The importance of energy efficiency today is felt across all domains of computing. In particular, personal computing platforms are becoming increasingly wireless and miniaturized, but limited by trade-offs between battery lifetimes and computational requirements. Developing energy-efficient solutions is critical to the survival of the semiconductor industry. This project seeks to develop new abstractions to effectively utilize network and cloud resources to enable a scalable and energy-efficient mobile computing paradigm. The proposed hierarchical cloud computing architecture with cross-layer parallelism provides innovation for teaching networking, system, and architecture courses. It provides a platform to engage undergraduate students and community service agencies. Finally, this project is expected to have a high impact on global societies and economies.
The proposed distributed system architecture leverages cross-layer parallelism to enable scalable, adaptive, and intelligent integration between edge mobile devices and clouds by intelligently partitioning and replicating computation between mobile devices and the cloud. This project redefines two classes of programming abstractions that enable seamless usage of: (1) network resources at transport layers and (2) any form of cloud resources whether local or remote. The proposed architecture strategically leverages network-level parallelism that transparently utilizes multiple network paths, in-network cloudlets that offer highly customized accelerators for offloaded computation, and remote cloud servers with more plentiful resources. These three components work in a coordinated way with awareness of the dynamic communication overhead, thereby ensuring scalability and energy efficiency of supporting both compute and network intensive distributed applications on mobile platforms.
