Proactive Recommendation of Composite Services in Multi-Access Edge Computing

Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users' service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

Orchestrating heterogeneous MEC-based applications for connected vehicles

The rapid increase of using cloud computing encourages service vendors to supply services with different features and provide them in a service pool. Service composition (SC) problem in cloud computing environment becomes a key issue because of the increase of service quantity and user requirements of the quality of service experience. To satisfy the demands on quality of service experience and realize an efficient algorithm for SC problem, a quality of experience (QoE) evaluation model based on fuzzy analytic hierarchy process (FAHP) for SC problem is put forward first. Then, an improved artificial bee colony (IABC) optimization algorithm for QoE based SC problem is proposed. The algorithm improves the updating mechanism of scout bees by introducing current global optimal solution to accelerate convergence velocity and eventually to improve the solution quality. Finally, the experimental results on QWS dataset show that IABC has a better performance on QoE based SC problem, compared with original ABC, PSO and DE.

Multi-access edge computing (MEC) promises to deliver localized computing power and storage. Coupled with low-latency 5G radio access, this enables the creation of high added-value services for mobile users, such as in-vehicle infotainment or remote driving. The performance of these services as well as their scalability will however depend on how MEC will be deployed in 5G systems. This paper evaluates different MEC deployment options, coherent with the respective 5G migration phases, using an accurate and comprehensive end-to-end (E2E) system simulation model (exploiting Simu5G for radio access and Intel CoFluent for core network and MEC), taking into account user-related metrics, such as response time or MEC latency. Our results show that 4G radio access is going to be a bottleneck, preventing MEC services from scaling up. On the other hand, the introduction of 5G will allow a considerable higher penetration of MEC services.

Virtual reality (VR) has been envisioned as the killer-application in the 5G mobile networks. Among numerous VR services, 360° video streaming is the most promising one. Nevertheless, its wide adoption is hindered by large latency incurred in cloud-based video delivery and insufficient bandwidth resource in Radio Access Network (RAN). Fortunately, the emergence of Multi-access Edge Computing (MEC) become an enabler to fulfill the potential of VR by providing caching and computing resources at network edges. Also, since a user can view only a part of the entire 360° video frame due to the limitation of eye vision, users' Quality of Experience (QoE) can be further enhanced if we can predict his Field of View (FoV). In this paper, we propose a novel MEC-assisted FoV-aware and QoE-driven Adaptive Streaming (MFQAS) scheme for 360° videos. Specifically, we first provide a comprehensive QoE model for 360° video streaming. Second, we adopt AutoRegression Moving Average (ARMA) model in FoV prediction. Finally, we propose a heuristic algorithm to optimize the caching and computing decision at MEC server based on predicted FoV so that users' QoE can be enhanced. The simulation results show that our proposed method can provide much better prediction accuracy and QoE compared with baseline algorithms.

A compelling need for Real Time decision making with detection, diagnosis & resolution of key industry related parameters from Industrial assets enabled with Digital Twins in hyper connectivity with IoT & low latency requirements as a primary driver for digitization of private industry. Focus on Industrial Revolution (IR) 4.0 with industry Digital Transformation solution is architected on Connectivity, Command, Control, Compute and Cyber Secure enabled industrial process control operations (5C Solution). 5G technology is chosen for its inherent high bandwidth capability and low latency features enabled by Multi-access Edge Computing (MEC) and cloudification architecture with virtualization-based solution. Quality of Service (QoS) and Quality of Experience (QoE) metrics with high reliability and high availability architectures are chosen as critical features. 5G New Radio - Unlicensed (NR-U) spectrum considered for realizing dense Radio Access Network (RAN) solution with Inter-site Distances (ISD) varying from 500mtrs to 1km with Rx sensitivity of upto −95dBm to enable the connectivity to distributed oil & gas field assets. 5G coverage design of the network is done with focus on interference minimization, neighbor relation management, load balancing and parameter optimization. Converged Connectivity Network with Industrial Automation Bus (IAB) based architecture is proposed to be realized with Digital Communication network Gateway (DCG) covering the functions of Operational and Information Technology (OT/IT). Oil & Gas industry's 3Rs (Rigs, Reservoirs & Refineries) with varying bandwidths - 10kbps to 1Gbps is supported by 5G NR-U RAN with Adaptive Antenna System (AAS) and beamforming technology using 4×4 Multiple Input Multiple Output (MIMO), 40/80MHz channel Bandwidth, 256 DL-QAM (Quadrature Amplitude Modulation) and latency requirements from - 100msec to 1msec enabled by MEC are met with use cases of process sensors, drones, Automated Guided Vehicle (AGV), Industrial robot (IR), Augment Reality/Virtual Reality (AR/VR) and Mission Critical Services (MCS) with highest degree of built-in cyber security.

