Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computing-based service placement scheme.

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift— relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

As more and more services are deployed on devices near the network edge, security operations (such as authentication and authorization) need to move with them. Typically, edge devices have fewer resources than data center servers and so the security operations need to make more efficient use of what is available while offering adequate performance. Authorization adds latency and requires system resources, but the need for security management with strong authorization at the network edge is growing. We have released the first open source, high-performance, resource-efficient, XACML3 standard-compatible Policy Decision Point (PDP) called Luas (means “speed'' in the Irish language) based on an event-driven architecture and a non-blocking computational model, using a Bloom Filter for better performance. We compared its performance, resource usage and reliability against existing open source PDPs. Like those we tested, it provides accurate decisions, but Luas offers much faster security policy evaluation while using fewer system resources, and provides responses in a reasonable timeframe even when resources are scarce.

Artificial neural stimulation of a peripheral nerve can create an in-body data communications channel. We propose the stimulation of a peripheral nerve using energy-harvesting arrays of nanodevices, embedded in biocompatible tissue patches. The resulting extracellular compound action potential (CAP) pulse can provide a data bit-stream for communicating with an embedded receiver. Our objective is to determine the maximum achievable transmission range of a CAP along a nerve and the maximum sustainable bit rate. We model the generation of a CAP and then compute the reduction in amplitude and the spreading of the pulse with propagation distance. The channel capacity is calculated for ON-OFF keying (OOK) and digital pulse interval modulation. We show that the transmission range depends on the number and diameters of the activated neurons contributing to the CAP amplitude and width. Our modulation analysis demonstrates the effects of attenuation, background noise, the neural refractory period, and pulse broadening on the achievable bit rate. We show how a maximum OOK bit rate of 200 bit/s can be sustained over transmission distances greater than 100 mm. The proposed approach provides a low bit rate, unidirectional asynchronous transmission system that could, for example, deliver simple instructions to an embedded drug-delivery system.

Managing network resources in highly dynamic vehicular environments is difficult. It is made more challenging when vehicular safety applications demand high bandwidth, reliability and extremely low latency. Standalone wireless access technologies cannot meet these requirements simultaneously and so heterogeneous wireless networking has been proposed as a solution. However heterogeneity introduces other problems, particularly network fragmentation and inefficient use of network resources. Software-defined Networking (SDN) offers logically centralized control and re-programmability of the control plane, with the potential to address these problems of heterogeneity. However, SDN has difficulty dealing with the high levels of mobility associated with highly dynamic vehicular networks. This paper presents ongoing work towards an SDN-based heterogeneous vehicular networking, employing edge-based caching to ensure extremely reliable and low-latency communication for safety-critical cooperative vehicular services.

Vehicles are an important source of data, with embedded sensors including built-in cameras. Their data can be processed close to where it was generated, using computation and network resources on collaborating vehicles. Processing at the network edge helps to reduce end-to-end service latencies. However, such mobile virtual resource pools require effective management, especially if those resources are to be leased to third-party service providers. We address the problem of service placement on moving, connected, vehicle nodes (V2V) supported by roadside infrastructure (V2I). We formulate a distributed service model and optimize the placement of services, subject to constraints related to node resource capacity, link capacity, distributed application deployment (full deployment, anti-collocation and adjacency constraint) and vehicle mobility.

The myriad of sensor information that can be collected using smartphones, wearables and other IoT devices greatly benefits context-aware applications. These applications rely heavily on mobile devices, present in locations of interest, to offload raw or processed sensor data in order to accurately capture, recognize and classify the surrounding real-time context. However, continuous sensing and offloading of large volumes of mainly redundant sensor data significantly impacts energy-constrained mobile devices. This results in a trade-off between sensing accuracy and the energy consumed by these devices. We propose the use of application-specific state machines that encode the context of interest to determine when sensed data should be offloaded to the cloud. Our control algorithm, `Assisted-Aggregation' applies frequent pattern mining to reduce the number of active devices by sharing sensed data between multiple applications. Our evaluation shows an improvement in terms of the residual energy of the mobile devices, the number of devices actively offloading and the volume of the offloaded data.

