Understanding the Impact of Self-Similarity on Network Utilization in Multimedia Networks

Satisfying Quality of Service (QoS) of multimedia traffic has been gaining more attentions recently. Data, voice and video share the same communication channel but have significantly different traffic characteristics and performance requirements in terms of packet loss, delay, etc. Self-similarity also imposes some new challenges for network dimensioning as more buffer and bandwidth are expected to accommodate the extra burstness of self-similarity of multimedia application. However, previous studies did not investigate how much extra bandwidth is needed to meet QoS requirements of self-similar multimedia traffic. In this study, we model delay and loss requirements of interactive voice as the constraints to simulate the maximum allowable link loads. Results of this study suggest that under a self-similar input traffic, most of the bandwidth resources of slow links will be wasted to accommodate the traffic burstness if quality requirements of voice traffic are to be satisfied in a best-effort manner. For fast links (\u3e10Mbps), self-similarity seems to have little impact on the maximum allowable load, which is above 82%

QoS provisioning in multimedia streaming

Multimedia consists of voice, video, and data. Sample applications include video conferencing, video on demand, distance learning, distributed games, and movies on demand. Providing Quality of Service (QoS) for multimedia streaming has been a difficult and challenging problem. When multimedia traffic is transported over a network, video traffic, though usually compressed/encoded for bandwidth reduction, still consumes most of the bandwidth. In addition, compressed video streams typically exhibit highly variable bit rates as well as long range dependence properties, thus exacerbating the challenge in meeting the stringent QoS requirements of multimedia streaming with high network utilization. Dynamic bandwidth allocation in which video traffic prediction can play an important role is thus needed. Prediction of the variation of the I frame size using Least Mean Square (LMS) is first proposed. Owing to a smoother sequence, better prediction has been achieved as compared to the composite MPEG video traffic prediction scheme. One problem with this LMS algorithm is its slow convergence. In Variable Bit Rate (VBR) videos characterized by frequent scene changes, the LMS algorithm may result in an extended period of intractability, and thus may experience excessive cell loss during scene changes. A fast convergent non-linear predictor called Variable Step-size Algorithm (VSA) is subsequently proposed to overcome this drawback. The VSA algorithm not only incurs small prediction errors but more importantly achieves fast convergence. It tracks scene changes better than LMS. Bandwidth is then assigned based on the predicted I frame size which is usually the largest in a Group of Picture (GOP). Hence, the Cell Loss Ratio (CLR) can be kept small. By reserving bandwidth at least equal to the predicted one, only prediction errors need to be buffered. Since the prediction error was demonstrated to resemble white noise or exhibits at most short term memory, smaller buffers, less delay, and higher bandwidth utilization can be achieved. In order to further improve network bandwidth utilization, a QoS guaranteed on-line bandwidth allocation is proposed. This method allocates the bandwidth based on the predicted GOP and required QoS. Simulations and analytical results demonstrate that this scheme provides guaranteed delay and achieves higher bandwidth utilization. Network traffic is generally accepted to be self similar. Aggregating self similar traffic can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly as compared to the LMS algorithm. Thus, it can be used to effectively predict the real time network traffic. The Differentiated Service (DiffServ) model is a less complex and more scalable solution for providing QoS to IP as compared to the Integrated Service (IntServ) model. We propose to transport MPEG frames through various service classes of DiffServ according to the MPEG video characteristics. Performance analysis and simulation results show that our proposed approach can not only guarantee QoS but can also achieve high bandwidth utilization. As the end video quality is determined not only by the network QoS but also by the encoded video quality, we consider video quality from these two aspects and further propose to transport spatial scalable encoded videos over DiffServ. Performance analysis and simulation results show that this can provision QoS guarantees. The dropping policy we propose at the egress router can reduce the traffic load as well as the risk of congestion in other domains

Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval

In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple diverse textual and visual features into feature pairs that lead to generating multiple joint feature spaces, which encode text-video pairs into comparable representations. To learn these representations our proposed network architecture is trained by following a multiple space learning procedure. Moreover, at the retrieval stage, we introduce additional softmax operations for revising the inferred query-video similarities. Extensive experiments in several setups based on three large-scale datasets (IACC.3, V3C1, and MSR-VTT) lead to conclusions on how to best combine text-visual features and document the performance of the proposed network. Source code is made publicly available at: https://github.com/bmezaris/TextToVideoRetrieval-TtimesVComment: Accepted for publication; to be included in Proc. ECCV Workshops 2022. The version posted here is the "submitted manuscript" versio

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Renegotiation based dynamic bandwidth allocation for selfsimilar VBR traffic

The provision of QoS to applications traffic depends heavily on how different traffic types are categorized and classified, and how the prioritization of these applications are managed. Bandwidth is the most scarce network resource. Therefore, there is a need for a method or system that distributes an available bandwidth in a network among different applications in such a way that each class or type of traffic receives their constraint QoS requirements. In this dissertation, a new renegotiation based dynamic resource allocation method for variable bit rate (VBR) traffic is presented. First, pros and cons of available off-line methods that are used to estimate selfsimilarity level (represented by Hurst parameter) of a VBR traffic trace are empirically investigated, and criteria to select measurement parameters for online resource management are developed. It is shown that wavelet analysis based methods are the strongest tools in estimation of Hurst parameter with their low computational complexities, compared to the variance-time method and R/S pox plot. Therefore, a temporal energy distribution of a traffic data arrival counting process among different frequency sub-bands is considered as a traffic descriptor, and then a robust traffic rate predictor is developed by using the Haar wavelet analysis. The empirical results show that the new on-line dynamic bandwidth allocation scheme for VBR traffic is superior to traditional dynamic bandwidth allocation methods that are based on adaptive algorithms such as Least Mean Square, Recursive Least Square, and Mean Square Error etc. in terms of high utilization and low queuing delay. Also a method is developed to minimize the number of bandwidth renegotiations to decrease signaling costs on traffic schedulers (e.g. WFQ) and networks (e.g. ATM). It is also quantified that the introduced renegotiation based bandwidth management scheme decreases heavytailedness of queue size distributions, which is an inherent impact of traffic self similarity. The new design increases the achieved utilization levels in the literature, provisions given queue size constraints and minimizes the number of renegotiations simultaneously. This renegotiation -based design is online and practically embeddable into QoS management blocks, edge routers and Digital Subscriber Lines Access Multiplexers (DSLAM) and rate adaptive DSL modems