List of Articles ابر

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  1 - Scheduling tasks in cloud environments using mapping framework - reduction and genetic algorithm
  nima khezr nima jafari novimipour
  20.1001.1.27170414.1397.10.37.2.7
  Task scheduling is a vital component of any distributed system such as grids, clouds, and peer-to-peer networks that refer tasks to appropriate resources for execution. Common scheduling methods have disadvantages such as high time complexity, inconsistent execution of More

  Task scheduling is a vital component of any distributed system such as grids, clouds, and peer-to-peer networks that refer tasks to appropriate resources for execution. Common scheduling methods have disadvantages such as high time complexity, inconsistent execution of input tasks, and increased program execution time. Exploration-based scheduling algorithms to prioritize tasks from Manuscript profile
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  2 - Using a multi-objective optimization algorithm for tasks allocate in the cloud-based systems to reduce energy consumption
  sara tabaghchimilan nima jafari novimipour
  20.1001.1.27170414.1399.12.43.5.6
  Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amount More

  Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amounts of electrical power. On the other hand, inadequate and inadequate cooling systems not only cause excessive heating of resources and shorten the life of the machines. It also produces carbon that plays an important role in the weather. Therefore, they should reduce the total energy consumption of these systems with proper methods. In this research, an efficient energy management approach is provided in virtual cloud data centers, which reduces energy consumption and operational costs, and brings about an increase in the quality of services. It aims to provide a resource allocation strategy for cloud systems with the goal of reducing energy, cost of implementation and examining its use in cloud computing. The results of the simulation show that the proposed method in comaprision to NPA, DVFS, ST and MM methods can reduce the average energy consumption up to 0.626 kWh, also the need to immigration and SLA violation declined up to 186 and 30.91% respectively. Manuscript profile
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  3 - Identification of the Factors and Indicators of the Model of Quality Assessment of the Game Industry Services on the Cloud Platform
  Mohammad Taghi Taghavifard mohamadali abaspour
  20.1001.1.27170411.1399.12.45.10.0
  n today's world, one of the most important media and entertainment sectors is the gaming industry. The number of users of this type of games is constantly increasing and Businesses in this industry have grown dramatically. This has led to a dollars million turnover boos More

  n today's world, one of the most important media and entertainment sectors is the gaming industry. The number of users of this type of games is constantly increasing and Businesses in this industry have grown dramatically. This has led to a dollars million turnover boost for developer companies of this kind of games. Therefore, the main aim of this study is to provide a model for measuring the quality of services on the cloud platform in the gaming industry. The present research is a developmental-applied objective and is a part of qualitative research. This research is also a survey research in terms of data gathering. In this study, 52 indicators were identified in 9 main dimensions after evaluating theoretical literature of the research and existing models and Delphi method for assessing the quality of the cloud computing services industry. The results of the research provide a comprehensive model for assessing the quality of cloud computing services industry services that can be used by industry and researchers. Manuscript profile
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  4 - A Multi-Objective Differential Evolutionary Algorithm-based Approach for Resource Allocation in Cloud Computing Environment
  Saeed Bakhtiari Mahan Khosroshahi
  20.1001.1.27170411.1401.14.51.9.5
  In recent years, the cloud computing model has received a lot of attention due to its high scalability, reliability, information sharing and low cost compared to separate machines. In the cloud environment, scheduling and optimal allocation of tasks affects the effectiv More

  In recent years, the cloud computing model has received a lot of attention due to its high scalability, reliability, information sharing and low cost compared to separate machines. In the cloud environment, scheduling and optimal allocation of tasks affects the effective use of system resources. Currently, common methods for scheduling in the cloud computing environment are performed using traditional methods such as Min-Min and meta-heuristic methods such as ant colony optimization algorithm (ACO). The above methods focused on optimizing one goal and do not estimate multiple goals at the same time. The main purpose of this research is to consider several objectives (total execution time, service level agreement and energy consumption) in cloud data centers with scheduling and optimal allocation of tasks. In this research, multi-objective differential evolution algorithm (DEA) is used due to its simple structure features and less adjustable parameters. In the proposed method, a new approach based on DEA to solve the problem of allocation in cloud space is presented which we try to be effective in improving resource efficiency and considering goals such as time, migration and energy by defining a multi-objective function and considering mutation and crossover vectors. The proposed method has been evaluated through a CloudSim simulator by testing the workload of more than a thousand virtual machines on Planet Lab. The results of simulation show that the proposed method in comparison with IqrMc, LrMmt and FA algorithms, in energy consumption by an average of 23%, number of migrations by an average of 29%, total execution time by an average of 29% and service level agreement violation (SLAV) by an average of 1% has been improved. In this case, use of the proposed approach in cloud centers will lead to better and appropriate services to customers of these centers in various fields such as education, engineering, manufacturing, services, etc. Manuscript profile
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  5 - An Intelligent Pricing System for Cloud Services aims at Increasing Implementation Simplicity and Flexibility
  Mahboubeh Zandieh Sepideh Adabi Samaneh Yazdani
  20.1001.1.27170411.1401.14.53.6.6
  Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goa More

  Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goals: 1) low computation complexity, 2) high accuracy, and 3) high implementation simplicity in real cloud environments. CMM (Cloud Market Maker) is one of the most popular dynamic pricing models that has two advantages of computation accuracy and the possibility to implement in the real cloud environments. This model calculates the bid price based on a linear function. In designing this linear function, the parameters: buyer’s urgency, number of competitors and number of opponents are considered. Despite the advantages of this pricing function, the importance ratio of the constructor parameters of it is considered the same in various market conditions. Ignoring this issue reduces both system flexibility and computation accuracy in tangible changes in the cloud market. Therefore, the authors of this paper focus on designing a new cloud market-aware intelligent pricing system (which developed in customer side of the market) to tackle the mentioned problem. At the same time, high implementation simplicity of the proposed system should be guaranteed. For this purpose, an agent-based intelligent pricing system by combining support vector machine (SVM) and hierarchical analysis process (AHP) techniques is proposed. Simulation results show the better performance of the proposed solution which is named as DPMA in comparison to CMM. Manuscript profile
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  6 - A review of the application of meta-heuristic algorithms in load balancing in cloud computing
  Mehdi Morsali Abolfazl Toroghi Haghighat Sasan Hosseinali-Zade
  20.1001.1.27170411.1401.14.53.14.4
  By widespread use of cloud computing, the need to improve performance and reduce latency in the cloud increases. One of the problems of distributed environments, especially clouds, is unbalanced load which results in reducing speed and efficiency and increasing delay in More

  By widespread use of cloud computing, the need to improve performance and reduce latency in the cloud increases. One of the problems of distributed environments, especially clouds, is unbalanced load which results in reducing speed and efficiency and increasing delay in data storage and retrieval time. Various methods for load balancing in the cloud environment have been proposed, each of which has addressed the issue from its own perspective and has its advantages and disadvantages. In this research, we first provide some criteria for measuring load balance in the cloud and then examine the use of Metaheuristic methods in load balancing in the cloud environment. After introducing Metaheuristic load balancing methods, we have compared them based on the aforementioned criteria and discussed the advantages and disadvantages of each. Ant Colony Algorithms, Artificial Ant Colony, Bee Colony, Artificial Bee Colony, Bee Foraging Algorithm, Particle Swarm, Cat Swarm, Simulated Annealing, Genetic Algorithm, Tabu Search, Fish Swarm and Hybrid Algorithms and etc. examined in this research. Manuscript profile
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  7 - An approach to prioritize quality dimensions of based on cloud computing using Multiple Criteria Decision Making method
  Zahra Abbasi Somayeh Fatahi Mohammad Javad  Ershadi
  20.1001.1.27170411.1401.14.53.10.0
  Today, quality is one of the most important factors in attracting customer satisfaction and loyalty to service organizations. Therefore, one of the main concerns of managers is to improve the quality of services. With the development of the Internet and the world of com More

  Today, quality is one of the most important factors in attracting customer satisfaction and loyalty to service organizations. Therefore, one of the main concerns of managers is to improve the quality of services. With the development of the Internet and the world of communications, a concept called cloud computing has expanded in the world of communications, which provides a new model for the supply, consumption and delivery of computing services. The purpose of this study is to make the optimal decision in choosing the appropriate cloud service according to the conditions of users so that they achieve the highest satisfaction. Fuzzy Delphi method, fuzzy hierarchical analysis method, fuzzy TOPSIS method and finally multi-criteria decision making method are the methods used in this research. The results of the fuzzy Delphi method show that the indicators of transparency, accessibility and reliability should be eliminated. The results of fuzzy hierarchical analysis identified the cost index as the most important index and the support index during demand as the least important index. According to the results of fuzzy TOPSIS based on the weights obtained from fuzzy hierarchical analysis, SAAS, IAAS and PAAS cloud services were ranked first to third, respectively. Using the SAAS service provides numerous benefits to employees and companies, such as reducing time and money spent on time-consuming tasks such as installing, managing, and upgrading software. Manuscript profile
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  8 - Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models
  Zeinab Khodaverdian Hossein Sadr Mojdeh Nazari Soleimandarabi Seyed Ahmad Edalatpanah
  20.1001.1.27170411.1402.15.55.9.0
  Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi More

  Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile
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  9 - WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling
  Arash Ghorbannia Delavar Reza Akraminejad sahar mozafari
  20.1001.1.27170411.1402.15.57.5.0
  Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for sche More

  Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for scheduling of compressed-instance workflows in things-internet by cluster processing in cloud. A method is presented for scheduling cloud workflows with aim of optimizing energy, throughput, and latency. In the proposed method, time and energy consumption has been improved in comparison to previous methods by creating distance parameters, clustering inputs, and considering real execution time. In WSTMOS method by considering special parameters and real execution time, we managed to reach the optimized objective function. Moreover, in the proposed method parameter of time distance of tasks to virtual machines for reduction of number of migration in virtual machines was applied. In WSTMOS method by organizing the workflow inputs to low, medium and heavy groups and also by distributing appropriate load on more suitable servers for processors threshold, we accomplished to optimize energy and cost. Energy consumption was reduced by 4.8 percent while the cost was cut down by 4.4 percent using this method in comparison to studied method. Finally, average delay time, power and workload are optimized in comparison to previous methods. Manuscript profile
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  10 - Improving the load balancing in Cloud computing using a rapid SFL algorithm (R-SFLA)
  Kiomars Salimi Mahdi Mollamotalebi
  20.1001.1.27170411.1402.15.57.13.8
  Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of th More

  Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of the load distribution among the processing nodes in order to have the best usage of resources while having minimum response time for the users’ requests. Several methods for load balancing in Cloud computing have been proposed in the literature. The shuffled frog leaping algorithm for load balancing is a dynamic, evolutionary, and inspired by nature. This paper proposed a modified rapid shuffled frog leaping algorithm (R-SFLA) that converge the defective evolution of frogs rapidly. In order to evaluate the performance of R-SFLA, it is compared to Shuffled Frog Leaping Algorithm (SFLA) and Augmented Shuffled Frog Leaping Algorithm (ASFLA) by the overall execution cost, Makespan, response time, and degree of imbalance. The simulation is performed in CloudSim, and the results obtained from the experiments indicated that the proposed algorithm acts more efficient compared to other methods based on the above mentioned factors. Manuscript profile