A Multi-User Tasks Offloading Scheme for Integrated Edge-Fog-Cloud Computing Environments

Abstract

This paper presents a multi-user, multi-class and multi-layer edge computing-based framework for effective task offloading and computation processes. Important system requirements that were not captured in the existing multi-layer solutions such as offloading, computations and deadline requirements were captured in the system modeling, while both wireless communications and task computation constraints were considered. We considered three layers system, where each device offloads its generated tasks in each time slot to any selected layer for computation. On its arrival at such a selected layer, the task is only accepted if the queue size is below the pre-defined threshold, otherwise, such a task is offloaded to the next layer. Tasks were classified into class 1 and class 2 tasks following tasks' quality of service requirements. We adopted stochastic geometry, parallel computing and queueing theory techniques to model the performance of the considered integrated edge-fog-cloud computing environment and obtained analysis for various performance metrics of interest. The obtained analyses demonstrate the importance of multi-layer and multiclass edge computing systems towards improving the experience of both delay-sensitive and mission-critical applications in any task offloading environment.

AI-Based Sustainable and Intelligent Offloading Framework for IIoT in Collaborative Cloud-Fog Environments

“…Our proposed algorithm categorizes each incoming task 𝒯𝜅(𝑡) into two classes as class 1 (C1) and class 2 (C2), using fuzzy inference logic. This grouping of tasks allows their concurrent execution at distinct layers, avoiding ageing, and being preemptive in a multiprocessing environment [16]. All tasks with a long length and a tight deadline or a low length with a hard deadline fall into class, C1.…”

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

“…However, complicated features with multi-layer modeling cannot be tackled with the abovementioned homogeneousfog-cloud-only task scheduling. There have been attempts to construct hierarchies among cloud nodes (e.g., cloudlet [1]) or FNs (e.g., multi-layer FNs [17]), aiming to increase utilization efficiency while reducing latency, as these are still not replaceable in fog-cloud hierarchical task scheduling with more diverse features. The most adopted strategies for fog-cloud task scheduling generally follow the principle that latencytolerant and large-size tasks are assigned to cloud nodes, and latency-sensitive tasks, to FNs [9], based on which minimizing the overall makespan, maximizing resource utilization efficiency, or load balancing is targeted [16,[18][19][20][21].…”

A Prediction-Enhanced Physical-to-Virtual Twin Connectivity Framework for Human Digital Twin

“…represents the marginal distribution of transmission power phases. The proof of (33) follows from [22], [28], [29] and is omitted to avoid unnecessary repetition. From this, we can obtain the average offloading success probability following the law of total probability and conditioned on the tagged LA m 0 ∈ Φ P T as…”