scholarly journals Scientific Applications in the Cloud: Resource Optimisation based on Metaheuristics

2020 ◽  
Vol 21 (4) ◽  
pp. 649-660
Author(s):  
Anas Mokhtari ◽  
Mostafa Azizi ◽  
Mohammed Gabli
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Cost Reduction ◽  
Complex Task ◽  
Scientific Applications ◽  
Cloud Data ◽  
Cloud Resource ◽  
Data Centres ◽  
Different Levels

The advent of emerging technologies such as 5G and Internet of Things (IoT) will generate a colossal amount ofdata that should be processed by the cloud computing. Thereby, cloud resources optimisation represents significant benefits in different levels: cost reduction for the user, saving energy consumed by cloud data centres, etc. Cloud resource optimisation is a very complex task due to its NP-hard characteristic. In this case, use of metaheuristic approaches is more rational. But the quality of metaheuristic solutions changes by changing the problem. In this paper we have dealt with the problem of determining the configuration of resources in order to minimise the payment cost and the duration of the scientific applications execution. For that, we proposed a mathematical model and three metaheuristic approaches, namely the Genetic Algorithm (GA), hybridisation of the Genetic Algorithm with Local Search (GA-LS) and the Simulated Annealing (SA). The comparison between them showed that the simulated annealing finds more optimal solutions than those proposed by the genetic algorithm and the GA-LS hybridisation.

scholarly journals Combining Interval Branch and Bound and Stochastic Search

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Dhiranuch Bunnag
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Branch And Bound ◽  
Lower Cost ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Constrained Problems ◽  
Interval Branch And Bound ◽  
Unconstrained Problems

This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen’s algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA.

scholarly journals A Heuristic Research of the Designing and Evaluation of Energy Efficiency using Dynamic VM Consolidation in Green Cloud Data Centres

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1532-1540
Keyword(s):  
Carbon Dioxide ◽  
Energy Efficiency ◽  
Quality Of Service ◽  
Virtual Machines ◽  
Web Based ◽  
Cloud Data ◽  
The Earth ◽  
Data Centres ◽  
Vm Consolidation

Allotted computing is a blasting innovation that tenders effective assets, and smooth accessibility through web based processing. however, the growing wishes of clients for such administrations are convincing the cloud professional corporations to send huge portions of strength hungry server farms which element awful effect to the earth with the aid of the usage of plenteous Carbon Dioxide discharge. To limit control usage and strengthen the quality of service (QoS) inside the server farm assesses the strength usage in an assortment of plans in IaaS of dispensed computing situation. Dynamic Virtual Machines’ Consolidation and Placement(DVMCP) is an in a position strategies for enhancing using assets and proficient power usage in Cloud DataCenters. in this exploration, we proposed a calculation, Energy Conscious Greeny Cloud Dynamic (ECGCD) set of rules that accomplishes live VM relocation that is turning off the inert has or located it to lowcontrol mode (i.e., rest or hibernation),that builds up power productivity and succesful usage of property in the dynamic hosts. The take a look at stop result confirmations with duplicate that, the proposed calculation achieves good sized diploma of lower in electricity usage in correlation with the modern-day-day VM combination calculations.

PROFESSIONAL TRAINING OF ELECTRONICS ENGINEERS: APPLYING US EXPERICENCE TO UKRAINAN HIGHER EDUCATION INSTITUTIONS

2020 ◽  
pp. 80-85
Author(s):  
Olha Pavlenko
Keyword(s):  
Higher Education ◽  
Education Research ◽  
Best Practices ◽  
Cost Reduction ◽  
Professional Training ◽  
Higher Education Institutions ◽  
Academic Staff ◽  
Research And Innovation ◽  
Current State

The article discusses the current state of professional training of engineers, in particular, electronics engineers in Ukrainian higher education institutions (HEIs) and explores best practices from US HEIs. The research outlines the features of professional training of electronics engineers and recent changes in Ukrainian HEIs. Such challenges for Ukrainian HEIs as lack of collaboration between higher education and science with industry, R&D cost reduction for HEIs, and downsizing the research and academic staff, the disparity between the available quality of human capital training and the demanded are addressed. The study attempts to identify successful practices of US HEIs professional training of engineers in order to suggest potential improvements in education, research, and innovation for training electronics engineers in Ukraine.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6401
Author(s):  
Kateryna Czerniachowska ◽  
Karina Sachpazidu-Wójcicka ◽  
Piotr Sulikowski ◽  
Marcin Hernes ◽  
Artur Rot
Keyword(s):  
Genetic Algorithm ◽  
Profit Maximization ◽  
Allocation Rule ◽  
Maximization Problem ◽  
Shelf Space ◽  
Allocation Model ◽  
Space Allocation ◽  
Shelf Space Allocation ◽  
To Receive

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

scholarly journals The Importance of Environmental Factors in the Planning of Container Terminals: The Case Study of the Port of Augusta

2021 ◽  
Vol 11 (5) ◽  
pp. 2153
Author(s):  
Nadia Giuffrida ◽  
Maja Stojaković ◽  
Elen Twrdy ◽  
Matteo Ignaccolo
Keyword(s):  
Environmental Impact ◽  
Container Terminal ◽  
Decision Support Tool ◽  
Container Terminals ◽  
Mitigation Measures ◽  
Support Tool ◽  
Clean Environment ◽  
Different Levels

Container terminals are the main hubs of the global supply chain but, conversely, they play an important role in energy consumption, environmental pollution and even climate change due to carbon emissions. Assessing the environmental impact of this type of port terminal and choosing appropriate mitigation measures is essential to pursue the goals related to a clean environment and ensuring a good quality of life of the inhabitants of port cities. In this paper the authors present a Terminal Decision Support Tool (TDST) for the development of a container terminal that considers both operation efficiency and environmental impacts. The TDST provides environmental impact mitigation measures based on different levels of evolution of the port’s container traffic. An application of the TDST is conducted on the Port of Augusta (Italy), a port that is planning infrastructural interventions in coming years in order to gain a new role as a reference point for container traffic in the Mediterranean.

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