Computility networks (CNs) enable large-scale computation scheduling and have emerged as a new computing paradigm. CNs have a broader service scope and more complex infrastructure than cloud and edge computing. Consequently, resource allocation and task scheduling in CNs face numerous challenges, such as unifying diverse computility resources in existing heterogeneous clouds, adequately incorporating network resource providers within the CN framework, and pricing computility resources. In this study, first, we extract computility and network resources to construct a task scheduling model for CNs. To maximize the number of tasks successfully scheduled, we represent this problem as a mixed-integer programming model that involves multiple roles, tasks, and resource constraints. Unlike most approaches, we explicitly incorporate network resource providers into the model. Second, we propose a double auction mechanism named Computility Double Auction (Computility_DA) to address the task scheduling problem in CNs. Specifically, we derive feasible solutions for task scheduling and network flow using optimization methods and then determine the final winners and payment pricing solution based on matching theory. Furthermore, we demonstrate that the proposed mechanism has economic properties such as individual rationality, truthfulness, and budget balance. Experimental results demonstrate that compared with existing algorithms, Computility_DA significantly increases the number of scheduled tasks and the utility and revenue for participants.

Metcalfe’s law states that the value of a network grows as the square of the number of its users ( $V\propto n^2$), which was validated by actual data of Facebook and Tencent in 2013–2015. Since then, the users and the values of Facebook and Tencent have increased significantly. Is Metcalfe’s law still valid? This paper leverages the latest data of Facebook and Tencent to fit the network effect laws and makes the following observations: 1) actual data of network values fit a cube law ( $V\propto n^3$) better than Metcalfe’s law; 2) actual data of network costs fit a cube law; 3) actual data of network sizes show a growth trend matching the netoid function well. We also discuss the underlying factors affecting such observations and the generality of the network effect laws.