A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Abstract

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

“…In the field of cloud-edge-end cooperation environments, Fang, C et al [33] have solved the issue of the overloaded mobile traffic that is resulted from the Internet services by designing a DRL-based optimization mechanism to allocate resources in heterogeneous environments with cloud-edge-end collaboration, focused on improving the distribution of content, where they schedule the tasks of the arrived content requests according to the historical requests from users. In another study supported by edge computing, Quan, T et al [34] have designed a seismic data query mechanism using DQN for mobile edge computing; they formulated a minimization optimization problem aiming to minimize the task delay in the long-term by considering the computing capacity constraints of edge servers, resulting in outstanding performance results.…”

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

“…The authors of the paper 77 tackled the issue of allocating resources effectively in environments with heterogeneous networks. Their goal was to enhance content delivery for various large‐scale services and ensure QoS.…”

Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments

“…The offloading problems are often modeled as combinatorial optimizations or mix-integer optimization problems to minimize the delay [6][7][8][9], energy consumption [10,11] or the trade-off of multiple performances [12][13][14][15][16]. A variety of optimization methods, like Lyapunov algorithm [8,12], heuristic algorithm [11,[17][18][19], swarm intelligence algorithm [13,20,21] and deep reinforcement learning [7,[22][23][24], are widely adopted to solve the task offloading problems. However, for applications that contain multiple time-dependent tasks, the computation offloading strategies should not only focus on "where" to offload but also "when" to offload.…”