A Cuckoo-based Workflow Scheduling Algorithm to Reduce Cost and Increase Load Balance in the Cloud Environment

Workflow scheduling is one of the important issues in implementing workflows in the cloud environment. Workflow scheduling means how to allocate workflow resources to tasks based on requirements and features of the tasks. The problem of workflow scheduling in cloud computing is a very important issue and is an NP problem. The relevant scheduling algorithms try to find optimal scheduling of tasks on the available processing resources in such a way some qualitative criteria when executing the entire workflow are satisfied. In this paper, we proposed a new scheduling algorithm for workflows in the cloud environment using Cuckoo Optimization Algorithm (COA). The aims of the proposed algorithm are reducing the processing and transmission costs as well as maintaining a desirable load balance among the processing resources. The proposed algorithm is implemented in MATLAB and its performance is compared with Cat Swarm Optimization (CSO). The results of the comparisons showed that the proposed algorithm is superior to CSO in discovering optimal solutions

Parzsweep: A Novel Parallel Algorithm for Volume Rendering of Regular Datasets

The sweep paradigm for volume rendering has previously been successfully applied with irregular grids. This thesis describes a parallel volume rendering algorithm called PARZSweep for regular grids that utilizes the sweep paradigm. The sweep paradigm is a concept where a plane sweeps the data volume parallel to the viewing direction. As the sweeping proceeds in the increasing order of z, the faces incident on the vertices are projected onto the viewing volume to constitute to the image. The sweeping ensures that all faces are projected in the correct order and the image thus obtained is very accurate in its details. PARZSweep is an extension of a serial algorithm for regular grids called RZSweep. The hypothesis of this research is that a parallel version of RZSweep can be designed and implemented which will utilize multiple processors to reduce rendering times. PARZSweep follows an approach called image-based task scheduling or tiling. This approach divides the image space into tiles and allocates each tile to a processor for individual rendering. The sub images are composite to form a complete final image. PARZSweep uses a shared memory architecture in order to take advantage of inherent cache coherency for faster communication between processor. Experiments were conducted comparing RZSweep and PARZSweep with respect to prerendering times, rendering times and image quality. RZSweep and PARZSweep have approximately the same prerendering costs, produce exactly the same images and PARZSweep substantially reduced rendering times. PARZSweep was evaluated for scalability with respect to the number of tiles and number of processors. Scalability results were disappointing due to uneven data distribution

Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms

Because the alternative process plans have significant contributions to the production efficiency of a manufacturing system, researchers have studied the integration of manufacturing functions, which can be divided into two groups, namely, integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Although IPPS and SWDDA are well-known and solved problems in the literature, there are limited works on integration of process planning, scheduling, and due date assignment (IPPSDDA). In this study, due date assignment function was added to IPPS in a dynamic manufacturing environment. And the studied problem was introduced as dynamic integrated process planning, scheduling, and due date assignment (DIPPSDDA). The objective function of DIPPSDDA is to minimize earliness and tardiness (E/T) and determine due dates for each job. Furthermore, four different pure metaheuristic algorithms which are genetic algorithm (GA), tabu algorithm (TA), simulated annealing (SA), and their hybrid (combination) algorithms GA/SA and GA/TA have been developed to facilitate and optimize DIPPSDDA on the 8 different sized shop floors. The performance comparisons of the algorithms for each shop floor have been given to show the efficiency and effectiveness of the algorithms used. In conclusion, computational results show that the proposed combination algorithms are competitive, give better results than pure metaheuristics, and can effectively generate good solutions for DIPPSDDA problems

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions

EXA2PRO programming environment:Architecture and applications

The EXA2PRO programming environment will integrate a set of tools and methodologies that will allow to systematically address many exascale computing challenges, including performance, performance portability, programmability, abstraction and reusability, fault tolerance and technical debt. The EXA2PRO tool-chain will enable the efficient deployment of applications in exascale computing systems, by integrating high-level software abstractions that offer performance portability and efficient exploitation of exascale systems' heterogeneity, tools for efficient memory management, optimizations based on trade-offs between various metrics and fault-tolerance support. Hence, by addressing various aspects of productivity challenges, EXA2PRO is expected to have significant impact in the transition to exascale computing, as well as impact from the perspective of applications. The evaluation will be based on 4 applications from 4 different domains that will be deployed in JUELICH supercomputing center. The EXA2PRO will generate exploitable results in the form of a tool-chain that support diverse exascale heterogeneous supercomputing centers and concrete improvements in various exascale computing challenges

Performance analysis and optimization of a cellular system simulation application

Aquest treball presenta l'anàlisi de rendiment i la prova de concepte d'optimització d'un sistema cel·lular heterogeni utiltzant el programari PhysiCell. PhysiCell és un entorn de modelització en base a agents per a la simulació de sistemes multicel·lulars 3-D. El treball inclou l'anàlisi de rendiment del cas d'ús, seguint la metodologia POP, per detectar problemes en l'escalabilitat, i la proposta i implementació d'exemples d'optimització per solucionar els problemes detectats.This work presents the performance analysis and the proof of concept optimization of a heterogeneous cell system simulation using PhysiCell. PhysiCell is an agent-based modeling framework for 3-D multicellular simulation. The work includes the performance analysis of the use-case using the POP methodology to detect scalability problems and the suggestion and implementation example of optimizations to solve the problems detected

Article a novel algorithm for capacitated vehicle routing problem for smart cities

Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph

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

Bee Colony Optimization - part II: The application survey

Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications. [Projekat Ministarstva nauke Republike Srbije, br. OI174010, OI174033, TR36002] Document type: Articl