On-line scheduling in the presence of overload

Baruah, Sanjoy; Koren, G.; Mishra, Bhubaneswar; Raghunathan, Arvind U.; Rosier, Louis E.; Shasha, Dennis

doi:10.1109/sfcs.1991.185354

Cited by 120 publications

(107 citation statements)

References 4 publications

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“…In our case study, five well-known scheduling policies, namely, EDF (earliest deadline first), LLF (least laxity first), SRT (shortest remaining time), SP (static priorities), and FIFO (first-in first-out), as well as some more elaborate algorithms that provide non-trivial performance guarantees, in particular, DSTAR (Baruah et al 1991), TD1 (Baruah et al 1992), and DOVER (Koren and Shasha 1995), were analyzed under a variety of tasksets (with and without additional constraints on the adversary). In addition, for TD1, we constructed a series of task sets according to the recurrence relation given in Baruah et al (1992), which confirms its worst-case competitive ratio of 1/4.…”

Section: Resultsmentioning

confidence: 99%

Automated competitive analysis of real-time scheduling with graph games

et al. 2017

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This paper is devoted to automatic competitive analysis of real-time scheduling algorithms for firm-deadline tasksets, where only completed tasks contribute some utility to the system. Given such a taskset T , the competitive ratio of an on-line scheduling algorithm A for T is the worst-case utility ratio of A over the utility achieved by a clairvoyant algorithm. We leverage the theory of quantitative graph games to address the competitive analysis and competitive synthesis problems. For the competitive analysis case, given any taskset T and any finite-memory online scheduling algorithm A, we show that the competitive ratio of A in T can be computed in polynomial time in the size of the state space of A. Our approach is flexible as it also provides ways to model meaningful constraints on the released task sequences that determine the competitive ratio. We provide an experimental study of many well-known on-line scheduling algorithms, which demonstrates the feasibility of our competitive analysis approach that effectively replaces human ingenuity (required Preliminary versions of this paper have appeared in Chatterjee et al. (2013 for finding worst-case scenarios) by computing power. For the competitive synthesis case, we are just given a taskset T , and the goal is to automatically synthesize an optimal on-line scheduling algorithm A, i.e., one that guarantees the largest competitive ratio possible for T . We show how the competitive synthesis problem can be reduced to a two-player graph game with partial information, and establish that the computational complexity of solving this game is Np-complete. The competitive synthesis problem is hence in Np in the size of the state space of the non-deterministic labeled transition system encoding the taskset. Overall, the proposed framework assists in the selection of suitable scheduling algorithms for a given taskset, which is in fact the most common situation in real-time systems design.

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Section: Resultsmentioning

confidence: 99%

Automated competitive analysis of real-time scheduling with graph games

et al. 2017

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“…If it is not possible to guarantee the successful completion of all the tasks, the goal is typically to maximize a performance metric. A common metric is to associate a value with each task and quantify the "goodness" of an algorithm by the accumulated values of successful tasks [6], [7], [8]. Aydin et al [9] studied reward-based scheduling for periodic tasks in which there is a reward associated with each task.…”

Section: A Related Workmentioning

confidence: 99%

Real-Time Competitive Environments: Truthful Mechanisms for Allocating a Single Processor to Sporadic Tasks

Mohammadi

Fisher

Grosu

2012

2012 24th Euromicro Conference on Real-Time Systems

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Abstract-In a non-competitive environment, sporadic realtime task scheduling on a single processor is well understood. In this paper, we consider a competitive environment comprising several real-time tasks vying for execution upon a shared single processor. Each task obtains a value if the processor successfully schedules all its jobs. Our objective is to select a feasible subset of these tasks to maximize the sum of values of selected tasks. There are algorithms for solving this problem in non-competitive settings. However, we consider this problem in an economic setting in which each task is owned by a selfish agent. Each agent reports the characteristics of her own task to the processor owner. The processor owner uses a mechanism to allocate the processor to a subset of agents and to determine the payment of each agent. Since agents are selfish, they may try to manipulate the mechanism to obtain the processor. We are interested in truthful mechanisms in which it is always in agents' best interest to report the true characteristics of their tasks. We design exact and approximate truthful mechanisms for this competitive environment and study their performance.

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“…In the current urgent period 7 , let J 0 be the last job admitted from Q wait to Q work (if no jobs have been admitted from Q wait so far, let J 0 be a dummy job of size zero admitted just before the current period starts). Consider all the jobs ever admitted to Q work that have become urgent after J 0 has been admitted to Q work , and let W denote the total original size of these jobs.…”

Section: A Job J Is Called T-urgent If D(j) ≤ Down-time(t) and Is Camentioning

confidence: 99%

“…In traditional scheduling (where speed is fixed) Koren and Shasha [16] gave an algorithm D over which is 4-competitive on throughput. Moreover Baruah et al [7] showed that this is the best possible throughput competitive ratio for any online algorithm. Thus running D over at speed T is clearly 4-competitive for throughput; however it can be arbitrarily worse with respect to energy.…”

Section: Introductionmentioning

confidence: 96%

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Scheduling for Speed Bounded Processors

Bansal¹,

Chan

Lam

et al. 2008

Automata, Languages and Programming

101

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Abstract. We consider online scheduling algorithms in the dynamic speed scaling model, where a processor can scale its speed between 0 and some maximum speed T . The processor uses energy at rate s α when run at speed s, where α > 1 is a constant. Most modern processors use dynamic speed scaling to manage their energy usage. This leads to the problem of designing execution strategies that are both energy efficient, and yet have almost optimum performance. We consider two problems in this model and give essentially optimum possible algorithms for them. In the first problem, jobs with arbitrary sizes and deadlines arrive online and the goal is to maximize the throughput, i.e. the total size of jobs completed successfully. We give an algorithm that is 4-competitive for throughput and O(1)-competitive for the energy used. This improves upon the 14 throughput competitive algorithm of Chan et al. [10]. Our throughput guarantee is optimal as any online algorithm must be at least 4-competitive even if the energy concern is ignored [7]. In the second problem, we consider optimizing the trade-off between the total flow time incurred and the energy consumed by the jobs. We give a 4-competitive algorithm to minimize total flow time plus energy for unweighted unit size jobs, and a (2 + o(1))α/ ln α-competitive algorithm to minimize fractional weighted flow time plus energy. Prior to our work, these guarantees were known only when the processor speed was unbounded (T = ∞) [4].

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On-line scheduling in the presence of overload

Cited by 120 publications

References 4 publications

Automated competitive analysis of real-time scheduling with graph games

Automated competitive analysis of real-time scheduling with graph games

Real-Time Competitive Environments: Truthful Mechanisms for Allocating a Single Processor to Sporadic Tasks

Scheduling for Speed Bounded Processors

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