Nonmigratory online deadline scheduling on multiprocessors

In this paper we consider multiprocessor scheduling with hard deadlines and investigate the cost of eliminating migration in the online setting. Let I be any set of jobs that can be completed by some migratory offline schedule on m processors. We show that I can also be completed by a nonmigratory online schedule using m speed-5.828 processors (i.e., processors 5.828 times faster). This result supplements the previous results that I can also be completed by a non-migratory offline schedule using 6m unit-speed processors [B. Kalyanasundaram and K. R. Pruhs, J. Algorithms, 38 (2001), pp. 2-24] or a migratory online schedule using m speed-2 processors [C. A. Phillips et al., Algorithmica. 32 (2002), pp. 163-200]. Our result is based on a simple conservative scheduling algorithm called PARK, which commits a processor to a job only when the processor has zero commitment before its deadline. A careful analysis of PARK further shows that the processor speed can be reduced arbitrarily close to 1 by exploiting more processors (say, using 16m speed-1.8 processors). PARK also finds application in overloaded systems; it gives the first online nonmigratory algorithm that can exploit moderately faster processors to match the performance of any migratory offline algorithm. © 2005 Society for Industrial and Applied Mathematics.published_or_final_versio

How to Schedule When You Have to Buy Your Energy

Abstract. We consider a situation where jobs arrive over time at a data center, consisting of identical speed-scalable processors. For each job, the scheduler knows how much income is lost as a function of how long the job is delayed. The scheduler also knows the fixed cost of a unit of energy. The online scheduler determines which jobs to run on which processors, and at what speed to run the processors. The scheduler's objective is to maximize profit, which is the income obtained from jobs minus the energy costs. We give a (1+ )-speed O(1)-competitive algorithm, and show that resource augmentation is necessary to achieve O(1)-competitiveness

Truthful Online Scheduling with Commitments

We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cloud scheduling, but there are strong lower bounds even for the algorithmic problem without incentive constraints. However, these lower bounds can be circumvented under the natural assumption of deadline slackness, i.e., that there is a guaranteed lower bound s > 1 on the ratio between a job's size and the time window in which it can be executed. In this paper, we construct a truthful scheduling mechanism with a constant competitive ratio, given slackness s > 1. Furthermore, we show that if s is large enough then we can construct a mechanism that also satisfies a commitment property: it can be determined whether or not a job will finish, and the requisite payment if so, well in advance of each job's deadline. This is notable because, in practice, users with strict deadlines may find it unacceptable to discover only very close to their deadline that their job has been rejected