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

Zhou, Chang; Gong, Chao; Hui, Hongwen; Lin, Fuhong; Zeng, Guangping

doi:10.23919/jcc.2021.10.016

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…A resource deficiency model is designed in [ 47 ], and a resource allocation algorithm for resource rejection rate, average resource waiting period, and resource utilization is proposed. The relationship between the resources consumed by task requests and the resources occupied by the system in a discrete computing network is analogized to the relationship between healthy cells and infected cells in epidemiology, and a task-resource model in a discrete computing network based on the classical virus dynamics model is built [ 48 ]. I. Al-Azzoni et al used genetic algorithms and ant colony algorithms to solve the problem of mapping a set of software components to the available computational units in a heterogeneous computing system, but the authors only considered the single-objective problem of component allocation [ 49 ].…”

Section: Related Workmentioning

confidence: 99%

Resource Mapping Allocation Scheme in 6G Satellite Twin Network

Deng

2022

Sensors

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The sixth generation (6G) satellite twin network is an important solution to achieve seamless global coverage of 6G. The deterministic geometric topology and the randomness of the communication behaviors of 6G networks limit the realism and transparency of cross-platform and cross-object communication, twin, and computing co-simulation networks. Meanwhile, the parallel-based serverless architecture has a high redundancy of computational resource allocation. Therefore, for the first time, we present a new hypergraph hierarchical nested kriging model, which provides theoretical analysis and modeling of integrated relationships for communication, twin, and computing. We explore the hierarchical unified characterization method which joins heterogeneous topologies. A basis function matrix for local flexible connectivity of the global network is designed for the connection of huge heterogeneous systems to decouple the resource mapping among heterogeneous networks. To improve the efficiency of resource allocation in communication, twin, and computing integrated network, a multi-constraint multi-objective genetic algorithm (MMGA) based on the common requirements of operations, storage, interaction, and multi-layer optimal solution conflict is proposed for the first time. The effectiveness of the algorithm and architecture is verified through simulation and testing.

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Section: Related Workmentioning

confidence: 99%

Resource Mapping Allocation Scheme in 6G Satellite Twin Network

Deng

2022

Sensors

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“…Dispersed computing is more capable of handling such tasks. The dispersed computing architecture is fully decentralized and efficiently mobile, which leverages geographically distributed hardware to provide diverse and flexible idle resources in a collaborative and shared manner [1]. It can expand the associated wireless network devices (e.g., servers, vehicles, UAVs, portable devices, smart equipments, etc.)…”

Section: Introductionmentioning

confidence: 99%

Dispersed Computing Resource Discovery Model and Algorithm for Polymorphic Migration Network Architecture

et al. 2023

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Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration of the network and the time and energy costs resulting from node selection and frequent interactions of information between nodes. The resource discovery problem for dispersed computing can be considered a dynamic multi-level decision problem. A bi-level programming model of dispersed computing resource discovery is developed, which is driven by time cost, energy consumption and accuracy of information acquisition. The upper-level model is to design a reasonable network structure of resource discovery, and the lower-level model is to explore an effective discovery mode. Complex network topology features are used for the first time to analyze the polymorphic migration characteristics of resource discovery networks. We propose an integrated calibration method for energy consumption parameters based on two discovery modes (i.e., agent mode and self-directed mode). A symmetric trust region based heuristic algorithm is proposed for solving the system model. The numerical simulation is performed in a dispersed computing network with multiple modes and topological states, which proves the feasibility of the model and the effectiveness of the algorithm.

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