Accelerating Big Data Processing in the Cloud with Scalable Communication and I/O Schemes

Student: Shashank Gugnani (Ohio State University)
Supervisor: Dhabaleswar K. Panda (Ohio State University)

Abstract: With the advent of cloud computing, the field of Big Data has seen rapid growth. Most cloud providers provide hardware resources such as NVMe SSDs, large memory nodes, and SR-IOV. This opens up the possibility of large-scale high-performance data analytics and provides opportunities to use these resources to develop new designs. Cloud computing provides flexibility, security, and reliability, which are important requirements for Big Data frameworks. However, several important requirements are missing, such as performance, scalability, consistency, and quality of service (QoS). The focus of this work revolves around developing communication and I/O designs and concepts which can provide these requirements to Big Data frameworks. Specifically, we explore new ways to provide QoS and consistency in cloud storage systems, and provide scalable and high-performance communication frameworks.

ACM-SRC Semi-Finalist: no

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