About

Jon Weissman is a professor in the Department of Computer Science & Engineering at the University of Minnesota. He joined the department in 1999 and was promoted to full professor in 2012. His research interests are in the broad area of parallel and distributed systems, with a focus on edge and cloud computing, mobile computing, Internet of Things, and high-performance computing. He works on developing techniques to improve performance, reliability, and energy efficiency in these areas. Weissman holds a Ph.D. in Computer Science from the University of Virginia, earned in 1995, along with an M.S. from the same institution and a B.S. from Carnegie Mellon University. His professional background includes a stint as an assistant professor at the University of Texas San Antonio before joining the University of Minnesota. He has been recognized with awards such as the George W. Taylor Distinguished Teaching Award in 2022 and the NSF CAREER Award in 1996. Weissman has also served as a visiting researcher at the University of Edinburgh and is a member of ACM and a senior member of IEEE. His research division is focused on Computing Systems, and he is actively involved in the Distributed Computing Systems Group.

Research topics

• Computer Science
• Real-time computing
• Distributed computing
• Computer network
• Operating system
• Embedded system

Selected publications

• SPAARC: Spatial Proximity and Association based prefetching for Augmented Reality in edge Cache

  ArXiv.org · 2025-02-21

  preprintOpen accessSenior author

  Mobile Augmented Reality (MAR) applications face performance challenges due to their high computational demands and need for low-latency responses. Traditional approaches like on-device storage or reactive data fetching from the cloud often result in limited AR experiences or unacceptable lag. Edge caching, which caches AR objects closer to the user, provides a promising solution. However, existing edge caching approaches do not consider AR-specific features such as AR object sizes, user interactions, and physical location. This paper investigates how to further optimize edge caching by employing AR-aware prefetching techniques. We present SPAARC, a Spatial Proximity and Association-based Prefetching policy specifically designed for MAR Caches. SPAARC intelligently prioritizes the caching of virtual objects based on their association with other similar objects and the user's proximity to them. It also considers the recency of associations and uses a lazy fetching strategy to efficiently manage edge resources and maximize Quality of Experience (QoE). Through extensive evaluation using both synthetic and real-world workloads, we demonstrate that SPAARC significantly improves cache hit rates compared to standard caching algorithms, achieving gains ranging from 3% to 40% while reducing the need for on-demand data retrieval from the cloud. Further, we present an adaptive tuning algorithm that automatically tunes SPAARC parameters to achieve optimal performance. Our findings demonstrate the potential of SPAARC to substantially enhance the user experience in MAR applications by ensuring the timely availability of virtual objects.

  PublisherOA PDFDOI

• ASTRA: Association, Spatial proximity and Temporal Relevance based Adaptive prefetching for Edge AR

  2025-09-23

  articleSenior author

  Mobile Augmented Reality (MAR) applications face performance challenges due to their high computational demands and need for low-latency responses. Traditional approaches like on-device storage or reactive data fetching from the cloud often result in limited augmented reality (AR) experiences. Edge caching, which caches AR objects closer to the user, provides a promising solution. However, existing edge caching approaches do not consider AR-specific features such as AR object sizes, user interactions, user’s field of view and physical location in a coherent manner. This paper investigates how to further optimize edge caching by employing AR-aware prefetching techniques. We present ASTRA, a prefetching framework tailored for mobile augmented reality edge caches. It integrates object associations derived from user interaction patterns with spatial awareness based on the user’s physical location and field of view. This approach employs an association factor per object that considers the recency of object co-access; and a lazy fetching strategy that prioritizes prefetching only when the user is in close proximity to the virtual objects. Furthermore, ASTRA incorporates an adaptive tuning algorithm for minimum support in association rule generation to minimize the computation overhead, making it a distinct and effective solution for enhancing user experience in AR applications by ensuring timely virtual object availability.Through extensive evaluation using both synthetic and real-world workloads, we demonstrate that ASTRA significantly improves cache hit rates compared to current prefetching algorithms, achieving gains in hit rate of upto 35% and end-to-end latency by upto 14%. Further, we demonstrate that the adaptive tuning algorithm that automatically tunes minimum support further improves the hit rate of ASTRA by 10%. Our findings demonstrate the potential of ASTRA to substantially enhance the user experience in MAR applications by ensuring the timely availability of virtual objects.

