List of Articles محاسبات ابری

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  1 - A Proposed Method of Decentralized Load Balancing Algorithm in Heterogeneous Cloud Environments
  S. Hourali S. Jamali F. Hourali
  One of the key strategies to improve the efficiency is load balancing. Choosing the appropriate VM to do any task, is function of various parameters such as the amount of required resources like CPU, memory, the size of VM resource, cost and maturity of VMs. In this pap More

  One of the key strategies to improve the efficiency is load balancing. Choosing the appropriate VM to do any task, is function of various parameters such as the amount of required resources like CPU, memory, the size of VM resource, cost and maturity of VMs. In this paper, by considering each of these criteria and design objectives such as load balancing, reducing the rate of create new VM, and VM migration, we modeling the problem in terms of effective parameters in performance. Then, we solving this model by using the PROMETHEE method, which is one of the most widely used method for MADM problems. In this method, selecting the best VM occurs based on the value assigned to each of criteria which is calculated based on fuzzy logic. To evaluate the performance of this approach, the necessary simulations have been carried out on CloudSim simulator and shown that the proposed method has better performance compared to FIFO, DLB and WRR methods on average in terms of response time, rate of success tasks, load variation and rate of VM migration. Manuscript profile
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  2 - An Intelligent Pricing System for Cloud Services aims at Increasing Implementation Simplicity and Flexibility
  Mahboubeh Zandieh Sepideh Adabi Samaneh Yazdani
  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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  3 - WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling
  Arash Ghorbannia Delavar Reza Akraminejad sahar mozafari
  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