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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Advances in Computer Engineering and Technology
- نوع مقاله: Journal Article
- کلمات کلیدی: Cloud computing,Task scheduling,Virtual Machines(Vms),Convariance Matrix Adaptation Evolution Strategy(CMA-ES)
- چکیده:
- چکیده انگلیسی: 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)- انتشار مقاله: 13-12-1395
- نویسندگان: Ghazaal Emadi,Amir Masoud Rahmani,Hamed Shahhoseini
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Advances in Computer Engineering and Technology
- نوع مقاله: Journal Article
- کلمات کلیدی: Job Scheduling,Response time,Data Replication,Index Terms— Cloud Computing,Data Access Time
- چکیده:
- چکیده انگلیسی: Abstract— High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications. Sometimes accessing data becomes a bottleneck for the whole cloud workflow system and decreases the performance of the system dramatically. Job scheduling and data replication are two important techniques which can enhance the performance of data-intensive applications. It is wise to integrate these techniques into one framework for achieving a single objective. In this paper, we integrate data replication and job scheduling with the aim of reducing response time by reduction of data access time in cloud computing environment. This is called data replication-based scheduling (DRBS). Simulation results show the effectiveness of our algorithm in comparison with well-known algorithms such as random and round-robin.
- انتشار مقاله: 13-11-1394
- نویسندگان: Bahareh Rahmati,Amir Masoud Rahmani,Ali Rezaei
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Advances in Computer Engineering and Technology
- نوع مقاله: Journal Article
- کلمات کلیدی: reliability,Data Grid,Availability,Dynamic Data Replication,Fault Injection
- چکیده:
- چکیده انگلیسی: Abstract - One of the important problems in grid environments is data replication in grid sites. Reliability and availability of data replication in some cases is considered low. To separate sites with high reliability and high availability of sites with low availability and low reliability, clustering can be used. In this study, the data grid dynamically evaluate and predict the condition of the sites. The reliability and availability of sites were calculated and it was used to make decisions to replicate data. With these calculations, we have information on the locations of users in grid with reliability and availability or cost commensurate with the value of the work they did. This information can be downloaded from users who are willing to send them data with suitable reliability and availability. Simulation results show that the addition of the two parameters, reliability and availability, assessment criteria have been improved in certain access patterns.
- انتشار مقاله: 24-11-1393
- نویسندگان: Ali Abbasi,Amir Masoud Rahmani,Esmaeil Zeinali Khasraghi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Advances in Computer Engineering and Technology
- نوع مقاله: Journal Article
- کلمات کلیدی: Feature Selection,classification,PSO Algorithm,Text mining,learning automata
- چکیده:
- چکیده انگلیسی: 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.
- انتشار مقاله: 29-10-1393
- نویسندگان: Mozhgan Rahimirad,Mohammad Mosleh,Amir Masoud Rahmani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: Data envelopment analysis,Banking industry,Financial Cloud Computing,Dynamic Network model,QoS Attributes
- چکیده:
- چکیده انگلیسی: Nowadays, the great benefits of cloud computing have dramatically increased the number of e-banking users. Hence, the competition in the banking industry has boosted and managers need to evaluate their branches on a regular basis. To this end, this study aims to evaluate cloud-based banking systems based on the Quality of Service (QoS) attributes using the Dynamic Network Data Envelopment Analysis (DNDEA) model. The main advantage of this research is that the efficiency of cloud-based bank branches can be estimated more realistically according to their internal structure over a specific time span. To conduct the experiment, 40 bank branches in Iran are analyzed by considering between-period and divisional interactions during 2018-2019. A cloud-based bank branch is conceptualized as a set of three inter-connected divisions including capabilities, intermediate process, and profitabilities. Some outputs of sub-DMUs 2 and 3 are treated as desirable and undesirable carry-overs between consecutive periods. In addition, the cost items and QoS attributes are considered as the inputs and outputs of divisions, respectively. The results indicate that 28 bank branches were efficient and all of the inefficiencies fall in divisions 1 and 3. Moreover, the number of efficient branches has been reduced from 2018 to 2019.
- انتشار مقاله: 09-02-1399
- نویسندگان: Alireza Poordavoodi,Mohammad Reza Moazami Goudarzi,Hamid Haj Seyyed Javadi,Amir Masoud Rahmani
- مشاهده