Presentation
Time:
Ph.D. Thesis Defense: Deepak Nadig
Date:
1:00 pm –
2:00 pm
Zoom
Ph.D. Thesis Defense: Deepak Nadig
Monday, June 21, 2021
1:00 PM (CST)
Zoom
https://unl.zoom.us/j/99988697274
“Application-awareness in Softwarized Networks: Building Intelligent Networks through Application and Network-layer Collaboration”
Increasingly, campus networks manage a multitude of large-scale data transfers. Big data plays a pivotal role in university research and impacts engineering, agriculture, natural sciences, and humanities. Data-intensive science workflows exhibit diverse characteristics and place varying demands on network resources. Software defined networking (SDN) and network functions virtualization (NFV), along with advances in cloud computing, makes building intelligent networks possible. Campus network infrastructures support multiple network traffic management goals, including commodity internet traffic and high-performance networks for scientific research. These goals often impose conflicting requirements on network design and management, and therefore, networks optimized and specially engineered for data-intensive tasks are necessary. Further, many aspects of campus networks are hard to change without impacting regular network operation. Over the years, numerous solutions have focused on the management and security of large-scale data transfers. These solutions severely degrade data transfer performance or result in data flows completely bypassing the campus network management and security controls. This dissertation will study application-aware architectures for data-intensive applications and present SDN and NFV-based solutions for data-intensive science. Our proposed application-aware software defined networking solutions span network monitoring, network management, service differentiation, and security for data-intensive applications. We first propose a novel application-aware architecture called SNAG (SDN-managed Network Architecture for GridFTP transfers). SNAG combines application-awareness with SDN-enabled network management to classify, monitor and manage network resources actively. At HCC, we also demonstrate how our system ensures the quality of service (QoS) for high-throughput workflows such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational-Wave Observatory (LIGO). Next, we investigate mapping service function chains (SFCs) across different data centers to reduce the flow processing costs. We develop integer linear programming (ILP) formulations and a novel application-aware flow reduction (AAFR) algorithm to optimally map SFCs to multiple data centers while adhering to the data center’s capacity constraints. We then present an application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site. Lastly, by developing a scalable application-aware edge computing framework, we focus on developing reliable service-to-service communication across distributed infrastructures using a service mesh network architecture. By building application-aware architectures and evolving data-intensive application domains to collaboratively and securely share application-layer metadata with the network-layer, we pave the way for intelligent networks that are secure, automated, dynamically composable and highly scalable.
Committee:
Dr. Byrav Ramamurthy, Advisor
Dr. Lisong Xu
Dr. Mehmet Can Vuran
Dr. Yi Qian
Monday, June 21, 2021
1:00 PM (CST)
Zoom
https://unl.zoom.us/j/99988697274
“Application-awareness in Softwarized Networks: Building Intelligent Networks through Application and Network-layer Collaboration”
Increasingly, campus networks manage a multitude of large-scale data transfers. Big data plays a pivotal role in university research and impacts engineering, agriculture, natural sciences, and humanities. Data-intensive science workflows exhibit diverse characteristics and place varying demands on network resources. Software defined networking (SDN) and network functions virtualization (NFV), along with advances in cloud computing, makes building intelligent networks possible. Campus network infrastructures support multiple network traffic management goals, including commodity internet traffic and high-performance networks for scientific research. These goals often impose conflicting requirements on network design and management, and therefore, networks optimized and specially engineered for data-intensive tasks are necessary. Further, many aspects of campus networks are hard to change without impacting regular network operation. Over the years, numerous solutions have focused on the management and security of large-scale data transfers. These solutions severely degrade data transfer performance or result in data flows completely bypassing the campus network management and security controls. This dissertation will study application-aware architectures for data-intensive applications and present SDN and NFV-based solutions for data-intensive science. Our proposed application-aware software defined networking solutions span network monitoring, network management, service differentiation, and security for data-intensive applications. We first propose a novel application-aware architecture called SNAG (SDN-managed Network Architecture for GridFTP transfers). SNAG combines application-awareness with SDN-enabled network management to classify, monitor and manage network resources actively. At HCC, we also demonstrate how our system ensures the quality of service (QoS) for high-throughput workflows such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational-Wave Observatory (LIGO). Next, we investigate mapping service function chains (SFCs) across different data centers to reduce the flow processing costs. We develop integer linear programming (ILP) formulations and a novel application-aware flow reduction (AAFR) algorithm to optimally map SFCs to multiple data centers while adhering to the data center’s capacity constraints. We then present an application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site. Lastly, by developing a scalable application-aware edge computing framework, we focus on developing reliable service-to-service communication across distributed infrastructures using a service mesh network architecture. By building application-aware architectures and evolving data-intensive application domains to collaboratively and securely share application-layer metadata with the network-layer, we pave the way for intelligent networks that are secure, automated, dynamically composable and highly scalable.
Committee:
Dr. Byrav Ramamurthy, Advisor
Dr. Lisong Xu
Dr. Mehmet Can Vuran
Dr. Yi Qian