ANEMONA: a programming language for network monitoring applications

This work presents ANEMONA: A language for programming NEtwork MONitoring Applications. The compilation of an ANEMONA program generates code for configuring a policy repository and the corresponding policy deployment and event monitoring. The language allows the definition of expressions of managed objects that are monitored, as well as triggers that when fired may indicate the occurrence of associated events, which are also defined by the language. A translator for the language was implemented that generates code for configuring both the policy repository and deployment. The current implementation of the language employs the Expression MIB and Event MIB. Experimental results are presented, including an ANEMONA program that detects TCP Syn Flooding attacks, and a program for detecting steep variations in the utilization of monitored links. Copyright © 2007 John Wiley & Sons, Ltd.     

Cellular multiadaptive analogic architecture: a computational framework for UAV applications

An efficient adaptive algorithm in real-time applications should make optimal use of the available computing power for reaching some specific design goals. Relying on appropriate strategies, the spatial resolution/temporal rate can be traded against computational complexity; and sensitivity traded against robustness, in an adaptive process. In this paper, we present an algorithmic framework where a spatial multigrid computing is placed within a temporal multirate structure, and at each spatial grid point, the computation is based on an adaptive multiscale approach. The algorithms utilize an analogic (analog and logic) architecture consisting of a high-resolution optical sensor, a low-resolution cellular sensor-processor and a digital signal processor. The proposed framework makes the acquisition of a spatio-temporally consistent image flow possible even in case of extreme variations (relative motion) in the environment. It ideally supports the handling of various difficult problems on a moving platform including terrain identification, navigation parameter estimation, and multitarget tracking. The proposed spatio-temporal adaptation relies on a feature-based optical-flow estimation that can be efficiently calculated on available cellular nonlinear network (CNN) chips. The quality of the adaptation is evaluated compared to nonadaptive spatio-temporal behavior where the input flow is oversampled, thus resulting in redundant data processing with an unnecessary waste of computing power. We also use a visual navigation example recovering the yaw-pitch-roll parameters from motion-field estimates in order to analyze the adaptive hierarchical algorithmic framework proposed and highlight the application potentials in the area of unmanned air vehicles.     

UniFAFF: a unified framework for implementing autonomic fault management and failure detection for self‐managing networks

Today's network management, as known within the Fault, Configuration, Accounting, Performance, Security (FCAPS) management framework, is moving towards the definition and implementation of ‘self‐managing’ network functions, with the aim of eliminating or drastically reducing human intervention in some of the complex aspects or daunting tasks of network management. The fault management plane of the FCAPS framework deals with the following functions: fault detection, fault diagnosis, localization or isolation, and fault removal. Task automation is at the very heart of self‐managing (autonomic) nodes and networks, meaning that all functions and processes related to fault management must be automated as much as possible within the functionalities of self‐managing (autonomic) nodes and networks, in order for us to talk about autonomic fault management. At this point in time there are projects calling for implementing new network architectures that are flexible to support on‐demand functional composition for context‐ or situation‐aware networking. A number of such projects have started, under the umbrella of the so‐called clean‐slate network designs. Therefore, this calls for open frameworks for implementing self‐managing (autonomic) functions across each of the traditional FCAPS management planes. This paper presents a unified framework for implementing autonomic fault management and failure detection for self‐managing networks, a framework we are calling UniFAFF. Copyright © 2008 John Wiley & Sons, Ltd.     

A spectral color correction framework for medical applications

This paper presents a new spectral approach to color correction for medical image analysis applications. Linear estimation with regularization by a constrained principal eigenvector method is used for calibration of the camera system and estimation of the illumination spectrum while spectral surface reflectivities are determined by Wiener inverse estimation. Nonlinear devices are handled by piecewise linear interpolation and any linear color preprocessing inside the camera is explicitly modeled. All measurement and estimation processes are combined into a spectral calibration framework for practical application in computer-assisted image analysis. The novelty of our approach lies in the generalization of the image formation model allowing for linear preprocessing inside the camera system. Such transforms would lead to erroneous results with positivity constraint based algorithms or a monochromator based measurement. We provide experimental results from a comprehensive set of reference measurements acquired with a video endoscopy system for gastroscopic application.     

A new programming model for dependable adaptive real-time applications

The CORTEX project envisions a new class of applications comprising (possibly many) smart components, called sentient objects, that can sense their environment and interact with it and each other by accepting and producing events. These interactions might occur in ways that demand predictable and sometimes guaranteed quality of service (QoS). However, achieving predictability is difficult given the objects' changing environments dictated by unstable and mobile object populations, unpredictable network load, varying connectivity, and failed system components. We devised an innovative programming model that aims to reconcile uncertainty with an application's required predictability. The model is based on fulfilling two predicates - coverage stability and no-contamination - by programs or protocols subject to varying conditions of system timeliness.     

