Capacity planning for computing clusters

This disclosure provides techniques that enable elastic computing service providers to optimize allocation of computing resources across multiple tiers of service level objectives, while allowing for periods of oversubscription to be met by cross-tier movement of resources. Further, the techniques enable providers of computing services to plan their computing investments given rates of oversubscription and anticipated demand across tiers. Service providers frequently plan investments based on peak, rather than average, demand and may therefore be left with surplus capacity during periods of below-peak demand. While such surplus can be resold at lower levels of service guarantees, such reselling increases demand (oversubscription). By accounting for oversubscriptions and inflations of service level objectives across tiers, the techniques of this disclosure enable optimal multi-tier resource allocation

Benchmarks and Standards for the Evaluation of Parallel Job Schedulers

The evaluation of parallel job schedulers hinges on the workloads used. It is suggested that this be standardized, in terms of both format and content, so as to ease the evaluation and comparison of different systems. The question remains whether this can encompass both traditional parallel systems and metacomputing systems. This paper is based on a panel on this subject that was held at the workshop, and the ensuing discussion; its authors are both the panel members and participants from the audience. Naturally, not all of us agree with all the opinions expressed here..

Application Scheduling over Supercomputers: A Proposal

We discuss how Resource Schedulers for Supercomputers can support Application Scheduling without degrading their own measures of performance. Initial results are presented and a proposal for future research is delineated.Pre-2018 CSE ID: CS1999-063

Application Scheduling over Supercomputers: A Proposal

Introduction Performance is a very important aspect of computer systems. This has been true since the very birth of modern computers, where they were seen as calculators whose reason d'tre was their ability to perform calculations faster than the human being. Performance is important to all areas of Computer Science, from Algorithms to Data Bases, from Operating Systems to Cryptography. Today, performance remains a major concern for both industry and academia. Among the factors that affect performance, scheduling is of fundamental importance. The execution time of an application strongly depends on the ordering of the pieces of work that form the application, which resources carry out each piece, and when such resources do so. Scheduling has been traditionally done by the operating system [Bunt 76]. This is because the operating system controls the resources of interest to most applications (processors, memory, disks, etc). One salient characteristic of traditional scheduling i