Sidath Handurukande

Magneto approach to QoS monitoring

Quality of Service (QoS) monitoring of end-user services is an integral and indispensable part of service management. However in large, heterogeneous and complex networks where there are many services, many types of end-user devices, and huge numbers of subscribers, it is not trivial to monitor QoS and estimate the status of Service Level Agreements (SLAs). Furthermore, the overwhelming majority of end-terminals do not provide precise information about QoS which aggravates the difficulty of keeping track of SLAs. In this paper, we describe a solution that combines a number of techniques in a novel and unique way to overcome the complexity and difficulty of QoS monitoring. Our solution uses a model driven approach to service modeling, data mining techniques on small sample sets of terminal QoS reports (from “smarter” end-user devices), and network level key performance indicators (N-KPIs) from probes to address this problem. Service modeling techniques empowered with a modeling engine and a purpose-built language hide the complexity of SLA status monitoring. The data mining technique uses its own engine and learnt data models to estimate QoS values based on N-KPIs, and feeds the estimated values to the modeling engine to calculate SLAs. We describe our solution, the prototype and experimental results in the paper.

RPig: A scalable framework for machine learning and advanced statistical functionalities

In many domains such as Telecom various scenarios necessitate the processing of large amounts of data using statistical and machine learning algorithms. A noticeable effort has been made to move the data management systems into MapReduce parallel processing environments such as Hadoop and Pig. Nevertheless these systems lack the features of advanced machine learning and statistical analysis. Frame-works such as Mahout on top of Hadoop support machine learning but their implementations are at the preliminary stage. For example Mahout does not provide Support Vector Machine (SVM) algorithms and it is difficult to use. On the other hand traditional statistical software tools such as R containing comprehensive statistical algorithms for advanced analysis are widely used. But such software can only run on a single computer and therefore it is not scalable. In this paper we propose an integrated solution RPig which takes the advantages of R (for machine learning and statistical analysis capabilities) and parallel data processing capabilities of Pig. The RPig framework offers a scalable advanced data analysis solution for machine learning and statistical analysis. Analysis jobs can be easily developed with RPig script in high level languages. We describe the design implementation and an eclipse-based RPigEditor for the RPig framework. Using application scenarios from the Telecom domain we show the usage of RPig and how the framework can significantly reduce the development effort. The results demonstrate the scalability of our framework and the simplicity of deployment for analysis jobs.

Autonomic MANET Routing Protocols

In Mobile Ad hoc Networks (MANETs), timers have been used widely to maintain routing (state) information. The use of fixed-interval timers is simple to implement but, in practise, may be difficult to configure in dynamic operational environments, and so may give reduced performance in the presence of frequent topology changes. This paper proposes a self-tuning timer approach within a simple control system for MANET routing protocols with the aim of allowing dynamic, autonomic, re-calibration of routing update frequencies. A novel dynamic timer algorithm is presented to automatically tune routing performance by adapting timer intervals to network conditions. Our simulation results have shown that, compared to the default fixed timer approach, the proposed algorithm could effectively improve routing throughput without manual configuration.

IPTV service modeling in Magneto networks

One of the main steps of service assurance is service monitoring using Key Performance Indicators (KPIs) and Service Level Agreements (SLAs). We show an approach for service modeling, first starting with an abstract service model that depends on the network. And then, we show how a corresponding model can be realized using a domain specific language. This solution is able to condense various sources of service model requirements into a condense formal and executable model including service decomposition and KPI aggregation. We have described this solution in the context of Magneto project and uses IPTV as a service in our description.

Autonomic Tuning of Routing for MANETs

In mobile ad hoc networks (MANETs), timers have been widely used to maintain routing (state) information. The use of fixed-interval timers is simple to implement but, in practise, may be difficult to configure in dynamic operational environments, and so may give reduced performance in the presence of frequent topology changes. This paper proposes a self-tuning timer approach within a simple control system for MANET routing protocols with the aim of allowing dynamic, autonomic, re-calibration of routing update frequencies. A novel dynamic timer algorithm is presented to automatically tune routing performance by adapting timer intervals to network conditions. Our simulation results have shown that, compared to the default fixed timer approach, the proposed algorithm could effectively improve routing throughput without manual configuration.

Real-world experiences with CQI-based analyses for dense LTE networks

Much work has been done in the area of telecommunications transmission parameters and, in particular, Channel Quality Indicators, to analyse the best ways to improve the performance and quality of communications. However, there is a gap in published papers when it comes to real-world experiences with CQI reports and network-side analysis of transmission parameters that can provide information about user equipment and network elements behaviour. In this paper, we present our experiences with a real-world analysis of dense LTE networks, focusing on two main aspects - what information can CQI reports give us about the behaviour of user equipment in the network, and what information do they provide in relation to the individual network elements. We show what insights can be derived from a network wide statistical analysis of CQI reports and how these insights can be further used by operators to understand potential problems in their network.

Automatic discovery of sub-optimal radio performance in LTE RAN networks

As telecommunication networks have become large complex systems, it is recognized today that there is an urgent need for providing comprehensive views about how the network operates, instead of fragmented reports about the different elements in the network. We propose a system that holistically analyzes the network along carefully balanced dimensions, both by using historic data and by using data across multiple LTE RAN networks. Our system learns baseline behaviours and provides insights to the operators alerting them about deviations from this learnt behaviour. The system can also help operators by discovering network configurations that lead to good radio performance, which can be used to bootstrap the network configuration process. All the learnt behaviour is made available globally through a global sharing service. We validated our approach using real 4G RAN network data, and show that we discover behaviours in the network which would not be obvious to the operator otherwise.

Efficient Management of Loosely Collaborative Service Networks: A Report on DANMS 2011

The IFIP/IEEE Workshop on Distributed Autonomous Network Management Systems 2011 (DANMS’11) [1] was held on May 27th, 2011 in Trinity College, Dublin, Republic of Ireland. DANMS’11 was co-located with the 12th IFIP/IEEE International Symposium on Integrated Network Management (IM) and it was the first time that the DANMS workshop was co-located with the IM Symposium. The DANMS’11 workshop is part of a series of workshops dedicated to advances in network management and the application of new management principles in network design. The previous workshops were co-located with IEEE GLOBECOM [2, 3] and The International Conference on Autonomic Computing [4]. Bringing industrial aspects to the workshop, this series was supported by Ericsson Ireland and was organized by researchers in the Network Management (NM) Lab in Ericsson Ireland together with distinguish academics in the network management domain. The NM Lab, being a part of Ericsson, a global leader in telecom network domain, was able to bring current industrial challenges into focus of the research community through this workshop series. The synergy of the collaboration between the industrial players and academics helped the workshop series on many fronts and contributed to the research field. The workshop series also received valuable support from European Marie Curie Actions Program. To enrich the workshop and to make it more relevant, the organizers collaborated with EU and Celtic telecommunication research projects members (such as member of Magneto and Madeira projects) who work in the network management domain.