Latency and Traffic Aware Container Placement in Distributed Cloud

Abstract: Distributed cloud is a key technology for 5G networks and is emerging as an alternative cloud infrastructure for hosting latency critical and traffic-intense applications. Placing computational resources on the edge of networks allows applications to be hosted closer to end users and traffic generating sources, which will reduce latency and traffic deeper in the network. This thesis presents a two-phase approach to solve the combinatorial op-timization problem of latency and traffic aware container placement in distributed cloud. Each phase is evaluated using a phase-specific simulated environment. The first phase involves placing containers in data centers and is solved using an integer programming model. Three different objective functions are presented and evaluated using acceptance ratio and average cost as performance metrics. In the second phase, containers are placed in servers. A traffic-aware heuristic is presented and evaluated against traditional bin packing heuristics. The traffic-aware heuristic managed to drastically reduce all traffic-related metrics at the cost of a few additional active servers in comparison to the bin packing heuristics. The traffic-aware heuristic can therefore be a good approach when placing traffic-intense applications in data centers in order to avoid network congestion.