Trust management for multimedia P2P applications in autonomic networking

In the last years, trust management has become a fundamental basis for facilitating the cooperation between different users in peer-to-peer (P2P) multimedia applications within autonomic networks. In these networks and applications, trust management should fulfill certain requirements (i.e. decentralisation, dynamism, simplicity, interoperability, etc.) for being functional. In this paper, we propose an evolutionary model of trust management that captures dynamic entities’ behaviour over time. Likewise, we explain protection mechanisms against several attacks, which are based on the cooperative behaviour of the entities, trust relationship properties, and trust rules. Finally, we successfully validate our model from several scenarios and compare it with other proposals in this field.     

A context management framework for supporting context-aware distributed applications

Service platform functions are enablers of adaptive communication services and applications that hide the heterogeneity of the infrastructure, manage personal reachability, manage implicit context, and adapt modality to the context. Furthermore, they can offer a diversity of services, whereby an optimal tradeoff can be made for end users in comfort, cost, security, and mobility. We consider the enablers of context-aware services in distributed environments and present a context management framework that is generic through its hierarchic ordering of context sources and extensible with multiple reasoning realizations. The article focuses on the architectural principles and reasoning methods of the framework.     

Constant-charge-injection programming: a novel high-speed programming method for multilevel flash memories

Constant-charge-injection programming (CCIP) has been proposed as a way to achieve high-speed multilevel programming in flash memories. In order to achieve high programming throughput in multilevel flash memory, programming method must provide: 1) high-speed cell-programming; 2) high programming efficiency; and 3) highly uniform programming characteristics. Conventional source-side channel-hot-electron injection (SSI) programming realizes both fast cell-programming and high programming efficiency, but the large cell-to-cell variation in programming speed with SSI is a problem. CCIP reduces the characteristic variation of SSI programming and satisfies all of the above requirements. By applying CCIP to 2-bit/cell AG-AND flash memory, the high programming throughput of 10.3 MB/s is obtained with no area penalty. This is 1.8 times faster than the throughput with conventional SSI programming.     

Mobile agent-based approach for efficient network management andresource allocation: framework and applications

Agent programming technology has emerged as a flexible and complementary way to manage resources of distributed systems due to the increased flexibility in adapting to the dynamically changing requirements of such systems. A very promising application of this technology is related to the control of forthcoming networking systems which will represent a competitive marketplace with a multitude of vendors, operators and customers. Thus, new reference models have to be investigated in order to better satisfy users' requirements in a framework where resource allocation is provided under the control of different and often competing stakeholders (users, network providers, service providers, etc.). We believe that autonomy is one of the features that will characterize the behavior of agents in such environment: autonomous choices will be taken as the result of coordination among different cooperating software entities. Following this direction, we describe the efficient integration and adoption of mobile agents and genetic algorithms in the implementation of a valuable strategy for the development of effective market based routes for brokering purposes in the future multioperator network marketplace. The proposed genetic algorithm provides a kind of stochastic algorithm searching process in order to identify optimal resource allocation strategies. The agent-based network management approach represents an underlying framework and structure for the multioperator network model, and can be used to facilitate the collection and dissemination of the required management data, as well as the efficient and distributed operation of the algorithm. We also present some numerical results to assess the performance and operation effectiveness of our approach, by applying it in some test case scenarios     

Dynamic programming framework for automatic video object segmentation and vision-assisted video pre-processing

A generic definition of video objects, which is a group of pixels with temporal motion coherence, is considered. The generic video object (GVO) is the superset of the conventional video objects considered in the object segmentation literature. Because of its motion coherence, the GVO can be easily recognised by the human visual system. However, due to its arbitrary spatial distribution, the GVO cannot be easily detected by the existing algorithms which often assume the spatial homogeneousness of the video objects. The concept of extended optical flow is introduced and a dynamic programming framework for the GVO detection and segmentation is developed, whose solution is given by the Viterbi algorithm. Using this dynamic programming formulation, the proposed object detection algorithm is able to discover the motion path of the GVO automatically and refine its spatial region of support progressively. In addition to object segmentation, the proposed algorithm can also be applied to video pre-processing, removing the so-called 'video mask' noise in digital videos. Experimental results show that this type of vision-assisted video pre-processing significantly improves the compression efficiency.     

