Design and Implementation of a High Performance Network Processor with Dynamic Workload Management

Internet plays a crucial part in today\u27s world. Be it personal communication, business transactions or social networking, internet is used everywhere and hence the speed of the communication infrastructure plays an important role. As the number of users increase the network usage increases i.e., the network data rates ramped up from a few Mb/s to Gb/s in less than a decade. Hence the network infrastructure needed a major upgrade to be able to support such high data rates. Technological advancements have enabled the communication links like optical fibres to support these high bandwidths, but the processing speed at the nodes remained constant. This created a need for specialised devices for packet processing in order to match the increasing line rates which led to emergence of network processors. Network processors were both programmable and flexible. To support the growing number of internet applications, a single core network processor has transformed into a multi/many core network processor with multiple cores on a single chip rather than just one core. This improved the packet processing speeds and hence the performance of a network node. Multi-core network processors catered to the needs of a high bandwidth networks by exploiting the inherent packet-level parallelism in a network. But these processors still had intrinsic challenges like load balancing. In order to maximise throughput of these multi-core network processors, it is important to distribute the traffic evenly across all the cores. This thesis describes a multi-core network processor with dynamic workload management. A multi-core network processor, which performs multiple applications is designed to act as a test bed for an effective workload management algorithm. An effective workload management algorithm is designed in order to distribute the workload evenly across all the available cores and hence maximise the performance of the network processor. Runtime statistics of all the cores were collected and updated at run time to aid in deciding the application to be performed on a core to to enable even distribution of workload among the cores. Hence, when an overloading of a core is detected, the applications to be performed on the cores are re-assigned. For testing purposes, we built a flexible and a reusable platform on NetFPGA 10G board which uses a FPGA-based approach to prototyping network devices. The performance of the designed workload management algorithm is tested by measuring the throughput of the system for varying workloads