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

The fast-paced development of power systems necessitates smart grids to facilitate real-time control and monitoring with bidirectional communication and electricity flows. Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas— energy management , information management , and security . In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid. We also highlight different challenges existing in the conventional smart grid (without cloud application) that can be overcome using cloud. In this survey, we present a synthesized overview of the current state of research on smart grid development. We also identify the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.

Cloud Computing Applications for Smart Grid: A Survey

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

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

A review of metaheuristic scheduling techniques in cloud computing

Symbiotic Organism Search optimization based task scheduling in cloud computing environment

Now-a-days, Cloud computing is a technology which eludes provision cost while providing scalability and elasticity to accessible resources on a pay-per-use basis. To satisfy the increasing demand of the computing power to execute large scale scientific workflow applications, workflow scheduling is the main challenging issue in Infrastructure-as-a-Service (IaaS) clouds. As workflow scheduling belongs to NP-complete problem, so, meta-heuristic approaches are more preferred option. Users often specified deadline and budget constraint for scheduling these workflow applications over cloud resources. But these constraints are in conflict with each other, i.e., the cheaper resources are slow as compared to the expensive resources. Most of the existing studies try to optimize only one of the objectives, i.e., either time minimization or cost minimization under user specified Quality of Service (QoS) constraints. But due to the complexity of workflows and dynamic nature of cloud, a trade-off solution is required to make a balance between execution time and processing cost. To address these issues, this paper presents a non-dominance sort based Hybrid Particle Swarm Optimization (HPSO) algorithm to handle the workflow scheduling problem with multiple conflicting objective functions on IaaS clouds. The proposed algorithm is a hybrid of our previously proposed Budget and Deadline constrained Heterogeneous Earliest Finish Time (BDHEFT) algorithm and multi-objective PSO. The HPSO heuristic tries to optimize two conflicting objectives, namely, makespan and cost under the deadline and budget constraints. Along with these two conflicting objectives, energy consumed of created workflow schedule is also minimized. The proposed algorithm gives a set of Pareto Optimal solutions from which the user can choose the best solution. The performance of proposed heuristic is compared with state-of-art multi-objective meta-heuristics like NSGA-II, MOPSO, and e -FDPSO. The simulation analysis substantiates that the solutions obtained with proposed heuristic deliver better convergence and uniform spacing among the solutions as compared to others. Hence it is applicable to solve a wide class of multi-objective optimization problems for scheduling scientific workflows over IaaS clouds.

A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.

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An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment

The emergence of Cloud Computing as a model of service provisioning in distributed systems instigated researchers to explore its pros and cons on executing different large scale scientific applications, i.e., Workflows. One of the most challenging problems in clouds is to execute workflows while minimizing the execution time as well as cost incurred by using a set of heterogeneous resources over the cloud simultaneously. In this paper, we present, Budget and Deadline Constrained Heuristic based upon Heterogeneous Earliest Finish Time (HEFT) to schedule workflow tasks over the available cloud resources. The proposed heuristic presents a beneficial trade-off between execution time and execution cost under given constraints. The proposed heuristic is evaluated for different synthetic workflow applications by a simulation process and comparison is done with state-of-art algorithm i.e. BHEFT. The simulation results show that our proposed scheduling heuristic can significantly decrease the execution cost while producing makespan as good as the best known scheduling heuristic under the same deadline and budget constraints.

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Cloud computing is a new technology and it is becoming popular day by day because of its great features. In this technology almost everything like hardware, software and platform are provided as a service. These services are charged from users on the pay-per-use bases. A cloud provider in cloud computing provides services on the basis of clients' requests. An important issue in cloud computing is the scheduling of users' requests means how to allocate resources to these requests, so that the requested tasks can be completed in a minimum time according to the user defined time. A good scheduling technique also helps in efficient utilization of the resources. Many scheduling algorithms have been researched like Min-Min, Max-Min, X-Sufferage, Genetic Algorithm, Particle Swarm Optimization etc. In this paper the three scheduling techniques Min-Min, Max-Min and Genetic Algorithm have been discussed and performance metrics of Min-Min and Max-Min have been shown. The performance of the standard Genetic Algorithm and the proposed Improved Genetic Algorithm have been checked against the sample data. A new scheduling idea is also proposed in which Min-Min and Max-Min can be combined in Genetic Algorithm.

Scheduling using improved genetic algorithm in cloud computing for independent tasks

Cloud Computing has become another buzzword after Web 2.0. The phrase cloud computing originated from the diagrams used to symbolize the internet. Cloud computing is not a completely new concept; it has intricate connection to the grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. With the development of cloud computing, a set of security problems appears. Security issues present a strong barrier for users to adapt into Cloud Computing systems. Several surveys of potential cloud adopters indicate that security is the primary concern hindering its adoption. This paper introduces the background and service model of cloud computing. Along with this, few of security issues and challenges are also highlighted.

Amandeep Verma

Papers

A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Scheduling using improved genetic algorithm in cloud computing for independent tasks

Cloud Computing Security Issues and Challenges: A Survey