Distributed processing frameworks, such as Yahoo!'s Hadoop and Google's MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. However, their effect on datacenter energy efficiency has not been scrutinized. Moreover, the filesystem component of these frameworks effectively precludes scale-down of clusters deploying these frameworks (i.e. operating at reduced capacity). This paper presents our early work on modifying Hadoop to allow scale-down of operational clusters. We find that running Hadoop clusters in fractional configurations can save between 9% and 50% of energy consumption, and that there is a tradeoff between performance energy consumption. We also outline further research into the energy-efficiency of these frameworks.

The simplest strategy to guarantee good quality of service (QoS) for a latency-sensitive workload with sub-millisecond latency in a shared cluster environment is to never run other workloads concurrently with it on the same server. Unfortunately, this inevitably leads to low server utilization, reducing both the capability and cost effectiveness of the cluster.In this paper, we analyze the challenges of maintaining high QoS for low-latency workloads when sharing servers with other workloads. We show that workload co-location leads to QoS violations due to increases in queuing delay, scheduling delay, and thread load imbalance. We present techniques that address these vulnerabilities, ranging from provisioning the latency-critical service in an interference aware manner, to replacing the Linux CFS scheduler with a scheduler that provides good latency guarantees and fairness for co-located workloads. Ultimately, we demonstrate that some latency-critical workloads can be aggressively co-located with other workloads, achieve good QoS, and that such co-location can improve a datacenter's effective throughput per TCO-$ by up to 52%.

Continuous evolution in process technology brings energyefficiency and reliability challenges, which are harder for memory system designs since chip multiprocessors demand high bandwidth and capacity, global wires improve slowly, and more cells are susceptible to hard and soft errors. Recently, there are proposals aiming at better main-memory energy efficiency by dividing a memory rank into subsets.We holistically assess the effectiveness of rank subsetting in the context of system-wide performance, energy-efficiency, and reliability perspectives. We identify the impact of rank subsetting on memory power and processor performance analytically, then verify the analyses by simulating a chipmultiprocessor system using multithreaded and consolidated workloads. We extend the design of Multicore DIMM, one proposal embodying rank subsetting, for high-reliability systems and show that compared with conventional chipkill approaches, it can lead to much higher system-level energy efficiency and performance at the cost of additional DRAM devices.

With scalable high-performance storage entirely in DRAM, RAMCloud will enable a new breed of data-intensive applications.

Abstract-While modern processors offer a wide spectrum of software-controlled power modes, most datacenters only rely on Dynamic Voltage and Frequency Scaling (DVFS, a.k.a. P-states) to achieve energy efficiency. This paper argues that, in the case of datacenter workloads, DVFS is not the only option for processor power management. We make the case for per-core power gating (PCPG) as an additional power management knob for multi-core processors. PCPG is the ability to cut the voltage supply to selected cores, thus reducing to almost zero the leakage power for the gated cores. Using a testbed based on a commercial 4-core chip and a set of real-world application traces from enterprise environments, we have evaluated the potential of PCPG. We show that PCPG can significantly reduce a processor's energy consumption (up to 40%) without significant performance overheads. When compared to DVFS, PCPG is highly effective saving up to 30% more energy than DVFS. When DVFS and PCPG operate together they can save up to almost 60%.