TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often used for recommendation, fluctuate over time. Thus, the QoS prediction is essential to identify a suitable service among functionally equivalent services over time. The contemporary temporal QoS prediction methods hardly achieved the desired accuracy due to various limitations, such as the inability to handle data sparsity and outliers and capture higher-order temporal relationships among user-service interactions. Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e.g., collaborative features) to comprehend the relationship among the user-service interactions. This paper addresses the above challenges and proposes a scalable strategy for Temporal QoS Prediction using Multi-source Collaborative-Features (TPMCF), achieving high prediction accuracy and faster responsiveness. TPMCF combines the collaborative-features of users/services by exploiting user-service relationship with the spatio-temporal auto-extracted features by employing graph convolution and transformer encoder with multi-head self-attention. We validated our proposed method on WS-DREAM-2 datasets. Extensive experiments showed TPMCF outperformed major state-of-the-art approaches regarding prediction accuracy while ensuring high scalability and reasonably faster responsiveness.Comment: 10 Pages, 7 figure

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Prediction Quality of Service in 5G Networks

Την παραμονή της συνδεδεμένης και αυτοματοποιημένης κινητικότητας (CAM) με δυνατότητα 5G, εμφανίστηκαν οι απαιτητικές υπηρεσίες όχημα-σε-οτιδήποτε (V2X) για αυτοματοποιημένη και ασφαλέστερη οδήγηση. Οι απαιτήσεις που απορρέουν από αυτές τις υπηρεσίες δημιουργούν πολύ αυστηρές προκλήσεις για το δίκτυο κυρίως όσον αφορά τον βασικό δείκτη απόδοσης (KPI) καθυστέρησης από άκρο σε άκρο (end-to-end delay). Ταυτόχρονα, η τεχνητή νοημοσύνη (AI) που εμφανίζεται εντός του δικτύου, αποκαλύπτει μια πληθώρα νέων δυνατοτήτων του δικτύου, να ενεργεί με προληπτικό τρόπο ως προς την ικανοποίηση των προαναφερθεισών απαιτήσεων. Αυτή η πτυχιακή εργασία παρουσιάζει έναν μηχανισμό πρόβλεψης ποιότητας υπηρεσιών (PreQoS), που υποστηρίζεται από τεχνητή νοημοσύνη, εστιάζει στις υπηρεσίες όχημα-σε-οτιδήποτε και είναι σε θέση να προβλέψει έγκαιρα συγκεκριμένες μετρήσεις ποιότητας υπηρεσίας. Παράδειγμα αυτών των υπηρεσιών είναι ο ρυθμός δεδομένων (data rate) και η καθυστέρηση στις ανερχόμενες (uplink) και κατερχόμενες ζεύξεις (downlink) από άκρο σε άκρο, προκειμένου να προσφέρει το απαιτούμενο χρονικό παράθυρο στο δίκτυο για να κατανείμει αποτελεσματικότερα τους πόρους του, καθώς και στις αντίστοιχες υπηρεσίες και εφαρμογές όχημα-σε-οτιδήποτε για την εκτέλεση των απαιτούμενων προσαρμογών. Η αξιολόγηση του προτεινόμενου μηχανισμού βασίζεται σε ένα ρεαλιστικό, προσομοιωμένο περιβάλλον όχημα-σε-οτιδήποτε που αποδεικνύει τη βιωσιμότητα και την εγκυρότητα μιας τέτοιας προσέγγισηςOn the eve of 5G-enabled Connected and Automated Mobility, challenging Vehicle-to-Everything services have emerged towards safer and automated driving. The requirements that stem from those services pose very strict challenges to the network primarily with regard to the end-to-end delay and service reliability. At the same time, the in-network Artificial Intelligence that is emerging, reveals a plethora of novel capabilities of the network to act in a proactive manner towards satisfying the aforementioned challenging requirements. This work presents PreQoS, a predictive Quality of Service mechanism that focuses on Vehicle-to-Everything services. PreQoS is able to timely predict specific Quality of Service metrics, such as uplink and downlink data rate and end to-end delay, in order to offer the required time window to the network to allocate more efficiently its resources. On top of that, the proactive management of those resources enables the respective Vehicle-to-Everything services and applications to perform any potential Quality of Service-related required adaptations in advance. The evaluation of the proposed mechanism based on a realistic, simulated, Connected and Automated Mobility environment proves the viability and validity of such an approach

