Poster: Processing of Latency- and Deadline-Aware Big Data Approaches at the Edge

Time-critical requests are becoming increasingly important for microservice service chains. For such service chains, a late response may be worthless or cause failures. Previous works proposed to prevent late responses by trading computation time for result quality, depending on the available resources. However, determining the Quality of Result (QoR) for each operation in a service chain ahead of time, as is the state of the art, cannot prevent requests that are currently processing from being late if resource availability changes, e.g., due to resource sharing at inelastic Edge sites. Therefore, we present a framework to control the QoR online by replanning QoR values whenever resource changes are detected.