Mícheál Ó Foghlú

Towards autonomic management of communications networks

As communications networks become increasingly dynamic, heterogeneous, less reliable, and larger in scale, it becomes difficult, if not impossible, to effectively manage these networks using traditional approaches that rely on human monitoring and intervention to ensure they operate within desired bounds. Researchers and practitioners are pursuing the vision of autonomic network management, which we view as the capability of network entities to self-govern their behavior within the constraints of business goals that the network as a whole seeks to achieve. However, applying autonomic principles to network management is challenging for a number of reasons, including: (1) A means is required to enable business rules to determine the set of resources and/or services to be provided. (2) Contextual changes in the network must be sensed and interpreted, because new management policies may be required when context changes. (3) As context changes, it may be necessary to adapt the management control loops that are used to ensure that system functionality adapts to meet changing user requirements, business goals, and environmental conditions. (4) A means is required to verify modeled data and to add new data dynamically so that the system can learn and reason about itself and its environment. This article provides an introduction to the FOCALE autonomic network management architecture, which is designed to address these challenges.

Biologically inspired self-governance and self-organisation for autonomic networks

The current complexity of network management has helped drive the need for autonomic capabilities. The vision of autonomic network management provides the ability for network devices to cooperatively self-organise and self-govern in the support of high level business goals. These principles are inspired by biological systems. In this paper, we propose key self-organisation and self-governance techniques that are drawn from principles of molecular biology including (i) blood glucose homeostasis, (ii) reaction diffusion like principles, (iii) microorganism mobility using chemotaxis techniques, and (iv) hormone signaling. Preliminary simulation results have also been presented to validate our model.

Beyond the Knowledge Plane: An Inference Plane to Support the Next Generation Internet

The Knowledge Plane was defined as a global, decentralized network overlay which used cognitive information processing to build a self-managing network. This paper builds on that work to address a number of its shortcomings, including a means to express business goals to drive the set of network services and resources provided, the ability to work with and control heterogeneous products and technologies, and the integration of wired and wireless networks using autonomic principles.

The Design of a New Context-Aware Policy Model for Autonomic Networking

This paper describes a new version of the DEN-ng context model, and how this model in conjunction with the DEN-ng policy model can be used for more effective and flexible context management. Both are part of the FOCALE autonomic network architecture. Context selects policies, which select roles that can be used, which in turn define allowed functionality for that particular context.

Hybrid DNA and Enzyme Based Computing for Address Encoding, Link Switching and Error Correction in Molecular Communication

This paper proposes a biological cell-based communication protocol to enable communication between biological nanodevices. Inspired by existing communication network protocols, our solution combines two molecular computing techniques (DNA and enzyme computing), to design a protocol stack for molecular communication networks. Based on computational requirements of each layer of the stack, our solution specifies biomolecule address encoding/decoding, error correction and link switching mechanisms for molecular communication networks.

Distributed formal concept analysis algorithms based on an iterative mapreduce framework

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganters classic algorithm by introducing a family of

A formal approach for the inference plane supporting integrated management tasks in the Future Internet

\mbox{MR}^\star

Ontology-based knowledge representation for self-governing systems

algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithms lineage. To evaluate the factors that impact distributed algorithm performance, we compare our

VoIP quality monitoring in LTE femtocells

\mbox{MR}^{*}

Modelling Context for Autonomic Networking

algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.