. The goal of the work presented in this paper is to investigate mechanisms how job size characteristics affect job scheduling performance. For this goal, this paper evaluates the performance of job scheduling algorithms under various workload models, each of which has a certain characteristic related

In this paper, we present a performance model and the experimental results of parallel execution of Java Remote Method Invocation (RMI). The Java RMI is used for remote execution of parallel jobs on the Internet. The main use of the model is to determine the job size of the distributed objects

Abstract—We study size-based schedulers, and focus on the impact of inaccurate job size information on response time and fairness. Our intent is to revisit previous results, which allude to performance degradation for even small errors on job size estimates, thus limiting the applicability of size

successive service requirements (job sizes) is limited to First-Come-First-Served scheduling. This leaves open fundamental questions: How do various scheduling policies interact with correlated job sizes? Can scheduling be used to mitigate the harmful effects of correlations? In this paper we take the first

probability, where V is the total work of all n con-V 2 Θ( tending jobs. This bound is significantly larger than when jobs are constant sized. A variant of exponential backoff, however, achieves makespan O(V log V) with high probability. Finally, we introduce a new protocol, size-hashed backoff, specifically

We investigate the problem of semi-online scheduling jobs on m identical parallel machines where the jobs arrive in order of decreasing sizes. We present a complete solution for the preemptive variant of semi-online scheduling with decreasing job sizes. We give matching lower and upper bounds

Abstract. We study the problem of contention resolution for differentsized jobs on a simple channel. When a job makes a run attempt, it learns only whether the attempt succeeded or failed. We first analyze binary exponential backoff, and show that it achieves a makespan of

Abstract. We study the problem of contention resolution for differentsized jobs on a simple channel. When a job makes a run attempt, it learns only whether the attempt succeeded or failed. We first analyze binary exponential backoff, and show that it achieves a makespan of

remaining sizes. In this paper, we introduce the class of ɛ-SMART policies, which formalizes the heuristic of “favoring small jobs” in a way that includes a wide range of policies that schedule using inexact job-size information. Examples of ɛ-SMART policies include (i) policies that use exact size

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