Effective Metaheuristic Algorithms for Bag-of-Tasks Scheduling Problems Under Budget Constraints on Hybrid Clouds

2020 ◽  
pp. 2150091
Author(s):  
Linhua Ma ◽  
Chunshan Xu ◽  
Haoyang Ma ◽  
Yujie Li ◽  
Jiali Wang ◽  
...  
Keyword(s):  
Initial Solution ◽  
Metaheuristic Algorithms ◽  
Initial Population ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Task Sequence ◽  
Hybrid Clouds ◽  
Data Intensive ◽  
Communication Time ◽  
The Impact

Cloud computing is an ideal platform for executing bag-of-task (BoT) applications due to its capability of delivering high-quality and pay-per-use computing services. This paper presents a family of genetic algorithm (GA)-based metaheuristics for scheduling the tasks of data-intensive BoT applications on hybrid clouds. The scheduling objective is to minimize the flowtime of BoT applications under a specified budget constraint. We take into account the impact of communication time and communication cost to formulate the optimization model for the data-intensive BoT scheduling problem. By using a task sequence to represent the scheduling solution, the proposed algorithms start with using a low-complexity strategy to generate an initial solution. The generated initial solution is identified as the best chromosome in the initial population of GA framework. We improve the standard crossover operator in GA’s evolutionary procedure by incorporating a probabilistic model. In addition, we design an efficient task dispatching method to evaluate the scheduling quality of each chromosome. Built upon the improved crossover scheme and task dispatching method, the proposed metaheuristic algorithms employ three crossover operators to solve the BoT scheduling problem considered in this work. Extensive experiments are performed to verify the performance of the proposed algorithms in scheduling data-intensive BoT applications.

scholarly journals Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities

2020 ◽  
Vol 7 (6) ◽  
pp. 761-774
Author(s):  
Kailash Changdeorao Bhosale ◽  
Padmakar Jagannath Pawar
Keyword(s):  
Production Planning ◽  
Demand Uncertainty ◽  
Clustering Algorithm ◽  
Real Life ◽  
Initial Population ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling

Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.

scholarly journals Music Modulates Cognitive Flexibility? An Investigation of the Benefits of Musical Training on Markers of Cognitive Flexibility

2021 ◽  
Vol 11 (4) ◽  
pp. 451
Author(s):  
Miriam Gade ◽  
Kathrin Schlemmer
Keyword(s):  
Everyday Life ◽  
Cognitive Flexibility ◽  
Musical Training ◽  
Rapid Change ◽  
Task Sequence ◽  
Switch Costs ◽  
Classification Tasks ◽  
Professional Contexts ◽  
The Impact ◽  
Task Sequences

Cognitive flexibility enables the rapid change in goals humans want to attain in everyday life as well as in professional contexts, e.g., as musicians. In the laboratory, cognitive flexibility is usually assessed using the task-switching paradigm. In this paradigm participants are given at least two classification tasks and are asked to switch between them based on valid cues or memorized task sequences. The mechanisms enabling cognitive flexibility are investigated through two empirical markers, namely switch costs and n-2 repetition costs. In this study, we assessed both effects in a pre-instructed task-sequence paradigm. Our aim was to assess the transfer of musical training to non-musical stimuli and tasks. To this end, we collected the data of 49 participants that differed in musical training assessed using the Goldsmiths Musical Sophistication Index. We found switch costs that were not significantly influenced by the degree of musical training. N-2 repetition costs were small for all levels of musical training and not significant. Musical training did not influence performance to a remarkable degree and did not affect markers of mechanisms underlying cognitive flexibility, adding to the discrepancies of findings on the impact of musical training in non-music-specific tasks.

scholarly journals Investigating the Impact of Root-Lesion Nematodes (Pratylenchus thornei) and Crown Rot (Fusarium pseudograminearum) on Diverse Cereal Cultivars in a Conservation Farming System

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 867
Author(s):  
John P. Thompson ◽  
Timothy G. Clewett
Keyword(s):  
Grain Yield ◽  
Soil Profile ◽  
Crown Rot ◽  
Tolerance Index ◽  
Initial Population ◽  
Fusarium Pseudograminearum ◽  
Conservation Farming ◽  
Pratylenchus Thornei ◽  
Fourth Experiment ◽  
The Impact

