Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

OPTIMAL POWER MANAGEMENT OF DGS AND DSTATCOM USING IMPROVED ALI BABA AND THE FORTY THIEVES OPTIMIZER

In this study an improved Ali Baba and the forty thieves Optimizer (IAFT) is proposed and successfully adapted and applied to enhance the technical performances of radial distribution network (RDN). The standard AFT governed by two sensible parameters to balance the exploration and the exploitation stages. In the proposed variant a modification is introduced using sine and cosine functions to create flexible balance between Intensification and diversification during search process. The proposed variant namely IAFT applied to solve various single and combined objective functions such as the improvement of total power losses (TPL), the minimization of total voltage deviation and the maximization of the loading capacity (LC) under fixed load and considering the random aspect of loads. The exchange of active powers is elaborated by integration of multi distribution generation based photovoltaic systems (PV), otherwise the optimal management of reactive power is achieved by the installation of multi DSTATCOM. The efficiency and robustness of the proposed variant validated on two RDN, the 33-Bus and the 69-Bus. The qualities of objective functions achieved and the statistical analysis elaborated compared to results achieved using several recent metaheuristic methods demonstrate the competitive aspect of the proposed IAFT in solving with accuracy various practical problems related to optimal power management of RDN

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost

A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles

Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical factors (e.g., transport and setup). This study integrates multiple automated guided vehicles (AGVs) into a job-shop environment. We propose a multiobjective scheduling model that considers machine processing, sequence-dependent setup and AGV transport, aiming to simultaneously minimize the makespan, total idle time of machines and total energy consumption of both machines and AGVs. To solve this problem, an effective multiobjective evolutionary algorithm (EMOEA) is developed. Within the EMOEA, an efficient encoding/decoding method is designed to represent and decode each solution. A new crossover operator is proposed for AGV assignment and AGV speed sequences. To balance the exploration and exploitation ability of the EMOEA, an opposition-based learning strategy is incorporated. A total of 75 benchmark instances and a real-world case are used for our experimental study. Taguchi analysis is applied to determine the best combination of key parameters for the EMOEA. Extensive computational experiments show that properly increasing the number of AGVs can shorten the waiting time of machines and achieve a balance between economic and environmental objectives for production systems. The experimental results confirm that the proposed EMOEA is significantly better at solving the problem than three other well-known algorithms. Our findings here have significant managerial implications for real-world manufacturing environments integrated with AGVs

A search algorithm for constrained engineering optimization and tuning the gains of controllers

In this work, the application of an optimization algorithm is investigated to optimize static and dynamic engineering problems. The methodology of the approach is to generate random solutions and find a zone for the initial answer and keep reducing the zones. The generated solution in each loop is independent of the previous answer that creates a powerful method. Simplicity as its main advantage and the interlaced use of intensification and diversification mechanisms--to refine the solution and avoid local minima/maxima--enable the users to apply that for a variety of problems. The proposed approach has been validated by several previously solved examples in structural optimization and scored good results. The method is also employed for dynamic problems in vibration and control. A modification has also been done on the method for high-dimensional test functions (functions with very large search domains) to converge fast to the global minimum or maximum; simulated for several well-known benchmarks successfully. For validation, a number of 9 static and 4 dynamic constrained optimization benchmark applications and 32 benchmark test functions are solved and provided, 45 in total. All the codes of this work are available as supplementary material in the online version of the paper on the journal website

Multi-Objective Multi-mode Time-Cost Tradeoff modeling in Construction Projects Considering Productivity Improvement

In today's construction industry, poor performance often arises due to various factors related to time, finances, and quality. These factors frequently lead to project delays and resource losses, particularly in terms of financial resources. This research addresses the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP), a real-world challenge that takes into account the time value of money and project payment planning. In this context, project activities exhibit discrete cost profiles under different execution conditions and can be carried out in multiple ways. This paper aims to achieve two primary objectives: minimizing the net present value of project costs and project completion times while simultaneously improving the project's productivity index. To accomplish this, a mathematical programming model based on certain assumptions is proposed. Several test cases are designed, and they are rigorously evaluated using the methodology outlined in this paper to validate the modeling approach. Recognizing the NP-hard nature of this problem, a multi-objective genetic algorithm capable of solving large-scale instances is developed. Finally, the effectiveness of the proposed solution is assessed by comparing it to the performance of the NSGA-II algorithm using well-established efficiency metrics. Results demonstrate the superior performance of the algorithm introduced in this study.Comment: 40 pages, 20 figures, 7 table

CIOA : Circle-Inspired Optimization Algorithm, an algorithm for engineering optimization

This paper presents a new, robust and very efficient metaheuristic optimization algorithm, called Circle Inspired Optimization Algorithm (CIOA), for solving constrained and unconstrained engineering optimization problems. The inspiration for the proposed algorithm consists of well-known formulations of the trigonometric circle. CIOA is compared with five other very famous algorithms in ten benchmark function optimization problems, five real-world engineering constrained optimization problems, and also four structural optimization problems for plane and spatial trusses subjected to multiple and different types of constraints. The results obtained demonstrate that the proposed algorithm is more efficient than other famous algorithms, contributing to the accurate and fast solution of complex optimization problems

A comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of the facilities conditions their market shares and profits, the competitive facility location problem (CFLP) has been extensively applied in the literature. This problem generally falls within the class of NP-hard problems, which are difficult to solve. Therefore, choosing a proper solution method to optimize the problem is a key factor. Even though CFLPs have been consistently solved and investigated, an important question that keeps being neglected is how to choose an appropriate solution technique. Since there are no specific criteria for choosing a solution method, the reasons behind the selection approach are mostly unclear. These models are generally solved using several optimization techniques. As harder-to-solve problems are usually solved using meta-heuristics, we apply different meta-heuristic techniques to optimize a new version of the CFLP that incorporates reliability and congestion. We divide the algorithms into four categories based on the nature of the meta-heuristics: evolution-based, swarm intelligence-based, physics-based, and human-based. GAMS software is also applied to solve smaller-size CFLPs. The genetic algorithm and differential evolution of the first category, particle swarm optimization and artificial bee colony optimization of the second, Tabu search and harmony search of the third, and simulated annealing and vibration damping optimization of the fourth are applied to solve our CFLP model. Statistical analyses are implemented to evaluate and compare their relative performances. The results show the algorithms of the first and third categories perform better than the others