Network functions virtualization (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating network function from traditional middleboxes, NFV is expected to lead to reduced capital expenditure and operating expenditure, and to more agile services. However, one of the main challenges to achieving these objectives is how physical resources can be efficiently, autonomously, and dynamically allocated to virtualized network function (VNF) whose resource requirements ebb and flow. In this paper, we propose a graph neural network-based algorithm which exploits VNF forwarding graph topology information to predict future resource requirements for each VNF component (VNFC). The topology information of each VNFC is derived from combining its past resource utilization as well as the modeled effect on the same from VNFCs in its neighborhood. Our proposal has been evaluated using a deployment of a virtualized IP multimedia subsystem, and real VoIP traffic traces, with results showing an average prediction accuracy of 90%, compared to 85% obtained while using traditional feed-forward neural networks. Moreover, compared to a scenario where resources are allocated manually and/or statically, our technique reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.

Network Functions Virtualisation (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating Network Functions (NFs) from traditional middleboxes, NFV is expected to lead to reduced CAPEX and OPEX, and to more agile services. However, one of the main challenges to achieving these objectives is on how physical resources can be efficiently, autonomously, and dynamically allocated to Virtualised Network Functions (VNFs) whose resource requirements ebb and flow. In this paper, we propose a Graph Neural Network (GNN)-based algorithm which exploits Virtual Network Function Forwarding Graph (VNF-FG) topology information to predict future resource requirements for each Virtual Network Function Component (VNFC). The topology information of each VNFC is derived from combining its past resource utilisation as well as the modelled effect on the same from VNFCs in its neighbourhood. Our proposal has been evaluated using a deployment of a virtualised IP Multimedia Subsystem (IMS), and real VoIP traffic traces, with results showing an average prediction accuracy of 90%. Moreover, compared to a scenario where resources are allocated manually and/or statically, our proposal reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.

Datacenter networks are commonly structured with hierarchical topologies in which multipaths are provided to create redundant paths between end-to-end servers. Without a dedicated flow control mechanism, different sizes of traffic flows are statically allocated to links without sensing the current link utilization, which can result in transient network congestion that impacts on the latency experienced by users of the hosted applications. Existing works aim to realize load balancing by efficiently scheduling flows based on their sizes and link utilization. However, many applications' performance is bounded with specific QoS targets, so load balancing may not directly address their QoS requirements. In this paper, we present a latency-aware flow scheduling system that schedules flows based on a tenant application's QoS target and empirical estimations of the effective bandwidth required to meet these QoS targets. The approach seeks to ensure that, following a network-aware placement of VMs hosting application components, the latency associated with communications between these VMs is controlled even as the inter-VM traffic patterns change, or as the background traffic associated with other applications changes. We describe a prototype implementation of the approach that extends our MAPLE network-aware VM placement system. Our experiments in an emulated datacenter indicate that our scheduling approach leads to fewer QoS violations compared to ECMP.

Service-Oriented Architectures can experience a highly diversified and complex set of interactions between web services for them to compute a response to an incoming service request. Existing approaches based on modelling and control theory are good for strategic and long-term development of a data center resource dimensioning necessary to respond within the boundaries of Service Level Agreements. When operating at a scale of many thousands of incoming service requests per second, combined with a highly diversified set of a priori unknown service interactions, other more reactive approaches are required to cope with performance bottlenecks in a live production system. We have taken an experimental approach to look at how techniques inspired by SDN principles could potentially mitigate problems related to excessive response times while service requests are being processed. We conclude that significant improvements are possible and further observe, following the results from this study, the need to extend the scope of SDN to also incorporate programmability of application server nodes.

Future wireless nanosensor networks are envisioned to operate in the THz band, due to the tiny size of the network components. Among the diverse range of applications that such networks promise, high-resolution plant monitoring systems are the categories which can benefit from the size and high sensitivity of nanosensor devices and also the high bandwidth provided by them. However, communications at the THz frequency band, especially within a hybrid channel like plant foliage, undergo peculiar types of attenuation and distortion. These phenomena, which can challenge the feasibility of the aforementioned applications, need to be addressed/modeled precisely. Therefore, in this paper, we propose the first THz path-loss model within a plant environment. In addition, we provide a simplified model of plant structure as well as a model for the probability of successful transmissions between nanosensors and microscale receivers mounted on the plant stem. The introduced models consider the limited capability of THz radiation to propagate through plant leaves, and also the high free-space path-loss as the main sources of signal loss in the network communications. Furthermore, these models can be customized based on the structural characteristics of a plant, e.g., leaves size and distribution, to account for a variety of plant species. Finally, the performance of communications based on the provided models is evaluated for different network scenarios.

