AppFlow: Autonomic Performance-Per-Watt Management of Large-Scale Data Centers

The characteristic of dramatic fluctuation in the resource provisioning for real-time applications calls for an elastic delivery of computing services. Current data center deployment schemes, which feature a strong tie between servers and applications, are increasingly challenged to ensure power efficiency in terms of multiple peak loads provisioning, optimal average resources utilization, variable runtime workloads profiling, data center manageability and overhead control on the data center Total Cost of Ownership (TCO). Researchers have exploited paradigms such as virtualization and migration for large-scale computing systems, however, there is still a long way before we can optimally address the power-performance trade-off. This paper provides an autonomic power management scheme for the resource provisioning process for large-scale data centers while meeting the Service-Level Agreement (SLA) and power requirements. The system status is continuously monitored using a cross-layered hierarchy to optimally scale up and down the virtual machine resources such that power and performance can be optimized. We have applied our technique to autonomically manage high performance platforms with multi-core processors and multi rank memory subsystems. Our experimental results show around 56.25 percent platform energy savings for memory-intensive workload, 63.75 percent platform energy savings for processor-intensive workload and 47.5 percent platform energy savings for mixed workload while maintaining.