Advances in Traffic Classification Techniques and in Network-aware Allocation of Datacenter Resources

Emerging applications create tremendous volume of data traffic every day, increasingly stressing the capabilities of the networks hosting these applications. Understanding the communication patterns of these applications can significantly benefit the corresponding network management tasks. Motivated by today’s wide use of data analysis techniques, this thesis studies how to perform effective network management through using network data analysis. We investigate problems in managing the QoS targets and resources in enterprize networks and datacenter networks, presenting two applications of traffic analysis. The first application of traffic analysis addresses the utilisation of traffic classification techniques in enterprize networks to differentiate servicing flows of different classes or applications. The traffic classification techniques developed based on statistical analysis produce advantages over the existing techniques based on deep packet inspections or port matching in the scenarios that new applications emerge and packets are encrypted. This dissertation presents enhanced traffic classification techniques based on traffic analysis. The second application addresses the requirement of effective resource allocation in datacenters, where predictable performance is essential to the applications hosted in datacenters. This thesis proposes resource allocation techniques that can allocate datacenter resources in a way that the QoS requirements of Virtual Machines (VM) are met while minimizing the amounts of required resources. This is achieved through the integrated use of bin packing algorithms, effective bandwidth estimation techniques and software-defined networking technologies, where network data are analyzed to identify the minimum amounts of bandwidth required to meet the QoS targets for provisioning VMs on demand.