Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive studies quantitatively characterizing the properties of fog computing architectures have been conducted. In this paper we examine the statistical properties of fog computing task completion latencies, which are important to understand to develop algorithms that match IoT nodes’ tasks with the best execution points within the fog computing substrate. Towards characterizing task completion latencies, we developed and deployed a set of benchmarks in 6 different locations, which included local nodes of different grades, conventional cloud computing services in two different regions, and Amazon Web Services (AWS) and Microsoft Azure serverless computing options. Using the developed infrastructure, we conducted a series of targeted experiments with a node invoking our benchmarks from different locations and in different conditions. The empirical study elucidated several important properties of task execution latencies, including latency variation across different execution points and execution options, and stability with respect to time. The study also demonstrated important properties of serverless execution options, and showed that statistical structure of computing latenciesmore »
This content will become publicly available on January 25, 2024
Many-to-many matching based task allocation for dispersed computing
Dispersed computing is a new resource-centric computing paradigm, which makes
use of idle resources in the network to complete the tasks. Effectively allocating tasks
between task nodes and networked computation points (NCPs) is a critical factor for
maximizing the performance of dispersed computing. Due to the heterogeneity of
nodes and the priority requirements of tasks, it brings great challenges to the task
allocation in dispersed computing. In this paper, we propose a task allocation model
based on incomplete preference list. The requirements and permissions of task nodes
and NCPs are quantitatively measured through the preference list. In the model, the
task completion rate, response time, and communication distance are taken as three
optimizing parameters. To solve this NP-hard optimization problem, we develop a new
many-to-many matching algorithm based on incomplete preference list. The unilateral
optimal and stable solution of the model are obtained. Taking into account the needs
for location privacy-preserving, we use the planar Laplace mechanism to produce
obfuscated locations instead of real locations. The mechanism satisfies ε-differential privacy. Finally, the efficacy of the proposed model is demonstrated through extensive
numerical analysis. Particularly, when the number of task nodes and NCPs reaches
1:2, the task completion rate can reach 99.33%.
- Award ID(s):
- 2148788
- Publication Date:
- NSF-PAR ID:
- 10397366
- Journal Name:
- Computing
- ISSN:
- 0010-485X
- Sponsoring Org:
- National Science Foundation
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