Envisioned low-latency services in 5G, like automated\n\t\t\t\t driving, will rely mainly on Multi-access Edge Computing\n\t\t\t\t (MEC) to reduce the distance, and hence latency, between users\n\t\t\t\t and the remote applications. MEC hosts will be deployed close to\n\t\t\t\t mobile base stations, constituting a highly distributed computing\n\t\t\t\t platform. However, user mobility may raise the need to migrate a\n\t\t\t\t MEC application among MEC hosts to ensure always connecting\n\t\t\t\t users to the optimal server, in terms of geographical proximity,\n\t\t\t\t Quality of Service (QoS), etc. However, service migration may\n\t\t\t\t introduce: (i) latency for users due to the downtime duration;\n\t\t\t\t (ii) cost for the network operator as it consumes bandwidth\n\t\t\t\t to migrate services. One solution could be the use of service\n\t\t\t\t replication, which pro-actively replicates the service to avoid service\n\t\t\t\t migration and ensure low latency access. Service replication\n\t\t\t\t induces cost in terms of storage, though, requiring a careful\n\t\t\t\t study on the number of service to replicate and distribute in\n\t\t\t\t MEC. In this paper, we propose to compare service migration\n\t\t\t\t and service replication via an analytical model. The proposed\n\t\t\t\t model captures the relation between user mobility and service\n\t\t\t\t duration on service replication as well as service migration costs.\n\t\t\t\t The obtained results allow to propose recommendations between\n\t\t\t\t using service migration or service replication according to user\n\t\t\t\t mobility and the number of replicates to use for two types of\n\t\t\t\t service.

The increasing popularity of virtual reality and 360-degree video streaming has created a demand for streaming high-quality video content over cellular networks. However, streaming 360-degree videos over cellular networks poses a challenge due to the need for ultra-low latency and high bandwidth. Although viewport adaptive streaming presents a promising solution to reduce the bandwidth requirement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video, it remains challenging to predict the Field of View (FoV) due to the adverse impact of prediction errors on the user's Quality of Experience (QoE). Moreover, bitrate adaptation gets complicated due to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> videos and the dynamic nature of the cellular network. Therefore, in this paper, we propose an intelligent framework, termed Seer, to fully realize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video streaming over cellular networks by leveraging Multi-access Edge Computing (MEC). It incorporates an FoV prediction scheme and a bitrate adaptation policy into the MEC platform. In particular, the FoV prediction scheme uses perceived video features coupled with time-series based viewport prediction to achieve higher prediction accuracy. For an effective bitrate selection in cellular network conditions, a Reinforcement Learning (RL) based bitrate adaptation policy is further employed. The results demonstrate that Seer's FoV prediction scheme delivers higher prediction accuracy, while the streaming framework significantly reduces the bandwidth requirements and improves the user's QoE.

This paper presents a novel web-based crowdsourcing platform for the assessment of the subjective and objective quality of experience (QoE) of the video service in the cloud-server environment. The user has the option to enter subjective QoE data for video service by filling out a web questionnaire. The objective QoE data of the cloud-server, network condition, and the user device is automatically captured by the crowdsourcing platform. Our proposed system collects both objective and subjective QoE simultaneously in real-time. The paper presents the key technologies used in the development of the platform and describes the functional requirements and design ideas of the system in detail. The system collects real-time comprehensive data to enhance the quality of the user experience to provide a valuable reference. The system is tested in a real-time environment and the test results are given in terms of the system performance. The crowdsourcing platform has new features of real-time network monitoring, the client device, and cloud monitoring, which currently has not been provided by existing web platforms and crowdsourcing frameworks. The results show that 1MB buffer is filled 100% very soon after starting watching videos from the crowdsourcing platform.

The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer’s a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoS-aware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedy-policy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

Next generation optical access networks have to cope with the contradiction between the intense computation and ultra-low latency requirements of the immersive applications and limited resources of smart mobile devices. In this chapter, after presenting a brief overview of the related work on multi-access edge computing (MEC), the authors explore the potential of full and partial decentralization of computation by leveraging mobile end-user equipment in an MEC-enabled FiWi-enhanced LTE-A HetNet, by designing a two-tier hierarchical MEC-enabled FiWi-enhanced HetNet-based architecture for computation offloading, which leverages both local (i.e., on-device) and nonlocal (i.e., MEC/cloud-assisted) computing resources to achieve low response time and energy consumption for mobile users. They also propose a simple yet efficient task offloading mechanism to achieve an improved quality of experience (QoE) for mobile users.

