No abstract available.

A wide-area distributed message delivery system can use replication to improve performance and availability. However, without safeguards, replicated messages may be delivered to a mobile device more than once, making the device's user repeat actions (e.g. making unnecessary phone calls, firing weapons repeatedly). Alternatively, they may not be delivered at all, making the user miss important messages. In this paper, we address the problem of exactly-once delivery to mobile clients when messages are replicated globally. We define exactly-once semantics and propose algorithms to guarantee it. We also propose and define a relaxed version of exactly-once semantics which is appropriate for limited-capability mobile devices. We study the relative performance of our algorithms compared to the weaker at-least-once semantics, and find that the performance overhead of exactly-once can be minimized in most cases by careful design of the system.

A condition monitoring system tracks real-world variables and alerts users when a predefined condition becomes true, e.g., when stock price drops, or when a nuclear reactor over-heats. Replication of monitoring servers can reduce the probability that an important alert is missed. However, replicated independent servers can sometimes report “conflicting” alerts to the user, causing confusion. In this paper, we study the problem of replicated condition monitoring. We identify and formally define three desirable properties of a replicated system, namely, orderedness, consistency, and completeness. We propose new monitoring algorithms that enforce some or all of the desired properties in different scenarios.

Despite the fact that large-scale shared-memory multiprocessors have been commercially available for several years, system software that fully utilizes all their features is still not available, mostly due to the complexity and cost of making the required changes to the operating system. A recently proposed approach, called Disco, substantially reduces this development cost by using a virtual machine monitor that laverages the existing operating system technology. In this paper we present a system called Cellular Disco that extends the Disco work to provide all the advantages of the hardware partitioning and scalable operating system approaches. We argue that Cellular Disco can achieve these benefits at only a small fraction of the development cost of modifying the operating system. Cellular Disco effectively turns a large-scale shared-memory multiprocessor into a virtual cluster that supports fault containment and heterogeneity, while avoiding operating system scalability bottlenecks. Yet at the same time, Cellular Disco preserves the benefits of a shared-memory multiprocessor by implementing dynamic, fine-grained resource sharing, and by allowing users to overcommit resources such as processors and memory. This hybrid approach requires a scalable resource manager that makes local decisions with limited information while still providing good global performance and fault containment. In this paper we describe our experience with a Cellular Disco prototype on a 32-processor SGI Origin 2000 system. We show that the execution time penalty for this approach is low, typically within 10% of the best available commercial operating system formost workloads, and that it can manage the CPU and memory resources of the machine significantly better than the hardware partitioning approach.

Despite the fact that large-scale shared-memory multiprocessors have been commercially available for several years, system software that fully utilizes all their features is still not available, mostly due to the complexity and cost of making the required changes to the operating system. A recently proposed approach, called Disco, substantially reduces this development cost by using a virtual machine monitor that leverages the existing operating system technology.In this paper we present a system called Cellular Disco that extends the Disco work to provide all the advantages of the hardware partitioning and scalable operating system approaches. We argue that Cellular Disco can achieve these benefits at only a small fraction of the development cost of modifying the operating system. Cellular Disco effectively turns a large-scale shared-memory multiprocessor into a virtual cluster that supports fault containment and heterogeneity, while avoiding operating system scalability bottle-necks. Yet at the same time, Cellular Disco preserves the benefits of a shared-memory multiprocessor by implementing dynamic, fine-grained resource sharing, and by allowing users to overcommit resources such as processors and memory. This hybrid approach requires a scalable resource manager that makes local decisions with limited information while still providing good global performance and fault containment.In this paper we describe our experience with a Cellular Disco prototype on a 32-processor SGI Origin 2000 system. We show that the execution time penalty for this approach is low, typically within 10% of the best available commercial operating system for most workloads, and that it can manage the CPU and memory resources of the machine significantly better than the hardware partitioning approach.