Efficient Management of Composite Edge Applications

Edge computing reduces latency for mobile applications (Apps) by processing data closer to users, while containerized microservices (MSs) enable their modular deployment. Managing such Apps involves three key challenges: (i) strategi- cally placing MSs to minimize response latency and resource consumption, (ii) managing MS migration/relocation during user mobility or traffic load changes while limiting App downtime, and (iii) enabling MS sharing across Apps while ensuring target performance. We formulate this as an optimization problem (proven to be NP-hard) and propose STEP, a polynomial-time heuristic. In contrast to prior art, STEP (i) jointly considers state- ful and stateless MSs in its decisions, (ii) exploits MS shareability to reduce resource usage, (iii) balances response latency, App downtime, and resource utilization, and (iv) leverages multiple versions of the same MS to adapt QoS to available edge resources. Results show that STEP achieves near-optimal performance with only 1.6% higher deployment cost while reducing CPU usage by 42% compared to baselines. Also, it enables real-time App deployment in a large-scale scenario on a Kubernetes cluster with sub-second order execution time and reduced deployment cost by 16–17% compared to its benchmarks.