We present LL-MEC, the first open source Low-Latency Multi-access Edge Computing (MEC) platform enabling mobile network monitoring, control, and programmability while retaining compatibility with 3GPP and ETSI specifications. LL-MEC achieves coordinated resource programmability in end-to-end slicing scenarios by leveraging SDN towards an appropriate allocation of resources, thus drastically improving the performance of slices. We evaluate LL-MEC in three practical case studies, namely, (i) end-to-end mobile network slicing, (ii) RAN-aware video content optimization and (iii) IoT gateway, and show that it achieves a 2-4x lower user plane latency compared to LTE, while enabling low latency edge applications to operate on a per millisecond basis. Also, we highlight the benefits of RAN-aware applications in improving user Quality of Experience (QoE), showing a significant user latency reduction along with a much lower variability compared to legacy LTE. Last, a compatibility evaluation of LL-MEC over the OpenAirInterface real-time LTE platform demonstrates the scalability merits of LL-MEC due to the use of an OpenFlow Virtual Switch for the user plane function, rather than a Linux kernel in typical LTE setups.

A number of studies have highlighted issues around the relationship between service users and providers. The recovery model is predominant in mental health as is the recognition of the importance of person-centred practice. The authors completed an in-depth search of the literature to answer the question: What are service users' experiences of the mental health service? Three key themes emerged: acknowledging a mental health problem and seeking help; building relationships through participation in care; and working towards continuity of care. The review adds to the current body of knowledge by providing greater detail into the importance of relationships between service users and providers and how these may impact on the delivery of care in the mental health service. The overarching theme that emerged was the importance of the relationship between the service user and provider as a basis for interaction and support. This review has specific implications for mental health nursing. Despite the recognition made in policy documents for change, issues with stigma, poor attitudes and communication persist. There is a need for a fundamental shift in the provider-service user relationship to facilitate true service-user engagement in their care. The aim of this integrative literature review was to identify mental health service users' experiences of services. The rationale for this review was based on the growing emphasis and requirements for health services to deliver care and support, which recognizes the preferences of individuals. Contemporary models of mental health care strive to promote inclusion and empowerment. This review seeks to add to our current understanding of how service users experience care and support in order to determine to what extent the principles of contemporary models of mental health care are embedded in practice. A robust search of Web of Science, the Cochrane Database, Science Direct, EBSCO host (Academic Search Complete, MEDLINE, CINAHL Plus Full-Text), PsycINFO, PsycARTICLES, Social Sciences Full Text and the United Kingdom and Ireland Reference Centre for data published between 1 January 2008 and 31 December 2012 was completed. The initial search retrieved 272 609 papers. The authors used a staged approach and the application of predetermined inclusion/exclusion criteria, thus the numbers of papers for inclusion were reduced to 34. Data extraction, quality assessment and thematic analysis were completed for the included studies. Satisfaction with the mental health service was moderately good. However, accessing services could be difficult because of a lack of knowledge and the stigma surrounding mental health. Large surveys document moderate satisfaction ratings; however, feelings of fear regarding how services function and the lack of treatment choice remain. The main finding from this review is while people may express satisfaction with mental health services, there are still issues around three main themes: acknowledging a mental health problem and seeking help; building relationship through participation and care; and working towards continuity of care. Elements of the recovery model appear to be lacking in relation to user involvement, empowerment and decision making. There is a need for a fundamental shift in the context of the provider-service user relationship to fully facilitate service users' engagement in their care.

Optimal resource management for multi-access edge computing without using cross-layer communication

In multi-access edge computing (MEC) networks, parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. These VNFs are unreliable due to software faults and server failures. A practical way to address this is to deploy idle backup VNFs (BVNFs) near these active VNFs and activate them when active VNFs fail. However, deploying BVNFs preempts server resources and decreases the number of accepted user requests. Thus, this paper proposes a reliability enhancement approach that uses BVNFs satisfying the delay requirement as active VNFs to form P-SFCs, which contributes to the delay reduction and reliability enhancement. Since the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs, establishing how to deploy the minimum number of P-SFCs and BVNFs to satisfy the delay and reliability requirements of mobile users and maximize the number of accepted user requests is a challenging problem. In this paper, we first model the dynamics of delay and reliability caused by VNF parallelization and BVNFs deployment, then formulate the joint deployment problem of P-SFCs and BVNFs. Next, we design an approximation algorithm to deploy critical VNFs and BVNFs on a target edge server and schedule the data traffic of user requests processed by P-SFCs. Experimental results based on real-world datasets show that our proposed algorithm outperforms two benchmark algorithms in terms of throughput, delay, reliability, and resource utilization.