Geo-distributed application deployment assistance based on past routing information

Abstract: Cloud computing platforms allow users to deploy geographically distributed applications on servers around the world. Applications may be simple to deploy on these platforms, but it is up to the user and the application to decide which regions and servers to use for application placement. Furthermore, network conditions and routing between the geo-distributed servers change over time, which can lead to sub-optimal performance of applications deployed on such servers. A user could either employ a static deployment configuration of servers, or attempt to use a more dynamic configuration. However, both have inherent limitations. A static configuration will be sub-optimal, as it will be unable to adapt to changing network conditions. A more dynamic approach where an application could switch over or transition to a more suitable server could be beneficial, but this can be very complex in practice. Furthermore, such a solution is more about adapting to change as it happens, and not beforehand. This thesis will investigate the possibility of forecasting impending routing changes between servers, by leveraging messages generated by the Border Gateway Protocol (BGP) and past knowledge about routing changes. BGP routers can delay BGP updates due to factors such as the minimum route advertisement interval (MRAI). Thus, out proposed solution involves forwarding BGP updates downstream in the network, before BGP routers process them. As routing between servers changes, so does the latency, meaning that the latency then could be predicted to some degree. This observation could be applied to realize when the latency to a server increases or decreases past another server. This in turn facilitates the decision process of selecting the most optimal servers in terms of latency for application deployment. The solution presented in this thesis can successfully predict routing changes between end-points in an enclosed environment, and inform users ahead of time that the latency is about to change. The time gained by such predictions depend on factors such as the number of ASs between the end-points, the MRAI, and the update processing delay imposed on BGP routers. Time gains between tens of milliseconds to over 2 minutes has been observed.