Accurate and Reliable Service Recommendation Based on Bilateral Perception in Multi-Access Edge Computing

Multi-Access Edge Computing (MEC) is an emerging computing paradigm that brings services from centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). With massive services from different domains being emerging in MEC, various powerful composite services can be created with simple services to satisfy users' complex needs. However, existing service composition methods follow a passive service model and cannot proactively recommend optimal composite services to users in MEC, which seriously affect users' service experience. To tackle this issue, we propose an approach for proactive recommendation of composite services based on demand prediction. Our approach consists of three steps. First, we predict a user's service demand based on an attention enhanced deep interaction network (AEDIN) model trained with clustered data. Then, we create the optimal composite service to satisfy the predicted demand with a mobility-aware services composition method, and finally, we proactively recommend the optimal composite service to the user. The extensive experiments have been carried out to verify our proposed approach and prove its performance superiority.

Wireless link quality is closely related to the received signal strength. Hence, transmit power control can be used to adjust the communication link quality to avoid asymmetric or weak links. This paper proposes a spatial radio resource model for quality of service (QoS) reliability in General Packet Radio Service (GPRS) networks. To model the propagation parameters, we adopt the empirical modeling approach by measuring signal outages at various base stations from a well established network, operating in Nigeria. We collect these data over a period of three months and apply the resultant data as parameters to the proposed model. We simulate the model under ideal conditions and evaluate the system’s performance as well as the power control tradeoff. A qualitative analysis using processed GIS maps, calls for effective power control in the system. Simulation results indicate that the existing system still requires further improvements to enhance its QoS reliability.

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.

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.

QoS prediction for service recommendations in mobile edge computing

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.

As the demand for mobile services grows exponentially, the focus on wireless network optimization has been changed from Quality of Service (QoS) for the network to Quality of Experience (QoE) for users. The network optimization research about QoS in the past cannot surely meet the requirements of the users' QoE. Therefore, in this paper, a handover solution is proposed to improve the QoE while considering the QoE balance for a LTE network that provides different services. Compared with other optimization of QoE nowadays, the proposed solution balances and improves the QoE of users. The proposed solution controls handover parameter by running a handover optimization algorithm based on the dynamic particle swarm optimization (DPSO) in a central controller, which finally optimizes the overall QoE and reduces the proportion of users with extremely poor QoE. The simulation results show that the DPSO algorithm ensures the quality of the solution and increases the speed of convergence by nearly twice that of the standard particle swarm optimization algorithm (SPSO). After adopting the DPSO handover solution, the overall QoE of users is increased by 6.22 % and 4.59 % while the variance of users' QoE is decreased by 14.2 % and 22.6 %, compared with the full handover solution and the traditional handover solution respectively. The number of users with QoE less than 1.9 is reduced to 0 with the proposed solution.

A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

Recently, the evaluation on quality of the air interface in a wireless network has shifted from emphasis on QoS (quality of service) only to incorporation of QoE (quality of experience). However, it seems that QoE metrics have not been related to the surroundings of the served users so far, which may have a great influence on users' expected QoE. For instance, with a high level of QoS, a user's expectation of QoE could still be terrible because of the serious interference from the surroundings such as noise, ambient light or shaking of terminals due to mobility. From another perspective, it means that in such a case a high level of QoS may be helpless to enhance users' QoE and the requirement could be relaxed accordingly. In this paper, an approach called surrounding-adaptive QoE adjustment (SAQA) is proposed to optimize the capacity and the quality of air interface with voice service as an example. The basic idea of the approach is that user equipment (UE) detects the surroundings interference which may impact users' QoE and then adjusts QoS according to the detected results: a lower level of QoS is chosen in a bad environment to enhance the capacity, while a higher level of QoS is selected to guarantee the users' QoE in a better circumstance. Simulation results show that this method can remarkably enhance the voice capacity of the communication systems as well as guarantee users' QoE.

Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.

Multi-access edge computing (MEC) provides processing and storage capabilities of the cloud into the radio access network. In this paper, we study the deployment of bandwidth management service in MEC environment. The bandwidth management service procedures are mapped onto functionality of the control protocol between radio access network and core network. An extension of the bandwidth management service is proposed that enables detecting of packets generated of specific applications and applying the appropriate enforcement actions. The proposed extension is described by typical use cases, information flows, required information, data model, as well as respective application programming interfaces. Models representing the status of bandwidth allocation as seen by the mobile edge application and network are proposed, formally described and verified. Formal model verification enables mathematical demonstration that the proposed extension is consistently implementable.

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

In an era of rapid urbanization, the quality of network services in urban environments is becoming increasingly important. This article explores efforts to improve network service quality in several sampang cities through an evaluation of Quality of Service (QoS) and user perceptions of Quality of Experience (QoE). The research is based on a survey conducted at various locations in sampang city to collect data on the reliability, speed, and availability of network services. The survey involved a diverse sample of users, covering a wide range of ages, professions, and levels of technology usage. In addition, we also analyzed users' perceptions of service quality based on their experiences of using the network in an urban environment. The results highlighted several key challenges in improving QoS, such as user density, signal interference, and network infrastructure limitations. However, we also found that strategies such as the use of advanced network technologies, such as 5G and beamforming, as well as traffic prioritization based on application type, can help to significantly improve QoS. In addition, we found a strong positive correlation between improved QoS and improved QoE, suggesting that improved quality of service has a direct impact on improving user experience. These findings provide valuable insights for network operators and policy makers in their efforts to improve network services in dense urban environments. The research also recommends further investment in network technologies and the development of policies that support more effective management of network traffic to ensure optimal quality of service.

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.

The internet currently hosts a large number of web services with highly volatile quality of service (QoS), which makes it difficult for users to quickly access highly reliable online services. Hence, the selection of the optimal service composition based on fast and reliable QoS has emerged as a challenging and popular problem in the field of service computing. In this paper, we propose a service selection approach based on QoS prediction. We consider historical QoS information as time series and predict QoS values using the autoregressive integrated moving average model, which can provide more accurate QoS attribute values. We then calculate the uncertainty in the prediction results using an improved coefficient of variation to prune redundant services. In order to downsize the search space, we employ Skyline computing to prune redundant services and perform Skyline service selection using 0-1 mixed-integer programming. Experimental results based on real-world dataset showed that our approach yields satisfactory performance in terms of reliability and efficiency.