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As cloud computing is growing rapidly, efficient task scheduling algorithm plays a vital role to improve the resource utilization and enhance overall performance of the cloud computing environment. However, task scheduling is the severe challenge needed to solve urgently in cloud computing. Therefore, the simulated annealing multi-population genetic algorithm (SAMPGA) is proposed for task scheduling...
Task scheduling is ones of the most important issues in cloud computing environment, which directly affects the overall performance of the cloud platform. QoS-aware Task scheduling in cloud computing is NP-hard problem. There is no efficient method to solve it, and most of current task scheduling algorithms bias total task completion time than single task completion time. This paper proposes a template-based...
Multiprocessor task scheduling is one of the hardest combinatorial optimization problems in parallel and distributed systems. It is known as NP-hard and therefore, scanning the whole search space using an exact algorithm to find the optimal solution is not practical. Instead, metaheuristics are mostly employed to find a near-optimal solution in a reasonable amount of time. In this paper, a multi-population...
Task scheduling is a key problem in Grid computing in order to benefit from the large computing capacity of such systems. The need of allocating a number of tasks to different resources for the efficient utilization of resources with minimal completion time and economic cost is the essential requirement in such systems. The problem is multi-objective in its general formation, with the objectives being...
Cloud environments consist of a collection of tasks and heterogeneous resources, the problem of mapping tasks to resources is a major issue. In this paper, we proposed an Improved Adaptive heuristic algorithm (IAHA). At first, the IAHA algorithm makes tasks prioritization in complex graph considering their impact on each other, based on graph topology. Through this technique, completion time of application...
This research discusses a system architecture that comes up with potentially better resiliency and faster recovery from failures based on the renowned genetic algorithm. Additionally, we aim to achieve a globally optimized performance as well as a service solution that can remain financially and operationally balanced according to customer preferences. The proposed methodology has undergone numerous...
Genetic algorithm has better global search ability, so has a wide application prospect in scientific research. In this paper, the improved genetic algorithm is applied to the cloud task scheduling, through simulation experiment on the CloudSim platform, the results show that application of genetic algorithm in task scheduling, the average waiting time is shorter and scheduling costs required lower...
Cloud computing is a provider of dynamic services which offers readily elastic on demand computing resources as per the customer's request in economical way. As there are a lot of requests fabricated by cloud users which are processed by the accessible resources, there exists a need for better and effective scheduling mechanism for efficient allocation of resources. In this paper, a genetic algorithm...
In the testing Cloud platform, there exist too many testing tasks waiting for scheduling at the same time. How to design scheduling strategy is really a challenging problem. In this paper, we firstly analyze the relationship between the testing tasks and establish the task relationship model. Based on these analyses, we propose a dynamic task scheduling strategy using genetic algorithm, which not...
Task scheduling is one of the most critical issues on cloud platform. The number of users is huge and data volume is tremendous. Requests of asset sharing and reuse become more and more imperative. Efficient task scheduling mechanism should meet users' requirements and improve the resource utilization, so as to enhance the overall performance of the cloud computing environment. In order to solve this...
Mobile cloud computing, which comes up in recent years, is a new computing paradigm. In mobile cloud, mobile users can access and schedule the resources or services in remote clouds via wireless networks, which we call mobile cloud task scheduling. They even can build mobile micro-cloud (MuCloud) with mobile device to provide lightweight service. However, unreliable wireless connection and dynamic...
Task allocation and scheduling in MAS systems utilized genetic algorithm is a focus for more and more computer scholars. Aiming at the low speed of typical genetic algorithm, the global convergence for traditional genetic algorithm, and the local convergence for simulated annealing algorithm, this paper proposes a new task allocation algorithm in multiple Agent systems with the advantages of both...
For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic...
Task scheduling has vital importance in heterogeneous systems because efficient task scheduling can enhance overall system performance considerably. This paper addresses the task scheduling problem by effective utilization of evolution based algorithm. Genetic algorithms are promising to provide near optimal results even in the large problem space but at the same time the time complexity of Genetic...
The allocating and scheduling of tasks in parallel and distributed systems has been considered to be an NP-Complete problem, which has received much attention. Although plentiful algorithms have been developed to obtain suboptimal solutions, many of them didn't consider the total execution time and load balancing among processors simultaneously. To solve this problem efficiently, this paper presents...
Tasks scheduling problem is a key factor for distributed systems to gain better performance. Even in the best conditions, the scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering...
Task scheduling and task allocation, which are vital parts of mapping parallel programs to concurrent architectures, must take into account the interprocessor communication, whose overheads have emerged as the major performance limitation in parallel applications. Furthermore, its power consumption is an important research focus which must be addressed. Finding an optimal solution requires information...
To solve high real-time and complexity calculation problems such as feature extraction and pattern classification when wireless sensor network real-time diagnosis and equipment health record of the mine coal underground equipments monitoring, this paper purpose a optimal algorithm for task scheduling underground wireless monitoring network based on distributed computing, this method use the fast convergence...
Task scheduling algorithms are designed mostly with the sole goal of minimizing makespan (completion time). Almost all research works related to this kind of algorithms do not pay much attention to energy consumption. In this paper, we investigate the energy issue in task scheduling particularly on high-performance computing systems (HCSs). We propose a new island-based bi-objective hybrid algorithm...
In grid computing, load balancing is a technique to distribute workload evenly across two or more computing nodes, in order to get optimal resource utilization, maximize throughput, minimize response time, and avoid overload. This paper takes advantages of genetic algorithm, brings forward a novel heuristic genetic load balancing algorithm and applied to solve grid computing load balancing problem...
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