1,762 research outputs found
Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices
Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth
Generation (5G) mobile networks. MEC facilitates distributed cloud computing
capabilities and information technology service environment for applications
and services at the edges of mobile networks. This architectural modification
serves to reduce congestion, latency, and improve the performance of such edge
colocated applications and devices. In this paper, we demonstrate how reactive
service migration can be orchestrated for low-power MEC-enabled Internet of
Things (IoT) devices. Here, we use open-source Kubernetes as container
orchestration system. Our demo is based on traditional client-server system
from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As
the use case scenario, we post-process live video received over web real-time
communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1
handovers, demonstrating MEC-based software defined network (SDN). Now, edge
applications may reactively follow the UE within the radio access network
(RAN), expediting low-latency. The collected data is used to analyze the
benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end
(E2E) latency and power requirements of the UE are improved. We further discuss
the challenges of implementing such schemes and future research directions
therein
Resource Management in Container-based Mobile Edge Computing
Mobile edge computing is a promising technology which provides support to time-sensitive applications by pushing centralized cloud processing capabilities to distributed Fog nodes. These fog nodes are deployed at one-hop distance from end-user and provide real-time data processing capabilities at the edge of network. Due to service provisioning at the edge of network, no congestion occurs at the core of network, quality of service (QoS) is improved and the overall network operational cost is significantly reduced. However, these nodes have limited capabilities such as processing, storage and coverage so, they face challenge of mobility support for a mobile user when continued service (i.e. zero downtime) is required during handovers between edge nodes. Furthermore, they also need an effective task allocation and resource management strategy to ensure smooth operation of edge services. Unlike traditional VM based environment in Fog Computing, this work explores lightweight Docker containers to deploy and migrate services. In this work, an interactive event-driven dashboard is developed for real-time edge node registration, system monitoring, service initiation and migration. Then, motivated by Fog Following Me, a couple of resource allocation schemes (i.e. algorithm-I & II) have been introduced to dynamically manage the compute resources among fog nodes. For smooth service operation and stable migration, an application profiling feature has been introduced which assigns the needed quota for an application requirement in terms of CPU, GPU and RAM. The developed system's performance is evaluated by conducting various experiments. The experimental results clearly demonstrate and verify the working feasibility of the whole system's operation in context of edge computing. However, the observed processing delays during service migration marks the limitation of Docker and suggest the need to use latest optimization tools to cut down the network delays and ensure zero-downtime service migration
Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges
The growing landscape of emerging wireless applications is a key driver
toward the development of novel wireless system designs. Such a design can be
based on the metaverse that uses a virtual model of the physical world systems
along with other schemes/technologies (e.g., optimization theory, machine
learning, and blockchain). A metaverse using a virtual model performs proactive
intelligent analytics prior to a user request for efficient management of the
wireless system resources. Additionally, a metaverse will enable
self-sustainability to operate wireless systems with the least possible
intervention from network operators. Although the metaverse can offer many
benefits, it faces some challenges as well. Therefore, in this tutorial, we
discuss the role of a metaverse in enabling wireless applications. We present
an overview, key enablers, design aspects (i.e., metaverse for wireless and
wireless for metaverse), and a novel high-level architecture of metaverse-based
wireless systems. We discuss metaverse management, reliability, and security of
the metaverse-based system. Furthermore, we discuss recent advances and
standardization of metaverse-enabled wireless system. Finally, we outline open
challenges and present possible solutions
Microservice Transition and its Granularity Problem: A Systematic Mapping Study
Microservices have gained wide recognition and acceptance in software
industries as an emerging architectural style for autonomic, scalable, and more
reliable computing. The transition to microservices has been highly motivated
by the need for better alignment of technical design decisions with improving
value potentials of architectures. Despite microservices' popularity, research
still lacks disciplined understanding of transition and consensus on the
principles and activities underlying "micro-ing" architectures. In this paper,
we report on a systematic mapping study that consolidates various views,
approaches and activities that commonly assist in the transition to
microservices. The study aims to provide a better understanding of the
transition; it also contributes a working definition of the transition and
technical activities underlying it. We term the transition and technical
activities leading to microservice architectures as microservitization. We then
shed light on a fundamental problem of microservitization: microservice
granularity and reasoning about its adaptation as first-class entities. This
study reviews state-of-the-art and -practice related to reasoning about
microservice granularity; it reviews modelling approaches, aspects considered,
guidelines and processes used to reason about microservice granularity. This
study identifies opportunities for future research and development related to
reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table
Multi-Criteria Decision-Making Approach for Container-based Cloud Applications: The SWITCH and ENTICE Workbenches
Many emerging smart applications rely on the Internet of Things (IoT) to provide solutions to time-critical problems. When building such applications, a software engineer must address multiple Non-Functional Requirements (NFRs), including requirements for fast response time, low communication latency, high throughput, high energy efficiency, low operational cost and similar. Existing modern container-based software engineering approaches promise to improve the software lifecycle; however, they fail short of tools and mechanisms for NFRs management and optimisation. Our work addresses this problem with a new decision-making approach based on a Pareto Multi-Criteria optimisation. By using different instance configurations on various geo-locations, we demonstrate the suitability of our method, which narrows the search space to only optimal instances for the deployment of the containerised microservice.This solution is included in two advanced software engineering environments, the SWITCH workbench, which includes an Interactive Development Environment (IDE) and the ENTICE Virtual Machine and container images portal. The developed approach is particularly useful when building, deploying and orchestrating IoT applications across multiple computing tiers, from Edge-Cloudlet to Fog-Cloud data centres
Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices
Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications
6G Vision: Towards Future Collaborative Cognitive Communication (3C) Systems
6G networks are expected to have a breakthrough by enabling the emergence of collaborative cognitive communication services over heterogeneous environments for industry 5.0 applications. These applications are required to adapt human-centric approach to make the most of human intuition and intelligence in Industry 4.0 automation.It calls for a transdisciplinarity research domain to investigate innovative systems with overlapping realms of Psychology, Sociology, Communication networks, Artificial Intelligence , Natural Language Processing and Collaborative Computing. The author at the Cognitive Systems Research Centre, London South Bank University has coined the expression “3C Systems" to refer to such artifacts which stands for "Collaborative Cognitive Communication Systems”. In this paper, an innovative framework for 3C Systems is proposed that is able to analyze and predict both the human as well as machine behaviors. It proactively diagnoses issues and recommends solutions without requiring any human intervention. The proposed concept of 3C Systems would potentially contribute towards 6G standardization. The automation and orchestration aspects of this research have variety of applications stretched across city infrastructures, retail, business, tourism, health, law, education and travel. A thorough insight to a broad view of 6G vision has been presented towards envisioned 3C Systems, while covering its enabling technologies. The experimental results for the proof of concept implementation has been presented. Results affirm the technical capabilities of the concept, to contribute to several industry 5.0 applications including, but not limited to holographic communication, self-driving vehicles, context-aware infrastructure and personalized interfaces
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
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