Efficient algorithms for periodic scheduling

Bar-Noy, Amotz; Dreizin, Vladimir; Patt-Shamir, Boaz

doi:10.1016/j.comnet.2003.12.017

Cited by 16 publications

(13 citation statements)

References 19 publications

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“…It follows that the E e M t D2M ration is ≥ 2 − for any small > 0. Several deterministic schedules for singletons with ratio at most 2 (and better than 2 when possible for the particular instance, in particular when priorities are small) were previously proposed [10,11]. Tree schedules are of interest to us here because they can be "properly" randomized to yield good performance in our treatment of general instances.…”

Section: Resultsmentioning

confidence: 99%

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Probe scheduling for efficient detection of silent failures

Cohen

Hassidim

Kaplan

et al. 2014

Performance Evaluation

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Most discovery systems for silent failures work in two phases: a continuous monitoring phase that detects presence of failures through probe packets and a localization phase that pinpoints the faulty element(s). We focus on the monitoring phase, where the goal is to balance the probing overhead with the cost associated with longer failure detection times.We formulate a general model for the underlying fundamental subset-test scheduling problem. We unify the treatment of schedulers and cost objectives and make several contributions: We propose Memoryless schedulesa natural subclass of stochastic schedules which is simple and suitable for distributed deployment. We show that the optimal memoryless schedulers can be efficiently computed by convex programs (for SUM objectives, which minimize average detection time) or linear programs (for MAX objectives, which minimize worst-case detection time), and surprisingly perhaps, are guaranteed to have expected detection times that are not too far off the (NP hard) stochastic optima. We study Deterministic schedules, which provide a guaranteed bound on the maximum (rather than expected) cost of undetected faults, but like general stochastic schedules, are NP hard to optimize. We develop novel efficient deterministic schedulers with provable approximation ratios.Finally, we conduct an experimental study, simulating our schedulers on real networks topologies, demonstrates a significant performance gains of the new memoryless and deterministic schedulers over previous approaches.

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Section: Resultsmentioning

confidence: 99%

“…Observe that with our choice of weighting, on any schedule opt-MAX e ≥ opt-SUM e . Therefore if the stochastic optimum of either the SUM e or MAX e objectives is (k + 1)/2, then opt-SUM e is also (k + 1)/2 which implies, from (11), that opt D -E e E t = (k + 1)/2, which implies exact cover. Proof.…”

Section: Proof Of Theorem 51mentioning

confidence: 96%

Probe scheduling for efficient detection of silent failures

Cohen

Hassidim

Kaplan

et al. 2014

Performance Evaluation

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“…The next obvious step is to embed these algorithms within heuristics for meeting the requests of mobile telecommunication clients, according to different service level quality measures, see Bar-Noy et al (2004), Brakerski and PattShamir (2006). There is potential for accommodating a wide range of combinations of periodicities, by incorporating multiples of the three basic periodicities in particular power of 2.…”

Section: Discussionmentioning

confidence: 99%

“…For perfect periodic scheduling with identical processing times, Bar-Noy et al (2004) consider two objective measures of maximum and weighted average ratios between the allocated periodicity and requested periodicity. They present a few efficient heuristic algorithms using a methodology, called tree scheduling, based on an hierarchical round-robin approach, where the hierarchy is a form of tree.…”

Section: Introductionmentioning

confidence: 99%

Perfect periodic scheduling for three basic cycles

Kim

Glass

2013

J Sched

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Periodic scheduling has many attractions for wireless telecommunications. It offers energy saving where equipment can be turned off between transmissions, and high-quality reception through the elimination of jitter, caused by irregularity of reception. However, perfect periodic schedules, in which each (of n) client is serviced at regular, prespecified intervals, are notoriously difficult to construct. The problem is known to be NP-hard even when service times are identical. This paper focuses on cases of up to three distinct periodicities, with unit service times. Our contribution is to derive a O(n 4 ) test for the existence of a feasible schedule, and a method of constructing a feasible schedule if one exists, for the given combination of client periodicities. We also indicate why schedules with a higher number of periodicities are unlikely to be useful in practice. This methodology can be used to support perfect periodic scheduling in a wide range in real world settings, including machine maintenance service, wireless mesh networks and various other telecommunication networks transmitting packet size data.

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“…The special case of singletons, where each test contains a single element, was extensively studied in the context of scheduling Teletext [2], broadcast disks [1,10,3,3,3,4], and search in unstructured p2p networks [7].…”

Section: Introductionmentioning

confidence: 99%

Scheduling Subset Tests: One-Time, Continuous, and How They Relate

Cohen

Kaplan

Mansour

2013

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques

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Abstract. A test scheduling instance is specified by a set of elements, a set of tests, which are subsets of elements, and numeric priorities assigned to elements. The schedule is a sequence of test invokations with the goal of covering all elements. This formulation had been used to model problems in multiple application domains from network failure detection to broadcast scheduling. The modeling considered both SUMe and MAXe objectives, which correspond to average or worst-case cover times over elements (weighted by priority), and both one-time testing, where the goal is to detect if a fault is currently present, and continuous testing, performed in the background in order to detect presence of failures soon after they occur. Since all variants are NP hard, the focus is on approximations. We present combinatorial approximations algorithms for both SUMe and MAXe objectives on continuous and MAXe on one-time schedules. The approximation ratios we obtain depend logarithmically on the number of elements and significantly improve over previous results. Moreover, our unified treatment of SUMe and MAXe objectives facilitates simultaneous approximation with respect to both. Since one-time and continuous testing can be viable alternatives, we study the overhead of continuous testing, captured by the ratio of optimal one-time to continuous cover times. We establish that the worst-case ratio is O(log n), but also provide evidence, by considering Zipf distributions, that the typical ratio is lower.

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Efficient algorithms for periodic scheduling

Cited by 16 publications

References 19 publications

Probe scheduling for efficient detection of silent failures

Probe scheduling for efficient detection of silent failures

Perfect periodic scheduling for three basic cycles

Scheduling Subset Tests: One-Time, Continuous, and How They Relate

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