CNS Core: Small: Embracing cross stack heterogeneity in next-generation cloud platforms

Cloud computing has emerged as an attractive computing paradigm to achieve better economy of scale and flexibility for many application domains. For catering to the increasing service demands of these applications, cloud platforms have steadily evolved to offer a myriad of resource and service types. This has complicated the cloud adoption process due to challenges with respect to resource provisioning, billing complexity, and inaccurate resource demand estimation. This leaves cloud-bound tenants with an uphill task of achieving an optimal cost-to-performance ratio when hosting their applications on public cloud resources. On the other hand, the ever-growing cloud market has pushed providers to embrace heterogeneity, both at the hardware and software levels, through the rapid adoption of state-of-the-art hardware (e.g. Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), etc.,) and software (e.g. Virtual Machines (VMs), containers, and serverless functions) developments. However, the main mediator between the applications and the underlying cloud computing infrastructure, which is the resource manager and scheduler, is not fully equipped to make the best use of the ever-evolving system heterogeneity and software complexity. Thus, the cloud resource management and scheduling framework needs a fresh look in order to adapt to the emerging hardware and software heterogeneity to maximize the deliverable performance and energy efficiency of cloud infrastructures. This project adresses these challenges by investigating a two-fold approach for developing a tenant- and provider-cognizant scalable cloud scheduling framework. First, from a tenant's perspective, we are developing an efficient cloud configuration framework that caters to the application requirements in terms of cost and performance. Second, from a provider's perspective, we are developing an efficient and adaptive resource management framework that can exploit the available heterogeneity across the cloud stack. The resource management framework will be integrated with commercial resource orchestrators like Kubernetes and will be evaluated using public cloud platforms with real-world workloads. The outcomes of this project should enable the effective utilization of heterogeneity offered by cloud providers for enhanced performance and energy efficiency across many application domains. The results of this research will foster new research directions in several areas of cloud computing, and the scheduling framework will be made publicly available. Furthermore, student training and research opportunities for undergraduate students will be facilitated through this research. The outreach activities include mentoring of women and minority students, participation in K-12 activities, and strengthening the Broadening Participation in Computing (BPC) program in the department.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.