Optimal Service Provisioning in IoT Fog-based Environment for QoS-aware Delay-sensitive Application

This paper addresses the escalating challenges posed by the ever-increasing data volume, velocity, and the demand for low-latency applications, driven by the proliferation of smart devices and Internet of Things (IoT) applications. To mitigate service delay and enhance Quality of Service (QoS), we introduce a hybrid optimization of Particle Swarm (PSO) and Chemical Reaction (CRO) to improve service delay in FogPlan, an offline framework that prioritizes QoS and enables dynamic fog service deployment. The method optimizes fog service allocation based on incoming traffic to each fog node, formulating it as an Integer Non-Linear Programming (INLP) problem, considering various service attributes and costs. Our proposed algorithm aims to minimize service delay and QoS degradation. The evaluation using real MAWI Working Group traffic data demonstrates a substantial 29.34% reduction in service delay, a 66.02% decrease in service costs, and a noteworthy 50.15% reduction in delay violations compared to the FogPlan framework

TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization

Technology providers heavily exploit the usage of edge‐cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network‐traffic effectiveness. In this study, we present a multi‐objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR, which utilizes an artificial bee colony optimization algorithm for power and network‐aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three‐tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters

How eLearning drives organisational product adoption: An exploratory multi-case approach

Purpose: Both industry and researchers have been discussing the need to close the learning skill gap in new product adoption for years. New business designs focusing on subscription models enabling online purchasing and cancellation of products in every country have changed the rules of the game. In this research, today’s usage of eLearning in organisations has been investigated and barriers and challenges are discussed. The guiding question is: How shall organisations adopt a product if, on top of the skills gap, there is nobody guiding them how to use it and ensure adoption, and, hence, market success? Methodology: Academic literature shows that eLearning can be efficient if well-designed and developed. Most research around product adoption goes back to Rogers and his product adoption model. This research uses a case-based approach to understand how eLearning could drive organisational product adoption in the future. It uses a unique two-level network sampling to avoid bias, meaning the author used his network but had no prior relationship to anybody eventually interviewed. The author interviewed the SVPs, VPs or Directors responsible globally for Learning and Development to discover first-hand which of the eLearning trends have the power to be the eLearning innovation changing product adoption in the future. The results blend expert opinions from automotive, hospitality, medical products, distribution, medical care, eLearning and consulting, most of them Fortune 100 and Fortune 500 companies Impact: This research contributes to knowledge by suggesting extending Rogers’ model of diffusion of innovation in two ways. First, by including a phase of very early conceptual eLearning and, in addition, it suggests replacing the communication channels by ubiquitous access to AI-influenced personalised eLearning. From a practice point of view, it suggests that developers need to listen to this case approach’s appeal to provide “short” eLearning with “immediate” and “ubiquitous availability” to the learners, making eLearning available on any platform. Furthermore, it highlights that, wherever uniformity is needed as learning outcome, to rethink the existing method and potentially replace it by eLearning. Finally, it suggests investigating whether the success of simulations in certain industries could be also useful in others