With the increased complexity of platforms coupled with data centers' servers sprawl, power consumption is reaching unsustainable limits. Researchers have addressed data centers' performance-per-watt management at different hierarchies going from server clusters to servers to individual components within the server platform. This paper addresses performance-per-watt maximization of memory subsystems in a data center. Traditional memory power management techniques rely on profiling the utilization of memory modules and transitioning them to some low-power mode when they are sufficiently idle. However, fully interleaved memory presents an interesting research challenge because data striping across memory modules reduces the idleness of individual modules to warrant transitions to low-power states. In this paper, we present a novel technique for performance-per-watt maximization of interleaved memory by dynamically reconfiguring (expanding or contracting) the degree of interleaving to adapt to incoming workload. The reconfigured memory hosts the application's working set on a smaller set of modules in a manner that exploits the platform's memory hierarchy architecture. This creates the opportunity for the remaining memory modules to transition to low-power states and remain in those states for as long as the performance remains within given acceptable thresholds. The memory power expenditure is minimized subject to application memory requirements and end-to-end memory access delay constraints. This is formulated as a performance-per-watt maximization problem and solved using an analytical memory power and performance model. Our technique has been validated on a real server using SPECjbb benchmark and on a trace-driven memory simulator using SPECjbb and gcc memory traces. On the server, our techniques are shown to give about 48.8 percent (26.7 kJ) energy savings compared to traditional techniques measured at 4.5 percent. The maximum improvement in performance-per-watt was measured at 88.48 percent. The simulator showed 89.7 percent improvement in performance-per-watt compared to the best performing traditional technique.

With the increased complexity of platforms coupled with data centers' servers sprawl, power consumption is reaching unsustainable limits. Researchers have addressed data centers' power & performance management at different hierarchies going from server clusters to servers to individual components within the server. This paper presents a novel technique for autonomic power & performance management of a high-performance server platform that consists of multi-core processor and multi-rank memory subsystems. Both the processor and/or the memory subsystem are dynamically reconfigured (expanded or contracted) to suit the application resource requirements. The reconfigured platform creates the opportunity for power savings by transitioning any unused platform capacity (processor/memory) into low-power states for as long as the platform performance remains within given acceptable thresholds. The platform power expenditure is minimized subject to platform performance parameters, which is formulated as an optimization problem. Our experimental results show around 58.33% savings in power as compared to static power management techniques.

With the rapid growth of servers and applications spurred by the Internet, the power consumption of servers has become critically important and must be efficiently managed. High energy consumption also translates into excessive heat dissipation which in turn, increases cooling costs and causes servers to become more prone to failure. This paper presents a theoretical and experimental framework and general methodology for hierarchical autonomic power & performance management in high performance distributed data centers. We optimize for power & performance (performance/watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach to provide the application with the required amount of memory at runtime. This enables us to transition the unused memory capacity to a low power state. Our experimental results show a maximum performance/watt improvement of 88.48% compared to traditional techniques. We also present preliminary results of using game theory to optimize performance/watt at the cluster level of a data center. Our cooperative technique reduces the power consumption by 65% when compared to traditional techniques (min-min heuristic).