258 research outputs found
Vehicle Speed Aware Computing Task Offloading and Resource Allocation Based on Multi-Agent Reinforcement Learning in a Vehicular Edge Computing Network
For in-vehicle application, the vehicles with different speeds have different
delay requirements. However, vehicle speeds have not been extensively explored,
which may cause mismatching between vehicle speed and its allocated computation
and wireless resource. In this paper, we propose a vehicle speed aware task
offloading and resource allocation strategy, to decrease the energy cost of
executing tasks without exceeding the delay constraint. First, we establish the
vehicle speed aware delay constraint model based on different speeds and task
types. Then, the delay and energy cost of task execution in VEC server and
local terminal are calculated. Next, we formulate a joint optimization of task
offloading and resource allocation to minimize vehicles' energy cost subject to
delay constraints. MADDPG method is employed to obtain offloading and resource
allocation strategy. Simulation results show that our algorithm can achieve
superior performance on energy cost and task completion delay.Comment: 8 pages, 6 figures, Accepted by IEEE International Conference on Edge
Computing 202
AI-Based Sustainable and Intelligent Offloading Framework for IIoT in Collaborative Cloud-Fog Environments
The cloud paradigm is one of the most trending areas in today’s era due to its rich profusion of services. However, it fails to serve the latency-sensitive Industrial Internet of Things (IIoT) applications associated with automotives, robotics, oil and gas, smart communications, Industry 5.0, etc. Hence, to strengthen the capabilities of IIoT, fog computing has emerged as a promising solution for latency-aware IIoT tasks. However, the resource-constrained nature of fog nodes puts forth another substantial issue of offloading decisions in resource management. Therefore, we propose an Artificial Intelligence (AI)-enabled intelligent and sustainable framework for an optimized multi-layered integrated cloud fog-based environment where real-time offloading decisions are accomplished as per the demand of IIoT applications and analyzed by a fuzzy based offloading controller. Moreover, an AI based Whale Optimization Algorithm (WOA) has been incorporated into a framework that promises to search for the best possible resources and make accurate decisions to ameliorate various Quality-of-Service (QoS) parameters. The experimental results show an escalation in makespan time up to 37.17%, energy consumption up to 27.32%, and execution cost up to 13.36% in comparison to benchmark offloading and allocation schemes
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
We formulate computation offloading as a decentralized decision-making
problem with autonomous agents. We design an interaction mechanism that
incentivizes agents to align private and system goals by balancing between
competition and cooperation. The mechanism provably has Nash equilibria with
optimal resource allocation in the static case. For a dynamic environment, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, and a reward signal that reduces
information need to a great extent. Empirical results confirm that through
learning, agents significantly improve both system and individual performance,
e.g., 40% offloading failure rate reduction, 32% communication overhead
reduction, up to 38% computation resource savings in low contention, 18%
utilization increase with reduced load variation in high contention, and
improvement in fairness. Results also confirm the algorithm's good convergence
and generalization property in significantly different environments
Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN
Today, vehicles use smart sensors to collect data from the road environment.
This data is often processed onboard of the vehicles, using expensive hardware.
Such onboard processing increases the vehicle's cost, quickly drains its
battery, and exhausts its computing resources. Therefore, offloading tasks onto
the cloud is required. Still, data offloading is challenging due to low latency
requirements for safe and reliable vehicle driving decisions. Moreover, age of
processing was not considered in prior research dealing with low-latency
offloading for autonomous vehicles. This paper proposes an age of
processing-based offloading approach for autonomous vehicles using unsupervised
machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge
Computing in Open Radio Access Network (O-RAN). We design a collaboration space
of edge clouds to process data in proximity to autonomous vehicles. To reduce
the variation in offloading delay, we propose a new communication planning
approach that enables the vehicle to optimally preselect the available RATs
such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local
resources are insufficient. We formulate an optimization problem for age-based
offloading that minimizes elapsed time from generating tasks and receiving
computation output. To handle this non-convex problem, we develop a surrogate
problem. Then, we use the Lagrangian method to transform the surrogate problem
to unconstrained optimization problem and apply the dual decomposition method.
The simulation results show that our approach significantly minimizes the age
of processing in data offloading with 90.34 % improvement over similar method
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