A hybrid decision approach for the association problem in heterogeneous networks

The area of networking games has had a growing impact on wireless networks. This reflects the recognition in the important scaling advantages that the service providers can benefit from by increasing the autonomy of mobiles in decision making. This may however result in inefficiencies that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized protocols keep being considered and compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal centric radio resource management schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. We derive the utilities related to the Quality of Service (QoS) perceived by the users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE systems.Comment: 5 pages, 4 figures, IEEE Infocom, San Diego, USA, March 2010

Fundamental Limits on Latency in Transceiver Cache-Aided HetNets

Stringent mobile usage characteristics force wire- less networks to undergo a paradigm shift from conventional connection-centric to content-centric deployment. With respect to 5G, caching and heterogeneous networks (HetNet) are key technologies that will facilitate the evolution of highly content- centric networks by facilitating unified quality of service in terms of low-latency communication. In this paper, we study the impact of transceiver caching on the latency for a HetNet consisting of a single user, a receiver and one cache-assisted transceiver. We define an information-theoretic metric, the delivery time per bit (DTB), that captures the delivery latency. We establish coinciding lower and upper bounds on the DTB as a function of cache size and wireless channel parameters; thus, enabling a complete characterization of the DTB optimality of the network under study. As a result, we identify cache beneficial and non-beneficial channel regimes.Comment: 5 pages, ISIT 201

Denial of Service (DoS) in Internet Protocol (IP) Network and Information Centric Network (ICN): An Impediment to Network Quality of Service (QoS).

This paper compares and analyses the Denial-of-Service attacks in the two different Network architectures. The two architectures are based on different routing approaches: Hop-by-Hop IP routing and source-routing using Bloom filters. In Hop-by-Hop IP routing, the packet header contains the address, and the route is decided node by node. Forwarding in this method requires a node to have a routing table which contains the port through which the packet should traverse depending on the address of the destination. Instead in source-routing, the forwarding identifier is encoded with the path a packet should take and it is placed in the packet header. The forwarding identifier in this approach does not require a forwarding table for look ups like the IP routing; it relies on Line Speed Publish/Subscribe (LIPSIN) forwarding solution that focuses on using named links not nodes or interfaces. The forwarding identifier encompasses a set of Link ID’s which specifies the path to the recipient and they are encoded in a Bloom filter. The In-packet Bloom filters serve as both path selectors and as capabilities, and they are generated dynamically. However, this thesis is going to focus on the latter network technology by looking at both its benefits and drawbacks as well as analysing the possibilities of having a Denial of service attack. Keywords: DoS, DDoS, TCP/IP Protocol Suite, ICMP flood, E-mail Bomb, Ping of Death, TCP and UD

Quality of experience evaluation of voice communication: an affect-based approach

Voice communication systems such as Voice-over IP (VoIP), Public Switched Telephone Networks, and Mobile Telephone Networks, are an integral means of human tele-interaction. These systems pose distinctive challenges due to their unique characteristics such as low volume, burstiness and stringent delay/loss requirements across heterogeneous underlying network technologies. Effective quality evaluation methodologies are important for system development and refinement, particularly by adopting user feedback based measurement. Presently, most of the evaluation models are system-centric (Quality of Service or QoS-based), which questioned us to explore a user-centric (Quality of Experience or QoE-based) approach as a step towards the human-centric paradigm of system design. We research an affect-based QoE evaluation framework which attempts to capture users\u27 perception while they are engaged in voice communication. Our modular approach consists of feature extraction from multiple information sources including various affective cues and different classification procedures such as Support Vector Machines (SVM) and k-Nearest Neighbor (kNN). The experimental study is illustrated in depth with detailed analysis of results. The evidences collected provide the potential feasibility of our approach for QoE evaluation and suggest the consideration of human affective attributes in modeling user experience

Service-centric networking for distributed heterogeneous clouds

Optimal placement and selection of service instances in a distributed heterogeneous cloud is a complex trade-off between application requirements and resource capabilities that requires detailed information on the service, infrastructure constraints, and the underlying IP network. In this article we first posit that from an analysis of a snapshot of today's centralized and regional data center infrastructure, there is a sufficient number of candidate sites for deploying many services while meeting latency and bandwidth constraints. We then provide quantitative arguments why both network and hardware performance needs to be taken into account when selecting candidate sites to deploy a given service. Finally, we propose a novel architectural solution for service-centric networking. The resulting system exploits the availability of fine-grained execution nodes across the Internet and uses knowledge of available computational and network resources for deploying, replicating and selecting instances to optimize quality of experience for a wide range of services

Efficient management of virtualized information-centric networks

The Internet has rapidly evolved from a network, connecting a couple of dozens of computers, to a network containing billions of devices. Furthermore, the current Internet is mostly used to deliver complex services with increasingly stringent Quality of Service (QoS) requirements. However, the underlying network model has remained the same, making the Internet not well suited to optimally support the current user trends and services. Currently, a lot of effort is being made in the area of network virtualization and Information-Centric Networking (ICN) to support the evolution towards the QoS constraint distribution of large amounts of information. Even though both directions offer a lot of opportunities, multiple important challenges have to be faced when managing the placement of content inside the network and guaranteeing delivery efficiency. These challenges are further increased when a combination of both trends is considered. This paper gives an overview of these challenges and how this PhD will deal with the mutual influences of network virtualization and ICN in an efficient way

Smart PIN: performance and cost-oriented context-aware personal information network

The next generation of networks will involve interconnection of heterogeneous individual networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore, there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches for data delivery across the heterogeneous network are also required. In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed: Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD). MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches

Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.Peer reviewe

Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.Peer reviewe