Enterprises are rapidly migrating or creating workloads of all levels of criticality to cloud environments, thanks to the convenient, on-demand, pay-as-you-go resource allocation model, and the ease of provisioning and deployment of machines and software. Even life sciences organizations, biomedical companies, and healthcare providers - who are subject to strict governmental regulations around data privacy and confidentiality – have joined this trend. In such applications it is necessary to provide resiliency, security, privacy, and performance, to a high degree, all at the same time.

Enterprise-class resiliency in the cloud (including the traditional areas of fault tolerance, high availability, disaster recovery, and planned outage prevention) has been extensively studied and, although cloud environments offer unique challenges, suitable solutions exist. However, meeting these resiliency challenges is greatly compounded by and interacts with the need for privacy-preserving cloud data access, confidentiality-assured cloud data operations, and extensive logging of security-related activities, all of which are mandated by regulations in many countries and several sectors (e.g., finance, biomedicine, and life sciences). As is well known, providing high levels of resiliency and security can have significant performance overhead, which in turn contributes to the challenges of providing adequate performance to the application.

Resiliency, security, privacy, and performance on the cloud are active areas of research and development for some time now, and numerous schemes have been developed to address one or a subset of these needs. However, integrating these schemes to coherently achieve all properties simultaneously is a hard problem, involving many tricky trade-offs – which must be solved to meet these applications’ requirements.