Datacenter operators are increasingly deploying virtualization platforms to improve resource usage efficiency and to simplify the management of tenant applications. Although there are significant efficiency gains to be made, predicting performance becomes a major challenge, especially given the difficulty of allocating datacenter network bandwidth to multitier applications, which generate highly variable traffic flows between their constituent software components. Static bandwidth allocation based on peak traffic rates ensures SLA compliance at the cost of significant overprovisioning, while allocation based on mean traffic rates ensures efficient usage of bandwidth at the cost of QoS violations. We describe MAPLE, a network-aware VM ensemble placement system that uses empirical estimations of the effective bandwidth required between servers to ensure that QoS violations are within targets specified in the SLAs for tenant applications. Moreover, we describe an extended version of MAPLE, termed MAPLEx, which allows the specification of anticolocation constraints relating to the placement of application VMs. Experimental results, obtained using an emulated datacenter, show that, in contrast to the Oktopus network-aware VM placement system, MAPLE can allocate computing and network resources in a manner that balances efficiency of resource utilization with performance predictability.

As a step towards the realization of wireless nanosensor networks in agricultural monitoring applications, a theoretical path-loss model is proposed. This model can predict the average attenuation of THz radiation in a channel composed of air and plant leaves, given the composition of air as well as attributes of leaves such as their permitivity. In addition, a Monte Carlo-based log-distance path-loss model in the vicinity of a plant is proposed, which takes the shadow fading nature of such a channel into account. The latter model, which is based on the transmission distance, can approximate variations of THz path-loss from its mean value. Through simulation results, it is shown that the proposed log-distance/Monte Carlo-based model is in good agreement with the theoretical model.

We study the design of energy-efficient stochastic leader-selection algorithms in environments which are sensed by mobile handsets. This work optimizes the energy usage of the interaction between mobile handsets and an application server. The energy- efficiency of mobile handset communications is of increasing importance given the convergence of mobile computing and cloud computing. In some scenarios the unavailability of WiFi signals forces mobile devices to expend additional energy transmitting application specific sensed data over cellular networks. This paper focuses on 1) fairly allocating the overhead of transmitting sensed data to a cellular base station amongst the mobile devices by selecting a cluster-head (Fairness is measured in terms of the relative energy expended); 2) successively improving the location of the selected leader such that the efficiency of the entire system is improved; and finally, 3) allowing the mobile devices to gradually forget what they have learned so that the life-time of the mobile devices is increased (without significantly affecting energy savings). We present two algorithms in this paper and demonstrate that the battery life-time associated with sensor data transmissions of the mobile handsets may be increased by 20-40% without incurring a sensing accuracy penalty.

Calcium (Ca2+)-signaling-based molecular communication is a short-range communication process that diffuses and propagates ions between the cells of a tissue. The communication process is initiated via stimulation and amplification of the production of Ca2+ions within a cell; these ions then diffuse through a physical connection between cells called a gap junction. Ca2+signaling can be found in different classes of cell. In excitable cells, initiation of the Ca2+-signaling process is accompanied by an electrical component; for nonexcitable cell types, the electrical component is absent; while hybrid cells exhibit both behaviors. This paper provides a comparison and analysis of the communication behavior in tissues comprised three specific cell types that utilize Ca2+signaling: epithelium cells (nonexcitable), smooth muscle cells (excitable), and astrocytes (hybrid). The analysis focuses on spatiotemporal Ca2+concentration dynamics and how they are influenced by the intracellular signaling process, the molecular diffusion delay, the gain and capacity of the communication channel, as well as intracellular signaling interference. This analysis of the communication behavior in the context of tissues provides insights useful for, inter alia, the design of nanomachines that are situated within tissues and that use analysis of the communication channel to infer tissue health.

As the need for more pervasive and more complex access controls grows, the challenge of ensuring the performance of access control systems is becoming apparent. Researchers have proposed several solutions to mitigate performance problems, including: adjusting the policy set; re-engineering the policy decision point (PDP); and decomposing the policies and distributing their evaluation. However, since the benefits and tradeoffs depend heavily upon the actual scenario, security administrators typically do not have objective justification for adopting particular mitigation actions. In response, we present the ATLAS framework, comprising: 1) DomainManager, which facilitates modelling the domain as closely as possible and automatically generates large numbers of representative policies and associated requests; 2) STACS, which enables controlled experiments to be performed using the generated policies/requests, to collect comprehensive measurements of PDP performance; and 3) PARPACS, which aids the understanding and worth of the measurement data and, by using rigorous validation techniques, reduces the risk of spurious insights or incorrect recommendations. We present a discussion of ATLAS as applied to an enterprise communication scenario, where access control is realised via XACML PDPs. Notable insights include that the SunXacml 2.0 PDP performs relatively poorly in terms of policy evaluation performance and that adding additional memory and/or processor cores to a XACML PDP server is not guaranteed to improve performance significantly.

