Computer Networks and Distributed Systems
Alireza Enami; Javad Akbari Torkestani
Volume 7, Issue 1 , February 2021, , Pages 19-34
Abstract
Fog computing is being seen as a bridge between smart IoT devices and large scale cloud computing. It is possible to develop cloud computing services to network edge devices using Fog computing. As one of the most important services of the system, the resource allocation should always be available to ...
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Fog computing is being seen as a bridge between smart IoT devices and large scale cloud computing. It is possible to develop cloud computing services to network edge devices using Fog computing. As one of the most important services of the system, the resource allocation should always be available to achieve the goals of Fog computing. Resource allocation is the process of distributing limited available resources among applications based on predefined rules. Because the problems raised in the resource management system are NP-hard, and due to the complexity of resource allocation, heuristic algorithms are promising methods for solving the resource allocation problem. In this paper, an algorithm is proposed based on learning automata to solve this problem, which uses two learning automata: a learning automata is related to applications (LAAPP) and the other is related to Fog nodes (LAN). In this method, an application is selected from the action set of LAAPP and then, a Fog node is selected from the action set of LAN. If the requirements of deadline, response time and resources are met, then the resource will be allocated to the application. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different Fog configurations. The obtained results are compared with several existing methods in terms of the makespan, average response time, load balancing and throughput.
Computer Networks and Distributed Systems
Somayeh Taherian Dehkordi; Vahid Khatibi Bardsiri
Volume 1, Issue 3 , August 2015, , Pages 17-22
Abstract
Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper scheduling ...
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Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This research studies the existing approaches of task scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages. Afterwards, a compound algorithm is presented in order to allocate tasks to resources properly and decrease runtime. In this paper we proposed a new method for task scheduling by learning automata (LA). This method where has named RAOLA is trained by historical information of task execution on the cloud, then divide task to many classes and evaluate them. Next, manage virtual machine for capture physical resources at any period based on rate of task classes, such that improve efficiency of cloud network.
Pattern Analysis and Intelligent Systems
Mozhgan Rahimirad; Mohammad Mosleh; Amir Masoud Rahmani
Volume 1, Issue 2 , May 2015, , Pages 1-8
Abstract
With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to ...
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With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.