A Grid Computing Task Scheduling Method Based on Target Genetic Algorithm

Current representatives of Grid systems are Globus and Web Services, however, they have poor scalability and single point failure. It is a hot research topic to build a P2P and grid hybrid framework for resource management and task schedule. We propose Information Pool Based Grid Architecture (IPBGA), which is a real sense hybrid of P2P and grid instead of only introducing P2P methods into grid systems for resource management. The key of IPBGA, information pool protocol, is presented. In our information pool protocol, all of resources and tasks are abstracted into information, and resource requests for tasks and task appeals for resources are viewed as information services, then grid resource management and task schedule are treated as information matching. Therefore, our architecture is very adaptive to heterogeneous, dynamic, and distributed grid systems. We use tri-information centers for collecting information, which strengthens the robustness of our system. Simulation experiments show information pool protocol of IPBGA is more efficient in resource management and task schedule, and has less bandwidth and processing cost compared with other hybrid P2P systems.

Starting from the actual business needs of power grid production, operation, and management, extract common key technologies of artificial intelligence, build an electric power artificial intelligence platform, provide sample labeling and management, model training, and model reasoning for business personnel, model developers, and application developers, and support power inspection, scheduling, safety supervision, marketing and other businesses through online service invocation. This paper studies the core issues of task scheduling and resource management of the electric power artificial intelligence platform. To begin with, it introduces its capabilities and overall architecture and analyzes its service objects, task types and processing processes. Then container technology and Kubernetes are employed to achieve resource-elastic management for multiple heterogeneous resources; a task scheduling method is put forward based on load balancing; last but not least, the functional components, deployment and application of the artificial intelligence platform are presented. By building the basic support capabilities of electric power artificial intelligence, reducing the threshold of artificial intelligence model training, gathering high-quality samples and high-quality models, unified model services are provided for business applications, thus accelerating the implementation of artificial intelligence applications and promoting the digital transformation and intelligent upgrade of the power grid.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

While resource management and task scheduling are identified challenges of Grid computing, current Grid scheduling systems mainly focus on CPU and network availability. Recent performance improvement of CPU and computer network has made memory usage a significant factor of overall performance. In this study, we consider memory availability as a performance factor and introduce memory conscious task partition and scheduling. Three task partition policies are discussed. They are CPU-based, memory-based, and CPU-memory combined partition. We first investigate the three task partition policies on dedicated resources and verify the effectiveness of the CPU-memory combined partition algorithm in finding an optimal solution. We then extend the task partition policies in non-dedicated environments with the consideration of resource sharing. Analytical and experimental results show that the CPU-memory combined scheduling approach outperforms either the CPU-based or memory-based scheduling approach considerably for memory-intensive applications in Grid environments.

Most of the existing solutions on task scheduling and resource management in grid computing are based on the traditional client/server model, enforcing a homogeneous policy on making decisions and limiting the flexibility, unpredictable reliability and scalability of the system. Thus, we need a well organised system architecture to provide high system availability with resource scheduling scheme for grid environment. In this paper, we integrate grid with P2P on to the hypercube topology with additional neighbourhood links for task scheduling and load balancing, which gives better makespan and balances the load. We propose the SPA-based task scheduling methodology in order to enhance system performance in hypercubic P2P grid (HPGRID). The simulation results show that the SPA-based min-mean scheduling minimises the makespan along with load balancing and guarantees the high system availability in system performance. At last, the min-mean algorithm is compared with min-min and max-min algorithm using SPA, by simulate experimentation, and it shows that the new algorithm has a better quality of system load balancing and the utilisation of system resources.

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing.

The current representatives of Grid systems are Globus and Web services, however, they have poor scalability and single point failure. It is these two factors which make the building of an improved P2P and grid hybrid framework for resource management and task schedule such a popular research topic. This paper differs from current research because it puts forward an Information Pool Based Grid Architecture (IPBGA), which is a real sense hybrid of P2P and grid instead of only introducing P2P methods into grid systems for resource management. Based on virtualizations, abstract physical resources and tasks to be, the information requests from resources for tasks and appeals from tasks for resources are upgraded as information services by using an information pool protocol (IPP). Thus, grid resource management and task scheduling are regarded as information matching by IPP which is adaptive to the heterogeneous, dynamic, and distributed characteristics of a grid system. Tri-Information Center (Tri-IC) and source ranking mechanisms are presented in IPP to improve robustness, prevent sybil attack, and to discourage free riding. Experiments and theory analysis show that the IPP of the IPBGA is more efficient and robust in dealing with information while both the bandwidth and process costs are less.

Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost. Index Terms— Task Scheduling, Multiobjective, Cloud Computing, Cost, Makespan.