As mobile devices increasingly incorporate a range of sensors, there is significant potential to apply opportunistic sensing techniques to allow collections of these devices to provide context information to applications. Focussing on a use case involving the use of mobile devices to sense and localize increasing levels of gases in a work environment, we show that the use of application-specific state machines that control the rate at which sensed data is reported, can lead to a significant reduction in battery consumption by the devices in comparison to continuous sensing approaches wherein the reporting rate remains constant.

We propose and evaluate a number of of frequency selection strategies for nano-scale devices using graphene-based nano-antennas (“graphennas”), which operate in the Terahertz band. The strategies take into account the limitations of Terahertz channel and aim to optimize the overall network transmission rate of a network of nano-devices, while maximizing various objectives. We investigate the trade-off between cases where: 1) frequency duplication within the network is allowed or prevented; 2) limiting the spread of frequencies over the entire Terahertz range is required; and 3) balancing the load between the network sink nodes is required. We compare the network performance for the different objectives proposed against a random frequency selection strategy. Our simulation study demonstrates the efficiency of the proposed algorithms and indicates their usefulness in different application scenarios.

One form of molecular communications for short range transmission between nanomachines is Calcium Signaling. This form of signaling is commonly found in cellular tissues, which consist of tightly packed cells, whereby Ca2+ions propagate and diffuse between the cells. However, the natural flexible structure of cells usually leads to them dynamically changing shapes under certain strains and forces. Since the interconnected cells form a tissue, changes in the shape of one cell will change the shape of neighboring cells and the tissue as a whole. This may in turn significantly impair the communication channel between the nanomachines (which we assume to be embedded within the cells). In order to counter this problem, we propose an adaptive transmission protocol for Ca2+signaling based molecular communications in cellular tissues. The protocol operates in two phases. The first phase utilizes information metrics to infer the state of the tissue; second phase then involves the determination of the most appropriate time-slot for bit transmission. In this way, we aim to improve the information rate by using a time slot length that is appropriate for the prevailing type of tissue deformation. Through simulation studies we show that, for two types of deformation and two different topologies, our protocol can improve the information rate performance by 15%.

Algorithms for self-organizing networks into node clusters have up to this point been limited to the organization of basically homogeneous networks of a limited size and application, having been targeted mainly towards wireless ad-hoc networks and sensor networks. We present a new distributed technique for how heterogeneous networks at any size, topology, and technologies can be self-organized into a set of domains using a novel method based on finding a centre node. The expression of finding the centre node is inspired by the law of finding the centre of gravity where the crucial problem sits in finding the node around which other nodes would “gravitate” and around which a domain can be formed. In this paper, we present the core principles and features of the GRANET concept, as well as presenting initial results from simulations of GRANET where its ability to scale and converge have been studied.

Traffic classification plays an important role in many short to medium term network management tasks and in long term network dimensioning/planning. In recent years a number of traffic classifiers have been proposed, in particular classifiers based on machine learning techniques exhibit high levels of accuracy. However, in practice, even if classifiers can be accurately trained at a given time, their accuracy will subsequently degrade when the characteristics of the network traffic change. In this paper, we propose an adjustable traffic classification system, the key technique of which is ensemble classification, assisted with a change detection method. Our system enables a traffic classifier to be effectively updated in response to the changing traffic distributions. Experimental results show that our classifier produces improved accuracy with relatively shorter updating time.

Increases in broadband access bandwidth in recent years have meant that it has become viable to offer games as cloud-hosted applications. This is attractive for many games users as they are not required to own relatively expensive games consoles. Whilst most cloud hosted games rely on traditional game controller inputs, it is likely that in the near future that: 1) they will seek to support controller-free sensed inputs such as those gathered by Microsoft's Kinect; and 2) games will require continuously updated images of the gamer(s) for inclusion in the game itself. In this paper we investigate the use of the Kinect as an input device for cloud-hosted games. We propose a protocol that allows a game server request specific information from the game and specify a simple image segmentation algorithm that can be applied at the client to decrease the volume of data that needs to be transferred to the server. Our implementation shows that the use of this protocol should make it feasible to support the use of Kinect for this purpose on typical consumer broadband access band-widths.

