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.

Efficient Bandwidth Allocation and Computation Configuration in Industrial IoT

With the advancement of the Industrial Internet of Things(IoT),the rapidly growing demand for data collection and processing poses a huge challenge to the design of data transmission and computation resources in the industrial scenario.Taking advantage of improved model accuracy by machine learning algorithms,we investigate the inner relationship of system performance and data transmission and computation resources,and then analyze the impacts of bandwidth allocation and computation resources on the accuracy of the system model in this paper.A joint bandwidth allocation and computation resource configuration scheme is proposed and the Karush-Kuhn-Tucker(KKT)conditions are used to get an optimal bandwidth allocation and computation configuration decision,which can minimize the total computation resource requirement and ensure the system accuracy meets the industrial requirements.Simulation results show that the proposed bandwidth allocation and computation resource configuration scheme can reduce the computing resource usage by 10%when compared to the average allocation strategy.