1. Detection of Anomalous Variations in Dynamic Networks
The intranet is fast becoming the preferred enterprise solution for delivering interoperable communications for internal information exchange. The term intranet implies a private data network that makes use of communication protocols and services of the Internet, such as the TCP/IP protocol suite. Over recent years these data networks have experienced significant growth in size and complexity resulting in an increase in frequency, type and severity of network problems. To ensure early detection and identification of these problems better network management techniques must be employed. In the management of large enterprise intranets (data networks), it becomes difficult to detect and identify causes of abnormal change in traffic distributions when the underlying logical topology is dynamic. Network management techniques use statistical trending methods and visualization tools to monitor network performance. These techniques are good for managing traffic but can be inadequate when networks are very dynamic (physical and logical structures of time-varying nature added to traffic variations). This project aims to complement these existing techniques with suitable metrics that allow the automatic detection of significant change within a network and alert operators to when and where the change occurred. Applications are manifold: discovery and prediction of network faults and abnormalities, overload, congestion, hotspots, etc. Possible topics: network reconstruction out of routing tables, where to put (a given number of) probes in order to get maximal coverage of network abnormalities, how does network monitoring depend on network protocols? If one has a time series of network transaction files, can one not monitor network (when?) and not loose too much information? What to do if there are “holes” in time series or in network(s)? In other words: Can a network be monitored without full knowledge of the entire network (network inference?)
2. Modelling the Energy use of Data Centre Networks
This project will investigate how energy is consumed in data centres and model the consumption with different workloads running in data centres. Specifically, the project will examine the link between the data centre energy use and the performance of the underlying network technology. The approach of this project is to conduct experiments in a prototype data centre and extract workload patterns, and to develop energy-efficient algorithms for the movement of data within the data centre. The second phase of the project will also investigate the interplay between data centre energy use and the wider electricity network (Smart Grid).
3. Resource Management for Network Function Virtualization
With the growing demand of the Cloud services, Network Function Virtualization (NFV) is gaining popularity among the application service providers, Internet service providers and Cloud service providers. NFV is proving to be an effective and flexible alternative for the service deployments across multiple-clouds. NFV is an emerging network architecture to increase flexibility and agility within operator’s networks by placing virtualized services on demand in Cloud Data Centers (CDCs). One of the main challenges for the NFV environment is how to efficiently allocate Virtual Network Functions (VNFs) to Virtual Machines (VMs) and how to minimize network latency in the rapidly changing network environments. Although a significant amount of work/research has been already conducted for the generic VNF placement problem and VM migration for efficient resource management in CDCs, network latency among various network components and VNF migration problem have not been comprehensively considered yet to the best of our knowledge. Firstly, to address VNF placement problem, we need to design more comprehensive models based on real measurements to capture network latency among VNFs with more granularity to optimize placement of VNFs in CDCs. We also need to consider resource demand of VNFs, resource capacity of VMs and network latency among various network components. Our objectives are to minimize both network latency and lead time (the time to find a VM to host a VNF).