In the era of data-intensive edge computing, the orchestration of Data Distributed Inferencing (DDI) tasks poses a formidable challenge, demanding real-time adaptability to varying network conditions and compute resources. This study introduces an innovative approach to address this challenge, leveraging Gradient Boosting Regression (GBR) as the core predictive modeling technique. The primary objective is to estimate inferencing time based on crucial factors, including bandwidth, compute device type, and the number of compute nodes, allowing for dynamic task placement and optimization in a DDI environment. Our model employs an online learning framework, continuously updating itself as new data streams in, enabling it to swiftly adapt to changing conditions and consistently deliver accurate inferencing time predictions. This research marks a significant step forward in enhancing the efficiency and performance of DDI systems, with implications for real-world applications across various domains, including IoT, edge computing, and distributed machine learning.
This work develops a principled approach for coordinating decentralized systems that can exchange information, issue and service requests, and perform outcome-focused actions together. It harnesses the resiliency and robustness offered by blockchain frameworks to enable sustained activity in the presence a diverse set of failure modes, operating constraints, and changing task requirements. We accomplish this through the Tactical Distributed Ledger, a distributed computer that can accommodate a wide range of sensors, agents, vehicles, and devices to create a decentralized system. We have used this framework to deploy a multi-participant system that combines sensing, vehicle mobility, and situational awareness to perform coordinated activities in a simulated city environment. The key mechanism that enables this interaction is a robust auction framework implemented on top of a smart contract blockchain system. This paper details the design, components, and features of the auction system.
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