Title :
Multi-model prediction for enhancing content locality in elastic server infrastructures
Author :
Tirado, Juan M. ; Higuero, Daniel ; Isaila, Florin ; Carretero, Jesus
Author_Institution :
Comput. Archit. & Technol. Area, Univ. Carlos III de Madrid, Leganes, Spain
Abstract :
Infrastructures serving on-line applications experience dynamic workload variations depending on diverse factors such as popularity, marketing, periodic patterns, fads, trends, events, etc. Some predictable factors such as trends, periodicity or scheduled events allow for proactive resource provisioning in order to meet fluctuations in workloads. However, proactive resource provisioning requires prediction models forecasting future workload patterns. This paper proposes a multi-model prediction approach, in which data are grouped into bins based on content locality, and an autoregressive prediction model is assigned to each locality-preserving bin. The prediction models are shown to be identified and fitted in a computationally efficient way. We demonstrate experimentally that our multi-model approach improves locality over the uni-model approach, while achieving efficient resource provisioning and preserving a high resource utilization and load balance.
Keywords :
Internet; autoregressive processes; file servers; resource allocation; autoregressive prediction model; content locality enhancement; elastic server infrastructure; future workload pattern forecasting; load balancing; locality-preserving bin; multimodel prediction; online applications; prediction models; proactive resource provisioning; resource utilization; Computational modeling; Data models; Load modeling; Mathematical model; Predictive models; Web servers;
Conference_Titel :
High Performance Computing (HiPC), 2011 18th International Conference on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4577-1951-6
Electronic_ISBN :
978-1-4577-1949-3
DOI :
10.1109/HiPC.2011.6152728