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Autonomic computing systems are designed to be self-diagnosing and self-healing, such that they detect performance and correctness problems, identify their causes, and react accordingly. These abilities can improve performance, availability, and security, while simultaneously reducing the effort and skills required of system administrators. One way that systems can support these abilities is by allowing monitoring code, diagnostic code, and function implementations to be dynamically inserted and removed in live systems. This "hot swapping" avoids the requisite prescience and additional complexity inherent in creating systems that have all possible configurations built in ahead of time. For already-complex pieces of code such as operating systems, hot swapping provides a simpler, higher-performance, and more maintainable method of achieving autonomic behavior. In this paper, we discuss hot swapping as a technique for enabling autonomic computing in systems software. First, we discuss its advantages and describe the required system structure. Next, we describe K42, a research operating system that explicitly supports interposition and replacement of active operating system code. Last, we describe the infrastructure of K42 for hot swapping and several instances of its use demonstrating autonomic behavior.As computer systems become more complex, they become more difficult to administer properly. Special training is needed to configure and maintain modern systems, and this complexity continues to increase. Autonomic computing systems address this problem by managing themselves. Central to autonomic computing is the ability of a system to identify problems and to reconfigure itself in order to address them. In this paper, we investigate hot swapping as a technology that can be used to address systems software's autonomic requirements. Hot swapping is accomplished either by interpositioning of code, or by replacement of code. Interpositioning involves inserting a new component between two existing ones. This allows us, for example, to enable more detailed monitoring when problems occur, while minimizing run-time costs when the system is performing acceptably. Replacement allows an active component to be switched with a different implementation of that component while the system is running, and while applications continue to use resources managed by that component. As conditions change, upgraded components, better suited to the new environment, dynamically replace the ones currently active.

The specific demands of high-performance computing (HPC) often mismatch the assumptions and algorithms provided by legacy operating systems (OS) for common workload mixes. While feature- and application-rich OSes allow for flexible and low-cost hardware configurations, rapid development, and flexible testing and debugging, the mismatch comes at the cost of --- oftentimes significant --- performance degradation for HPC applications.The ubiquitous availability of virtualization support in all relevant hardware architectures enables new programming and execution models for HPC applications without loosing the comfort and support of existing OS and application environments. In this paper we discuss the trends, motivations, and issues in hardware virtualization with emphasis on their value in HPC environments.

Current operating systems offer poor performance when a numeric application's working set does not fit in main memory. As a result, programmers who wish to solve "out-of-core" problems efficiently are typically faced with the onerous task of rewriting an application to use explicit I/O operations (e.g., read/write). In this paper, we propose and evaluate a fully automatic technique which liberates the programmer from this task, provides high performance, and requires only minimal changes to current operating systems. In our schemethe compiler provides the crucial information on future access patterns without burdening the programmer; the operating system supports nonbinding prefetch and release hints for managing I/O; and the operating system cooperates with a run-time layer to accelerate performance by adapting to dynamic behavior and minimizing prefetch overhead. This approach maintains the abstraction of unlimited virtual memory for the programmer, gives the compiler the flexibility to aggressively insert prefetches ahead of references, and gives the operating system the flexibility to arbitrate between the competing resource demands of multiple applications. We implemented our compiler analysis within the SUIF compiler, and used it to target implementations of our run-time and OS support on both research and commercial systems (Hurricane and IRIX 6.5, respectively). Our experimental results show large performance gains for out-of-core scientific applications on both systems: more than 50% of the I/O stall time has been eliminated in most cases, thus translating into overall speedups of roughly twofold in many cases.

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Designing and implementing system software so that it scales well on shared-memory multiprocessors (SMMPs) has proven to be surprisingly challenging. To improve scalability, most designers to date have focused on concurrency by iteratively eliminating the need for locks and reducing lock contention. However, our experience indicates that locality is just as, if not more, important and that focusing on locality ultimately leads to a more scalable system.In this paper, we describe a methodology and a framework for constructing system software structured for locality, exploiting techniques similar to those used in distributed systems. Specifically, we found two techniques to be effective in improving scalability of SMMP operating systems: (i) an object-oriented structure that minimizes sharing by providing a natural mapping from independent requests to independent code paths and data structures, and (ii) the selective partitioning, distribution, and replication of object implementations in order to improve locality. We describe concrete examples of distributed objects and our experience implementing them. We demonstrate that the distributed implementations improve the scalability of operating-system-intensive parallel workloads.

Cloud computing promises to bring about a fundamental shift in the computer industry where consumers of IT enjoy on-demand access to massive compute capacity and producers of IT benefit from economies of scale and automation. We believe that the advantages of cloud computing will be best realized if there is a highly competitive marketplace. We describe our vision of a marketplace of clouds, discuss what is needed to make this vision a reality, and then describe what VMware is doing to help enable this marketplace model of cloud computing

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