Optimal Resource Allocation Policy for Multi-Rate Opportunistic Forwarding

Author(s):  
Fabian Hohmann ◽  
Andrea Ortiz ◽  
Anja Klein
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiquan Bai ◽  
Tongtong Wang ◽  
Piming Ma ◽  
Yanbo Ma ◽  
Kyungsup Kwak

We investigate a secure multiuser time division multiple access (TDMA) system with statistical delay quality of service (QoS) guarantee in terms of secure effective capacity. An optimal resource allocation policy is proposed to minimize the β-fair cost function of the average user power under the individual QoS constraint, which also balances the energy efficiency and fairness among the users. First, convex optimization problems associated with the resource allocation policy are formulated. Then, a subgradient iteration algorithm based on the Lagrangian duality theory and the dual decomposition theory is employed to approach the global optimal solutions. Furthermore, considering the practical channel conditions, we develop a stochastic subgradient iteration algorithm which is capable of dynamically learning the intended wireless channels and acquiring the global optimal solution. It is shown that the proposed optimal resource allocation policy depends on the delay QoS requirement and the channel conditions. The optimal policy can save more power and achieve the balance of the energy efficiency and the fairness compared with the other resource allocation policies.


2019 ◽  
Vol 8 (4) ◽  
pp. 2565-2571

Cloud Computing is mainly attracted by people for its unlimited storage space and worldwide accessibility from anywhere and anytime. The data that is stored in the cloud has to be retrieved in a faster manner as well as without any faults. Content Delivery Networks dominates Cyberspace traffic heavily due to its increasing demand. Resource allocation plays a vital role in determining the performance of the cloud. Over allocation leads to wastage which can be used for instances that are running short of resources. This work proposes an optimal resource allocation through Genetic algorithm. They help in increasing the download speed which in turn would be helpful in controlling traffic to greater extent. Recently cloud downloading Services have been emerged, in which cloud storage hoards the user interested content and updates the cloud cache. This process takes place in two modes, server mode and the helper mode. The proposed Resource Allocation Policy can well support the server mode of processing by the way it can increment the download speed with dynamic load balancing and fault tolerance.


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