In two experiments on a farm practicing conservation agriculture, the grain yield of a range of wheat cultivars was significantly (p < 0.001) negatively related to the post-harvest population densities of Pratylenchus thornei in the soil profile to 45 cm depth. In a third and fourth experiment with different rotations, methyl bromide fumigation significantly (p < 0.05) decreased (a) a low initial population density of P. thornei in the soil profile to 90 cm depth and (b) a high initial population of P. thornei to 45 cm depth, and a medium level of the crown rot fungus, Fusarium pseudograminearum, at 0–15 cm depth to a low level. For a range of wheat and durum cultivars, grain yield and response to fumigation were highly significantly (p < 0.001) related to (a) the P. thornei tolerance index of the cultivars in the third experiment, and (b) to both the P. thornei tolerance index and the crown rot resistance index in the fourth experiment. In the latter, grain yield was significantly (p < 0.001) positively related to biomass at anthesis and negatively related to percentage whiteheads at grain fill growth stage. One barley cultivar was more tolerant to both diseases than the wheat and durum cultivars. Crop rotation, utilizing crop cultivars resistant and tolerant to both P. thornei and F. pseudograminearum, is key to success for conservation farming in this region.

scholarly journals Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 830
Author(s):  
Filipe F. C. Silva ◽  
Pedro M. S. Carvalho ◽  
Luís A. F. M. Ferreira
Keyword(s):  
Distributed Generation ◽  
Optimal Solution ◽  
Dynamic Reconfiguration ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Distribution Grid ◽  
Medium Voltage ◽  
Classical Computing ◽  
The Impact ◽  
Distribution Grids

The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in the course of the day, resulting in higher losses. The dynamic reconfiguration of the grid could mitigate daily losses and be used to minimize or defer the need for network reinforcement. Yet, dynamic reconfiguration has to be carried out in near real-time in order to make use of the most updated load and generation forecast, this way maximizing operational benefits. Given the need to quickly find and update reconfiguration decisions, the computational complexity of the underlying optimal scheduling problem is studied in this paper. The problem is formulated and the impact of sub-optimal solutions is illustrated using a real medium-voltage distribution grid operated under a heavy generation scenario. The complexity of the scheduling problem is discussed to conclude that its optimal solution is infeasible in practical terms if relying upon classical computing. Quantum computing is finally proposed as a way to handle this kind of problem in the future.

Systematische Navigation für die Reihenfolgeplanung/Systematic solution space navigation for job shop scheduling problems

2020 ◽  
Vol 110 (07-08) ◽  
pp. 563-571
Author(s):  
Edzard Weber ◽  
Eduard Schenke ◽  
Luka Dorotic ◽  
Norbert Gronau
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Solution Space ◽  
Comparative Test ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Space Navigation ◽  
Systematic Solution

Dieser Beitrag stellt einen Algorithmus für das Job-shop-Scheduling-Problem vor, welcher den Lösungsraum indexiert und eine systematische Navigation zur Lösungssuche durchführt. Durch diese problemadäquate Aufbereitung wird der Lösungsraum nach bestimmten Kriterien vorzustrukturiert. Diese Problemrepräsentation wird formal beschrieben, sodass ihre Anwendung als Grundlage für ein navigationsorientiertes Suchverfahren dienen kann. Ein vergleichender Test mit anderen Optimierungsansätzen zeigt die Effizienz dieser Lösungsraumnavigation. &nbsp; This paper presents an algorithm for the job-shop scheduling problem indexing the solution space and performing systematic navigation to find good solutions. By this problem-adequate preparation of the solution space, the solution space is pre-structured according to certain criteria. This problem representation is formally described so that its application can serve as a basis for a navigation-oriented search procedure. A comparative test with other optimization approaches shows the efficiency of this solution space navigation.

scholarly journals InterRC: An Inter-Resources Collaboration Heuristic for Scheduling Independent Tasks on Heterogeneous Distributed Environments

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 179-188
Author(s):  
Abdelhamid Khiat ◽  
Abdelkamel Tari
Keyword(s):  
Task Scheduling ◽  
Initial Solution ◽  
Distributed Environments ◽  
Scheduling Problem ◽  
Reasonable Time ◽  
Independent Task ◽  
Np Hard Problem ◽  
Computing Environments ◽  
Scheduling Heuristic ◽  
Independent Tasks

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.

Sign in / Sign up

Export Citation Format

Share Document