Daniel Bachar

With the advent of cloud and container technologies, enterprises develop applications using a microservices architecture, managed by orchestration systems (e.g. Kubernetes), that group the microservices into clusters. As the number of application setups across multiple clusters and different clouds is increasing, technologies that enable communication and service discovery between the clusters are emerging (mainly as part of the Cloud Native ecosystem).
In such a multi-cluster setting, copies of the same microservice may be deployed in different geo-locations, each with different cost and latency penalties. Yet, current service selection and load balancing mechanisms do not take into account these locations and corresponding penalties.
We present \emph{MCOSS}, a novel solution for optimizing the service selection, given a certain microservice deployment among clouds and clusters in the system. Our solution is agnostic to the different multi-cluster networking layers, cloud vendors, and discovery mechanisms used by the operators. Our simulations show a reduction in outbound traffic cost by up to 72% and response time by up to 64%, compared to the currently-deployed service selection mechanisms.

With the advent of cloud and container technologies, enterprises develop applications through microservices architecture. This design pattern is mostly managed by orchestration systems (e.g. Kubernetes), that groups the microservices into clusters. Current deployments (e.g., Submariner) provide the ability to connect multiple clusters such that various microservices can communicate with each other. In such a multi-cluster setting, copies of the same microservice may be deployed in different geo-locations, each with different cost and latency penalties. Yet, current service selection and load balancing mechanisms do not take into account these locations and corresponding penalties. We present KOSS, a novel solution for optimizing the service selection, given a certain microservice deployment among the clusters in the system. Our solution transparently utilizes the current service discovery process. Our simulations show a significant reduction of the outbound traffic cost by up to 29% and the latency by up to 85% when comparing our solution to the currently deployed service selection mechanisms(e.g. Submariner’s).