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.

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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.

Supercomputers and clouds both strive to make a large number of computing cores available for computation. More recently, similar objectives such as low-power, manageability at scale, and low cost of ownership are driving a more converged hardware and software. Challenges remain, however, of which one is that current cloud infrastructure does not yield the performance sought by many scientific applications. A source of the performance loss comes from virtualization and virtualization of the network in particular. This paper provides an introduction and analysis of a hybrid supercomputer software infrastructure, which allows direct hardware access to the communication hardware for the necessary components while providing the standard elastic cloud infrastructure for other components.