Service quality assurance for the IPTV networks

The objective of the proposed research is to design and evaluate end-to-end solutions to support the Quality of Experience (QoE) for the Internet Protocol Television (IPTV) service. IPTV is a system that integrates voice, video, and data delivery into a single Internet Protocol (IP) framework to enable interactive broadcasting services at the subscribers. It promises significant advantages for both service providers and subscribers. For instance, unlike conventional broadcasting systems, IPTV broadcasts will not be restricted by the limited number of channels in the broadcast/radio spectrum. Furthermore, IPTV will provide its subscribers with the opportunity to access and interact with a wide variety of high-quality on-demand video content over the Internet. However, these advantages come at the expense of stricter quality of service (QoS) requirements than traditional Internet applications. Since IPTV is considered as a real-time broadcast service over the Internet, the success of the IPTV service depends on the QoE perceived by the end-users. The characteristics of the video traffic as well as the high-quality requirements of the IPTV broadcast impose strict requirements on transmission delay. IPTV framework has to provide mechanisms to satisfy the stringent delay, jitter, and packet loss requirements of the IPTV service over lossy transmission channels with varying characteristics. The proposed research focuses on error recovery and channel change latency problems in IPTV networks. Our specific aim is to develop a content delivery framework that integrates content features, IPTV application requirements, and network characteristics in such a way that the network resource utilization can be optimized for the given constraints on the user perceived service quality. To achieve the desired QoE levels, the proposed research focuses on the design of resource optimal server-based and peer-assisted delivery techniques. First, by analyzing the tradeoffs on the use of proactive and reactive repair techniques, a solution that optimizes the error recovery overhead is proposed. Further analysis on the proposed solution is performed by also focusing on the use of multicast error recovery techniques. By investigating the tradeoffs on the use of network-assisted and client-based channel change solutions, distributed content delivery frameworks are proposed to optimize the error recovery performance. Next, bandwidth and latency tradeoffs associated with the use of concurrent delivery streams to support the IPTV channel change are analyzed, and the results are used to develop a resource-optimal channel change framework that greatly improves the latency performance in the network. For both problems studied in this research, scalability concerns for the IPTV service are addressed by properly integrating peer-based delivery techniques into server-based solutions.Ph.D

Networks: A study in Analysis and Design

In this dissertation, we will look at two fundamental aspects of Networks: Network Analysis and Network Design. In part A, we look at Network Analysis area of the dissertation which involves finding the densest subgraph in each graph. The densest subgraph extraction problem is fundamentally a non-linear optimization problem. Nevertheless, it can be solved in polynomial time by an exact algorithm based on the iterative solution of a series of max-flow sub-problems. To approach graphs with millions of vertices and edges, one must resort to heuristic algorithms. We provide an efficient implementation of a greedy heuristic from the literature that is extremely fast and has some nice theoretical properties. An extensive computational analysis shows that the proposed heuristic algorithm proved very effective on many test instances, often providing either the optimal solution or near-optimal solution within short computing times. In part-B, we discuss Network design, which is a cornerstone of mathematical optimization, is about defining the main characteristics of a network satisfying requirements on connectivity, capacity, and level-of-service. In multi-commodity network design, one is required to design a network minimizing the installation cost of its arcs and the operational cost to serve a set of point-to-point connections. This prototypical problem was recently enriched by additional constraints imposing that each origin-destination of a connection is served by a single path satisfying one or more level-of-service requirements, thus defining the Network Design with Service Requirements. These constraints are crucial, e.g., in telecommunications and computer networks, in order to ensure reliable and low-latency communication. We provide a new formulation for the problem, where variables are associated with paths satisfying the end-to-end service requirements. A fast algorithm for enumerating all the exponentially-many feasible paths and, when this is not viable, a column generation scheme that is embedded into a branch-and-cut-and-price algorithm is provided

Autonomic service configuration for telecommunication MASs with extended role-based GAIA and JADEx

Autonomie Communications have attracted huge attention recently for the management of telecommunication networks in the European Network Research Community. The purpose of this research is to offer the abilities such as autonomy, scalability, adaptation as well as simplicity for management application in complex networks. The accomplished networks inspired by biological mechanisms or market-based concepts could enable agents to be of intelligence, scalablility, and interoperabliliry in the management functional domains with regards to the large volume requirements from services' fulfillment perspective in decentralized Multi-Agent Systems. In accordance with TMF and FIPA specifications and requirements, the autonomy attributes self-configuring, self-adapting, self-limiting, self-preserving, and self-optimizing are involved into our simulation. Resource allocation requests are bidded for a long session in the multi-unit Vickrey-Clarke-Groves auction. This design adopts the software development methodology-GAIA and the framework-JADEx. We have shown multiple service configuration in dynamic network can be nearly optimized by autonomie behaviors via bidding according to business objectives for getting maximum revenues. We conclude this end-to-end approach maintains self-managing capability, easy-to-implement scalability, and more incentively compatible and efficient over other common implementation so that it could achieve the optimal solution to the flexible requirements for the Service Fulfillment for advanced IP networks. © 2005 IEEE

