Rebecca Steinert

Toward decentralized probabilistic management

In recent years, data communication networks have grown to immense size and have been diversified by the mobile revolution. Existing management solutions are based on a centralized deterministic paradigm, which is appropriate for networks of moderate size operating in relatively stable conditions. However, it is becoming increasingly apparent that these management solutions are not able to cope with the large dynamic networks that are emerging. In this article, we argue that the adoption of a decentralized and probabilistic paradigm for network management will be crucial to meet the challenges of future networks, such as efficient resource usage, scalability, robustness, and adaptability. We discuss the potential of decentralized probabilistic management and its impact on management operations, and illustrate the paradigm by three example solutions for real-time monitoring and anomaly detection.

Long-Term Adaptation and Distributed Detection of Local Network Changes

We present a statistical approach to distributed detection of local latency shifts in networked systems. For this purpose, response delay measurements are performed between neighbouring nodes via probing. The expected probe response delay on each connection is statistically modelled via parameter estimation. Adaptation to drifting delays is accounted for by the use of overlapping models, such that previous models are partially used as input to future models. Based on the symmetric Kullback-Leibler divergence metric, latency shifts can be detected by comparing the estimated parameters of the current and previous models. In order to reduce the number of detection alarms, thresholds for divergence and convergence are used. The method that we propose can be applied to many types of statistical distributions, and requires only constant memory compared to e.g., sliding window techniques and decay functions. Therefore, the method is applicable in various kinds of network equipment with limited capacity, such as sensor networks, mobile ad hoc networks etc. We have investigated the behaviour of the method for different model parameters. Further, we have tested the detection performance in network simulations, for both gradual and abrupt shifts in the probe response delay. The results indicate that over 90% of the shifts can be detected. Undetected shifts are mainly the effects of long convergence processes triggered by previous shifts. The overall performance depends on the characteristics of the shifts and the configuration of the model parameters.

Service provider DevOps for large scale modern network services

Network service providers are facing challenges for deploying new services mainly due to the growing complexity of software architecture and development process. Moreover, the recent architectural innovation of network systems such as Network Function Virtualization (NFV), Software-defined Networking (SDN), and Cloud computing increases the development and operation complexity yet again. One of the emerging solutions to this problem is a novel software development concept, namely DevOps, that is widely employed by major Internet software companies. Although the goals of DevOps in data centers are well-suited for the demands of agile service creation, additional requirements specific to the virtualized and software-defined network environment are important to be addressed from the perspective of modern network carriers. In this paper, we thoroughly debate DevOps requirements for developing a modern service creation platform by taking EU FP7 project UNIFY as a reference architecture and suggest the corresponding extensions of UNIFY interfaces that meet the discovered requirements.

Towards Distributed and Adaptive Detection and Localisation of Network Faults

We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through probing between nodes, probe response delay and packet drop are modelled via parameter estimation for each link. Estimated model parameters are used for autonomous configuration of algorithm parameters, related to probe intervals and detection mechanisms. Expected fault-detection performance is formulated as a cost instead of specific parameter values, significantly reducing configuration efforts in a distributed system. The benefit offered by using our algorithm is fault-detection with increased certainty based on local measurements, compared to other methods not taking observed network conditions into account. We investigate the algorithm performance for varying user parameters and failure conditions. The simulation results indicate that more than 95% of the generated faults can be detected with few false alarms. At least 80% of the link faults and 65% of the node faults are correctly localised. The performance can be improved by parameter adjustments and by using alternative paths for communication of algorithm control messages.

Roles of DevOps tools in an automated, dynamic service creation architecture

Software Defined Networking (SDN) and Network Functions Virtualization facilitate, with their advanced programmability features, the design of automated dynamic service creation platforms. Applying DevOps principles to service design can further reduce service creation times and support continuous operation. Monitoring, troubleshooting, and other DevOps tools can have different roles within virtualised networks, depending on virtualization level, type of instantiation, and user intent. We have implemented and integrated four key DevOps tools that are useful in their own right, but showcase also an integrated scenario, where they form the basis for a more complete and realistic DevOps toolkit. The current set of tools include a message bus, a rudimentary configuration tool, a probabilistic congestion detector, and a watchpoint mechanism. The demo also presents potential roles and use-cases for the tools.

Estimating the Parameters of Randomly Interleaved Markov Models

Sequences that can be assumed to have been generated by a number of Markov models, whose outputs are randomly interleaved but where the actual sources are hidden, occur in a number of practical situations where data is captured as an unlabeled stream of events. We present a practical method for estimating model parameters on large data sets under the assumption that all sources are identical. Results on representative examples are presented, together with a discussion on the accuracy and performance of the proposed estimation algorithms. Finally, we describe a real-world case study where we apply the technique to the sequence of events recorded in the technical support database of an IT vendor.

Service Provider DevOps

Although there is consensus that software defined networking and network functions virtualization overhaul service provisioning and deployment, the community still lacks a definite answer on how carrier-grade operations praxis needs to evolve. This article presents what lies beyond the first evolutionary steps in network management, identifies the challenges in service verification, observability, and troubleshooting, and explains how to address them using our Service Provider DevOps (SP-DevOps) framework. We compendiously cover the entire process from design goals to tool realization and employ an elastic version of an industry-standard use case to show how on-the-fly verification, software-defined monitoring, and automated troubleshooting of services reduce the cost of fault management actions. We assess SP-DevOps with respect to key attributes of software-defined telecommunication infrastructures both qualitatively and quantitatively, and demonstrate that SP-DevOps paves the way toward carrier-grade operations and management in the network virtualization era.

A Distributed Spatio-Temporal Event Correlation Protocol for Multi-Layer Virtual Networks

We present a distributed spatio-temporal event correlation protocol for multi-layer networks. The problems that we address relate to scalability in stacked overlay networks and network equipment with asynchronous clocks, which complicates the problem of event correlation. We describe a cross-layer protocol designed to address these problems, operating in a fully distributed manner and taking into account asynchronous timestamps. It is assumed that events in one layer may arise from a series of events in lower layers. Detected events that are spatially related in one layer are aggregated using a gossip-like protocol, and constitute a root cause. The set of aggregated events is disseminated to lower layers and used for temporal correlation. We have tested the scalability and the performance of the distributed event protocol, using both synthetically generated and real-world topologies. The results indicate that the average overhead produced for collecting events down the stack of overlays increases with the number of layers. For a fixed number of layers, the protocol scales similarly with the graph-theoretic properties for a network of increasing size.

Translation of probabilistic QoS in hierarchical and decentralized settings

In this paper we build on methods from probabilistic management for overcoming two issues in the translation of QoS into configurations of network nodes in dynamic, decentralized, and hierarchical networks. First, the inherent uncertainty about node performance in such networks (due to network dynamics) may impede adequate specification of QoS. We suggest how a probabilistic variant of QoS via optimization can be translated into objectives that can be handled with probabilistic management methods. Second, the dynamics of the network may make the interpretation of QoS dependent on the availability of network resources. We use methods from probabilistic management to suggest how the expression of QoS may be restricted in order to be able to ensure the existence of a translation of given QoS. The suggestions are applied to our own network architecture. The aim is to further develop our approach to a viable management solution for cloud computing and virtualized networks.

Searching for Gas Turbine Maintenance Schedules

Preventive maintenance schedules occurring in industry are often suboptimal with regard to maintenance coal-location, loss-of-production costs and availability. We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines, with the goal of reducing the direct maintenance costs and the often costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that the feasibility version is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using integer programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, the use of our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days by 12%. Compared to a integer programming approach, our algorithm is not optimal, but is much faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based< on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals.