Configuration and orchestration techniques for federated cloud resources

Abstract

Cloud resources are central to the operation of modern software-driven organizations. Due to the exponential growth of the number and diversity of cloud resources, organizations are inspired to build and deploy services, processes and applications by intermixing a set of best-of-breed cloud resources. It is estimated that nearly half of all large enterprises will have such intermixed (or we call federated) cloud resource deployments by the end of 2017.

Federated cloud resources, a special category of composite resources, which draws component resources from one or more public clouds and one or more private clouds, combined at the behest of its users. In this dissertation, we investigate the problems of configuration and orchestration of federated cloud resources. Addressing this problem is challenging, as component resources of federated cloud resources are distributed across multiple heterogeneous, autonomous and evolving cloud providers. Moreover, cloud-based applications may possess dynamic resource requirements during different phases of their life-cycle. Consequently, designing interoperable, portable and effective cloud resource configuration and orchestration techniques that cope with both heterogeneous and dynamic environments remains a deeply challenging problem.

To address these challenges, we first propose a taxonomy framework for cloud resource consumers to improve the awareness of the fundamental building blocks within the domain of cloud resource orchestration. Our taxonomy framework allows consumers to efficiently explore, understand, compare, contrast and thereby be able to wisely and rationally evaluate cloud resource orchestration techniques based on consumers’ requirements. We then present model-driven and process-driven techniques to describe, reuse and orchestrate elementary and federated cloud resource configurations. In conjunction with, we also propose a pluggable architecture to translate these high-level models into resource descriptions and management rules which can be interpreted by external configuration and orchestration tools such as Juju and Docker. We next propose a rule-based configuration and orchestration knowledge recommender service which empowers incremental acquisition, curation, and recommendation of knowledge based on users’ contexts. Finally we introduce a language for effective comprehension and visualization of cloud resource orchestration concerns. This language allows to visually represent, monitor and control cloud resource configurations. All aforementioned proposals have been implemented tools and experimentally validated based on real-world user scenarios and user studies.