SWAVES: Softwarized deployment of services in waves around moving users

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In many application scenarios, users interact with services deployed in a server infrastructure. To ensure low latency in such interactions, it is often advantageous to deploy these services close to a user; for example, at or near the network's edge in a mobile network – hence the name edge computing. This raises interesting problems in managing such distributed services, e.g., how many instances of a service to run where. These aspects have been extensively researched, but often under simplifying assumptions. For example, it is often assumed that executable code for all services is available everywhere. But this is unrealistic due to limited storage size (especially in Internet-of-Things scenarios), limited data rate to distribute code, and the often small probability that a particular service is required at a particular site. It is hence the goal of this project to research approaches to distribute executable code to edge sites where it is actually needed, balancing distribution overhead and storage space, using, e.g., user mobility predictions. In addition, we will consider different forms of code, ranging from mere source code that is very small but needs building before execution to ready-to-execute virtual machines as well as running code in various standby levels (e.g., cold standby with code being locally available but not running up to active/active replication). To do so, we will investigate both conventional optimization approaches and machine-learning-based ones. We will characterize which schemes can achieve what kinds of tradeoffs under which circumstances (e.g., prediction accuracy). From a practical side, we will provide code characterizations (e.g., build times) and performance profiles for popular services to our SNDZoo, making collected data available to the community at large.