Age of Computing: A Metric of Computation Freshness in Communication and Computation Cooperative Networks
In communication and computation cooperative networks (3CNs), timely computation is crucial but not always guaranteed. There is a strong demand for a computational task to be completed within a given deadline. The time taken involves both processing time, communication time, and the impact of the deadline. However, a measure of such timeliness in 3CNs is lacking. In this paper, we introduce the novel concept, Age of Computing (AoC), to capture computation freshness in 3CNs. We analyze AoC in a line topology consisting of a source, a transmitter, a receiver, and a computational node. Tasks generated by the source are immediately available at the transmitter, where they enter a communication queue. These tasks then pass to the receiver and subsequently to a computation queue at the computational node for processing. Each task has a deadline, requiring completion within this timeframe. AoC is evaluated under two types of deadlines: (i) soft deadline, tasks can be fed back to the source if delayed beyond the deadline, but with additional latency; (ii) hard deadline, tasks delayed beyond the deadline are discarded. Under both deadlines, we derive the AoC formula and a general expression for the time-average AoC. For the first-come, first-serve discipline, we obtain a closed-form expression for the average AoC under the soft deadline and an approximation for the hard deadline. In addition to freshness, we define computation throughput, providing a general expression and an approximation. To explore the relationship between freshness and throughput, we construct an optimization problem and prove that the objective pair is a weakly Pareto-optimal point. Numerical results validate all the theoretical findings. Additionally, they reveal that under the hard deadline, the computation throughput serves as a reliable proxy for the average AoC.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach
Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, b…
Edge-computingLearning-based sensing and computing decision for data freshness in edge computing-enabled networks
As the demand on artificial intelligence (AI)-based applications increases, the freshness of sensed data becomes crucial in the wireless sensor networks. Since those applications require a large amount of computation for…
Edge-computingReinforcement Learning (RL)Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems
This paper studies the problem of information freshness-aware task offloading in an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). A third-party real-time…
Deep Reinforcement LearningEdge-computingReinforcement Learning (RL)Redefining Information Freshness: AoGI for Generative AI in 6G Networks
Generative Artificial Intelligence (GenAI) is playing an increasingly important role in enriching and facilitating human life by generating various useful information, of which real-time GenAI is a significant part and h…
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intens…
Deep Reinforcement LearningEdge-computingFederated LearningGraph Neural Network+1