Delay Aware Survivable Routing with Network Coding in Software Defined Networks

It was demonstrated in transport networks that network (diversity) coding can provide sufficient redundancy to ensure instantaneous single link failure recovery, while nearoptimal bandwidth efficiency can be reached. However, in the resulting multi-path routing problem the end-to-end delays were not considered. On the other hand, even in a European-scale network the delay difference of the paths has severe effect on the Quality-of-Service of application scenarios, such as video streaming. Thus, in this paper we thoroughly investigate survivable routing in Software Defined Networks (SDNs) with several additional delay bounds to the bandwidth cost minimization problem. We build on the fact that, if the user data is split into at most two parts, then the minimum cost coding solution has a well-defined acyclic structure of subsequent paths and disjoint path-pairs between the communication end-points. Complexity analysis and integer linear programs are provided to solve these delay aware survivable routing problems in SDNs

Diversity Coding-Based Survivable Routing with QoS and Differential Delay Bounds

Survivable routing with instantaneous recovery gained much attention in the last decade, as in optical backbone networks even the shortest disruption of a connection may cause tremendous loss of data. Recently, strict delay requirements emerges with the growing volume of multimedia and video streaming applications, which have to be ensured both before and after a failure. Diversity coding provides a nice trade-off between the simplicity of dedicated protection and bandwidth-efficiency of network coding to ensure instantaneous recovery for the connections. Hence, in this paper we thoroughly investigate the optimal structure of diversity coding-based survivable routing, which has a well-defined acyclic structure of subsequent paths and disjoint path-pairs between the communication end-points. We define the delay of these directed acyclic graphs, and investigate the effect of Qualityof- Service and differential delay bounds on the solution cost. Complexity analysis and integer linear programs are provided to solve these delay aware survivable routing problems. We discuss their approximability and provide some heuristic algorithms, too. Thorough experiments are conducted to demonstrate the benefits of diversity coding on randomly generated and real-world optical topologies

Energy Efficient Survivable IP over WDM Networks with Network Coding

In this work we investigate the use of network coding in 1+1 survivable IP over WDM networks by encoding the protection paths of multiple flow with each other at intermediate nodes. We study the energy efficiency of this scheme through MILP, and a heuristic with five operating options. We evaluate the MILP and the heuristics on typical and regular network topologies. Our results show that implementing network coding can produce savings up to 37% on the ring topology and 23% considering typical topologies. We also study the impact of varying the demand volumes on the network coding performanc

PON-Based Connectivity for Fog Computing

Fog computing plays a crucial role in satisfying the requirements of delay-sensitive applications such as connected vehicles, smart grids, and actuator networks by moving data processing close to end users. Passive optical networks (PONs) are widely used in access networks to reduce the power consumption while providing high bandwidth to end users under flexible designs. Typically, distributed fog computing units in access networks have limited processing and storage capacities that can be under or over utilized depending on instantaneous demands. To extend the available capacity in access network, this paper proposes a fog computing architecture based on SDN-enabled PONs to achieve full connectivity among distributed fog computing servers. The power consumption results show that this architecture can achieve up to about 80% power savings in comparison to legacy fog computing based on spine and leaf data centers with the same number of servers