Predictive control for energy aware consolidation in cloud datacenters
0 likes33 views
This paper presents an energy-aware consolidation strategy using predictive control for cloud datacenters to optimize power consumption and reduce operational costs. It introduces a discrete-time dynamic model and migration strategies based on solving optimal control problems with integer variables. The effectiveness of the approach is validated through simulations against heuristics from existing literature, demonstrating a balance between energy savings and service level agreement compliance.
Related topics:1 of 1
Download to read offline
More Related Content
Ad
Viewers also liked (13)
Ad
Similar to Predictive control for energy aware consolidation in cloud datacenters (20)
More from ieeepondy (20)
Recently uploaded (20)
Predictive control for energy aware consolidation in cloud datacenters
- 1. Predictive Control for Energy-Aware Consolidation in Cloud Datacenters Abstract: Infrastructure-as-a-Service is one of the most used paradigms of cloud computing and relies on large-scale datacenters with thousands of nodes. As a consequence of this success, the energetic demand of the infrastructure may lead to relevant economical costs and environmental footprint. Thus, the search for power optimization is of primary importance. In this perspective, this paper introduces an energy-aware consolidation strategy based on predictive control, in which virtual machines are properly migrated among physical machines to reduce the amount of active units. To this aim, a discrete-time dynamic model and suitable constraints are introduced to describe the cloud. The migration strategies are obtained by solving finite-horizon optimal control problems involving integer variables. The proposed method allows one to trade among power savings and violations of the service level agreement. To prove its effectiveness, a simulation campaign is conducted in different scenarios using both synthetic and real workloads, also by performing a comparison with three heuristics selected from the reference literature.