No abstract

Following the adoption of cloud computing, the proliferation of cloud data centers in multiple regions, and the emergence of computing paradigms such as fog computing, there is a need for integrated and efficient management of geodistributed clusters. Geo-distributed deployments suffer from resource fragmentation, as the resources in certain locations are over-allocated while others are under-utilized. Orchestration platforms such as Kubernetes and Kubernetes Federation offer the conceptual models and building blocks that can be used to build integrated solutions that address the resource fragmentation challenge. In this work, we propose mck8s -an orchestration platform for multi-cluster applications on multiple geo-distributed Kubernetes clusters. It offers controllers that automatically place, scale, and burst multi-cluster applications across multiple geo-distributed Kubernetes clusters. mck8s allocates the requested resources to all incoming applications while making efficient use of resources. We designed mck8s to be easy to use by development and operation teams by adopting Kubernetes' design principles and manifest files. We evaluated mck8s in a geo-distributed experimental testbed in Grid'5000. Our results show that mck8s balances the resource allocation across multiple clusters and reduces the fraction of pending pods to 6% as opposed to 65% in the case of Kubernetes Federation for the same workload.

Fog computing was designed to support the specific needs of latency-critical applications such as augmented reality, and IoT applications which produce massive volumes of data that are impractical to send to faraway cloud data centers for analysis. However this also created new opportunities for a wider range of applications which in turn impose their own requirements on future fog computing platforms. This article presents a study of a representative set of 30 fog computing applications and the requirements that a general-purpose fog computing platform should support.

As resources in geo-distributed environments are typically located in remote sites characterized by high latency and intermittent network connectivity, delays and transient network failures are common between the management layer and the remote resources. In this paper, we show that delays and transient network failures coupled with static configuration, including the default configuration parameter values, can lead to instability of application deployments in Kubernetes Federation, making applications unavailable for long periods of time. Leveraging on the benefits of configuration tuning, we propose a feedback controller to dynamically adjust the concerned configuration parameter to improve the stability of application deployments without slowing down the detection of hard failures. We show the effectiveness of our approach in a geo-distributed setup across five sites of Grid'5000, bringing system stability from 83-92% with no controller to 99.5-100% using the controller.