Deepak Nadig

Purdue University West Lafayette | Purdue · Department of Computer and Information Technology (IT)

Microservices have emerged as a popular pattern for developing large-scale applications in cloud environments for their flexibility, scalability, and agility benefits. Furthermore , orchestration services like Kubernetes have simplified the deployment of cloud-native applications. However, monitoring and debugging these complex networked applicatio...

As machine learning workloads become computationally demanding, there is an increased focus on distributed machine learning to train and deploy models across multiple machines in a cloud-native cluster. However, optimizing a machine learning model's lifecycle to facilitate efficient resource utilization is still an active area of research. The appr...

Increasingly, academic campus networks support large-scale data transfer workflows for data-intensive science. These data transfers rely on high-performance, scalable, and reliable protocols for moving large amounts of data over a high-bandwidth, high-latency network. GridFTP is a widely used protocol for wide area network (WAN) data movement. Howe...

Named Data Networking (NDN) is a promising approach to provide fast in-network access to compact muon solenoid (CMS) datasets. It proposes a content-centric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture...

Increasingly, campus networks manage a multitude of large-scale data transfers. Big data plays a pivotal role in university research and impacts domains such as engineering, agriculture, natural sciences, and humanities. Over the years, numerous solutions have been proposed to manage and secure large-scale data transfers efficiently. Examples consi...

In this paper, we propose 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...

Named Data Networking (NDN) proposes a content-centric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture relies on forwarding strategies that are dependent upon on-path caching and is therefore inefficient....

Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However,...

Network management for applications that rely on large-scale data transfers is challenging due to the volatility and the dynamic nature of the access traffic patterns. Predictive ana-lytics and forecasting play an important role in providing effective resource allocation strategies for large data transfers. We propose a predictive analytics solutio...

Network anomaly detection systems can be used to identify anomalous transfers or threats, which, when undetected, can trigger large-scale malicious events. Data-intensive science projects rely on high-throughput computing and high-speed networking resources for data analysis and processing. In this paper, we propose an anomaly detection framework a...

Experimental science workflows from projects such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational Wave Observatory (LIGO) are characterized by data-intensive computational tasks over large datasets transferred over encrypted channels. The Science DMZ approach to network design favors lossless packet forwarding through a separa...