COMPARATIVE ANALYSIS OF SOFTWARE DEFINED NETWORKS (SDN) AND CONVENTIONAL NETWORKS USING ROUTING PROTOCOLS

Conventional routing protocols such as RIP, OSPF, EIGRP and BGP have a very rigid and intricate system thus narrowing the adaptability of networks to the ever changing Internet, the emergence of Software Defined Networking (SDN) provides a solution for this problem. Due to the handiness of a centralized controller, SDN has provided an effective method in terms of routing computation and fine control over data packets. Due to the increase in unpredicted failures taking place the ability to predict/ know the approximate maximum time it takes for these networks to converge in order to avoid and/or minimize loss of packets/data during these failures has become crucial in today's world. This time that the routers in the network take to converge via the implemented routing protocol to resume communication or transfer of information again is called the routing convergence time. In this thesis, the performance is evaluated by measuring the routing convergence time during link failure with respect to the topology scale of the networks to show that SDN routing/forwarding is better compared to conventional routing. Further the results indicate that the routing convergence time is less in SDN networks on comparison with conventional networks when the topology scale is increased, indicating that SDN networks converge faster during link/node failures in comparison with Conventional networks and that routing convergence time is greatly influenced with the changing topological size/increasing network size. I believe that this work can throw light upon many advantages in SDN with regards to faster convergence during failures in contrast to archaic conventional networks

LHView: Location Aware Hybrid Partial View

The rise of the Cloud creates enormous business opportunities for companies to provide global services, which requires applications supporting the operation of those services to scale while minimizing maintenance costs, either due to unnecessary allocation of resources or due to excessive human supervision and administration. Solutions designed to support such systems have tackled fundamental challenges from individual component failure to transient network partitions. A fundamental aspect that all scalable large systems have to deal with is the membership of the system, i.e, tracking the active components that compose the system. Most systems rely on membership management protocols that operate at the application level, many times exposing the interface of a logical overlay network, that should guarantee high scalability, efficiency, and robustness. Although these protocols are capable of repairing the overlay in face of large numbers of individual components faults, when scaling to global settings (i.e, geo-distributed scenarios), this robustness is a double edged-sword because it is extremely complex for a node in a system to distinguish between a set of simultaneously node failures and a (transient) network partition. Thus the occurrence of a network partition creates isolated sub-sets of nodes incapable of reconnecting even after the recovery from the partition. This work address this challenges by proposing a novel datacenter-aware membership protocol to tolerate network partitions by applying existing overlay management techniques and classification techniques that may allow the system to efficiently cope with such events without compromising the remaining properties of the overlay network. Furthermore, we strive to achieve these goals with a solution that requires minimal human intervention