Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.Peer ReviewedPostprint (published version

Maximizing Computational Profit in Grid Resource Allocation Using Dynamic Algorithm

Grid computing, one of the most trendy phrase used in IT, is emerging vastly distributed computational paradigm. A computational grid provides a collaborative environment of the hefty number of resources capable to do high computing performance to reach the common goal. Grid computing can be called as super virtual computer, it ensemble large scale geographically distributed heterogeneous resources. Resource allocation is a key element in the grid computing and grid resource may leave at anytime from grid environment. Despite a number of benefits in grid computing, still resource allocation is a challenging task in the grid. This work investigates to maximize the profits by analyzing how the tasks are allocated to grid resources effectively according to quality of service parameter and gratifying user requisition. A fusion of SS-GA algorithm has introduced to answer the above raised question about the resource allocation problem based on grid user requisition. The swift uses genetic algorithms heuristic functions and makes an effective resource allocation process in grid environment. The result of proposed fusion of SS-GA algorithm ameliorates the grid resource allocation

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

An Enhanced Model for Job Sequencing and Dispatch in Identical Parallel Machines

This paper has developed an efficient scheduling model that is robust and minimizes the total completion time for job completion in identical parallel machines. The new model employs Genetic-Fuzzy technique for job sequencing and dispatch in identical parallel machines. It uses genetic algorithm technique to develop a job scheduler that does the job sequencing and optimization while fuzzy logic technique was used to develop a job dispatcher that dispatches job to the identical parallel machines. The methodology used for the design is the Object Oriented Analysis and Design Methodology (OOADM) and the system was implemented using C# and .NET framework. The model was tested with fifteen identical parallel machines used for printing. The parameters used in analyzing this model include the job scheduling length, average execution time, load balancing and machines utilization. The result generated from the developed model was compare with the result of other job scheduling models like First Come First Sever (FCFS) scheduling approach and Genetic Model (GA) scheduling approach. The result of the new model shows a better load balancing and high machine utilization among the individual machines when compared with the First Come First Serve (FCFS) scheduling model and Genetic Algorithm (GA) scheduling model. Keywords:  Parallel Machines, Genetic Model, Job Scheduler, Fuzzy Logic Technique, Load Balancing, Machines   Utilization DOI: 10.7176/CEIS/11-2-05 Publication date: March 31st 202

A Niched Pareto GA Approach for Scheduling Scientific Workflows in Wireless Grids

We present a Niched Pareto Genetic Algorithm (NPGA) approach to the scheduling of scientific workflows in a wireless grid environment that connects computational resources, wired grids and wireless device resources such as cameras, microphones, network interfaces and so on where the maximization of job completion ratio and minimization of lateness is crucial. Our approach supports handling uncertainty in the field of decision analysis, a rigorous technique for combining multiple objectives simultaneously. We made comparisons of our approach with respect to other scheduling policies; it performed significantly better than the majority of cases, and in worst cases, it was as good as the best of the others

Optimization grid scheduling with priority base and bees algorithm

Grid computing depends upon sharing large-scales in a network that is widely connected within itself such as the Internet. Therefore, many grid computing researchers and scholars have focused on task scheduling, which is considered one of the NP-Complete issues. The main aim of this current research to propose an optimization of the initial scheduler for grid computing using the bees algorithm. Modern algorithms informed this research. The suggested procedure means that a newly developed algorithm can implement the schedule grid task while accounting for priorities and deadlines to decrease the completion time required for the tasks. The average waiting time of the grid environment can be minimized, and this minimization, in turn, creates an increase in the throughput of the environment

Resource Schedulingin Grid Computing: A Survey

Grid computing is a computing framework to meet growing demands for running heterogeneous grid enables applications. A grid system is composed of computers which are separately located and connected with each other through a network. Grids are systems that involve resource sharing and problem solving in heterogeneous dynamic grid environments. Here we present five different approaches/algorithms for resource allocation/ Scheduling in grid computing environment