Approximation Algorithms for Scheduling with Reservations

Diedrich, Florian; Jansen, Klaus; Pascual, Fanny; Trystram, Denis

doi:10.1007/s00453-008-9271-2

Cited by 14 publications

(15 citation statements)

References 26 publications

(43 reference statements)

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“…Concerning further results, we refer the reader to Leung [2004, Chapter 22], or Sanlaville and Schmidt [1998] for surveys. For the sake of completeness, some results about single-machine problems can be found in the articles [Diedrich et al 2010;Kacem 2009;Lee 1991]. Finally, for scheduling with nonavailability, our new technique yields an improved approximation ratio independent from ρ which is tight.…”

Section: Introductionmentioning

confidence: 93%

“…The inapproximability is circumvented by requiring at least one machine to be permanently available. The case with m constant, arbitrary nonavailability intervals, and at least one machine permanently available, is strongly NP-hard but Diedrich et al [2010] gave a PTAS for it. For general m, researchers so far have only studied the problem where there is at most one interval of nonavailability per machine.…”

Section: Introductionmentioning

confidence: 98%

“…In the literature, scheduling with nonavailability is also called nonresumable scheduling with availability constraints [Diedrich et al 2010;Lee 1996;Leung 2004;Liao et al 2003]. The makespan C max is one of the most well-studied objectives in the field of scheduling; for this objective, most problem formulations permit good approximation algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from 27:2 F. Diedrich et al et al 2011], production planning and control for the beverage industry [Scharbrodt et al 1999], and distributed computing [Diedrich et al 2010].…”

Section: Introductionmentioning

confidence: 99%

“…In parallel machine scheduling, an important issue is the scenario where either some jobs are already fixed in the system [Scharbrodt et al 1999] or intervals of nonavailability of some machines must be taken into account [Diedrich et al 2010;Hwang et al 2005;Lee 1996;Leung 2004;Liao et al 2003]. The first problem occurs since high-priority jobs are present in the system while the latter problem is due to regular maintenance of machines; both models are relevant for turnaround scheduling [Megow This is a combined version of the work with the title "Improved Approximation Algorithms for Scheduling with Fixed Jobs" presented at the 20 th ACM-SIAM Symposium on Discrete Algorithms (SODA09) and the work with title "Faster Approximation Algorithms for Scheduling with Fixed Jobs" presented at the 17 th Computing: The Australian Theory Symposium (CATS11).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Tight approximation algorithms for scheduling with fixed jobs and nonavailability

Diedrich

Jansen

Prädel

et al. 2012

ACM Trans. Algorithms

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We study two closely related problems in nonpreemptive scheduling of jobs on identical parallel machines. In these two settings there are either fixed jobs or nonavailability intervals during which the machines are not available; in both cases, the objective is to minimize the makespan. Both formulations have different applications, for example, in turnaround scheduling or overlay computing.For both problems we contribute approximation algorithms with an improved ratio of 3/2. For scheduling with fixed jobs, a lower bound of 3/2 on the approximation ratio has been obtained by Scharbrodt et al. [1999]; for scheduling with nonavailability we provide the same lower bound.We use dual approximation, creation of a gap structure, and a PTAS for the multiple subset sum problem, combined with a postprocessing step to assign large jobs.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tight approximation algorithms for scheduling with fixed jobs and nonavailability

Diedrich

Jansen

Prädel

et al. 2012

ACM Trans. Algorithms

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A Bi-Objective Scheduling Algorithm for Desktop Grids with Uncertain Resource Availabilities

Canon

Essafi

Mounié

et al. 2011

Euro-Par 2011 Parallel Processing

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In this work, we consider the execution of applications on desktop grids. Such parallel systems use idle computing resources of desktops distributed over the Internet for running massively parallel computations. The applications are composed of workflows of independent non-preemptive sequential jobs that are submitted by successive batches. Then, the corresponding jobs are executed on the distributed available resources according to some scheduling policy. However, most resources are not continuously available over time since the users give their idle CPU time only for some time when they are not using their desktops. Moreover, even if the dates of unavailability periods are estimated in advance, they are subject to uncertainties. This may drastically impact the global performances by delaying the completion time of the applications. The aim of this paper is to study how to schedule efficiently a set of jobs in the presence of unavailability periods on identical machines. In the same time, we are interested in reducing the impact of disturbances on the unavailability periods. This is achieved by maximizing the stability that measures the distance between the makespan of the disturbed instance over the initial one. Our main contribution is the design of a new parametrized algorithm and the analysis of its performance through structural properties. This algorithm reduces the impact of disturbances on availability periods without worsening too much the makespan. Its interest is assessed by running simulations based on realistic workflows. Moreover, theoretical results are obtained under the assumption that the size of every availability interval is at least twice the size of the largest job.

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Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem

Shewale,

Mokhade,

Lipare

et al. 2023

Arab J Sci Eng

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The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.

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Approximation Algorithms for Scheduling with Reservations

Cited by 14 publications

References 26 publications

Tight approximation algorithms for scheduling with fixed jobs and nonavailability

Tight approximation algorithms for scheduling with fixed jobs and nonavailability

A Bi-Objective Scheduling Algorithm for Desktop Grids with Uncertain Resource Availabilities

Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem

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