Tools and process for management of network infrastructure typically assume that network administrators are technically literate, with a willingness to devote significant time to ensuring that devices are correctly configured to behave as desired. Whilst this is largely the case for service provider and enterprise networks, it is typically not the case for home area network users. The growing complexity and heterogeneity of home area networks mean that management and configuration tasks are becoming increasingly complex. In this paper we outline an approach for the semantic uplift of monitoring data from a home area network router and the selection of appropriate policies drive the (re-)configuration of the router to provide desired behaviour in response to the monitored events. We outline the use of an ontology-based semantic model to conceptualize monitoring data and select the policies to apply. The major contributions of this paper are the algorithms developed for semantic uplift and policy selection. The approach is explained and evaluated via two example scenarios that have been realized on a home area network test-bed.

We present a server selection and admission control algorithm for IPTV networks that uses available bandwidth estimation to assess bandwidth available on the path from an end-user point of attachment to one or more IPTV content servers and that employs a revenue maximising admission decision process that prioritizes requests for high revenue content item types over requests for lower revenue item types. The algorithm operates by estimating expected request arrival rates for different content item types based on past arrival rates and, based on these and available bandwidth estimates decides whether to accept a new request and, when accepting requests, which of the available content servers to use. Results of a simulation study show that the algorithm succeeds in 1) maintaining acceptable packet delays for accepted flows in the presence of fluctuating background traffic on network paths and 2) when available bandwidth is limited prioritizing requests for higher revenue content types.

Creating composed services dynamically to meet the demand of every individual customer can be an inexpensive way for a service provider to differentiate itself from its competitors. Charging and billing systems are one of the key components supporting this business model. However, many existing charging and billing systems suffer from their time-consuming scheme1configuration process. This limits the flexibility of service providers to offer composed services. This paper proposes a framework named Accounting Logic Generator (ALG) that automates the generation and deployment of charging schemes for composed services. It processes a two-part charging scheme which contains rules that can be used to govern how the usage of a service is charged for when it is executed standalone, used in the context of a composed service, invoked within specific time intervals of interest or completed successfully in the context of composed service invocations where one or more other services fail. Furthermore, an approach that estimates service cost for the selection process of service composition is presented.

We outline a policy conflict analysis process for the analysis of newly specified federation-level policies against previously deployed local/federation policies. The process is generic in the sense that it can be employed by each domain participating in a federation to help maintain the consistency of their local system with that of the federation. The conflict analysis process utilises an information model and associated ontology for representing both the static and dynamic application-specific aspects of the local operating environment and the federation to aid in the detection of potential inconsistencies. It employs two algorithms, one for the selection of previously deployed policies related to the federation-level policy and the other for analysing the returned policies against the federation-level policy. The selection algorithm reduces the number of deployed policies required to be retrieved for analysis against any newly specified federation-level policy, while the conflict analysis algorithm detects inconsistencies relating to the conditional element of a policy rule. We discuss a concrete example in the form of a federated XMPP communication scenario.

The performance and scalability of access control systems is a growing concern as organisations deploy ever more complex communications and content management systems. This paper describes how an (offline) experimental testbed may be used to address performance concerns. To begin, timing measurements are collected from a server component incorporating the Policy Decision Point (PDP) under test, using representative policies and corresponding requests. Our experiments with two XACML PDP implementations show that measured request service times are typically clustered by request type; thus an algorithm for request cluster identification is presented. Cluster characterisations are used as inputs to a PDP performance model for a given policy/request mix and an analytic (queueing) model is used to estimate the equilibrium server load for different mixes of request clusters. The analytic performance prediction model is validated and extended by discrete event simulation of a PDP subject to additional load. These predictive models enable network administrators to explore the capacity of the PDP for different overall loadings (requests per unit time) and profiles (relative frequencies) of requests.