Route selection for QoS over Mobile IP

Influenced by the availability of powerful portable computers, and the expansion of Internet-based services, consumers demand mobile Internet. This implies supporting user mobility while sustaining network connectivity which in turn requires more sophisticated routing methods than simple static Internet protocol. Routing optimization is critical for the efficiency of mobile Internet as it directly impacts resource utilization in the network. On the other hand, while connected to the Internet, users want to enjoy real-time services. Granting quality of service for real-time multimedia applications is one of the major concerns for Internet Service Providers. In the mobile Internet context, as a result of a signalling conflict, providing quality of service declines in presence of routing optimisation. This conflict must be resolved to achieve efficient resource utilization in the networks and provide quality of service for real-time Internet-based services. This thesis presents a solution for resolving the aforementioned conflict. Initially a set of requirements are established for a new solution. The fundamental idea is developed based on exercising routing optimization according to quality of service requirements and network conditions. A new protocol architecture for Mobile IP (MIP) is built based on a cross-layer design technique. This design implies that a new data flow collects the necessary parameters in the network. This data is passed to a new entity in the network layer of the MIP protocol stack that inter-connects IP, routing, mobility and resource reservation protocols. Based on network conditions and the quality of service requirements the optimal path is selected over which the resources are reserved for the duration of the connection. The new solution named MIP with Routing Optimization and QoS (MIP-ROQS) removes the signalling conflict and furthermore, results in increased network performance. Simulation of this solution demonstrates reduction in application end-to-end delay and delay variation, improved core network by reducing the amount of control traffic and dropped traffic and improved access network by reducing its delay. Moreover, we integrate Multi Protocol Label Switching (MPLS) architecture and MIP-ROQS to take advantage of MPLS labelling mechanism to label the optimal path selected by MIP-ROQS for the duration of a session. This integration makes IP-in-IP tunnelling in data forwarding redundant. MPLS is used to switch packets and label the optimal path over which the resources are reserved. Using this approach, the transmission delay, and packet processing overhead are further reduced

Power Control for Full-Duplex Relay-Enhanced Cellular Networks With QoS Guarantees

Full-duplex (FD) has emerged as a new communication paradigm with the potential advantage of enhancing the capacity of the wireless communication systems. In this paper, we consider an FD relay-enhanced cellular network, wherein the residual self-interference, the uplink-downlink interference, as well as the relay-access-link interference are the vital restrictions to network performance. To this end, we investigate power control design for the FD relay-enhanced cellular networks, so as to maximize the system spectral efficiency while fulfilling the quality of service (QoS) requirements of both the uplink and downlink user equipments (UEs). We characterize the properties of the optimal transmit power allocation, and propose a power control algorithm based on signomial programming to coordinate the transmit power of the uplink UE, base station, and relay stations to mitigate the interference. Meanwhile, we also derive the closed-form optimal transmit power allocation for the conventional half-duplex (HD) transmission mode. Moreover, we conduct extensive simulation experiments to study the network-level gain of the FD mode over the HD mode in the relay-enhanced cellular networks. Simulation results demonstrate that FD relaying outperforms HD relaying on improving the spectral and energy efficiency, as well as provisioning QoS guarantees for both the uplink and downlink users

Power Control for Full-Duplex Relay-Enhanced Cellular Networks With QoS Guarantees

Full-duplex (FD) has emerged as a new communication paradigm with the potential advantage of enhancing the capacity of the wireless communication systems. In this paper, we consider an FD relay-enhanced cellular network, wherein the residual self-interference, the uplink-downlink interference, as well as the relay-access-link interference are the vital restrictions to network performance. To this end, we investigate power control design for the FD relay-enhanced cellular networks, so as to maximize the system spectral efficiency while fulfilling the quality of service (QoS) requirements of both the uplink and downlink user equipments (UEs). We characterize the properties of the optimal transmit power allocation, and propose a power control algorithm based on signomial programming to coordinate the transmit power of the uplink UE, base station, and relay stations to mitigate the interference. Meanwhile, we also derive the closed-form optimal transmit power allocation for the conventional half-duplex (HD) transmission mode. Moreover, we conduct extensive simulation experiments to study the network-level gain of the FD mode over the HD mode in the relay-enhanced cellular networks. Simulation results demonstrate that FD relaying outperforms HD relaying on improving the spectral and energy efficiency, as well as provisioning QoS guarantees for both the uplink and downlink users

