Reducing Microservices Interference and Deployment Time in Resource-Constrained Cloud Systems

In resource-constrained cloud systems, e.g., at the network edge or in private clouds, it is essential to deploy microservices (MSs) efficiently. Unlike most of the existing approaches, we tackle this issue by accounting for two important facts: (i) the interference that arises when MSs compete for the same resources and degrades their performance, and (ii) the MSs’ deployment time. In particular, we first present some experiments highlighting the impact of interference on the throughput of MSs co-located in the same server, as well as the benefits of MSs’ parallel deployment. 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, clustering MSs also allows us to exploit the benefit of parallel deployment, which greatly reduces the deployment time as compared to the sequential approach applied in prior art and by default in state-of-the-art orchestrators. Our numerical results show that iPlace closely matches the optimum and uses 21-92% fewer servers compared to alternative schemes while proving to be highly scalable. Further, by deploying MSs in parallel using Kubernetes, iPlace reduces the deployment time by 69% compared to state-of-the-art solutions.