An Efficient Task Scheduling of Multiprocessor Using Genetic Algorithm Based on Task Height

A novel intelligent method for task scheduling in multiprocessor systems using genetic algorithm

Fault-tolerant LPT task scheduling in multiprocessor systems

We develop a new method to map (i.e. allocate and schedule) real-time applications into certain multiprocessor systems. Its objectives are: the minimization of the number of processors used; and the minimization of the deadline missing time. Given a parallel program with real time constraints and a multiprocessor system, our method finds schedules of the program in the system which satisfy all the real time constraints with minimum number of processors. The minimization is carried out through a Pareto-based genetic algorithm which independently considers the both goals, because they are non-commensurable criteria. Experimental results show that our scheduling algorithm achieved better performance than previous ones. The advantage of our method is that the algorithm produces not a single solution but a family of solutions known as the Pareto-optimal set, out of which designers can select optimal solutions appropriate for their environmental conditions.

The objective of the scheduling soft real-time tasks is to minimize total tardiness and the scheduling these tasks on multiprocessor system is NP-hard problem. In this chapter, scheduling algorithms for soft real-time tasks using genetic algorithm (GA) are introduced. GA has been known to offer significant advantages against conventional heuristics by using simultaneously several search principles and heuristics. The objective of this study is to propose reasonable solutions for NP-hard scheduling problem which much less difficulties than those of traditional mathematical methods. A continuous task scheduling, real-time task scheduling on homogeneous system and real-time task scheduling on heterogeneous system are included in this chapter.

Scheduling tasks in a multiprocessor system is found to be a NP-hard problem and a considerable amount of time is used up when it is solved using conventional techniques. Therefore, evolutionary algorithms like Genetic Algorithms (GA) have been explored for scheduling tasks in a multiprocessor system. GA can be implemented in various manners. This paper investigates the performance of GA with two different selection operators. This paper also studies how introducing elitism effects the performance of GA. Extensive simulations have been carried out in order to find the better candidate among the two selection operators. The decision is made depending on stability of the GA output, the rate of convergence of output and the ability of GA to give an output which is as close as possible to the actual output.

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.

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 task scheduling in real-time multiprocessor systems is to map tasks onto processors and order their execution so that the precedence relationships between tasks are maintained and the minimum schedule length is obtained. This is a well-known NP-completed problem. And many heuristic methods have existed, but their performance still needs to be improved. Particle swarm optimization has received much attention as a class of robust stochastic search algorithm for various optimization problems. This paper presents a novel task scheduling algorithm for real-time multiprocessor systems, which takes task's height and particle's position as the task's priority values, and applies the list scheduling strategy to generate the feasible solutions. Simulation results demonstrate that the proposed algorithm, compared with genetic algorithm, produces encouraging results in terms of quality of solution and time complexity.

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

With the development of high‐performance computing, it is possible to solve large‐scale computing problems. However, the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation. Improving the performance of a single core makes it challenging to maintain Moore's law, and multi‐core processors emerge. A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi‐core processor. In high‐performance computing, the granularity of computing tasks leads to the complexity of scheduling strategies. Satisfying high system performance, load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high‐performance field, especially in specific multi‐core hardware architecture. In this study, global real‐time task scheduling is implemented in a high‐performance multi‐core system. The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real‐time task scheduling strategy. In the cluster scheduling policy, tasks are allowed to preempt the core with low priority, and the priority of tasks that access memory is dynamically improved, higher than that of all the tasks without memory access. An intelligent fitting method is also proposed. When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold, the priority of the task is promoted to the highest priority, preempting the core without the access memory task. The results show that the intelligently fitting global scheduling strategy for multi‐core systems has better performance in the nuclear utilisation rate and task schedulability.

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

We consider using hardware and software redundancy to guarantee task deadlines in a hard real-time mu1 tiprocessor system even in the presence of processor failures. A set of scheduling requirements for the real-time fault-tolerant multiprocessor scheduling problem is first identified and a heuristic algorithm is then proposed to solve the problem. Experimental results show that the algorithm finds optimal solutions in most of the cases.

Communication-aware task scheduling and voltage selection for total energy minimization in a multiprocessor system using Ant Colony Optimization

Real-time embedded systems must operate reliably and predictably from both a functional and dynamic point of view. To achieve these objectives we need to use an integrated approach to the design of systems. Integrated, that is, in terms of designing the hardware and software as a co-design operation. We present a co-design tool for the evaluation and demonstration of real-time system architectures and performance tradeoffs. The tool enables simulation and animation techniques to be applied in the analysis and early design stages of a project. It is specifically intended for use with multiprocessor systems to determine the optimum task allocation strategy. Allocations must consider task priority, timing characteristics and node utilization. This is achieved by the application of a stochastic global search technique based on genetic algorithm (GA) principles. Solutions produced by the genetic algorithm can be simulated and presented graphically to allow users to more easily estimate the quality of the allocation strategy. >

Scheduling tasks in multiprocessor systems is a difficult problem. The paper describes a method based upon evolutionary strategies (using genetic algorithms) to aid in finding good task assignments. The technique is illustrated by scheduling a digital signal processing algorithm on a two processor distributed system. >