The Best-Worst Method for Resource Allocation and Task Scheduling in Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

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An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud

Journal of Circuits System and Computers ◽

10.1142/s0218126620502539 ◽

2020 ◽

Vol 29 (16) ◽

pp. 2050253

Author(s):

Pravin Albert ◽

Manikandan Nanjappan

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Task Scheduling ◽

Virtual Machines ◽

Optimal Resource Allocation ◽

Allocation Model ◽

Swarm Optimization ◽

Artificial Fish Swarm ◽

Optimal Resource ◽

And Task

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see text]-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.

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Optimization model for QoS based task scheduling in cloud computing environment

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i2.pp1081-1088 ◽

2020 ◽

Vol 18 (2) ◽

pp. 1081

Author(s):

Sirisha Potluri ◽

Katta Subba Rao

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Task Scheduling ◽

Optimization Model ◽

Execution Time ◽

Scheduling Algorithm ◽

Cloud Services ◽

Allocation Model ◽

Task Scheduling Algorithm ◽

And Task

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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