iPlace: An Interference-aware Clustering Algorithm for Microservice Placement

Efficiently deploying microservices (MSs) is critical, especially in data centers at the edge of the network infras- tructure where computing resources are precious. Unlike most of the existing approaches, we tackle this issue by accounting for the interference that arises when MSs compete for the same resources and degrades their performance. In particular, we first present some experiments highlighting the impact of interference on the throughput of co-located MSs. Then, we formulate an optimization problem that minimizes the number of used servers while meeting the MSs’ performance requirements. In light of the problem complexity, we design a low-complexity heuristic, called iPlace, that clusters together MSs competing for resources as diverse as possible and, hence, interfering as little as possible. Importantly, the choice of clustering MSs allows us to exploit the benefit of parallel MSs deployment, which, as shown by experimental evidence, greatly reduces the deployment time as compared to the sequential approach applied in prior art. Our numerical results show that iPlace closely matches the optimum and uses 10-63% fewer servers compared to alternative schemes, while proving to be highly scalable.