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In this paper we analyze the behavior of a gang-scheduling strategy that we are developing for the ASCI Blue-Pacific machines. Using actual job logs for one of the ASCI machines we generate a statistical model of the current workload with hyper Erlang distributions. We then vary the parameters of those distributions to generate various workloads, representative of different operating points of the machine. Through simulation we obtain performance parameters for three different scheduling strategies: (i) first-come first-serve, (ii) gang-scheduling, and (iii) backfilling. Our results show that backfilling,can be very effective for the common operating points in the 60-70% utilization range. However, for higher utilization rates, time-sharing techniques such as gang-scheduling offer much better performance.

A logical partition in an IBM pSeries TM symmetric multiprocessor (SMP) system is a subset of the hardware of the SMP that can host an operating system (OS) instance. Dynamic reconfiguration (DR) on these logically partitioned servers enables the movement of hardware resources (such as processors, memory, and I/O slots) from one logical partition to another without requiring reboots. This capability also enables an autonomic agent to monitor usage of the partitions and automatically move hardware resources to a needy OS instance nondisruptively. Today, as SMPs and nonuniform memory access (NUMA) systems become larger and larger, the ability to run several instances of an operating system(s) on a given hardware system, so that each OS instance plus its subsystems scale or perform well, has the advantage of an optimal aggregate performance, which can translate into cost savings for customers. Though static partitioning provides a solution to this overall performance optimization problem, DR enables an improved solution by providing the capability to dynamically move hardware resources to a needy OS instance in a timely fashion to match workload demands. Hence, DR capabilities serve as key building blocks for workload managers to provide selfoptimizing and self-configuring features. Besides dynamic resource balancing, DR also enables Dynamic Capacity Upgrade on Demand, and self-healing features such as Dynamic CPU Sparing, a winning solution for users in this age of rapid growth in Web servers on the Internet.One of the cardinal features of an autonomic component in an information technology (IT) infrastructure is the ability of the component to adapt itself smoothly to changes in its environment. Endowing a computing system with this self-management feature often translates to the implementation of selfprotecting, self-healing, self-optimizing, and self-configuring algorithms and subcomponents. Because the primary role of an operating system (OS) is to manage the physical resources of a computer system so as to optimize the performance of its applications (including middleware, which consists of applications from the perspective of the OS), an OS supporting autonomic computing 1 needs to handle the changes in the amount of physical resources allocated to it in a smooth fashion. Some of the most prominent physical resources of an OS are processors, physical memory, and I/O devices.The current tendency among the noncommodity symmetric multiprocessor (SMP) system vendors is to develop systems that are increasingly large in terms of the number of processors, number of I/O slots, and memory size. Although advances in the design of hardware continue to provide rapid increases in the

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Web 2.0 represents the evolution of the web from a source of information to a platform. Network advances have permitted users to migrate from desktop applications to so-called Rich Internet Applications (RIAs) characterized by thin clients, which are browser-based and store their state on managed servers. Other Web 2.0 technologies have enabled users to more easily participate, collaborate, and share in web-based communities. With the emergence of wikis, blogs, and social networking, users are no longer only consumers, they become contributors to the collective knowledge accessible on the web. In another Web 2.0 development, content aggregation is moving from portal-based technologies to more sophisticated socalled mashups where aggregation capabilities are greatly expanded.While Web 2.0 has generated a great deal of interest and discussion, there has not been much work on analyzing these emerging workloads. This paper presents a detailed characterization of several applications that exploit Web 2.0 technologies, running on an IBM Power5 system, with the goal of establishing, whether the server-side workloads generated by Web 2.0 applications are significantly different from traditional web workloads, and whether they present new challenges to underlying systems. In this paper, we present a detailed characterization of three Web 2.0 workloads, and a synthetic benchmark representing commercial workloads that do not exploit Web 2.0, for comparison.