Geo-Distributed IoT Data Analytics With Deadline Constraints Across Network Edge

Abstract

Owing to the advancement of the Internet of Things (IoT) and 5G mobile technologies, various IoT devices produce massive data which is usually transferred to nearby sites, such as edge nodes or datacenters. Many large-scale IoT applications need to analyze the data distributed across multiple sites to obtain final results. A dominant challenge of this type of data analytics is the heterogeneities of resource capacities across geodistributed sites. In this paper, we find that the resource capacity as well as the resource price differ among sites, and the price heterogeneity has a significant impact on geo-distributed IoT data analytics. Thus, each geo-distributed IoT data analytics job prefers to minimize the job execution cost while guaranteeing its deadline requirement under the resource constraints of involved sites. Specifically, we propose to jointly consider the resource heterogeneities of both capacity and price, and minimize the cost of each job before its deadline. We characterize this optimization problem as a Quadratically Constrained Quadratic Programming problem. To tackle such an NP-hard problem, we propose the MCGL method, which calculates a task placement solution by gradient adjustment strategy according to the remarkable negative correlation relationship between job completion time and job completion cost of geo-distributed IoT data analytics job. The task placement strategy can optimize resource cost with respect to the deadline requirement of any geo-distributed data analytics job. The trace-driven evaluations indicate that MCGL significantly reduces the total cost compared with existing methods; moreover, they satisfy the deadline constraints simultaneously.

A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

“…The evolution of blockchain has progressed through several phases, from the foundational Bitcoin system to the subsequent Ethereum application platform and onward to the integration of blockchain in diverse sectors. In the era marked by the swift advancement of artificial intelligence, blockchain assumes a pivotal role in various contexts, including big data, distributed computing, and construction work, among others [39][40][41]. Blockchain applications have grown more intricate and varied, holding the promise to invigorate emerging industries.…”