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The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

The primary objective of task scheduling involves scheduling the task on resources and minimizing the objective of the schedule. In this study, we proposed mean grey wolf optimization algorithm to enhance the system performance there by depleting the scheduling issues. The main objective of this method is minimizing the makespan and energy consumption. The objective of the proposed algorithms has been evaluated using CloudSim toolkit for standard workload (left-skewed & right-skewed). The outcome of the simulation result shows that the proposed Mean GWO algorithm renders comparatively ample result than the other existing algorithms.

With the rapid development of science technology, the multi-core processor system has been become one of the hottest issues in the high performance computation field at present. At the same time, there are some problems in the application and the process of development. In order to find the more efficient task scheduling algorithm, this paper will research the multi-core processors task scheduling algorithm. With exploring the existing task scheduling algorithm principle, the heterogeneous multi-core processor system task scheduling mathematical model is be built, and based on the genetic algorithm, the paper proposes the heterogeneous multi-core processor system scheduling based on population genetic algorithm. Then, through the feasibility, parameter analysis, verification algorithm, the improved genetic algorithm effectively improves the system performance, and reduces the running time. The model, in a certain extent, increases the application and development of artificial intelligence, and provides a theoretical basis for related research.

With the high performance demand, recent embedded systems are mostly based on NoC (Network-on-Chip) architectures, which would bring complex on-chip communication and scheduling problems. In this paper, a novel task scheduling algorithm which statically schedules both communication transactions and computation tasks onto heterogeneous NoC architectures under real-time constraints is presented. Our algorithm is capable of assigning tasks onto different processing elements (PE) automatically and scheduling their execution. We map tasks onto an 8× 8 NoC-based MPSoC to show that our scheduling algorithm leads to reduction in the total execution time, energy consumption. Experimental results show that for a multimedia application, more than 40% energy savings have been observed compared to the schedules generated by a standard scheduler.

The majority of recent embedded systems are based on MPSoCs (Multi-Processors System on Chip) architectures. The topologies and the interconnections inside multi processors almost adopt NoCs (Networks on Chip) whose topology and task scheduling algorithm have a direct impact on its performances. In this paper, by using static data flow, a task scheduling algorithm which would automatically assign the application tasks onto different processors is proposed based on complex network. The goal of our algorithm is to replace the static data flow subnetwork by a single dynamic data flow actor such that the global performance in terms of latency and throughput is optimized. Through complex network, it greatly enhances the power of our algorithm in terms of avoiding deadlock, saving energy and providing for integration with more general models of computation. Experimental results show up to 60% performance improvement for real-world examples.

Task scheduling and resource scheduling are the core issue in cloud computing. Pointing at the premature problem in the scheduling algorithm of particle swarm, we propose a scheduling algorithm of cloud task particle swarm based on “fission” mechanism in this paper. The particle in traditional particle swarm algorithm gets “fission” by the new algorithm in appropriate place, to get more kinds of the particles, contributing to the particle swarm diversity, avoiding premature convergence of the swarm. As the experimental result shows that, the algorithm in this paper has faster scheduling efficiency than the FIFO and the PSO, also solves the premature problem in PSO.

Based on the study of traditional min-min scheduling algorithm, the paper proposed a min-min task scheduling algorithm based on QOS constraints in cloud computing. According to the vector which is generated by QOS parameters, the algorithm processes the matching of resources and tasks, and then provides users with resources which meet their requirements. Experimental results show that the min-min task scheduling algorithm for cloud computing has better performance in such aspects as task execution time, rate of discarding task and QOS satisfaction compared with traditional min-min scheduling algorithm.

Efficient task scheduling mechanism in cloud computing can improve the resource utilization and enhance the overall performance of the cloud computing environment. However, the existing strategies based on static task requirements are difficult to guarantee the stability of the system, while dynamic mechanisms have higher complexity. In this paper, a new task scheduling algorithm based on potential game is proposed. We prove that the potential game will reach Nash equilibrium quickly. Also, the system load balancing level is adaptive with the number of users’ task changing. The experimental results show the priority of the proposed algorithm.

Cloud computing provides powerful and on-demand business environment. It is built on top of virtualized data centers. Virtualization provides the flexible infrastructure for cloud. But, managing the resources and scheduling the tasks in virtualized data center is a challenging task. This paper proposes a minimum makespan task scheduling framework named MMSF and minimum makespan task scheduling algorithm named as MMA. This algorithm is developed with two objectives. First, minimizing the total makespan and maximizing the virtual machine utilization. The task scheduling problem is formulated as multi-objective optimization problem. It is solved by using optimization techniques. Experiments shows that MMA outperforms the traditional task scheduling algorithms based on total makespan and virtual machine utilization.

