A deadline constrained scheduling algorithm for cloud computing system based on the driver of dynamic essential path

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.

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Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Neural Computing and Applications ◽

10.1007/s00521-021-06002-w ◽

2021 ◽

Author(s):

Poria Pirozmand ◽

Ali Asghar Rahmani Hosseinabadi ◽

Maedeh Farrokhzad ◽

Mehdi Sadeghilalimi ◽

Seyedsaeid Mirkamali ◽

...

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Energy Consumption ◽

Scheduling Algorithm ◽

Hybrid Genetic Algorithm ◽

Computing System ◽

Computing Systems ◽

Cloud Computing System ◽

Scheduling Heuristic ◽

The Cost

AbstractThe cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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Task Scheduling and Resource Allocation of Cloud Computing Based on QoS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.915-916.1382 ◽

2014 ◽

Vol 915-916 ◽

pp. 1382-1385 ◽

Cited By ~ 8

Author(s):

Bai Lin Pan ◽

Yan Ping Wang ◽

Han Xi Li ◽

Jie Qian

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Task Scheduling ◽

Scheduling Algorithm ◽

Computing System ◽

Computing Power ◽

Power Scheduling ◽

Cloud Computing System ◽

User Demand

With the enlargement of the scope of cloud computing application, the number of users and types also increases accordingly, the special demand for cloud computing resources has also improved. Cloud computing task scheduling and resource allocation are key technologies, mainly responsible for assigning user jobs to the appropriate resources to perform. But the existing scheduling algorithm is not fully consider the user demand for resources is different, and not well provided for different users to meet the requirements of its resources. As the demand for quality of service based on cloud computing and cloud computing original scheduling algorithm, the computing power scheduling algorithm is proposed based on the QoS constraints to research the cloud computing task scheduling and resource allocation problems, improving the overall efficiency of cloud computing system.

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An adaptive weight based particle swarm optimization scheduling algorithm on cloud computing system

Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science ◽

10.1145/3453187.3453402 ◽

2020 ◽

Author(s):

Fan Pei ◽

Dong Minggang ◽

Jing Chao

Keyword(s):

Cloud Computing ◽

Particle Swarm Optimization ◽

Scheduling Algorithm ◽

Particle Swarm ◽

Computing System ◽

Swarm Optimization ◽

Cloud Computing System ◽

Adaptive Weight ◽

Optimization Scheduling

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Performance Evaluation of Various Scheduling Algorithm Based on Cloud Computing System

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2018/v2i124768 ◽

2018 ◽

pp. 1-6

Author(s):

Nobo Chowdhury ◽

K. M. Aslam Uddin ◽

Sadia Afrin ◽

Apurba Adhikary ◽

Fazly Rabbi

Keyword(s):

Cloud Computing ◽

Scheduling Algorithm ◽

Computing System ◽

System Throughput ◽

Cloud Environment ◽

Cloud Systems ◽

Cloud Computing System ◽

Different Types ◽

High Level ◽

Working Procedure

Cloud computing is an information technology archetype which has been used significantly for providing various services through Internet. It ensures easier access to resources and high-level services. The working procedure of cloud systems must be scheduled, so as to efficiently provide services to people. The goal of task scheduling is to acquire best system throughput and to allocate various computing resources to applications. The unpredictable situation increases with the size of the task and becomes high potential to solve effectively. Numerous intellectual methods are recommended to clarify this situation in the territory of scheduling of cloud computing. In this research, a comparative analysis has been conducted for different types of existing scheduling algorithms in the cloud environment with their respective parameters.

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A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization

International Journal of Gaming and Computer-Mediated Simulations ◽

10.4018/ijgcms.2021040101 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1-15

Author(s):

Zhou Wu ◽

Jun Xiong

Keyword(s):

Cloud Computing ◽

Particle Swarm Optimization ◽

Task Scheduling ◽

Scheduling Algorithm ◽

Particle Swarm ◽

Resource Scheduling ◽

Computing System ◽

Swarm Optimization ◽

Task Scheduling Algorithm ◽

Cloud Computing System

With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algorithm is introduced to schedule applications' tasks and enhance load balancing. It uses Copula function to explore the relation of the random parameters random numbers and defines the local attractor to avoid the fitness function to be trapped into local optimum. The simulation results show that the proposed resource scheduling and allocation model can effectively improve the resource utilization of cloud computing and greatly reduce the completion time of tasks.

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