  PublisherDOI

• Efficient Compressed Sensing for Real-Time Electrocardiogram Acquisition on Low-Power Medical Devices

  2024-11-10

  articleSenior author

  Low cost wearable and implantable cardiac monitoring devices (WCM & ICM), combined with increasingly accurate disease and arrhythmia detection algorithms have proven effective to help slow the impact of cardiovascular disease, the worlds leading cause of death in 2022 [1]. Improvements to server-side detection algorithms along with hardware limitations of these devices such as slow processor speeds and minimal battery life has led to a desire to offload data from the devices for later analysis. Paired with limited storage capacity, this has led to a push to decrease the necessary storage for electrocardiogram (EKG) signals without sacrificing disease detection accuracy and device longevity. A promising recent innovation in EKG compression has come from Compressed Sensing (CS), which exposes inherent sparseness in the signal to selectively sample for eventual server-side reconstruction. Many CS approaches have been implemented on WCM devices which demonstrate a high compression ratio (CR) and accurate signal reconstruction, but quickly become impractical for the stricter hardware constraints of ICM devices due to the increased computation on the device and slow reconstruction time. In this paper we propose a CS approach known as Tailored Sensing (TS) which combines on-device prior knowledge, a custom transform basis, and optimal sense location selection to achieve improved CR and reconstruction accuracy while eliminating on-device computational burden and slow reconstruction time. Our approach offers equivalent or better signal reconstruction at previously unfathomable CRs due to our novel signal segmentation scheme. Additionally, our approach boasts a zero overhead on-device sensing strategy and a 93 % reduction in signal reconstruction time.

  PublisherDOI

• Jingle: IoT-Informed Autoscaling for Efficient Resource Management in Edge Computing

  2024-05-06 · 2 citations

  articleSenior author

  Edge computing is increasingly applied to various systems for its proximity to end-users and data sources. To facilitate the deployment of diverse edge-native applications, container technology has emerged as a favored solution due to its simplicity in development and resource management. However, deploying edge applications at scale can quickly overwhelm edge resources, potentially leading to violations of service-level objectives (SLOs). Scheduling edge containerized applications to meet SLOs while efficiently managing resources is a significant challenge. In this paper, we introduce Jingle, an autoscaler for edge clusters designed to efficiently scale edge-native applications. Jingle utilizes application performance metrics and domain-specific insights collected from IoT devices to construct a hybrid model. This hybrid model combines a predictive-reactive module with a lightweight learning model. We demonstrate Jingle’s effectiveness through a real-world deployment in a classroom setting, managing two edge-native applications across edge configurations. Our experimental results show that Jingle can fulfill SLO requirements while requiring up to 50% fewer containers than a state-of-the-art cloud scheduler, which highlights its resource management efficiency and SLO compliance.

  PublisherDOI

• SQuBA: Social Quorum Based Access Control for Open IoT Environments

  2023-07-01 · 1 citations

  articleSenior author

  Internet of things (IoT) devices have been ubiquitous in recent years. An emerging model for IoT deployment is an open edge-based infrastructure. Edge resources are commonly used to coordinate capabilities and manage access due to IoT device resource limitations and IoT vendor heterogeneity. The open IoT environment often exists in a multi-user setting, where multiple users interact with a single IoT device. In this setting, we assume that none of the users or the edges are fully trusted, thus IoT data privacy may be compromised. Limited attention has been paid to authorization and auditing in this environment. However, exploiting inter-user relationships gives us leverage. In this work, we propose a social quorum based architecture, SQuBA, as an access control mechanism for IoT which provides relationship-driven authorization and auditing. We present a tiered approach to support access control rules and relationship-based trustworthiness. We implemented a prototype and carried out experiments using a real-world dataset under various scenarios and configurations. The results demonstrate both SQuBA’s promising near real-time response latency that is in the order of milliseconds, and good resilience to different edge faulty models. We also compare with various baselines and SQuBA is able to improve end-to-end latency by up to 10X and tolerate the number of faulty edges by up to 2X.

  PublisherDOI

• Towards Elasticity in Heterogeneous Edge-dense Environments

  2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) · 2022-07-01 · 7 citations

  articleSenior author

  Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce a densely-distributed edge resource model that leverages capacity-constrained volunteer edge nodes to support elastic computation offloading. Our model also enables the use of geo-distributed edge nodes to further support elasticity. Collectively, these features raise the issue of edge selection. We present a distributed edge selection approach that relies on client-centric views of available edge nodes to optimize average end-to-end latency, with considerations of system heterogeneity, resource contention and node churn. Elasticity is achieved by fine-grained performance probing, dynamic load balancing, and proactive multi-edge node connections per client. Evaluations are conducted in both real-world volunteer environments and emulated platforms to show how a common edge application, namely AR-based cognitive assistance, can benefit from our approach and deliver low-latency responses to distributed users at scale.

