Decentralized Computation Market for Stream Processing Applications
September 2022·,
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Scott Eisele
Michael Wilbur
Taha Eghtesad
Kevin Silvergold
Fred Eisele
Aron Laszka
Abhishek Dubey
Abstract
While cloud computing is the current standard for outsourcing computation, it can be prohibitively expensive for cities and infrastructure operators to deploy services. At the same time, there are underutilized computing resources within cities and local edge-computing deployments. Using these slack resources may enable significantly lower pricing than comparable cloud computing; such resources would incur minimal marginal expenditure since their deployment and operation are mostly sunk costs. However, there are challenges associated with using these resources. First, they are not effectively aggregated or provisioned. Second, there is a lack of trust between customers and suppliers of computing resources, given that they are distinct stakeholders and behave according to their own interests. Third, delays in processing inputs may diminish the value of the applications. To resolve these challenges, we introduce an architecture combining a distributed trusted computing mechanism, such as a blockchain, with an efficient messaging system like Apache Pulsar. Using this architecture, we design a decentralized computation market where customers and suppliers make offers to deploy and host applications. The proposed architecture can be realized using any trusted computing mechanism that supports smart contracts, and any messaging framework with the necessary features. This combination ensures that the market is robust without incurring the input processing delays that limit other blockchain based solutions. We evaluate the market protocol using game-theoretic analysis to show that deviation from the protocol is discouraged. Finally, we assess the performance of a prototype implementation based on experiments with a streaming computer-vision application.
Type
Venue
10th IEEE International Conference on Cloud Engineering (IC2E 2022), 36–46
Authors

Ph.D. 2026
Taha Eghtesad joined the Applied Artificial Intelligence Lab as a Ph.D. student in Fall 2018 and received his Ph.D.
in 2026 under the supervision of Dr. Aron Laszka. He received a B.S. degree in Computer Engineering, with a minor in
Software Engineering, from Shahid Beheshti University, Tehran, Iran. His research lies at the intersection of
machine learning, game theory, cyber-physical systems, and systems security. In particular, his Ph.D. research
focused on using artificial intelligence and machine learning to make cyber-physical systems more secure.

Associate Professor
Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.
His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.
His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.