Welcome to the Research Laboratory for Virtualized Infrastructure, Systems, and Applications (VISA) at Arizona State University (ASU).
Our team current works on a variety of exciting projects on cloud, HPC, and big data systems as well as operating systems and storage systems in general.
EuroSys’18: “RTVirt: Enabling Time-sensitive Computing on Virtualized Systems through Cross-layer CPU Scheduling”, EDGE’18: “Are Existing Knowledge Transfer Techniques Effective For Deep Learning on Edge Devices?” …
VISA Lab is a place for fun and productivity! We are looking for talented students to join us! We have multiple PhD positions with full scholarships and paid undergraduate positions.
The spread of the Corona Virus Disease 2019 (COVID-19) has reached pandemic levels across the globe. There is an urgent need to develop CT apps that not only monitor but also intervene to limit COVID-19 spread while respecting user security and privacy. Sponsored by National Science Foundation, this project addresses this challenge via Federated Analytics[…]
The Fifth ACM/IEEE Symposium on Edge Computing (SEC) seeks to present exciting, innovative research related to the design, implementation, analysis, evaluation, and deployment of computer systems and applications at the network edge. SEC is a forum for top researchers, engineers, students, entrepreneurs, and government officials come together under one roof to discuss the opportunities and[…]
Call for Papers for the 3rd USENIX Workshop on Hot Topics in Edge Computing: due Thursday, February 20, 2020 HotEdge ‘20 (#HotEdge20)(https://lnkd.in/gBH_DKf) will take place on April 30, 2020, in Santa Clara, CA, co-located with the 2020 USENIX Conference on Operational Machine Learning. HotEdge ‘20 is the flagship workshop on edge computing (#edgecomputing) and a hot venue for researchers and practitioners from both academia and industry to discuss[…]
Congratulations to Ryunu for her presentation of USENIX ATC ’19 papers on “SmartDedup: Optimizing Deduplication for Resource-constrained Devices” and Yitao for his presentation of HotEdge ’19 workshop on “Exploring the Use of Synthetic Gradients for Distributed Deep Learning across Cloud and Edge Resources”.
Arizona State University Associate Professor Ming Zhao leads the development of GEARS, a big data computing infrastructure designed for today’s demanding big data challenges.
Dr. Ming Zhao brings cloud computing service companies a step closer to providing reliable performance guarantees.
Ragini’s EDGE’18 paper, “Are Existing Knowledge Transfer Techniques Effective For Deep Learning on Edge Devices?” studies distributed deep learning techniques that exploit the knowledge trained from a deep network in the cloud to improve the speed and accuracy of small networks on the devices.
Ragini Sharma successfully defended her master thesis on “A Study on Knowledge Transfer Techniques to Support Deep Learning on Edge Devices”. Her work studies the use of knowledge transfer techniques to support distributed deep learning that exploits the knowledge from deep networks trained in the cloud to improve the accuracy and speed of small networks[…]
RTVirt is a new solution for enabling time-sensitive applications (such as emergency planning and management applications) on virtualized systems (such as public and private cloud systems) through cross-layer scheduling. It allows the two levels of schedulers on a virtualized system to communicate key scheduling information and coordinate on the scheduling decisions. It enables optimal multiprocessor[…]
GEARS is an enerGy-Efficient big-datA Research System at Arizona State University, for studying heterogeneous and dynamic data by employing heterogeneous computing and storage resources and co-designing the software and hardware components of the system. GEARS is sponsored by National Science Foundation award CNS-1629888. Read more about it here.