  PublisherDOI

• Constellation: An Edge-Based Semantic Runtime System for Internet of Things Applications

  arXiv (Cornell University) · 2022-01-28 · 1 citations

  preprintOpen accessSenior author

  With the global Internet of Things IoT market size predicted to grow to over 1 trillion dollars in the next 5 years, many large corporations are scrambling to solidify their product line as the defacto device suite for consumers. This has led to each corporation developing their devices in a siloed environment with unique protocols and runtime frameworks that explicitly exclude the ability to work with the competitions devices. This development silo has created problems with programming complexity for application developers as well as concurrency and scalability limitations for applications that involve a network of IoT devices. The Constellation project is a distributed IoT runtime system that attempts to address these challenges by creating an operating system layer that decouples applications from devices. This layer provides mechanisms designed to allow applications to interface with an underlying substrate of IoT devices while abstracting away the complexities of application concurrency, device interoperability, and system scalability. This paper provides an overview of the Constellation system as well as details four new project expansions to improve system scalability.

  PublisherOA PDFDOI

• Armada: A Robust Latency-Sensitive Edge Cloud in Heterogeneous Edge-Dense Environments

  arXiv (Cornell University) · 2021-11-23 · 1 citations

  preprintOpen accessSenior author

  Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale in wide-area environments poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce Armada: a densely-distributed edge cloud infrastructure that explores the use of dedicated and volunteer resources to serve geo-distributed users in heterogeneous environments. We describe the lightweight Armada architecture and optimization techniques including performance-aware edge selection, auto-scaling and load balancing on the edge, fault tolerance, and in-situ data access. We evaluate Armada in both real-world volunteer environments and emulated platforms to show how common edge applications, namely real-time object detection and face recognition, can be easily deployed on Armada serving distributed users at scale with low latency.

  PublisherOA PDFDOI

• Network Cost-Aware Geo-Distributed Data Analytics System

  IEEE Transactions on Parallel and Distributed Systems · 2021-09-01 · 20 citations

  articleSenior author

  Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: inter-data center network bandwidth to improve performance. Unfortunately, these systems may encounter a <i>cost-bottleneck</i> ( <inline-formula><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> ) because they have not considered data transfer cost ( <inline-formula><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> ), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this article, we present <i>Kimchi</i> , a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kimchi is also mindful of data transfer cost in the presence of dynamics. Kimchi has been applied to two common GDA MapReduce models: synchronous barrier and asynchronous push-based shuffle. A Kimchi prototype has been implemented on Spark, and experiments show that it reduces cost by 5% <inline-formula><tex-math notation="LaTeX">$\scriptstyle \sim$</tex-math></inline-formula> 24% without impacting performance and reduces query execution time by 45% <inline-formula><tex-math notation="LaTeX">$\scriptstyle \sim$</tex-math></inline-formula> 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g., Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.

  PublisherDOI

• A Network Cost-aware Geo-distributed Data Analytics System

  2020 · 17 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Distributed computing

  Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: interdata center network bandwidth to improve performance. Unfortunately, these systems may encounter a cost-bottleneck ($) because they have not considered data transfer cost ($), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this paper, we present Kimchi, a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kim- chi is also mindful of data transfer cost in the presence of dynamics. A Kimchi prototype has been implemented on Spark and experiments show that it reduces cost by 14% ~ 24% without impacting performance and reduces query execution time by 45% ~ 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g. Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.

  PublisherDOI

Recent grants

• NGS: A Framework for Dynamic Service Adaptation in the Grid

  NSF · $240k · 2003–2008

• DC: Small: One Thousand Points of Light: Accelerating Data-Intensive Applications By Proxy

  NSF · $482k · 2009–2013

• CSR: Medium: Enriching Mobile User Experience Through The Cloud

  NSF · $732k · 2012–2017

Frequent coauthors

• Abhishek Chandra

  95 shared

• Albert Jonathan

  Tarumanagara University

  25 shared

• Kwangsung Oh

  University of Nebraska at Omaha

  20 shared

• Jinoh Kim

  Texas A&M University – Commerce

  15 shared

• Shantenu Jha

  Rutgers, The State University of New Jersey

  13 shared

• Darin England

  University of Minnesota

  13 shared

• Daniel S. Katz

  13 shared

• Emmanuel Jeannot

  11 shared

Labs

• Jon WeissmanPI

Awards & honors

• 2022: George W. Taylor Distinguished Teaching Award
• 1996: National Science Foundation Faculty Early Career Devel…