The complexity and dynamicity of networks continue to increase. Furthermore, specialization of network operations, the de-regulation of vertically integrated network operators, new demands from end users, as well as new business models for Over-The-Top players, give rise to new challenges and emphasizes that networking becomes ever more dynamic and organic in nature. To address these trends the research community has been investigating how to integrate management capabilities into the networks in order to meet the foremost requirements on reduced OPEX, but also to enable faster responses to agile business operations for the support of new value chains, applications, and services. These management capabilities are however envisaged as an add-on to the existing functionality. This would impose scalability, performance, and extensibility issues when such capabilities are deployed incrementally and ad-hoc. In this paper, we give an overview of selected recent advances in network architectures supporting self-management capabilities, and identify both their strengths and their limitations. This analysis provides input for a definition of the requirements and core properties for networks to become inherently self-managed. We then outline a generic model termed the “Netcell” that seeks to support the identified requirements and core properties.

Constructing composite services by using of services offered by third parties is an attractive and inexpensive way for service brokers and aggregators to enhance differentiation from their competitors. When multiple services provide the same or similar functionalities, selecting those that satisfy users' non-functional requirements is crucial. In many cases, non-functional properties of services are heavily dependent on the activity of the network delivering those services whilst the network activity follows certain time-sensitive rules. We present a service selection algorithm that takes into account time-sensitive variations of non-functional propensities of services to identify a service combination offering the highest quality within a specified time interval.

Pervasive mobile communication is set to be greatly enhanced by the development and deployment of miniature mobile base stations, suitable for workplace or home. These base stations, known as Femtocells and Picocells, connect to traditional broadband connections, to offer greatly improved 3G communications to mobile handsets. Developing an architecture that allows for targeted applications to be developed for end users of these enhanced mobile networks is of great importance. Examining how contextual data, extracted from these mobile networks, can be used to make inference and provide benefit to end users is both a technical and sophisticated challenge to be discussed in this paper.

Service providers offering IP-based video on demand services often replicate video content in multiple content servers with different network points of attachment. When a request for a content item arrives from an end-user, a decision must be made as to whether the request should be admitted and, if so, which server should be used. To ensure adequate quality-of service this admission control/server selection decision should be cognisant of the current link utilisation on the paths between the end-user's point of attachment and those of the servers. We have studied the conditions and parameters under which end to-end available bandwidth estimation tools (ABETs) pathChirp and Assolo can be used in this decision process. We specify an admission control/server selection algorithm that uses available bandwidth estimations. Simulation study results show pathChirp (but not Assolo), when appropriately parameterised, can generate available bandwidth estimates that can be used by our admission control/server selection algorithm to react appropriately to changes in background loads on network paths.

The performance and scalability of access control systems is growing more important as organisations deploy ever more complex communications and content management systems. Fine-grained access control is becoming more pervasive, so decisions are more frequent and policy sets are larger. We outline a flexible performance testing framework that accepts XACML PDP implementations (in the server component) and submits representative access control requests (from the client component) in a representative temporal ordering. The framework includes instrumentation and analysis modules to support performance experiments. We describe an initial realization of the framework and report on initial experiments comparing the performance of the SunXACML and Enterprise XACML PDPs.

Presence management, i.e., the ability to automatically identify the status and availability of communication partners, is becoming an invaluable tool for collaboration in enterprise contexts. In this paper, we argue for efficient presence management by means of a holistic view of both physical cont

This paper presents a purely empirical approach to estimating the effective bandwidth of aggregated traffic flows independent of traffic model assumptions. The approach is shown to be robust when used in a variety of traffic scenarios such as both elastic and streaming traffic flows of varying degrees of aggregation. The method then forms the basis of two Quality of Service related traffic performance optimisation strategies. The paper presents a cost efficient approach to supplying suitably accurate demand matrix input for QoS related network planning and a QoS provisioning, revenue maximising admission control algorithm for an IPTV services network. This paper summarises these approaches and discusses the major benefits of an appropriately accurate effective bandwidth estimation algorithm.

We present a policy conflict analysis process that makes use of pre-defined semantic models of an application to perform effective and efficient conflict analysis. The process is effective as it can be used to analyse for policy conflicts that may occur in different applications due to the separation of application specific information and constraints from the algorithms to semantic models, such as information model and ontologies. The process is efficient as it incorporates a pre-analysis policy selection step that reduces the number of policies that need to be analysed more extensively. Experimental results show that this process results in a significant reduction in the number of policies that needed to be analysed for potential conflict and that it is flexible enough to detect for policy conflict both for many popular applications and between different applications.

