A task-resource joint management model with intelligent control for mission-aware dispersed computing

Dispersed computing can link all devices with computing capabilities on a global scale to form a fully decentralized network, which can make full use of idle computing resources. Realizing the overall resource allocation of the dispersed computing system is a significant challenge. In detail, by jointly managing the task requests of external users and the resource allocation of the internal system to achieve dynamic balance, the efficient and stable operation of the system can be guaranteed. In this paper, we first propose a task-resource joint management model, which quantifies the dynamic transformation relationship between the resources consumed by task requests and the resources occupied by the system in dispersed computing. Secondly, to avoid downtime caused by an overload of resources, we introduce intelligent control into the task-resource joint management model. The existence and stability of the positive periodic solution of the model can be obtained by theoretical analysis, which means that the stable operation of dispersed computing can be guaranteed through the intelligent feedback control strategy. Additionally, to improve the system utilization, the task-resource joint management model with bi-directional intelligent control is further explored. Setting control thresholds for the two resources not only reverse restrains the system resource overload, but also carries out positive incentive control when a large number of idle resources appear. The existence and stability of the positive periodic solution of the model are proved theoretically, that is, the model effectively avoids the two extreme cases and ensure the efficient and stable operation of the system. Finally, numerical simulation verifies the correctness and validity of the theoretical results.