Software Engineering and Information Systems
Narges Akhound; Sahar Adabi; Ali Rezaee; Amir masoud Rahmani
Articles in Press, Accepted Manuscript, Available Online from 27 September 2022
Abstract
The advent of the Internet of Things (IoT) technology has made it possible for different devices to be widely connected to the Internet and interact. It has led to the production of large amounts of heterogeneous data. On the other hand, cloud computing is a convenient and efficient processing model ...
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The advent of the Internet of Things (IoT) technology has made it possible for different devices to be widely connected to the Internet and interact. It has led to the production of large amounts of heterogeneous data. On the other hand, cloud computing is a convenient and efficient processing model for storing and processing data. Still, the increasing demand for real-time and delay-sensitive applications is increasing day by day. Due to network bandwidth limitations, these problems cannot be solved using cloud computing alone. A fog layer located between the IoT devices and the cloud computing layer has been proposed to overcome the problem of resource constraints in mobile devices. delay-sensitive applications run that require more volume and power resources. In this paper, end-to-end architecture for integrating IoT, fog, and cloud layers into a large-scale dispatched application is proposed to support high availability to make efficient use of fog-cloud resources and achieve the appropriate quality of service (QoS) in terms of delay and failure probability criteria. The mentioned architecture consists of three hierarchal layers: IoT devices, fog nodes, and cloud data centers. Depending on the processing power of each layer's resources, user requests may be executed on the same layer or sent to a higher layer. Then, quality characteristics such as availability, performance, and interoperability for the proposed architecture are evaluated by the ATAM scenario-based method. The basis of architectural evaluation and analysis in this method is the study of the requirements and the quality characteristics of the system architecture.
Computer Networks and Distributed Systems
Hanieh Ghorashi; Meghdad Mirabi
Volume 6, Issue 3 , August 2020, , Pages 155-168
Abstract
Cloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental ...
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Cloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental issue in cloud computing which aims at distributing the load on the different resources of a distributed system in order to optimize resource utilization and response time. In this paper, an optimization-based method for task scheduling is presented in order to improve the efficiency of cloud computing. In the proposed approach, three criteria for scheduling, including the task execution time, the task transfer time, and the cost of task execution have been considered. Our method not only reduces the execution time of the overall tasks but also minimizes the maximum time required for task execution. We employ the Multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) for solving the scheduling problem. To evaluate the efficiency of the proposed method, a real cloud environment is simulated, and a similar method based on Multi-Objective Particle Swarm Optimization is applied. Experimental results show the superiority of our approach over the baseline technique.
Computer Networks and Distributed Systems
Scholastica Nwanneka Mallo; Francisca Nonyelum Ogwueleka
Volume 5, Issue 3 , August 2019, , Pages 169-180
Abstract
Cloud computing technology is providing businesses, be it micro, small, medium, and large scale enterprises with the same level playing grounds. Small and Medium enterprises (SMEs) that have adopted the cloud are taking their businesses to greater heights with the competitive edge that cloud computing ...
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Cloud computing technology is providing businesses, be it micro, small, medium, and large scale enterprises with the same level playing grounds. Small and Medium enterprises (SMEs) that have adopted the cloud are taking their businesses to greater heights with the competitive edge that cloud computing offers. The limitations faced by (SMEs) in procuring and maintaining IT infrastructures has been handled on the cloud platform for the SMEs that adopt it. In this research, the impact and challenges of cloud computing on SME’s that have adopted it in Nigeria has been investigated. The impacts identified ranges from provisioning IT infrastructures, reshaping and extending business values and outreach to giving competitive edge to businesses subscribed to it. Though Cloud computing has many benefits; however, it is not without some pitfalls. These pitfalls include data vulnerability, vendor lock-in, limited control over the infrastructure by the subscribers etc. To investigate the level of impacts and challenges being faced by SMEs in Nigeria on the cloud platform, questionnaires were administered to managers and employees of about fifty SMEs that have deployed cloud. The data collected were analyzed using Statistical Package for Social Sciences (SPSS), from which appropriate recommendations were made.Key Words: Cloud Computing, Impacts, Challenges, SME.
Computer Networks and Distributed Systems
Marzieh Bozorgi Elize; Ahmad KhademZadeh
Volume 3, Issue 4 , November 2017, , Pages 203-212
Abstract
Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, ...
