SCALABLE RESOURCE MANAGEMENT SYSTEM SOFTWARE FOR EXTREMESCALE DISTRIBUTED SYSTEMS

Distributed systems are growing exponentially in the computing capacity. On the high-performance computing (HPC) side, supercomputers are predicted to reach exascale with billion-way parallelism around the end of this decade. Scientific applications running on supercomputers are becoming more diverse, including traditional large-scale HPC jobs, small-scale HPC ensemble runs, and fine-grained many-task computing (MTC) workloads. Similar challenges are cropping up in cloud computing as data-centers host ever growing larger number of servers exceeding many top HPC systems in production today. The applications commonly found in the cloud are ushering in the era of big data, resulting in billions of tasks that involve processing increasingly large amount of data. However, the resource management system (RMS) software of distributed systems is still designed around the decades-old centralized paradigm, which is far from satisfying the ever-growing needs of performance and scalability towards extreme scales, due to the limited capacity of a centralized server. This huge gap between the processing capacity and the performance needs has driven us to develop next-generation RMSs that are magnitudes more scalable. In this dissertation, we first devise a general system software taxonomy to explore the design choices of system software, and propose that key-value stores could serve as a building block. We then design distributed RMS on top of key-value stores. We propose a fully distributed architecture and a data-aware work stealing technique for the MTC resource management, and develop the SimMatrix simulator to explore the distributed designs, which informs the real implementation of the MATRIX task execution framework. We also propose a partition-based architecture and resource sharing techniques for the HPC resource management, and implement them by building the Slurm++ real workload manager and the SimSlurm++ simulator. We study the distributed designs through real systems up to thousands of nodes, and through simulations up to millions of nodes. Results show that the distributed paradigm has significant advantages over centralized one. We envision that the contributions of this dissertation will be both evolutionary and revolutionary to the extreme-scale computing community, and will lead to a plethora of following research work and innovations towards tomorrow’s extremescale systems.Ph.D. in Computer Science, July 201