Speed is as powerful as clairvoyance

Kalyanasundaram, Bala; Pruhs, Kirk

doi:10.1145/347476.347479

Cited by 448 publications

(346 citation statements)

References 24 publications

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“…resource augmentation in performance analysis of approximation algorithms, as initiated in [7]). The results here extend the results in [5], who considered the techniques for dualcriticality systems, i.e., in which there are only two different criticality levels.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Real-Time Mixed-Criticality Jobs

Baruah

Bonifaci

D’Angelo

et al. 2010

Mathematical Foundations of Computer Science 2010

100

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Abstract. Many safety-critical embedded systems are subject to certification requirements; some systems may be required to meet multiple sets of certification requirements, from different certification authorities. Certification requirements in such "mixed-criticality" systems give rise to interesting scheduling problems, that cannot be satisfactorily addressed using techniques from conventional scheduling theory. In this paper, we study a formal model for representing such mixed-criticality workloads. We demonstrate first the intractability of determining whether a system specified in this model can be scheduled to meet all its certification requirements, even for systems subject to two sets of certification requirements. Then we quantify, via the metric of processor speedup factor, the effectiveness of two techniques, reservation-based scheduling and priority-based scheduling, that are widely used in scheduling such mixedcriticality systems, showing that the latter of the two is superior to the former. We also show that the speedup factors are tight for these two techniques.

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Section: Introductionmentioning

confidence: 99%

Scheduling Real-Time Mixed-Criticality Jobs

Baruah

Bonifaci

D’Angelo

et al. 2010

Mathematical Foundations of Computer Science 2010

100

View full text Add to dashboard Cite

show abstract

“…In fact, for m ≥ 2, there does not exist any optimal online algorithm [3]. To overcome this hardness, Phillips, Stein, Torng, and Wein [8] proposed the use of resource augmentation [5]: Given an online algorithm A we determine the speed s ≥ 1 such that A is optimal on m speed-s processors for any instance that is feasible for m processors of unit speed. We are interested in the smallest s for which…”

Section: Introductionmentioning

confidence: 99%

Meeting Deadlines: How Much Speed Suffices?

Anand

Gang

Megow

2011

Automata, Languages and Programming

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Abstract. We consider the online problem of scheduling real-time jobs with hard deadlines on m parallel machines. Each job has a processing time and a deadline, and the objective is to schedule jobs so that they complete before their deadline. It is known that even when the instance is feasible it may not be possible to meet all deadlines when jobs arrive online over time. We therefore consider the setting when the algorithm has available machines with speed s > 1. We present a new online algorithm that finds a feasible schedule on machines of speed e/(e − 1) ≈ 1.58 for any instance that is feasible on unit speed machines. This improves on the previously best known result which requires a speed of 2 − 2/(m + 1). Our algorithm only uses the relative order of job deadlines and is oblivious of the actual deadline values. It was shown earlier that the minimum speed required for such algorithms is e/(e − 1), and thus, our analysis is tight. We also show that our new algorithm outperforms two other well-known algorithms by giving the first lower bounds on their minimum speed requirement.

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“…This can be done by considering randomized online algorithms [5] or by allowing the online algorithms to use more resources [11]. More related to our approach are concepts like the diffuse adversary [14], average-case competitive analysis [22] and smoothed competitive analysis [3,21], which are also based on random request sequences.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Analysis of Online Bin Coloring Algorithms Via Stochastic Comparison

Hiller

Vredeveld

2008

Algorithms - ESA 2008

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This paper proposes a new method for probabilistic analysis of online algorithms that is based on the notion of stochastic dominance. We develop the method for the Online Bin Coloring problem introduced in [15]. Using methods for the stochastic comparison of Markov chains we establish the strong result that the performance of the online algorithm GreedyFit is stochastically dominated by the performance of the algorithm OneBin for any number of items processed. This result gives a more realistic picture than competitive analysis and explains the behavior observed in simulations.

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Speed is as powerful as clairvoyance

Cited by 448 publications

References 24 publications

Scheduling Real-Time Mixed-Criticality Jobs

Scheduling Real-Time Mixed-Criticality Jobs

Meeting Deadlines: How Much Speed Suffices?

Probabilistic Analysis of Online Bin Coloring Algorithms Via Stochastic Comparison

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