Static task scheduling in multiprocessor frameworks is one of the well-defined NP hard problem. Due to optimal utilization of processors and in addition investing less time, the scheduling of tasks in multiprocessor frameworks is of extraordinary significance. To solve NP hard problem using traditional strategies takes reasonable measures of time. Over the time, various heuristic procedures were presented for comprehending it. Therefore, heuristic methods such as genetic algorithms are appropriate methods for task scheduling in multiprocessor system. In this paper, a new GA for static task scheduling in multiprocessor systems has been presented whose priority of tasks’ execution is based on the height of task in graph and other mentioned parameters and then scheduling is performed. This proposed method is simulated and then compared with basic genetic algorithm.

Aimed at the problem of the task scheduling in the mobile grid environment, after introducing mobile devices and considering the stability of the network connection with the mobile terminal, we propose a task scheduling method based on genetic algorithm to shorten the time span and improve the utilization ratio of resources. By means of simulation experiments show that, using genetic algorithms for mobile grid task scheduling can reduce the system total execution time and task completion time, improve grid task analysis efficiency, and make a mobile grid system scheduling performance better.

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

The cloud computing is the new technique used most today for computation and storage. Edge computing is also a new computing domain that gives computation of tasks and storage of data nearer to the data sources. Various applications of edge computing are IOT, healthcare, retail, manufacturing etc. Virtualization technologies have enabled cloud platforms to provide various services such as virtual machines to the users. Task scheduling is the major issues faced in both cloud and edge domains. Since the usage of machines is high, it is difficult to assign tasks manually. So, an efficient and optimized algorithm is required in the cloud as well as the edge environment. Here, a task scheduling is implemented based on Heuristic Optimization Technique to examine the performance measures using cloudsim. Task scheduling and optimization techniques dynamically allocate resources in the cloud environment. Similarly, in edge computing, scheduling the task is performed with different optimization techniques such as random fit, and max fit algorithms. These task scheduling methods are allocated to the VM based on the resources that are available at the VM. These algorithms are implemented, and it increased the VM utilization by 4.82%. The processing time taken is reduced by 2.67% and the percentage of failed tasks is reduced by 3.68%. The experiment result shows the importance of the scheduling in the cloud and edge environment, in terms of processing time, failed task and increase in VM utilization in edge devices.

With the phenomenal growth of Internet, the technologies associated with the Internet like cloud computing and fog computing also grown massively. At the same time, the issues in this technology also need to be addressed while allocating and deploying resources. Regardless of rapid growth of cloud computing, plenty of issues are there which are necessary to be solved due to ingrained property of cloud computing such as location awareness, unreliable delay and lack of mobility support. Fog computing means bringing the centralized computing cloud resources to the edge of the network. Fog computing copes with the problems in cloud computing by giving flexible resources and services to end users, even cloud computing is more approximately imparting resource dispersed in the core network. The differences between compute-intensive applications and resource-limited gadgets result in restricting the quality of the system. In fog technology, the logical inconsistency should be tackled by task scheduling. Because of the mushrooming of Internet of things, effective means of allocating resource for the clients whenever it is requested is very challenging task in fog technology. This paper gives a survey of different mechanisms proposed for scheduling the tasks and allocating the resources in fog technology. The various techniques proposed by different authors are reviewed in detail. Finally, an analysis is made by comparing the disadvantages of each technique and suggestions are given for betterment in task scheduling and resource allocation in fog effectively.

Many applications involve the concepts of scheduling, such as communications, packet routing, production planning [Zhai et al., 2006], classroom arrangement [Mathaisel & Comm, 1991], aircrew scheduling [Chang, 2002], nurse scheduling [Ohki et al., 2006], food industrial [Simeonov & Simeonovova, 2002], control system [Fleming & Fonseca, 1993], resource-constrained scheduling problem [Chen, 2007] and grid computing. There are many different types of scheduling problems such as real-time, job-shop, permutation flow-shop, project scheduling and other scheduling problems have been studied intensively. However, in this work, the studied grid task scheduling problem is much more complex than above stated classic task scheduling problems. Restated, a grid application is regarded as a task scheduling problem involving tasks with inter-communication and distributed homogeneous or heterogeneous resources, and can be represented by a task interaction graph (TIG). Grid is a service for sharing computing power and data storage capacity over the Internet. The grid systems outperform simple communication between computers and aims ultimately to turn the global network of computers into one vast computational resource. Grid computing can be adopted in many applications, such as high-performance applications, large-output applications, data-intensive applications and community-centric applications. These applications major concern to efficiently schedule tasks over the available multi-processor environment provided by the grid. A grid is a collaborative environment in which one or more tasks can be submitted without knowing where the resources are or even who owns the resources [Foster et al., 2001]. The efficiency and effectiveness of grid resource management greatly depend on the scheduling algorithm [Lee et al., 2007]. Generally, in the grid environment, these resources are different over time, and such changes will affect the performance of the tasks running on the grid. In grid computing, tasks are assigned among grid system [Salman, 2002]. The purpose of task scheduling in grid is to find optimal task-processor assignment and hence minimize application completion time (total cost). Most scheduling problems in these applications are categorized into the class of NP-complete problems. This implies that it would take amount of computation time to obtain an optimal solution, especially for a large-scale scheduling problem. A variety of approaches have been applied to solve scheduling problems, such as