Energy-efficient Delay-aware and Profit Maximization Caching enabled, Congestion Control in Stochastic Network
G.Anandharaj1, V.Lazar Ramesh2
1Dr.G.Anandharaj is finished Doctorate in Computer Applications in Anna University, India, PH-01123456789.
2V.Lazar Ramesh2is currently pursuing M.Phil (Computer Science) Research Scholar in Adhiparasakthi College of Arts and Science, Kalavai, Tamil Nadu – 632506 PH-9841508044.
Manuscript received on July 15, 2019. | Revised Manuscript received on August 19, 2019. | Manuscript published on August 30, 2019. | PP: 4266-4274 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9127088619/2019©BEIESP | DOI: 10.35940/ijeat.F9127.088619
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Abstract: We are presenting a new unified structure for dynamic distributed forwarding and congestion-controlled network caching enabled. Improved use of data transfer capacity and storage resources in Stochastic networks in aspects of energy-efficient and profit-maximization. In the investigation of stochastic networks, a framework has been developed for combined implementation of caching, forwarding and traffic command called the Markov Decision Process in Stochastic Learning (MDPSL) strategy. The MDPSL structure uses a virtual plane that manages customer request prices, as well as a real plane that processes actual interest packets and data packets. It can accomplish dynamically structured transmission and caching. It can fulfill dynamically distributed forwarding and caching. Focus on MDPSL communication and queuing systems, including wireless networks with time-varying channels, mobility, and arrival of random traffic. Using this framework, estimates of the time are optimized such as throughput, utility throughput, energy, and distortion. Explicit performance-delay tradeoffs are provided to show the expense of attaining optimality. A congestion control algorithm is intended to improve client services subject to network stability when optimally coupled with forwarding and caching algorithms 
Keywords: Lyapunov stability, Markov decision process, stochastic learning, Delay-aware resource control