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Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, in addition to high operating costs and huge amounts of electrical power consumption. Insufficient cooling systems and inefficient, causing overheating sources, shortening the life of the machine and too much carbon dioxide is produced. In this paper, we aim to improve system performance; Cloud Computing based on a decrease in migration of among virtual machines (VM), and reduce energy consumption to be able to manage resources to achieve optimal energy efficiency. For this reason, various techniques such as genetic algorithms (GAs), virtual machine migration and ways Dynamic voltage and frequency scaling (DVFS), and resize virtual machines to reduce energy consumption and fault tolerance are used. The main purpose of this article, the allocation of resources with the aim of reducing energy consumption in cloud computing. The results show that reduced energy consumption and hold down the rate of virtual machines breach of contract, reduces migration as well.
Computer Networks and Distributed Systems
Ghazaal Emadi; Amir Masoud Rahmani; Hamed Shahhoseini
Volume 3, Issue 3 , August 2017, , Pages 135-144
Abstract
The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ...
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The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and user needs for these applications with high quality, as well as, the popularity of cloud computing among user and rapidly growth of them during recent years. This research presents the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm in the field of optimization for tasks scheduling in the cloud computing environment. The findings indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to SPT, LPT, and RLPT algorithms.Keywords: Cloud Computing, Task Scheduling, Virtual Machines (VMs), Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Computer Networks and Distributed Systems
Hadi Moei Emamqeysi; Nasim Soltani; Masomeh Robati; Mohamad Davarpanah
Volume 3, Issue 3 , August 2017, , Pages 173-180
Abstract
The issue of management and allocation of resources in cloud computing environments, according to the breadth of scale and modern technology implementation, is a complicated issue. Issues such as: the heterogeneity of resources, resource dependencies to each other, the dynamics of the environment, virtualization, ...
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The issue of management and allocation of resources in cloud computing environments, according to the breadth of scale and modern technology implementation, is a complicated issue. Issues such as: the heterogeneity of resources, resource dependencies to each other, the dynamics of the environment, virtualization, workload diversity as well as a wide range of management objectives of cloud service providers to provide services in this environment. In this paper, first, the description of cloud computing environment and related issues have been reported. According to the performed studies, challenges such as: the absence of a comprehensive management for resources in the cloud environment, the method of predicting the resource allocation process, optimum resource allocation methods to reduce energy consumption and reducing the time to access resources and also implementation of dynamic resources allocation methods in the mobile cloud environments, have been addressed. Finally, with regard to the challenges, some recommendations to improve the process of allocation of resources in a cloud computing environment is has been proposed.
Computer Networks and Distributed Systems
Somayeh Taherian Dehkordi; Vahid Khatibi Bardsiri
Volume 1, Issue 4 , November 2015, , Pages 25-32
Abstract
Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with task scheduling has ...
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Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with task scheduling has direct influence on cloud networks’ performance and efficiency. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This paper 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. The proposed algorithm is built according to conditions of compounding Min-min and Sufferage algorithms. In the proposed algorithm, task allocation between machines takes place alternatively and with continuous change of scheduling algorithms. The main idea of the proposed algorithm is to concentrate on the number of tasks instead of the existing resources. The simulation results reveal that the proposed algorithm can achieve higher performance in decreasing response time.
Computer Networks and Distributed Systems
Seyedeh Roudabeh Hosseini; Sepideh Adabi; Reza Tavoli
Volume 1, Issue 3 , August 2015, , Pages 23-32
Abstract
Migration of Virtual Machine (VM) is a critical challenge in cloud computing. The process to move VMs or applications from one Physical Machine (PM) to another is known as VM migration. In VM migration several issues should be considered. One of the major issues in VM migration problem is selecting an ...
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Migration of Virtual Machine (VM) is a critical challenge in cloud computing. The process to move VMs or applications from one Physical Machine (PM) to another is known as VM migration. In VM migration several issues should be considered. One of the major issues in VM migration problem is selecting an appropriate PM as a destination for a migrating VM. To face this issue, several approaches are proposed that focus on ranking potential destination PMs by addressing migration objectives. In this paper we propose a new hierarchal fuzzy logic system for ranking potential destination PMs for a migrating VM by considering following parameters: Performance efficiency, Communication cost between VMs, Power consumption, Workload, Temperature efficiency and Availability. Using hierarchal fuzzy logic systems which consider the mentioned six parameters which have great role in ranking of potential destination PMs for a migrating VM together, the accuracy of PMs ranking approach is increased, furthermore the number of fuzzy rules in the system are reduced, thereby reducing the computational time (which is critical in cloud environment). In our experiments, we compare our proposed approach that is named as (HFLSRPM: Hierarchal Fuzzy Logic Structure for Ranking potential destination PMs for a migrating VM) with AppAware algorithm in terms of communication cost and performance efficiency. The results demonstrate that by considering more effective parameters in the proposed PMs ranking approach, HFLSRPM outperforms AppAware algorithm.