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.

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Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

https://cyberleninka.org/article/n/468779.pdf

A review of metaheuristic scheduling techniques in cloud computing

Cloud computing has become an increasingly important research topic given the strong evolution and migration of many network services to such computational environment. The problem that arises is related with efficiency management and utilization of the large amounts of computing resources. This paper begins with a brief retrospect of traditional scheduling, followed by a detailed review of metaheuristic algorithms for solving the scheduling problems by placing them in a unified framework. Armed with these two technologies, this paper surveys the most recent literature about metaheuristic scheduling solutions for cloud. In addition to applications using metaheuristics, some important issues and open questions are presented for the reference of future researches on scheduling for cloud.

Metaheuristic Scheduling for Cloud: A Survey

Cloud computing is an emerging technology in distributed computing which facilitates pay per model as per user demand and requirement.Cloud consist of a collection of virtual machine which includes both computational and storage facility. The primary aim of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy according to the changing environment and the type of task. In this research paper we presented a Generalized Priority algorithm for efficient execution of task and comparison with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim toolkit and result shows that it gives better performance compared to other traditional scheduling algorithm.

Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment

Cloud computing is an emerging technology in distributed computing which facilitates pay per model as per user demand and requirement. Cloud consist of a collection of virtual machine which includes both computational and storage facility. The primary aim of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy according to the changing environment and the type of task. In this research paper we presented a Generalized Priority algorithm for efficient execution of task and comparison with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim toolkit and result shows that it gives better performance compared to other traditional scheduling algorithm.

/pdf/efficient-optimal-algorithm-of-task-scheduling-in-cloud-4ezsmwp09t.pdf

Scheduling is a very important part of the cloud computing system. This paper introduces an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. Algorithm considers the QOS requirements of different type tasks, the QOS parameters are dealt with dimensionless. The algorithm efficiently completes tasks scheduling in the cloud computing environment computing.

Genetic simulated annealing algorithm for task scheduling based on cloud computing environment

Background processing technology is the overall protection of cloud computing system. Large-scale data processing technology plays a key role in background data collection and arrangement of the cloud computing. Google's MapReduce programming mode is a parallel programming model, widely used in processing and generating large data sets, and its scheduling mechanism is an important part of the model. In this article, the implementation process of MapReduce programming environment is analyzed, and a improvement in scheduling mechanisms is proposed.

An improved schedule of MapReduce programming environment in cloud computing

Virtualization and cloud computing platform brought the new combination of resources integration and use mode. Cloud computing distributes computing tasks to large pools consists of computing resources to make all kinds of application system could acquire services according to need; The Virtualization technology can greatly improve the efficiency and availability of resources and application in cloud computing system, and greatly simplified IT infrastructure, optimize resource and reduce the risk. In order to reduce the spending of virtual machines in the virtualization environment, improve the virtualization implementation efficiency, based on the virtual cloud platform, we propose a computing resources load balance management strategy based on the time axis and introduce the CPU time assignment algorithm.

https://ieeexplore.ieee.org/iel7/6775580/6784688/06784754.pdf

An optimization strategy of resource management in Virtualization cloud platform

Ant Colony Algorithm has some advantages in solving complex optimization problems, in particular discrete optimization problem. A mine locomotive scheduling algorithm based on the improved Ant Colony Algorithm for the mine locomotive scheduling problem has proposed in this paper. The method takes simulated annealing algorithm as a local search strategy of ant colony algorithm, aim at expanding the solution search space, avoiding falling into local optimum that improve convergence rate of the algorithm by dynamic pheromone evaporation factor. Simulation results show that the algorithm is more efficiency in locomotive scheduling than the basic ant colony algorithm.

The application of the improved Ant Colony Algorithm in mine locomotive scheduling problem

With regard to the research on MANET mobile model and routing protocol issues, according to the behaviors of community mobile nodes, a community wireless mobile model based on AOC. Autonomous system was established by making use of AOC-by-prototyping combining with trial-and-Error process. This paper does simulation experiment through NS-2 simulation platform, showing that in this model DSR and AODV routing protocols are relatively stable in the aspect of routing overhead; however in the window size DSR routing protocol is the best.

Guo-ning Gan

Papers

Genetic simulated annealing algorithm for task scheduling based on cloud computing environment

An improved schedule of MapReduce programming environment in cloud computing

An optimization strategy of resource management in Virtualization cloud platform

The application of the improved Ant Colony Algorithm in mine locomotive scheduling problem

Wireless mobile community model based on AOC