Algorithms for Scheduling Imprecise Computations with Timing Constraints

Shih, Wei-Kuan; Liu, Jane W. S.; Chung, Jen-Yao

doi:10.1137/0220035

Cited by 96 publications

(44 citation statements)

References 5 publications

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“…All jobs in H are scheduled within the ν intervals of set W A , and no job j ∈ H is scheduled outside the interval [r( j), d(j)]. This problem is essentially the same as the one discussed in Hochbaum and Shamir (1990, Section 2) (see also Shih et al 1991). This is called the problem of minimizing the unweighted number of tardy units, where the objective is to minimize the total (unweighted) compression (or tardy units) j∈H (b(j)− q( j)) instead of maximizing j∈H q(j).…”

Section: Solving Ssp On a Single Machinementioning

confidence: 75%

Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints

Shioura¹,

Shakhlevich²,

Strusevich³

2017

INFORMS Journal on Computing

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Abstract. In this paper, we propose a new methodology for the speed-scaling problem based on its link to scheduling with controllable processing times and submodular optimization. It results in faster algorithms for traditional speed-scaling models, characterized by a common speed/energy function. Additionally, it efficiently handles the most general models with job-dependent speed/energy functions with single and multiple machines.

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Section: Solving Ssp On a Single Machinementioning

confidence: 75%

Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints

Shioura¹,

Shakhlevich²,

Strusevich³

2017

INFORMS Journal on Computing

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“…Their algorithm for the unweighted problem is of particular importance for this study, because we use its extended form as a subroutine in §5. Shih et al (1991) study the general case of 1 r j , p j =p j − x j pmtn C j ≤ d j w j x j and 1 r j p j =p j − x j pmtn C j ≤ d j x j with nonzero lower bounds on the processing times; the proposed algorithms have the same time complexity as those by Hochbaum and Shamir (1990). Notice that it is fairly easy to incorporate nonzero lower bounds if a network flow technique is used, whereas in an algorithm that relies on scheduling reasoning a move from zero lower bounds to nonzero ones requires additional effort.…”

Section: Review Of Scheduling With Fixed and Controllable Processing mentioning

confidence: 99%

“…It can be solved by algorithms developed by Hochbaum and Shamir (1990) and Shih et al (1991). In particular, the algorithm by Hochbaum and Shamir (1990) uses the UNION-FIND technique and guarantees that the actual processing times of all jobs and the corresponding optimal schedule are found in O h time, provided that the jobs are numbered in accordance with (26).…”

Section: Solving Single Machine Problem Via Decompositionmentioning

confidence: 99%

Application of Submodular Optimization to Single Machine Scheduling with Controllable Processing Times Subject to Release Dates and Deadlines

Shioura

Shakhlevich

Strusevich

2016

INFORMS Journal on Computing

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Full terms and conditions of use: http:/ / pubsonline. informs. org/ page/ terms-and-conditionsThis art icle m ay be used only for t he purposes of research, t eaching, and/ or privat e st udy. Com m ercial use or syst em at ic downloading ( by robot s or ot her aut om at ic processes) is prohibit ed wit hout explicit Publisher approval, unless ot herwise not ed. For m ore inform at ion, cont act perm issions@inform s.org.The Publisher does not warrant or guarant ee t he art icle's accuracy, com plet eness, m erchant abilit y, fit ness for a part icular purpose, or non-infringem ent . Descript ions of, or references t o, product s or publicat ions, or inclusion of an advert isem ent in t his art icle, neit her const it ut es nor im plies a guarant ee, endorsem ent , or support of claim s m ade of t hat product , publicat ion, or service. Copyright © 2016, I NFORMS Please scroll down for article-it is on subsequent pagesINFORMS is t he largest prof essional societ y in t he world f or prof essionals in t he f ields of operat ions research, management science, and analyt ics. For more inf ormat ion on INFORMS, it s publicat ions, membership, or meet ings visit ht t p: / / www. inf orms. org INFORMS Journal on ComputingVol. 28, No. 1, Winter 2016, pp. 148-161 ISSN 1091-9856 (print) ISSN 1526 http I n this paper, we study a scheduling problem on a single machine, provided that the jobs have individual release dates and deadlines, and the processing times are controllable. The objective is to find a feasible schedule that minimizes the total cost of reducing the processing times. We reformulate the problem in terms of maximizing a linear function over a submodular polyhedron intersected with a box. For the latter problem of submodular optimization, we develop a recursive decomposition algorithm and apply it to solving the single machine scheduling problem to achieve the best possible running time.

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“…If processing time is not enough to produce high-quality results within the deadlines, there could be enough time for producing approximate results with a lower quality. This concept has been formalized by many authors Liu et al, 1987;Natarajan, 1995;Shih et al, 1991) and specific techniques have been developed for designing programs that can produce partial results.…”

Section: Service Adaptationmentioning

confidence: 99%

Handling Overload Conditions in Real-Time Systems

Buttazzo¹

2012

Real-Time Systems, Architecture, Scheduling, and Application

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This chapter deals with the problem of handling overload conditions, that is, those critical situations in which the computational demand requested by the application exceeds the processor capacity. If not properly handled, an overload can cause an abrupt performance degradation, or even a system crash. Therefore, a real-time system should be designed to anticipate and tolerate unexpected overload situations through speciﬁc kernel mechanisms

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Algorithms for Scheduling Imprecise Computations with Timing Constraints

Cited by 96 publications

References 5 publications

Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints

Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints

Application of Submodular Optimization to Single Machine Scheduling with Controllable Processing Times Subject to Release Dates and Deadlines

Handling Overload Conditions in Real-Time Systems

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