Digital ecosystems are distributed software environments through which organisations can seamlessly access customised, potentially disposable, services to aid them carry out a myriad of tasks. Peer to peer networks are often cited as a suitable platform for digital ecosystem deployment. A typical feature of such systems is the lack of a point of control. In this regard these are untrusted environments. This lack of trust acts as a barrier to commercial applications emerging on these platforms. Suitable mechanisms for identity, authentication and trust evolution are required to overcome this. This paper provides a model for distributed accountability in digital ecosystems which can strengthen the trust in the system both from a external viewpoint (i.e. the system as a whole) and between individuals within the system.

A key part of the policy authoring process is analysis of the potential for newly created or modified policies to conflict with already deployed policies. We propose an approach for policy conflict analysis in which candidate policies (either newly created or modified) are analyzed on a pair-wise basis with already deployed policies, with potential conflicts between the policies being notified to the policy author. Central to the approach is a two-phase algorithm which, querying an information model, firstly determines the relationships between the pair of policies and, secondly, applies an application-specific conflict pattern to determine if the policies should be flagged as potentially conflicting. The algorithm is generic in the sense that all application specific information is encoded in the information model; as long as a minimal set of assumptions regarding the policy model are adhered to it can be applied in arbitrary application domains. In the paper we present the two phase algorithm and describe an implementation in which it is used to detect potential conflicts for both access control and filtering (firewall) policies.

In this paper we describe QoSPlan - a generalized process for preparing information relevant to QoS-aware IP network planning. QoSPlan is designed to reduce the cost of deployment and maintenance of traditional network monitoring systems for service providers, while maintaining a sufficient degree of precision. The process involves analysis of pre-existing accounting data to estimate a network-wide demand matrix. Part of this estimation process relates to the generalization of QoS-related effective bandwidth measurements taken from a set of collected packet traces. Based on experience, we offer recommendations on how to appropriately realise QoSPlan to maximise its accuracy and effectiveness when applied to different network traffic scenarios.

Providers of communications services aim to specify, realise and deploy services as quickly as possible in order to gain a competitive edge in meeting evolving consumer demands. One means of creating new service offerings is the composition of pre-existing services which, when orchestrated in a particular manner, provide novel functionality. Indeed, many industry analysts predict the emergence of virtual services providers, who do not themselves deploy and offer individual services, but instead orchestrate services offered by other providers. Crucial to the success of providers offering composed services is an efficient, automated process by which such services are charged and billed for. In this paper we present a process for automated generation of charging schemes for composed IMS services, based on analysis of the charging schemes associated with services comprising those composed services. Charging schemes are specified using a Domain Specific Language (DSL), so that they can then be mapped to platform specific representations that can be deployed onto one or more rating engines. Semi- automated configuration of charging schemes in this manner obviates the need for expensive manual configuration of accounting components every time a new composed services is specified and activated.

Abstract — The increase in complexity of network management systems and a consequent lack of association to business requirements has driven the need for autonomic communications. By integrating context information, autonomic computing can provide more efficient means to counter technical problems found in complex network systems and at the same time address associated business requirements. In this paper, we propose an autonomic communications architecture that manages complexity through policy-based management where we incorporate a shared information model integrated with knowledge-based reasoning mechanisms to provide selfgovernance behavior.

We present a framework enabling charging for composed services comprised of services offered by multiple providers. In the framework rating engines may generate charge information for individual services and provide this information upon request to other rating engines when these services are used as part of a composed service. Rating engines additionally employ a two-phase rating process which allows potentially complex business agreements between providers to be reflected in composed service charges. Charges can vary depending on the context in which a service is executed, for example, as part of a composed service which includes services offered by a partner provider. Crucially, the process allows rating engines to calculate these varying charges without having to be manually pre-configured with details of the structure of individual composed services. In the paper we provide an overview of the framework, specifying in detail the rating process and inter-rating engine communications, and describe via an example its deployment in a distributed environment supporting the execution of composed services

Signalling System No. 7 (SS7) networks are used to transport call control messages for both traditional telephony and advanced intelligent network (IN) based services. A large number of IN implementations have been successfully deployed, however a key factor in ensuring their ongoing commercial success will be that they continue to meet specified performance targets. Because network congestion may have a serious adverse effect on IN service performance, much research effort has been focused on IN congestion control. However, in general, this research has not taken into account operation of the standardised congestion controls operating at the SS7 plane of the network. This paper outlines an investigation that demonstrates that the interaction between SS7 and IN congestion controls is not always harmonious and can lead to serious performance degradation. A congestion control strategy, which resolves the problems identified, is presented and evaluated.