Performance Isolation in Cloud-Based Big Data Systems

Cloud-based big data systems usually have many different tenants that require access to the server’s functionality. In a non-isolated cloud system, the different tenants can freely use the resources of the server. Hereby, disruptive tenants who exceed their limits can easily cause degradation of performance of the provided services for other tenants. To ensure performance demands of the multiple tenants and meet fairness criteria various performance isolation approaches have been introduced including artificial delay, round robin, blacklist, and thread pool. Each of these performance isolation approaches adopts different strategies to avoid the performance interference in case of multiple concurrent tenant needs. In this project we will investigate performance isolation in cloud-based big data systems. To this end we will identify the alternative architecture designs of cloud-based big data systems and evaluate the integration of feasible performance isolation approaches. We will evaluate our approach using industrial case studies on cloud-based big data system from the agriculture application domains.