120 research outputs found
Service Prototyping Lab Report - 2018 (Y3)
The annual activity report of the Service Prototyping Lab at Zurich University of Applied Sciences. Research trends and initiatives, research projects, transfer to education and local industry, academic community involvement, qualification and scientific development over the period of one year are among the covered topics
Fault Localization in Large-Scale Network Policy Deployment
The recent advances in network management automation and Software-Defined
Networking (SDN) are easing network policy management tasks. At the same time,
these new technologies create a new mode of failure in the management cycle
itself. Network policies are presented in an abstract model at a centralized
controller and deployed as low-level rules across network devices. Thus, any
software and hardware element in that cycle can be a potential cause of
underlying network problems. In this paper, we present and solve a network
policy fault localization problem that arises in operating policy management
frameworks for a production network. We formulate our problem via risk modeling
and propose a greedy algorithm that quickly localizes faulty policy objects in
the network policy. We then design and develop SCOUT---a fully-automated system
that produces faulty policy objects and further pinpoints physical-level
failures which made the objects faulty. Evaluation results using a real testbed
and extensive simulations demonstrate that SCOUT detects faulty objects with
small false positives and false negatives.Comment: 10 pages, 10 figures, IEEE format, Conference, SDN, Network Polic
Fast network configuration in Software Defined Networking
Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%
LogUAD: Log unsupervised anomaly detection based on word2Vec
System logs record detailed information about system operation and are important for analyzing the system\u27s operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational cost and avoid log instability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takes original log messages as input to avoid the noise. LogUAD uses Word2Vec to generate word vectors and generates weighted log sequence feature vectors with TF-IDF to handle the evolution of log statements. At last, a computationally efficient unsupervised clustering is exploited to detect the anomaly. We conducted extensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25% compared to LogCluster
Beyond Data Markets: Opportunities and Challenges for Distributed Ledger Technology in Genomics
During the past decade, distributed ledger technology (DLT) has found its way into application areas outside finance, such as supply chain management, the Internet of Things, or health care. To this end, this novel technology phenomenon has recently also caught the attention of researchers and practitioners in genomics. Although various DLT-based data markets for genome data already exist or are in development, the potential of DLT in this context is far from exhausted, whereas the possible risks related to the application of DLT in genomics are not yet sufficiently known. In this work, we investigate the potential opportunities and challenges for the application of DLT in the field of genomics. Thus, we make an important contribution to the safe and socially acceptable use of DLT in this unique and highly relevant use context
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