Cloud computing is regarded as a new computing mode in recent years, and has been widely applied. Its task scheduling affects the performance of the cloud computing system directly, and people pay more and more attention to the security problems of cloud computing. The paper introduces the trust in scheduling algorithm, improves and fuses the PSO and SA in order to make them complementary. By applying that to the task scheduling strategy of cloud computing, we can get a higher scheduling efficiency. We implement the proposed algorithm and verify its high efficiency through the simulation platform (CloudSim).

Cloud computing is the technology that moves the information technology (IT) services out of the office. Unfortunately, Cloud computing has faced some challenges. The task scheduling problem is considered one of the main challenges because a good mapping between the available resources and the users' tasks is needed to reduce the execution time of the users’ tasks (i.e., reduce make-span), and increase resource utilization. The objective of this paper is to introduce and implement an enhanced task scheduling algorithm to assign the users' tasks to multiple computing resources. The aim of the proposed algorithm is to reduce the execution time, and cost, as well as, increase resource utilization. The proposed algorithm is considered an amalgamation of the Particle Swarm Optimization (PSO),the Best–Fit (BF), and Tabu-Search (TS) algorithms; called BFPSOTS. According to the proposed BFPSOTS algorithm, the BF algorithm has been used to generate the initial population of the standard PSO algorithm instead of to be random. The Tabu-Search (TS) algorithm has been used to improve the local research by avoiding the trap of the local optimality which could be occurred using the standard PSO algorithm. The proposed hybrid algorithm (i.e., BFPSOTS) has been implemented using Cloudsim. A comparative study has been done to evaluate the performance of the proposed algorithm relative to the standard PSO algorithm using five problems with different number of independent task, and Virtual Machines (VMs). The performance parameters which have been considered are the execution time (Makspan), cost, and resources utilization. The implementation results prove that the proposed hybrid algorithm (i.e., BFPSOTS) outperforms the standard PSO algorithm..

There’re huge numbers of users and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing need to solve. Aiming to the model structure of cloud computing, in this article it introduces the Particle Swarm Optimization algorithm (PSO) and Ant Colony Optimization algorithm (ACO) to combine with optimized task scheduling algorithm. First it takes the particle swarm optimization algorithm to generate the initial scheduling results, and introduces the random inertia weight to improve the scheduling ability of the algorithm, then to take the generated results of improved particle swarm optimization algorithm as the initial pheromones of the ant colony algorithm to find out the optimal scheduling scheme, and use the elitist strategy and crossover operator in the genetic algorithm to improve the ant colony algorithm, among the algorithms to use multistage optimization algorithm to improve the operating efficiency. The experimental results show that under the same conditions, the total task completion time of improved algorithm has been reduced and its performance advantage are getting more obvious with the increased task measures.

Scheduling algorithm is always a hot topic in cloud computing environment. In order to eliminate system bottleneck and balance load dynamically. A load balancing task scheduling algorithm based on weighted random and feedback mechanisms was proposed in this paper. At first the chosen cloud scheduling host chose resources by needs and made static quantification, and then sorted them; secondly the algorithm chose resources from which sorted by weight randomly; then it acquired corresponding dynamic information to make load filter and sort the left. At last it achieved the self-adaptively to system load through feedback mechanisms. The experiment shows that the algorithm has avoided the system bottleneck effectively and has achieved balanced load as well as self-adaptability to it.

Cloud computing is a computing paradigm where applications, resources and services are provided over the internet. Software and hardware can be used to pay as service basis, without buying them. The key role of scheduling is to manage different tasks in different cloud environment. Cloud computing service providers use the available resources efficiently to achieve maximum profit. This makes task scheduling as a challenging issue for cloud service providers. This paper gives an introduction about cloud computing, various existing scheduling algorithms in different task scheduling environments, existing problem and the future suggestions in existing algorithms.

In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.

A new task scheduling algorithm based on Hadoop is proposed to optimize scheduling of resources problems under the Distributed cloud-computing platforms. The core idea of the algorithm is full reference to the current network conditions and treat it as an important reference for system task scheduling, with the bandwidth management ability ,SDN architecture allows us to allocate bandwidth according to a time slot strategy, then according to the operation completed sooner or later the time to decide whether the task assigned to the local node or low load of non-local node .In this way, we will not only ensure the locality of the task from a global perspective , but also assign tasks in an optimal way to efficiently. In the end, we do experiments based on the scheduler to verify the quality of scheduling algorithm.

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.

The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.