Network-aware video streaming for future media Internet

272 p

AI-Driven, Predictive QoS for V2X Communications in 5G and beyond.

Με το ξεκίνημα της εποχής της συνδεδεμένης και αυτοματοποιημένης κινητικότητας με δυνατότητα 5G, έχουν προκύψει καινοτόμες υπηρεσίες Vehicle-to-Everything προς ασφαλέστερη και αυτοματοποιημένη οδήγηση. Οι απαιτήσεις που απορρέουν από αυτές τις υπηρεσίες θέτουν πολύ αυστηρές προκλήσεις στο δίκτυο κυρίως όσον αφορά την καθυστέρηση από άκρο σε άκρο και την αξιοπιστία των υπηρεσιών. Ταυτόχρονα, η τεχνητή νοημοσύνη εντός δικτύου που αναδύεται, αποκαλύπτει μια πληθώρα νέων δυνατοτήτων του δικτύου να ενεργεί με προληπτικό τρόπο προς την ικανοποίηση των προαναφερθέντων μεγάλων απαιτήσεων. Αυτή η διατριβή παρουσιάζει το PreQoS, έναν προγνωστικό μηχανισμό Ποιότητας Υπηρεσιών που εστιάζει στις υπηρεσίες Οχήματος-προς-Όλα (V2X). Το PreQoS είναι σε θέση να προβλέψει έγκαιρα συγκεκριμένες μετρήσεις Ποιότητας Υπηρεσιών, όπως ο ρυθμός δεδομένων uplink and downlink και η καθυστέρηση από άκρο σε άκρο, προκειμένου να προσφέρει το απαιτούμενο χρονικό διάστημα στο δίκτυο για την πιο αποτελεσματική κατανομή των πόρων του. Επιπλέον, η προληπτική διαχείριση αυτών των πόρων επιτρέπει στις αντίστοιχες υπηρεσίες και εφαρμογές του Οχήματος προς Όλα να εκτελούν εκ των προτέρων τυχόν ενδεχόμενες προσαρμογές που σχετίζονται με την Ποιότητα Υπηρεσιών. Η αξιολόγηση του προτεινόμενου μηχανισμού με βάση ένα ρεαλιστικό, προσομοιωμένο, συνδεδεμένο και αυτοματοποιημένο περιβάλλον κινητικότητας αποδεικνύει τη βιωσιμότητα και την εγκυρότητα μιας τέτοιας προσέγγισης.On the eve of 5G-enabled Connected and Automated Mobility, challenging Vehicle-to-Everything services have emerged towards safer and automated driving. The requirements that stem from those services pose very strict challenges to the network primarily with regard to the end-to-end delay and service reliability. At the same time, the in-network Artificial Intelligence that is emerging, reveals a plethora of novel capabilities of the network to act in a proactive manner towards satisfying the aforementioned challenging requirements. This thesis presents PreQoS, a predictive Quality of Service mechanism that focuses on Vehicle-to-Everything services. PreQoS is able to timely predict specific Quality of Service metrics, such as uplink and downlink data rate and end-to-end delay, in order to offer the required time window to the network to allocate more efficiently its resources. On top of that, the proactive management of those resources enables the respective Vehicle-to-Everything services and applications to perform any potential Quality of Service-related required adaptations in advance. The evaluation of the proposed mechanism based on a realistic, simulated, Connected and Automated Mobility environment proves the viability and validity of such an approach