Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

• Metrics to classify servers in a heterogeneous cloud environment based on server's power consumption and performance.
• Adaptive heterogeneous and thermal aware heuristic approaches for energy efficient dynamic consolidation of VMs.
• Accounted for server processor sleep states and state transition time latencies in a heterogeneous cloud environment.
• Hybrid heuristics approach, which works with both homogeneous and heterogeneous server cloud.
• Extensive simulation-based evaluation and analysis of the proposed algorithms.

Holistic datacenter energy minimization operation should consider interactions between computing and cooling source specific usage patterns. Decisions like workload type, server configuration, load, utilization etc., contributes to power consumption and influences datacenter's thermal profile and impacts the energy required to control temperature within operational thresholds. In this paper, we present an adaptive virtual machine placement and consolidation approach to improve energy efficiency of a cloud datacenter; accounting for server heterogeneity, server processor low-power SLEEP state, state transition latency and integrated thermal controls to maintain datacenter within operational temperature. Our proposed heuristic approach reduces energy consumption with acceptable level of performance.