495478
Optimized routing for fault management in optical burst-switched WDM networks
Optical burst switching (OBS) is a promising technique for supporting high-capacity, bursty data traffic over optical wavelength-division-multiplexed (WDM) networks. An optical link failure may result in a huge amount of data (and revenue) loss, and it has been an important survivability concern in optical networks. In this paper, we study the fault- management issues related with a link failure in an OBS network. We propose to use pre-planned global rerouting to balance network load and to reroute bursts after a link fails. We apply optimization techniques to pre-plan explicit backup routes for failure scenarios. Our objective is to achieve optimal load balancing both before and after a failure such that the network state can still remain stable with minimum burst-loss probability when a failure occurs. We apply the pre-planned normal and backup routing tables to an OBS network, and study the network performance after a failure occur using illustrative numerical examples. The results show that the average burst-loss probability can be significantly reduced by 60% - from an average of 0.10 to 0.04 (when the normalized link load is less than 0.5) using globally-rerouted backup routes, when compared with the scheme without global rerouting. We also observe that the burst-loss probability is reduced by 43% - from an average of 0.07 to 0.04 (when the link load is less than 0.5) if the rerouting is done using optimization techniques, when compared with shortest-path routing.for Fault Management
2367333
Building Accountability Middleware to Support Dependable SOA
The Intelligent Accountability Middleware Architecture (Llama) project supports dependable service-oriented architecture (SOA) monitoring, runtime diagnosis, and reconfiguration. At its core, Llama implements an accountability service bus that users can install on existing service-deployment infrastructures. It collects and monitors service execution data from a key subset of services; enables Llama users to incorporate others' advanced diagnosis models and algorithms into the framework; and provides enterprise service bus extensions for collecting service profiling data, thus making process problems transparent to diagnose. Finally, experimental results indicate that using Llama contributes a modest amount of system overhead.
36577118
A comprehensive study on backup-bandwidth reprovisioning after network-state updates in survivable telecom mesh networks
32231750
A Hybrid Restoration Scheme Based on Threshold Reaction Time in Optical Burst-Switched Networks
Optical Burst-Switched (OBS) networks usually employ one-way reservation by sending a burst control packet (BCP) with a specific offset time, before transmitting each data burst frame (BDF). Therefore, a fiber link failure may lead to loss of several BDFs as the ingress nodes sending these BDFs remain unaware of the failed link till they receive the failure indication signal (FIS). In this paper, we propose a hybrid restoration scheme, wherein we employ a novel combination of sub-path as well as path restoration schemes. In particular, the upstream node preceding the failed link employs sub-path restoration as soon as it detects the failure. Thereafter, when the source ingress node receives the FIS message, it (source ingress node) takes over the responsibility and employs path restoration. However, sub-path restoration is exercised by the upstream node only if the remaining time intervals left for the yet-to-arrive BDFs (for which BCPs have already left the upstream node) are less than a minimum time interval (called as threshold reaction time). Performance of the proposed scheme is evaluated through extensive computer simulation using OPNET. Our results indicate that the proposed restoration scheme significantly reduces burst losses due to link failure and, thus, outperforms the existing restoration schemes.
11999738
The LLAMA Middleware Support for Accountable Service-Oriented Architecture
Enterprises are turning to SOA for the flexible deployment of business processes. While current monitoring tools can detect service execution problems in an enterprise's servers and report such problems to human decision makers, they may not closely monitor the external services they use, diagnose the root cause of process problems, and automatically reconfigure the process to replace faulty services. This paper presents the LLAMA middleware support for service process monitoring, run-time management, and configuration. Instances of accountability agents are deployed to selectively monitor some services' performance. These agents in turn allow LLAMA's Accountability Authority (AA) to diagnose process problems and apply any necessary reconfiguration. The project also builds tools to simplify the setup and deployment of LLAMA components. Our experimental results indicate that using LLAMA contributes only a modest amount of system overhead, and that the diagnosis process is swift and sufficiently accurate.
31253731
Explicit Routing for Traffic Engineering in Labeled Optical Burst-Switched WDM Networks
Optical burst switching (OBS) is a promising technique for supporting high-capacity bursty data traffic over optical wavelength-division-multiplexed (WDM) networks. A label-switched path can be established to forward a burst control packet (BCP) if each OBS node is augmented with an IP/MPLS controller. Such a network is called a labeled OBS (LOBS) network, and it can exploit the explicit routing and constraint-based routing properties supported in the MPLS framework to perform traffic and resource engineering. In this paper, we consider the traffic-engineering problem in a LOBS network, using explicit routing to balance network load and to reduce the burst-loss probability. We model the traffic-engineering problem in a LOBS network as an optimization problem and propose two new objective functions to minimize network congestion. Our illustrative numerical examples show that the burst-loss probability can be significantly reduced using optimization technique, when compared with shortest-path routing.
62240401
A model-based approach to adding autonomic capabilities to network fault management system
Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.
15009651
Model-Driven Program Transformation of a Large Avionics Framework
Model-driven approaches to software development, when coupled with a domain-specific visual language, assist in capturing the essence of a large system in a notation that is familiar to a domain expert. From a high-level domain-specific model, it is possible to describe concisely the configuration features that a system must possess, in addition to checking that the model preserves semantic properties of the domain. With respect to large legacy applications written in disparate programming languages, the primary problem of transformation is the difficulty of adapting the legacy source to match the evolving features specified in the corresponding model. This paper presents an approach for uniting model-driven development with a mature program transformation engine. The paper describes a technique for performing widespread adaptation of source code from transformation rules that are generated from a domain-specific modeling environment for a large avionics framework.
2730805
Applying PageRank Algorithm in Requirement Concern Impact Analysis
As an important part of requirement management, managing requirement change plays a key role in controlling project schedule and costs at early stage. Effective requirement impact analysis would give proper assessment on the effect of certain requirement changes on the whole system, and provide useful information for making trade-off decisions on future system design and implementation. In this paper a quantitative approach to concern impact analysis at requirement level has been proposed with the application of PageRank algorithm, which is a successful link based web page sorting algorithm. At first, separation of concerns is applied during deriving formal requirement specification from textual requirement statements. Next, concerns are specified and concern relationship graph is established. Finally, PageRank algorithm is utilized on concern relationship graph for assessing the impact of concern changes. Our approach has been applied to hallway section in Light Control System and validation of analysis result has been stated.
8817054
The Application of Cultural Algorithm in Load Balancing of Hybrid P2P Network
This paper analyses the defect of the designing model of load balancing in hybrid P2P network, and proposes a new strategy of scheduling for load balancing, which based on culture algorithm of multilayer belief spaces. According to the current load status of each super-node in the system, it can calculate its load weight of the next time section by using the cultural algorithm, and assign the current requests of login with weighted round-robin. The extracted knowledge which is got by selecting the best belief space from the multilayer belief space is used to optimize the constrains of the evolutionary, in order to accelerate the optimization process of the calculation of load weight. Simulation experiments show that the allocation of login request to each node is to be balance much faster through using of cultural algorithm based on multilayer belief spaces.
60677377
Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality. Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliers in high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional stream data is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the outlying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams. 
In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensional data streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set of top sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding the outlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliers for high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.