List of Articles تخصیص منابع

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  1 - Improving resource allocation in mobile edge computing using gray wolf and particle swarm optimization algorithms
  seyed ebrahim dashti saeid shabooei
  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigate More

  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigated. Some tasks are processed locally and some are offloaded to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, it was introduced using the hybrid algorithm of particle swarm and gray wolf to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%. Manuscript profile
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  2 - Improving IoT resource management using fog calculations and ant lion optimization algorithm
  payam shams Seyedeh Leili Mirtaheri reza shahbazian ehsan arianyan
  20.1001.1.27170411.1402.15.57.16.1
  In this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (proc More

  In this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (processing power, storage memory, and bandwidth) are first extracted. This component determines the requested traffic status of the application in terms of real-time. If the application is not real-time and is somewhat resistant to latency, the request will be referred to the cloud environment, but if the application needs to respond promptly and is sensitive to latency, it will be dealt with as a fog calculation. It will be written to one of the Cloudletes. In this step, in order to select the best solution in allocating resources to serve the users of the IoT environment, the ant milk optimization algorithm was used. The proposed method is simulated in MATLAB software environment and to evaluate its performance, five indicators of fog cells energy consumption, response time, fog cell imbalance, latency and bandwidth have been used. The results show that the proposed method reduces the energy consumption, latency rate in fog cells, bandwidth consumption rate, load balance rate and response time compared to the base design (ROUTER) 22, 18, 12, 22 and 47, respectively. Percentage has improved. Manuscript profile
• Open Access Article
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  3 - Improving Resource Allocation in Mobile Edge Computing Using Particle Swarm and Gray Wolf Optimization Algorithms
  seyed ebrahim dashti saeid shabooei
  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigat More

  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigated. Some tasks are uploaded and processed locally and some to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, a multi-objective hybrid algorithm of particle swarm and gray wolf was introduced to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. Local search in the particle swarm algorithm has good results in the problem, but it will cause the loss of global optima, so in this problem, in order to improve the model, the gray wolf algorithm was used as the main basis of the proposed algorithm, in the wolf algorithm Gray, due to the graphical approach to the problem, the set of global searches will reach the optimal solution, so by combining these functions, we tried to improve the operational conditions of the two algorithms for the desired goals of the problem. In order to create a network in this research, the network creation parameters in the basic article were used and the LCG data set was used in the simulation. The simulation environment in this research is the sim cloud environment. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%. Manuscript profile