A general framework for vertex orderings with applications tocircuit clustering

Vertex orderings have been successfully applied to problems in netlist clustering and for system partitioning and layout. We present a vertex ordering construction that encompasses most reasonable graph traversals. Two parameters-an attraction function and a window-provide the means for achieving various graph traversals and addressing particular clustering requirements. We then use dynamic programming to optimality split the vertex ordering into a multiway clustering. Our approach outperforms several clustering methods in the literature in terms of three distinct clustering objectives. The ordering construction, by itself, also outperforms existing graph ordering constructions for this application. Tuning our approach to “meta-objectives”, particularly clustering for two-phase Fiduccia-Mattheyses bipartitioning, remains an open area of research     

A software defined radio modeling framework for enabling cognitive applications

The rapid advances in software defined radio (SDR) technologies over the past decade encouraged their transition from the lab to field deployment, and shifted the usage from disparate communication devices to the creation of large resilient wireless networks capable of adapting to highly variable environment conditions in a cognitive manner. Simulation models capable of accurately representing the behavior and properties of SDR devices operating in a networked environment thus become a prerequisite for both the evaluation of such networks as well as for providing a development platform for creating new cognitive capabilities. This paper describes our work in creating a network simulation model framework for software defined radios that takes into account some of the unique behaviors and requirements of SDRs not previously seen in purely hardware devices. Factors such as large and variable communication delays between software modules, contention to shared resources, such as CPU or memory, as well as continuous tuning and environment awareness functionality essential to SDR demand a different modeling approach as well as novel techniques that enable accurate scale testing.     

Giotto: a time-triggered language for embedded programming

Giotto provides an abstract programmer's model for the implementation of embedded control systems with hard real-time constraints. A typical control application consists of periodic software tasks together with a mode-switching logic for enabling and disabling tasks. Giotto specifies time-triggered sensor readings, task invocations, actuator updates, and mode switches independent of any implementation platform. Giotto can be annotated with platform constraints such as task-to-host mappings, and task and communication schedules. The annotations are directives for the Giotto compiler, but they do not alter the functionality and timing of a Giotto program. By separating the platform-independent from the platform-dependent concerns, Giotto enables a great deal of flexibility in choosing control platforms as well as a great deal of automation in the validation and synthesis of control software. The time-triggered nature of Giotto achieves timing predictability, which makes Giotto particularly suitable for safety-critical applications.     

Viper: a framework for responsive television

Web content commonly incorporates user profile and tracking data to personalize information to clients. Video post-production and delivery systems, however, generally promote a one-size-fits-all authoring approach. Viper lets producers create complex video programs that can re-edit themselves in response to various audience-related factors.     

Coding for system-on-chip networks: a unified framework

Global buses in deep-submicron (DSM) system-on-chip designs consume significant amounts of power, have large propagation delays, and are susceptible to errors due to DSM noise. Coding schemes exist that tackle these problems individually. In this paper, we present a coding framework derived from a communication-theoretic view of a DSM bus to jointly address power, delay, and reliability. In this framework, the data is first passed through a nonlinear source coder that reduces self and coupling transition activity and imposes a constraint on the peak coupling transitions on the bus. Next, a linear error control coder adds redundancy to enable error detection and correction. The framework is employed to efficiently combine existing codes and to derive novel codes that span a wide range of tradeoffs between bus delay, codec latency, power, area, and reliability. Using simulation results in 0.13-/spl mu/m CMOS technology, we show that coding is a better alternative to repeater insertion for delay reduction as it reduces power dissipation at the same time. For a 10-mm 4-bit bus, we show that a bus employing the proposed codes achieves up to 2.17/spl times/ speed-up and 33% energy savings over a bus employing Hamming code. For a 10-mm 32-bit bus, we show that 1.7/spl times/ speed-up and 27% reduction in energy are achievable over an uncoded bus by employing low-swing signaling without any loss in reliability.     

MicroNF:a microservice-based hybrid framework for NFV

Network function virtualization (NFV) brought significant flexibility.However,such flexibility came with considerable compromises,since virtual machine carried monolithic functions could introduce significant performance overhead.A novel high-performance and programmable framework called MicroNF was proposed,which combines programmable hardware infrastructure and traditional software infrastructure in NFV to achieve both high performance and flexibility.In particular,microservice,a new design approach in software architecture,was leveraged by MicroNF to re-architect NFV to enable functional reusability among services and improve performance.MicroNF was implemented in a test bed based on OpenStack and ONetCard.Experimental results show that MicroNF reduces the forwarding latency of a service chain by an average of 70% compared with DPDK-based software implementation.