An Energy Aware Cost Recovery Approach for Virtual Machine Migration

Datacenters provide an IT backbone for today's business and economy, and are the principal electricity consumers for Cloud computing. Various studies suggest that approximately 30% of the running servers in US datacenters are idle and the others are under-utilized, making it possible to save energy and money by using Virtual Machine (VM) consolidation to reduce the number of hosts in use. However, consolidation involves migrations that can be expensive in terms of energy consumption, and sometimes it will be more energy efficient not to consolidate. This paper investigates how migration decisions can be made such that the energy costs involved with the migration are recovered, as only when costs of migration have been recovered will energy start to be saved. We demonstrate through a number of experiments, using the Google workload traces for 12,583 hosts and 1,083,309 tasks, how different VM allocation heuristics, combined with different approaches to migration, will impact on energy effciency. We suggest, using reasonable assumptions for datacenter setup, that a combination of energy-aware ll-up VM allocation and energy-aware migration, and migration only for relatively long running VMs, provides for optimal energy efficiency

Renewable Energy Curtailment via Incentivized Inter-datacenter Workload Migration

Continuous Grid balancing is essential for ensuring the reliable operation of modern smart grids. Current smart grid systems lack practical large-scale energy storage capabilities and therefore their supply and demand levels must always be kept equal in order to avoid system instability and failure. Grid balancing has become more relevant in recent years following the increasing desire to integrate more Renewable Energy Sources (RESs) into the generation mix of modern grids. RESs produce intermittent energy supply that can’t always be predicted accurately [1] and necessitates that effective balancing mechanisms are put in place to compensate for their supply variability [2, 3]. In this work, we propose a new energy curtailment scheme for balancing excess RESs energy using data centers as managed loads. Our scheme uses incentivized inter-datacenter workload migration to increase the computational energy consumption at a destination datacenter by the amount necessary to balance the grid. Incentivised workload migration is achieved by offering discounted energy prices (in the form of Energy Credits) to large-scale cloud clients in order to influence their workload placement algorithms to favor datacenters where the energy credits can be used. Implementations of our system using the CPLEX ILP solver as well as the Best Fit Decreasing (BFD) heuristic [4] for workload placement on data centers showed that using energy credits is an effective mechanism to speed-up/control the energy consumption rates at datacenters especially at low system loads and that they result in increased profits for the cloud clients due to the higher profit margins associated with using the proposed credits