Parallel Machine Scheduling to Minimize Energy Consumption

Antoniadis, Antonios; Garg, Naveen; Kumar, Gunjan; Kumar, Neelesh

doi:10.1137/1.9781611975994.168

Cited by 17 publications

(35 citation statements)

References 18 publications

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“…The authors in [43] addressed an identical parallel machine scheduling problem considering release dates, deadlines, and energy consumption. The objective is the minimization of the total consumed energy.…”

Section: Green Parallel Machine Scheduling Problem With Idle Energy C...mentioning

confidence: 99%

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

et al. 2021

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Global warming and climate change are threatening life on earth. These changes are due to human activities resulting in the emission of greenhouse gases. This is caused by intensive industrial activities and excessive fuel energy consumption. Recently, the scheduling of production systems has been judged to be an effective way to reduce energy consumption. This is the field of green scheduling, which aims to allocate jobs to machines in order to minimize total costs, with a focus on the sustainable use of energy. Several studies have investigated parallel-machine shops, with a special focus on reducing and minimizing the total consumed energy. Few studies explicitly include the idle energy of parallel machines, which is the energy consumed when the machines are idle. In addition, very few studies have considered the elimination of idle machine times as an efficient way to reduce the total consumed energy. This is the no-idle machine constraint, which is the green aspect of the research. In this context, this paper addresses the green parallel-machine scheduling problem, including release dates, delivery times, and no-idle machines, with the objective of minimizing the maximum completion time. This problem is of practical interest since it is encountered in several industry processes, such as the steel and automobile industries. A mixed-integer linear programming (MILP) model is proposed for use in obtaining exact solutions for small-sized instances. Due to the NP-hardness of the studied problem, and encouraged by the successful adaptation of metaheuristics for green scheduling problems, three genetic algorithms (GAs) using three different crossover operators and a simulated annealing algorithm (SA) were developed for large-sized problems. A new family of lower bounds is proposed. This was intended for the evaluation of the performance of the proposed algorithms over the average percent of relative deviation (ARPD). In addition, the green aspect was evaluated over the percentage of saved energy, while eliminating the idle-machine times. An extensive experimental study was carried out on a benchmark of test problems with up to 200 jobs and eight machines. This experimental study showed that one GA variant dominated the other proposed procedures. Furthermore, the obtained numerical results provide strong evidence that the proposed GA variant outperformed the existing procedures from the literature. The experimental study also showed that the adoption of the no-idle machine time constraints made it possible to reduce the total consumed energy by 29.57%, while the makespan (cost) increased by only 0.12%.

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Section: Green Parallel Machine Scheduling Problem With Idle Energy C...mentioning

confidence: 99%

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

et al. 2021

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“…Related work. In recent years, energy conservation in data centers has received much attention, see for example [1,8,33] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Right-Sizing Heterogeneous Data Centers

Albers

Quedenfeld

2021

Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures

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Power consumption is a dominant and still growing cost factor in data centers. In time periods with low load, the energy consumption can be reduced by powering down unused servers. We resort to a model introduced by Lin, Wierman, Andrew and Thereska [23,24] that considers data centers with identical machines, and generalize it to heterogeneous data centers with d different server types. The operating cost of a server depends on its load and is modeled by an increasing, convex function for each server type. In contrast to earlier work, we consider the discrete setting, where the number of active servers must be integral. Thereby, we seek truly feasible solutions. For homogeneous data centers (d = 1), both the offline and the online problem were solved optimally in [3,4].In this paper, we study heterogeneous data centers with general time-dependent operating cost functions. We develop an online algorithm based on a work function approach which achieves a competitive ratio of 2d + 1 + ϵ for any ϵ > 0. For time-independent operating cost functions, the competitive ratio can be reduced to 2d + 1. There is a lower bound of 2d shown in [5], so our algorithm is nearly optimal. For the offline version, we give a graph-based (1+ϵ )-approximation algorithm. Additionally, our offline algorithm is able to handle time-variable data-center sizes. CCS CONCEPTS• Theory of computation → Online algorithms; Approximation algorithms analysis; Scheduling algorithms; Discrete optimization; • Hardware → Enterprise level and data centers power issues; Switching devices power issues.

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“…The design of energy-efficient algorithms has received quite some research interest over the last years, see e.g. [3,10,21] and references therein. Specifically, data center right-sizing has attracted considerable attention lately.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Energy Conservation in Heterogeneous Data Centers

Albers

Quedenfeld

2021

Lecture Notes in Computer Science

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Power consumption is the major cost factor in data centers. It can be reduced by dynamically right-sizing the data center according to the currently arriving jobs. If there is a long period with low load, servers can be powered down to save energy. For identical machines, the problem has already been solved optimally by [25] and [1].In this paper, we study how a data-center with heterogeneous servers can dynamically be right-sized to minimize the energy consumption. There are d different server types with various operating and switching costs. We present a deterministic online algorithm that achieves a competitive ratio of 2d as well as a randomized version that is 1.58d-competitive. Furthermore, we show that there is no deterministic online algorithm that attains a competitive ratio smaller than 2d. Hence our deterministic algorithm is optimal. In contrast to related problems like convex body chasing and convex function chasing [17, 30], we investigate the discrete setting where the number of active servers must be an integral, so we gain truly feasible solutions.

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Parallel Machine Scheduling to Minimize Energy Consumption

Cited by 17 publications

References 18 publications

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

Algorithms for Right-Sizing Heterogeneous Data Centers

Algorithms for Energy Conservation in Heterogeneous Data Centers

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