Environment Construction of Virtual Reality Technology Based on Edge Computing in Immersive Communication

Li, Jianxiong; Ma, Yan; Wang, Yan

doi:10.1109/ctmcd53128.2021.00033

Cited by 1 publication

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any delay or failure to detect the movements may lead to severe safety consequences. In Virtual Reality gaming [4], rendering 72 frames per second is the minimum requirement for the smooth running of the application. The user movements need to be accurately captured and processed for an immersive experience.…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

Decentralized Cache-aided Offloading in Edge Cloud Collaborative Environment using Deep Q Reinforcement Learning

Guddeti¹

View full text Add to dashboard Cite

Many emerging applications such as augmented reality, facial recognition, autonomous cars, and e-health require heavy computation, and the processed results have to be available to the user in the order of milliseconds. Edge computing combined with cloud computing can address this challenge by distributing the load (offloading) on different connected computing resources. However, effective task offloading requires an efficient resource management framework. Many existing offloading methodologies consider only latency and energy consumption in a pre-defined network configuration implemented on a small scale. In addition, the effect of the location of the offloading algorithm has not been extensively studied. This thesis introduces a novel adaptive offloading framework using Online Deep Q reinforcement learning. The proposed framework considers strict latency constraints, high state space, rapidly changing user mobility, heterogeneous resources, and stochastic task arrival rate. The proposed research also highlights the importance of caching and introduces a novel concept called "container caching" that caches the dependencies of popular applications. Therefore, offloading decisions are taken to minimize energy consumption, latency, and caching costs. Moreover, the significance of deployment location of the offloading algorithm is also reviewed, and a distributed offloading method is proposed. Extensive simulations in a discrete event simulator implemented in Java using realistic profiles of tasks have been conducted. Simulation results and comparisons with existing benchmarking algorithms showed remarkable performance in terms of energy consumption, network traffic, task failures, remaining power on a large scale demonstrated the feasibility of the proposed approach.

show abstract

Section: Chapter 1: Introductionmentioning

confidence: 99%

Decentralized Cache-aided Offloading in Edge Cloud Collaborative Environment using Deep Q Reinforcement Learning

Guddeti¹

View full text Add to dashboard Cite

show abstract

Environment Construction of Virtual Reality Technology Based on Edge Computing in Immersive Communication

Cited by 1 publication

References 1 publication

Decentralized Cache-aided Offloading in Edge Cloud Collaborative Environment using Deep Q Reinforcement Learning

Decentralized Cache-aided Offloading in Edge Cloud Collaborative Environment using Deep Q Reinforcement Learning

Contact Info

Product

Resources

About