Towards end-to-end security in internet of things based healthcare

Healthcare IoT systems are distinguished in that they are designed to serve human beings, which primarily raises the requirements of security, privacy, and reliability. Such systems have to provide real-time notifications and responses concerning the status of patients. Physicians, patients, and other caregivers demand a reliable system in which the results are accurate and timely, and the service is reliable and secure. To guarantee these requirements, the smart components in the system require a secure and efficient end-to-end communication method between the end-points (e.g., patients, caregivers, and medical sensors) of a healthcare IoT system. The main challenge faced by the existing security solutions is a lack of secure end-to-end communication. This thesis addresses this challenge by presenting a novel end-to-end security solution enabling end-points to securely and efficiently communicate with each other. The proposed solution meets the security requirements of a wide range of healthcare IoT systems while minimizing the overall hardware overhead of end-to-end communication. End-to-end communication is enabled by the holistic integration of the following contributions. The first contribution is the implementation of two architectures for remote monitoring of bio-signals. The first architecture is based on a low power IEEE 802.15.4 protocol known as ZigBee. It consists of a set of sensor nodes to read data from various medical sensors, process the data, and send them wirelessly over ZigBee to a server node. The second architecture implements on an IP-based wireless sensor network, using IEEE 802.11 Wireless Local Area Network (WLAN). The system consists of a IEEE 802.11 based sensor module to access bio-signals from patients and send them over to a remote server. In both architectures, the server node collects the health data from several client nodes and updates a remote database. The remote webserver accesses the database and updates the webpage in real-time, which can be accessed remotely. The second contribution is a novel secure mutual authentication scheme for Radio Frequency Identification (RFID) implant systems. The proposed scheme relies on the elliptic curve cryptography and the D-Quark lightweight hash design. The scheme consists of three main phases: (1) reader authentication and verification, (2) tag identification, and (3) tag verification. We show that among the existing public-key crypto-systems, elliptic curve is the optimal choice due to its small key size as well as its efficiency in computations. The D-Quark lightweight hash design has been tailored for resource-constrained devices. The third contribution is proposing a low-latency and secure cryptographic keys generation approach based on Electrocardiogram (ECG) features. This is performed by taking advantage of the uniqueness and randomness properties of ECG's main features comprising of PR, RR, PP, QT, and ST intervals. This approach achieves low latency due to its reliance on reference-free ECG's main features that can be acquired in a short time. The approach is called Several ECG Features (SEF)-based cryptographic key generation. The fourth contribution is devising a novel secure and efficient end-to-end security scheme for mobility enabled healthcare IoT. The proposed scheme consists of: (1) a secure and efficient end-user authentication and authorization architecture based on the certificate based Datagram Transport Layer Security (DTLS) handshake protocol, (2) a secure end-to-end communication method based on DTLS session resumption, and (3) support for robust mobility based on interconnected smart gateways in the fog layer. Finally, the fifth and the last contribution is the analysis of the performance of the state-of-the-art end-to-end security solutions in healthcare IoT systems including our end-to-end security solution. In this regard, we first identify and present the essential requirements of robust security solutions for healthcare IoT systems. We then analyze the performance of the state-of-the-art end-to-end security solutions (including our scheme) by developing a prototype healthcare IoT system

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

Parametric Design Synthesis of Distributed Embedded Systems

This paper presents a design synthesis method for distributed embedded systems. In such systems, computations can flow through long pipelines of interacting software components, hosted on a variety of resources, each of which is managed by a local scheduler. Our method automatically calibrates the local resource schedulers to achieve the system's global end-to-end performance requirements. A system is modeled as a set of distributed task chains (or pipelines), where each task represents an activity requiring nonzero load from some CPU or network resource. Task load requirements can vary stochastically, due to second-order effects like cache memory behavior, DMA interference, pipeline stalls, bus arbitration delays, transient head-of-line blocking, etc. We aggregate these effects -- along with a task's per-service load demand -- and model them via a single random variable, ranging over an arbitrary discrete probability distribution. Load models can be obtained via profiling tasks in isolation, or simply by using an engineer's hypothesis about the system's projected behavior. The end-to-end performance requirements are posited in terms of throughput and delay constraints. Specifically, a pipeline's delay constraint is an upper bound on the total latency a computatation can accumulate, from input to output. The corresponding throughput constraint mandates the pipeline's minimum acceptable output rate -- counting only outputs which meet their delay constraints. Since per-component loads can be generally distributed, and since resources host stages from multiple pipelines, meeting all of the system's end-to-end constraints is a nontrivial problem. Our approach involves solving two sub-problems in tandem: (A)~finding an optimal proportion of load to allocate each task and channel; and (B)~deriving the best combination of service intervals over which all load proportions can be guaranteed. The design algorithms use analytic approximations to quickly estimate output rates and propagation delays for candidate solutions. When all parameters are synthesized, the estimated end-to-end performance metrics are re-checked by simulation. The per-component load reservations can then be increased, with the synthesis algorithms re-run to improve performance. At that point the system can be configured according to the synthesized scheduling parameters -- and then re-validated via on-line profiling. In this paper we demonstrate our technique on an example system, and compare the estimated performance to its simulated on-line behavior. (Also cross-referenced as UMIACS-TR-98-18