Christopher Stewart

Christopher Stewart
The Ohio State University | OSU · Department of Computer Science and Engineering

PhD Computer Science

About

97
Publications
8,036
Reads
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1,425
Citations
Citations since 2016
54 Research Items
758 Citations
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2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
Introduction
Skills and Expertise
Additional affiliations
September 2009 - September 2016
The Ohio State University
Position
  • Professor (Associate)

Publications

Publications (97)
Conference Paper
Full-text available
Digital agriculture, hailed as the fourth great agricultural revolution, employs software-driven autonomous agents for in-field crop management. Edge computing resources deployed near crop fields support autonomous agents with substantial computational needs for tasks such as AI inference. In large fields, using multiple autonomous agents, called s...
Conference Paper
Full-text available
Unmanned aerial vehicles (UAV) are revolutionizing critical industries. Their inexpensive and accessible nature makes them useful for a number of broad applications including agriculture, infrastructure inspection, and more. In response to this popularity, UAV manufacturers, hobbyists, and researchers have developed myriad software packages for UAV...
Conference Paper
Full-text available
Unmanned aerial vehicles (UAV) play a critical role in many edge computing deployments and applications. UAV are prized for their maneuverability, low cost, and sensing capacity , facilitating many applications that would otherwise be prohibitively expensive or dangerous without them. UAV are cheaper than alternative aerial analysis methods, but st...
Article
Full-text available
Data-driven science is an emerging paradigm where scientific discoveries depend on the execution of computational AI models against rich, discipline-specific datasets. With modern machine learning frameworks, anyone can develop and execute computational models that reveal concepts hidden in the data that could enable scientific applications. For im...
Preprint
Full-text available
Data-driven science is an emerging paradigm where scientific discoveries depend on the execution of computational AI models against rich, discipline-specific datasets. With modern machine learning frameworks, anyone can develop and execute computational models that reveal concepts hidden in the data that could enable scientific applications. For im...
Preprint
The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm divides nodes into leaders and followers, to satisfy read/write requests spanning geo-diverse sites. With the increase of workload, Raft shall provide scale-out performance in proportion. However, traditional scale-out techniques encounter bottlenecks in R...
Article
Full-text available
In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets and considerable processing...
Article
Severe crop defoliation caused by insects and pests is linked to low agricultural productivity. If the root cause is not addressed, severe defoliation spreads, damaging whole crop fields. Understanding which areas are afflicted by severe defoliation can help farmers manage crops. Unmanned Aerial Vehicles (UAV) can fly over whole crop fields capturi...
Article
The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm places nodes, i.e., leader and follower, to serve read/write requests spanning geo-diverse sites. As the workload increases, Raft shall provide proportional scale-out performance. However, traditional scale-out techniques are bottlenecked in Raft with an e...
Conference Paper
Full-text available
Control firmware in unmanned aircraft systems (UAS) manage the subsystems for in-flight dynamics, navigation and aircraft sensors. Computer systems on-board the aircraft and on gateway machines can now support rich features in the control firmware, such as GPS-driven waypoint missions and autonomy. However, the source code behind control firmware c...
Preprint
Full-text available
Autonomous systems (AS) carry out complex missions by continuously observing the state of their surroundings and taking actions toward a goal. Swarms of AS working together can complete missions faster and more effectively than single AS alone. To build swarms today, developers handcraft their own software for storing, aggregating, and learning fro...
Article
The articles in this special section focus on fully autonomous and networked vehicles. Around 5,000 years ago, our ancestors invented the wheel, sparking generations of revolutionary innovations from the wheelbarrow to the automobile. Today, we are poised to revolutionize the wheel again. Autonomous and networked vehicles can observe their surround...
Conference Paper
Full-text available
Unmanned aerial vehicles (UAVs) are gaining popularity in many governmental and civilian sectors. The combination of aerial mobility and data sensing capabilities facilitates previously impossible workloads. UAVs are normally piloted by remote operators who determine where to fly and when to sense data. Fully autonomous aerial systems (FAAS) have e...
Preprint
Full-text available
Multiple applications running on Edge computers can be orchestrated to achieve the desired goal. Orchestration of applications is prominent when working with Internet of Things based applications, Autonomous driving and Autonomous Aerial vehicles. As the applications receive modified classifiers/code, there will be multiple applications that need t...
Thesis
Full-text available
Autonomous systems such as self driving cars, smart traffic lights, smart homes and smart cameras are increasingly being deployed. Such systems deployed at the edge make use of low-powered edge devices and machine learning techniques in order to process inferences faster. However, such AI inference consumes precious energy, drains batteries and sho...
Article
Full-text available
Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it is envisioned that a large-scale, geographically dispersed, and resource-rich distributed system will emerge and play a key role in the future Internet. However , given t...
Article
Full-text available
Unmanned aerial systems (UAS) are increasingly used in precision agriculture to collect crop health related data. UAS can capture data more often and more cost-effectively than sending human scouts into the field. However, in large crop fields, flight time, and hence data collection, is limited by battery life. In a conventional UAS approach, human...
Article
Full-text available
Rice is a globally important crop that will continue to play an essential role in feeding our world as we grapple with climate change and population growth. Lodging is a primary threat to rice production, decreasing rice yield, and quality. Lodging assessment is a tedious task and requires heavy labor and a long duration due to the vast land areas...
Conference Paper
Full-text available
Fully autonomous aerial systems (FAAS) are an increasingly relevant and intriguing research topic which require considerable systems support. FAAS use unmanned aerial vehicles and edge and cloud compute resources to dynamically sense and respond to their environments without human piloting. FAAS are useful in a number of domains, but are very diffi...
Preprint
Full-text available
Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores for delivering more responsive services. Thus, it is envisioned that a large-scale, geographically dispersed and resource-rich distributed system will emerge and become the backbone of the future Inter-net. However, given...
