Power densities have been increasing rapidly at all levels of server systems. To counter the high temperatures resulting from these densities, systems researchers have recently started work on softwarebased thermal management. Unfortunately, research in this new area has been hindered by the limitations imposed by simulators and real measurements. In this paper, we introduce Mercury, a software suite that avoids these limitations by accurately emulating temperatures based on simple layout, hardware, and componentutilization data. Most importantly, Mercury runs the entire software stack natively, enables repeatable experiments, and allows the study of thermal emergencies without harming hardware reliability. We validate Mercury using real measurements and a widely used commercial simulator. We use Mercury to develop Freon, a system that manages thermal emergencies in a server cluster without unnecessary performance degradation. Mercury will soon become available from http://www.darklab.rutgers.edu.

The previous research on cluster-based servers has focused on homogeneous systems. However, real-life clusters are almost invariably heterogeneous in terms of the performance, capacity, and power consumption of their hardware components. In this paper, we argue that designing efficient servers for heterogeneous clusters requires defining an efficiency metric, modeling the different types of nodes with respect to the metric, and searching for request distributions that optimize the metric. To concretely illustrate this process, we design a cooperative Web server for a heterogeneous cluster that uses modeling and optimization to minimize the energy consumed per request. Our experimental results for a cluster comprised of traditional and blade nodes show that our server can consume 42 % less energy than an energy-oblivious server, with only a negligible loss in throughput. The results also show that our server conserves 45 % more energy than an energy-conscious server that was previously proposed for homogeneous clusters. 1

Energy conservation without performance degradation is an important goal for battery-operated computers, such as laptops and hand-held assistants. We study application-supported device management for optimizing energy and performance. In particular, we consider application transformations that increase device idle times and inform the operating system about the length of each upcoming, period of idleness. We use modeling and experimentation to assess the potential energy and performance benefits of this type of application support for a laptop disk. Furthermore, we propose and evaluate a compiler framework for performing the transformations automatically. Our main modeling results show that the transformations are potentially beneficial. However, our experimental results with six real laptop applications demonstrate that, unless applications are transformed, they cannot accrue any of the predicted benefits. In addition, they show that our compiler can produce almost the same performance and energy results as hand-modifying applications. Overall, we find that the transformations can reduce disk energy consumption from 55 percent to 89 percent with degradation in performance of at most 8 percent.

We demonstrate a framework for improving the availability of cluster based Internet services. Our approach models Internet services as a collection of interconnected components, each possessing well defined interfaces and failure semantics. Such a decomposition allows designers to engineer high availability based on an understanding of the interconnections and isolated fault behavior of each component, as opposed to ad-hoc methods. In this work, we focus on using the entire commodity workstation as a component because it possesses natural, fault-isolated interfaces. We define a failure event as a reboot because not only is a workstation unavailable during a reboot, but also because reboots are symptomatic of a larger class of failures, such as configuration and operator errors. Our observations of 3 distinct clusters show that the time between reboots is best modeled by a Weibull distribution with shape parameters of less than 1, implying that a workstation becomes more reliable the longer it has been operating. Leveraging this observed property, we design an allocation strategy which withholds recently rebooted workstations from active service, validating their stability before allowing them to return to service. We show via simulation that this policy leads to a 70-30 rule-of-thumb: For a constant utilization, approximately 70% of the workstation failures can be masked from end clients with 30% extra capacity added to the cluster, provided reboots are not strongly correlated. We also found our technique is most sensitive to the burstiness of reboots as opposed to absolute lengths of workstation uptimes.

This work quantifies how persistent increases in processor speed compared to I/O speed reduce the performance gap between specialized, high performance messaging layers and general purpose protocols such as TCP/IP and UDP/IP. The comparison is important because specialized layers sacrifice considerable system connectivity and robustness to obtain increased performance. We first quantify the scaling effects on small messages by measuring the LogP performance of two Active Message II layers, one running over a specialized VIA layer and the other over stock UDP as we scale the CPU and I/O components. We then predict future LogP performance by mapping the LogP model's network parameters, particularly overhead into architectural components. Our projections show that the performance benefit afforded by specialized messaging for small messages will erode to a factor of 2 in the next 5 years. Our models further show that the performance differential between the two approaches will continue to erode without a radical restructuring of the I/O system. For long messages, we quantify the variable per-page instruction budget that a zero-copy messaging approach has for page table manipulations if it is to outperform a single-copy approach. Finally we conclude with an examination of future I/O advances that would result in substantial improvements to messaging performance.