23,196 research outputs found
Special cases of online parallel job scheduling
In this paper we consider the online scheduling of jobs, which require processing on a number of machines simultaneously. These jobs are presented to a decision maker one by one, where the next job becomes known as soon as the current job is scheduled. The objective is to minimize the makespan. For the problem with three machines we give a 2.8-competitive algorithm, improving upon the 3-competitive greedy algorithm. For the special case with arbitrary number of machines, where the jobs appear in non-increasing order of machine requirement, we give a 2.4815-competitive algorithm, improving the 2.75-competitive greedy algorithm
Approximation Results for Preemptive Stochastic Online Scheduling
We present first constant performance guarantees for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release dates on identical parallel machines we derive policies with a guaranteed performance ratio of 2 which matches the currently best known result for the corresponding deterministic online problem. Our policies apply to the recently introduced stochastic online scheduling model inwhich jobs arrive online over time. In contrast to the previously considered nonpreemptivesetting, our preemptive policies extensively utilize information on processing time distributions other than the first (and second) moments. In order to derive our results we introduce a new nontrivial lower bound on the expected value of an unknown optimal policy that we derive from an optimal policy for the basic problem on a single machine without release dates. This problem is known to be solved optimally by a Gittins index priority rule. This priority index also inspires the design of our policies.computer science applications;
Reallocation Problems in Scheduling
In traditional on-line problems, such as scheduling, requests arrive over
time, demanding available resources. As each request arrives, some resources
may have to be irrevocably committed to servicing that request. In many
situations, however, it may be possible or even necessary to reallocate
previously allocated resources in order to satisfy a new request. This
reallocation has a cost. This paper shows how to service the requests while
minimizing the reallocation cost. We focus on the classic problem of scheduling
jobs on a multiprocessor system. Each unit-size job has a time window in which
it can be executed. Jobs are dynamically added and removed from the system. We
provide an algorithm that maintains a valid schedule, as long as a sufficiently
feasible schedule exists. The algorithm reschedules only a total number of
O(min{log^* n, log^* Delta}) jobs for each job that is inserted or deleted from
the system, where n is the number of active jobs and Delta is the size of the
largest window.Comment: 9 oages, 1 table; extended abstract version to appear in SPAA 201
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