MRoCO: A Novel Approach to Structured Application Scheduling with a Hybrid Vehicular Cloud-Edge Environment

Xu, Xifeng; Chen, Peng; Xia, Yunni; Long, M. I. E.; Peng, Qinglan; Long, Tianhe

doi:10.1109/scc55611.2022.00024

Cited by 4 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FT − MAACC is compared against the following benchmark algorithms: Thompson sampling (TS) [33], Random Selection Algorithm (RSA) [34], Greedy Algorithm (GA) [35], MARL [12], and MAACC (The algorithm in this paper does not consider fault tolerance).…”

Section: Comparison Algorithmsmentioning

confidence: 99%

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Ma,

Zhao,

Guo

et al. 2024

CMES

View full text Add to dashboard Cite

Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network's periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions for real-time mobile applications, especially those sensitive to latency, by effectively utilizing mobility information, continues to be a significant challenge. In response to this challenge, this paper introduces FT-MAACC, a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms. This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content. Furthermore, it relies on collaborative caching strategies based on multi-agent deep reinforcement learning models and establishes a fault-tolerance model to ensure the system's reliability, availability, and persistence. Empirical results unequivocally demonstrate that FT-MAACC outperforms its peer methods in cache hit rates and transmission latency.

show abstract

Section: Comparison Algorithmsmentioning

confidence: 99%

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Ma,

Zhao,

Guo

et al. 2024

CMES

View full text Add to dashboard Cite

show abstract

“…Xu et al [23] interpreted the scheduling problem as a combinatorial optimization formula. Through the application of the priority ranking mechanism of task dependency perception and the hybrid task mapping mechanism based on edge cloud, an efficient offload algorithm is proposed.…”

Section: Computation Offloadingmentioning

confidence: 99%

“…From the formula (23), data queue length is positively correlated with the value of L(G (t )). Data queue dynamics can be described using as Lyapunov function for differences of between the adjacent time slots of the system, that is, Lyapunov drift Δ(G (t )), which is expressed as follows:…”

Section: Model Transformationmentioning

confidence: 99%

Energy‐efficient adaptive dependent task scheduling in cooperative vehicle‐infrastructure system

Su,

Dai,

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

show abstract