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As networks of workstations (NOW) emerge as a viable platform for a wide range of workloads, a new scheduling approach is needed to allocate the collection of resources across competing users. In this paper, we show that extensions to a proportional-share scheduler for improving response time can still fairly allocate resources to a mix of sequential, interactive, and parallel jobs in this distributed environment. We find that a proportional-share scheduler, specifically a stride-scheduler, running on each node in the cluster is a good building-block. Simple extensions are implemented and analyzed which provide better response-times for interactive jobs by giving those jobs their share of resources over a longer time-interval. When scheduling jobs across the cluster, we show that fairness can be guaranteed if each local scheduler knows the number of tickets issued to each user and if the tickets are balanced across all workstations. Finally, we show that a proportional-share of resourc...

Parallel applications can be executed using the idle computing capacity of workstation clusters. However, it remains unclear how to most effectively schedule the processors among different applications. Processor scheduling algorithms that were successful for shared-memory machines have proven to be inadequate for distributed memory environments due to the high costs of remote memory accesses and redistributing data. We investigate how knowledge of system load and application characteristics can be used in scheduling decisions. We propose the new algorithm AEP(2) which, by properly exploiting both the information types above, performs better than other non-preemptive scheduling rules, and nearly as well as idealized versions of preemptive rules (with free preemption). We conclude that AEP(2) is suitable for use in scheduling parallel applications on networks of workstations. 1

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Multiprocessor systems should exist in the the larger context of distributed systems, allowing multiprocessor resources to be shared by those that need them. Unfortunately, typical multiprocessor resource management techniques do not scale to large networks. The Prospero Resource Manager (PRM) is a scalable resource allocation system that supports the allocation of processing resources in large networks and multiprocessor systems. To manage resources in such distributed parallel systems, PRM employs three types of managers: system managers, job managers, and node managers. There exist multiple independent instances of each type of manager, reducing bottlenecks. The complexity of each manager is further reduced because each is designed to utilize information at an appropriate level of abstraction.

With the proliferation of workstation clusters connected by high-speed networks, providing efficient system support for concurrent applications engaging in nontrivial interaction has become an important problem. Two principal barriers to harnessing parallelism are: one, efficient mechanisms that achieve transparent dependency maintenance while preserving semantic correctness, and two, scheduling algorithms that match coupled processes to distributed resources while explicitly incorporating their communication costs. This paper describes a set of performance features, their properties, and implementation in a system support environment called DUNES that achieves transparent dependency maintenance—IPC, file access, memory access, process creation/termination, process relationships—under dynamic load balancing. The two principal performance features are push/pull-based active and passive end-point caching and communication-sensitive load balancing. Collectively, they mitigate the overhead introduced by the transparent dependency maintenance mechanisms. Communication-sensitive load balancing, in addition, affects the scheduling of distributed resources to application processes where both communication and computation costs are explicitly taken into account. DUNES ’ architecture endows commodity operating systems with distributed operating system functionality while achieving transparency with respect to their existing application base. DUNES also preserves semantic correctness with respect to single processor semantics. We show performance measurements of a UNIX based implementation on Sparc and x86 architectures over high-speed LAN environments. We show that significant performance gains in terms of system throughput and parallel application speed-up are achievable.

Over the past several years, distributed systems composed of computer workstations have become common. To make full use of their often enormous aggregate computing capacity, distributed schedulers must be developed that are appropriate to the unique features of these systems. In this paper, we show that the approach (referred to as conservative) commonly used by distributed schedulers designed for such systems is both unduly costly and fails to exploit large portions of their computing capacities. The goal of the Stealth Distributed Scheduler is to explore a liberal approach to distributed scheduling in this environment. We discuss the design and performance of Stealth, showing that it is better able to exploit computing capacity, yet incurs less overhead than conservative distributed schedulers. 1. Introduction The Stealth Distributed Scheduler is designed specifically for a class of distributed systems that has emerged in recent years -- those composed of computer workstations. These...