A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.</p

Monitoring in fog computing: state-of-the-art and research challenges

Fog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privacy issues. Fog computing resources and applications dynamically vary at run-time, and they are highly distributed, mobile, and appear-disappear rapidly at any time over the internet. Therefore, to ensure the quality of service and experience for end-users, it is necessary to comply with a comprehensive monitoring approach. However, the volatility and dynamism characteristics of fog resources make the monitoring design complex and cumbersome. The aim of this article is therefore three-fold: 1) to analyse fog computing-based infrastructures and existing monitoring solutions; 2) to highlight the main requirements and challenges based on a taxonomy; 3) to identify open issues and potential future research directions.This work has been (partially) funded by H2020 EU/TW 5G-DIVE (Grant 859881) and H2020 5Growth (Grant 856709). It has been also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52 PID2019-108713RB-C52 / AEI / 10.13039/501100011033)

Security analysis of mobile edge computing in virtualized small cell networks

Based upon the context of Mobile Edge Computing (MEC) actual research and within the innovative scope of the SESAME EU-funded research project, we propose and assess a framework for security analysis applied in virtualised Small Cell Networks, with the aim of further extending MEC in the broader 5G environment. More specifically, by applying the fundamental concepts of the SESAME original architecture that aims at providing enhanced multi-tenant MEC services through Small Cells coordination and virtualization, we focus on a realistic 5G-oriented scenario enabling the provision of large multi-tenant enterprise services by using MEC. Then we evaluate several security issues by using a formal methodology, known as the Secure Tropos

A Framework for Prediction in a Fog-Based Tactile Internet Architecture for Remote Phobia Treatment

Tactile Internet, as the next generation of the Internet, aims to transmit the modality of touch in addition to conventional audiovisual signals, thus transforming today’s content-delivery into skill-set delivery networks, which promises ultra-low latency and ultra reliability. Besides voice and data communication driving the design of the current Internet, Tactile Internet enables haptic communications by incorporating 5G networks and edge computing. A novel use-case of immersive, low-latency Tactile Internet applications is haptic-enabled Virtual Reality (VR), where an extremely low latency of less than 50 ms is required, which gives way to the so-called Remote Phobia treatment via VR. It is a greenfield in the telehealth domain with the goal of replicating normal therapy sessions with distant therapists and patients, thereby standing as a cost-efficient and time-saving solution. In this thesis, we consider a recently proposed fog-based haptic-enabled VR system for remote treatment of animal phobia consisting of three main components: (1) therapist-side fog domain, (2) core network, and (3) patient-side fog domain. The patient and therapist domains are located in different fog domains, where their communication takes place through the core network. The therapist tries to cure the phobic patient remotely via a shared haptic virtual reality environment. However, certain haptic sensation messages associated with hand movements might not be reached in time, even in the most reliable networks. In this thesis, a prediction model is proposed to address the problem of excessive packet latency as well as packet loss, which may result in quality-of-experience (QoE) degradation. We aim to use machine learning to decouple the impact of excessive latency and extreme packet loss from the user experience perspective. For which, we propose a predictive framework called Edge Tactile Learner (ETL). Our proposed fog-based framework is responsible for predicting the zones touched by the therapist’s hand, then delivering it immediately to the patient-side fog domain if needed. The proposed ETL builds a model based on Weighted K-Nearest Neighbors (WKNN) to predict the zones touched by the therapist in a VR phobia treatment system. The simulation results indicate that our proposed predictive framework is instrumental in providing accurate and real-time haptic predictions to the patient-side fog domain. This increases patient’s immersion and synchronization between multiple senses such as audio, visual and haptic sensory, which leads to higher user Quality of Experience (QoE)

Pre-study on Multi-access Edge Computing at Communication Technology lab: simulator/emulator

Multi-access edge computing has been on the rise since the evolution of 5G. There are challenges that 5G has been fighting with, such as latency and data being transferred. In this paper, it will talk about the background of Multi-access edge computing, the state of the art of Multi-access edge computing research and then implementing a simulator to demonstrate Multi-access edge computing functionalities

Reactive vs Predictive Live Migration in Edge Cloud

Migrating services in an edge-cloud environ- ment poses unique challenges, including heterogeneous en- vironments, potential failures, and uneven resource distri- bution. This paper studies and evaluate reactive and predic- tive migration approaches to support live migration in case of edge cloud computing failures. Telemetry information relate to edge cloud computing have been considered to trigger migration, whereas deadlock prevention algorithm has been used to determine and select the target device to migrate services. The paper evaluates these strategies by comparing resource utilization, assessing differences between predictive and reactive migration and handling multiple migrations for tenants hosting numerous appli- cations. Experimental results have shown that predictive migration can reduce the downtime of the hosted services. Additionally, the total migration cost can be increased for both scenarios where the containers can be migrated to different edge devices due to lack of available resource

A Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

The In-Network Computing (COIN) paradigm is a promising solution that leverages unused network resources to perform some tasks to meet up with computation-demanding applications, such as metaverse. In this vein, we consider the metaverse partial computation offloading problem for multiple subtasks in a COIN environment to minimise energy consumption and delay while dynamically adjusting the offloading policy based on the changing computation resources status. We prove that the problem is NP and thus transformed it into two subproblems: task splitting problem (TSP) on the user side and task offloading problem (TOP) on the COIN side. We modelled the TSP as an ordinal potential game (OPG) and proposed a decentralised algorithm to obtain its Nash Equilibrium (NE). Then, we model the TOP as Markov Decision Process (MDP) proposed double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, our COIN agent explores the NE of the TSP and the deep neural network. Finally, simulation results show that our proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.Comment: 14 pages, 9 figure