according to certain system criteria. An evaluation of two well known switching policies - the proportional switching policy (PSP) and the bottleneck aware switching policy (BSP) - is conducted in the context of seven workload prediction algorithms. This study uses real-world workload traces consisting of approximately 3.5M requests, and models a multi-tiered, cluster-based, multi-server solution. The results show that a combination of the bottleneck aware switching policy and workload predictions based on an autoregressive, integrated, moving-average model can improve system revenue by as much as 43%.
KW - Dynamic resource allocation
KW - Enterprise applications
KW - Predictors
KW - Switching policies
UR - http://www.scopus.com/inward/record.url?scp=78249281360&partnerID=8YFLogxK
U2 - 10.1109/CIT.2010.463
DO - 10.1109/CIT.2010.463
M3 - Conference contribution
AN - SCOPUS:78249281360
SN - 9780769541082
T3 - Proceedings - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, ScalCom-2010
SP - 2776
EP - 2783
BT - Proceedings - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, ScalCom-2010
T2 - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010
Y2 - 29 June 2010 through 1 July 2010
ER -