CNS Core: Small: Principled Methodologies and Systems Support for Automated Cost-Effective Service Blending in the Emerging Public Cloud

A growing number of individuals and organizations rely on public cloud providers for their information technology (IT) needs. Many of these cloud users are budget-constrained and, therefore, interested in ways to reduce their cloud bills while still meeting their applications' performance needs. Cloud providers offer myriad service types (spanning infrastructure-, platform-, and software-as-a-service and diversity within each of these) and blending these can offer significant cost savings to users over prevalent techniques that tend to be limited to a small number of service types. However, getting such blending right is non-trivial and may itself pose significant effort and cost. This project aims to help users overcome such hurdles by significantly automating the process of cost-effectively blending and sizing cloud services. In particular, this automation will be realized via a cloud cost optimizing compiler called CoCo. A framework for application code annotation will allow users to convey blending-related hints based on their domain expertise. CoCo will require fundamentally novel optimization techniques and heuristics to transform user code into its cloud-ready form which will be cost-effective while meeting performance requirements. Finally, a runtime system for continual adaptation to dynamic workload changes will also be developed. All of these ideas will be prototyped on state of the art public cloud platforms and open-sourced.

This project has the potential to significantly simplify the task of migrating user applications to the public cloud with attendant cost savings. Perhaps more importantly, the transformed code is expected to incur lower recurring cloud bills owing to careful blending and sizing of cloud service types that adapts to dynamic conditions. These innovations are likely to be especially useful to small/medium-sized users for whom cloud migration can pose significant technical and cost hurdles. The educational and outreach components of the project will create awareness of such cost savings offered by service blending and, in combination with our open-source prototypes, will help spur further innovations on related themes within the cloud computing research community.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.