Conference Paper
Full-text available
AI-driven Internet of Things (IoT) use AI inference to characterize data harvested from IoT sensors. Together, AI inference and IoT support smart buildings, smart cities and autonomous vehicles. However, AI inference consumes precious energy, drains batteries and shortens IoT lifetimes. Deep sleep modes on IoT processors can save energy during long...
Poster
Full-text available
AI-driven Internet of Things (IoT) use AI Inference to characterize data processed from various sensors. Together, AI and IoT support smart buildings, cities, cars and drones. However , AI Inference requires updates when executed in new contexts. Frequent updates consume more energy and drain precious IoT batteries. Updates can be batched together...
Conference Paper
Full-text available
Fully autonomous aerial systems (FAAS) fly complex missions guided wholly by software. If users choose software, compute hardware and aircraft well, FAAS can complete missions faster and safer than unmanned aerial systems piloted by humans. On the other hand, poorly managed edge resources slow down missions, waste energy and inflate costs. This pap...
Conference Paper
Full-text available
AI inference services receive requests, classify data and respond quickly. These services underlie AI-driven Internet of Things, recommendation engines and video analytics. Neural networks are widely used because they provide accurate results and fast inference, but it is hard to explain their classifications. Tree-based deep learning models can pr...
Conference Paper
The Translational Data Analytics Institute (TDAI) at the Ohio State University (OSU) aims to promote cross discipline work in order to improve data science and analytics in many areas of university-wide research for use in solving real world problems including areas such as smart city, smart agriculture, and biomedical research. As a result of thei...
Conference Paper
Full-text available
Raft is a protocol to maintain strong consistency across data replicas in cloud. It is widely used, especially by workloads that span geographically distributed sites. As these workloads grow, Raft's costs should grow, as least proportionally. However, auto scaling approaches for Raft inflate costs by provisioning at all sites when one site exhaust...
Conference Paper
Full-text available
Computational sprinting speeds up query execution by increasing power usage for short bursts. Sprinting policy decides when and how long to sprint. Poor policies inflate response time significantly. We propose a model-driven approach that chooses between sprinting policies based on their expected response time. However, sprinting alters query execu...
Conference Paper
Full-text available
Ever tightening power caps constrain the sustained processing speed of modern processors. With computational sprinting, processors reserve a small power budget that can be used to increase processing speed for short bursts. Computational sprinting speeds up query executions that would otherwise yield slow response time. Common mechanisms used for s...
Article
Full-text available
Online data-intensive (OLDI) services use anytime algorithms to compute over large amounts of data and respond quickly. Interactive response times are a priority, so OLDI services parallelize query execution across distributed software components and return best effort answers based on the data so far processed. Omitted data from slow components co...
Conference Paper
Computer systems constrain their processing rates to stay within power, cost and answer quality budgets. Sprinting mechanisms increase processing rates by exceeding budgets for short bursts before reverting back to safe processing rates. Sprinting mechanisms can speed up query processing and reduce queuing delay, but it is challenging to set polici...
Conference Paper
State of the art schedulers use workload profiles to help determine which resources to allocate. Traditionally, threads execute on every available core, but increasingly, too much power is consumed by using every core. Because peak power can occur at any point in time during the workload, workloads are commonly profiled to completion multiple times...
Article
Full-text available
Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure t...
Patent
Full-text available
A technique includes monitoring a first cumulative number of transactions arriving into a processing station and monitoring the second cumulative number of transactions completed by the station. The technique includes based on the first and second cumulative numbers, determining at least one of a transaction waiting time of the station and a predic...
Article
Full-text available
With the advent of internet services, data started growing faster than it can be processed. To personalize user experience, this enormous data has to be processed in real time, in interactive fashion. In order to achieve faster data processing often a statistical method called subsampling. Subsampling workloads compute statistics from a random subs...
Conference Paper
Full-text available
Subsampling workloads compute statistics from a set of observed samples using a random subset of sample data (i.e., a subsample). Data-parallel platforms group these samples into tasks, each task subsamples its data in parallel. In this paper, we study subsampling workloads that benefit from tiny tasks-i.e., tasks comprising few samples. Tiny tasks...
Patent
Full-text available
Embodiments of the present invention pertain to dynamically resizing a virtual machine container. According to one embodiment, an optimal utilization is determined based on a desired performance for a multi-tiered application and transaction mix information that describes a mix of transactions that result from executing the multi-tiered application...
Conference Paper
Full-text available
Networking bandwidth and latency have improved in recent years, prompting a wide range of workloads to move back to key value stores, databases, and other types of networked storage. However, networked storage has a well known drawback: Outlier access times create a heavy tailed distribution. Outlier accesses can take much longer than normal access...
Article
Full-text available
Internet services access networked storage many times while processing a request. Just a few slow storage ac- cesses per request can raise response times a lot, making the whole service less usable and hurting profits. This paper presents Zoolander, a key value store that meets strict, low latency service level objectives (SLOs). Zo- olander scales...
Article
Full-text available
Energy production must continuously match demand on the electric grid. A deficiency can lead to service disruptions, and a surplus can place tremendous stress on grid components, potentially causing major blackouts. To manage this balance, grid operators must increase or lower power generation, with only a few minutes to react. The grid balancing p...
Conference Paper
Full-text available
Search engines parse diverse, natural language datasets in search of answers to a user query. Not only are they expected to find good answers, they must find them quickly. For public search engines, like Bing and Google, answers that are returned slowly cost more and produce less revenue than answers returned within a second [3]. For private search...
Conference Paper
Full-text available