# Resource Cube: Multi-Virtual Resource Management for Integrated Satellite-Terrestrial Industrial IoT Networks

^{1}, Beijing Institute of Technology

^{2}, Lancaster University

^{3}, Ryerson University

^{4}, University of Idaho

^{5}

TL;DR: A matching considered preferences (MCPR) algorithm is designed to match IIoT nodes with service sides to achieve higher resource utilization and smarter connections and considers the resource cube (MCRC) algorithm based on MCPR algorithm to lower the total system delay.

Abstract: Industrial Internet of Things (IIoT) has found wider research, and satellite-terrestrial network (STN) can provide large-scale seamless connections for IIoT. With virtualization, we design resource cube to describe the integration and state of multi-dimensional virtual resources. To achieve higher resource utilization and smarter connections, we design a matching considered preferences (MCPR) algorithm to match IIoT nodes with service sides. The matching design considers the resource cube (MCRC) algorithm based on MCPR algorithm to lower the total system delay. In addition, in order to simplify the analysis of resource management, we adopt a layered architecture and multiple M/M/1 queuing models. We analyze the resource utilization and the total system delay for three different combinations of arrival rate and service rate of each resource cube. With MCRC algorithm, the utilization of resources is slightly reduced, while the total system delay is greatly reduced compared with MCPR algorithm.

## Summary (2 min read)

### I. INTRODUCTION

- With the development of the next generation networks and the Industry 4.0, there will be multiple traffic types of Internet applications under different network scenarios, leading to different requirements and different representative applications.
- Meanwhile, a large number of IIoT applications increase the demand for network resources, and the terrestrial network is difficult to provide network resources that meet all IIoT requirements in some cases.
- In the meantime, using the STN to provide services for IIoT applications has become a tendency in industry and academia [6] - [11] .

### II. SYSTEM ARCHITECTURE

- It contains IIoT nodes in transoceanic logistics, virtual resource controller (VRC), the terrestrial networks (TN) and the satellite networks (SN).
- Each terrestrial or satellite node contains computation, storage and communication resources.
- Since the application scenario under consideration is transoceanic logistics, the distribution of IIoT nodes will be particularly extensive.
- As mentioned above, VRC plays an important role in the whole process.
- Like a link, VRC matches IIoT nodes and network service sides when IIoT nodes and service sides are not in contact.

### III. RESOURCE CUBE AND PROBLEM FORMULATION

- For ease of understanding, the main symbol definitions used are listed in table I.
- It is well known that transoceanic logistics in the IIoT is characterized by massive connections, and data volume of each connection is very small.
- The unit resource cube is composed of one portion communication resource, one portion computation resource and one portion of storage resource.
- Constraint C3 can be divided into two parts based on services provided by different networks, as shown below.

### B. Matching Game between IIoT Nodes and TN or SN

- As mentioned above, the authors adopt several M/M/1 queuing models to describe the system.
- Within a VRC, there is a many-to-many matching between IIoT nodes and TNs or SNs.
- These matching results ignore the fact that the service sides may have insufficient resources, so the authors change some of the matching results.
- According to the resource utilization optimization problem solved by Markov approximation and Markov chain, the authors can adjust the matching results of some IIoT nodes that TNs or SNs can hardly meet its demand, and steps are shown in Algorithm 2: matching considering resource cubes (MCRC) algorithm.
- By doing so, the service delay can be reduced significantly, which are presented in the next section.

### V. SIMULATION RESULTS

- The authors will present the performance of the proposed MCRC algorithm and MCPR algorithm comparing them with the random selection way and the Hungary assignment algorithm [35] .
- The simulation scenario includes satellite networks consisting of one GEO, two MEO and three LEO satellites and the terrestrial networks consisting of 15 base stations.
- In the case of satellites as service sides, taking the GEO satellites with highest time delay as an example to explain the set of delay tolerance.
- The LEO and the MEO satellites are preferred by the preference lists when they can provide services, therefore data transmission delays are not as large.

### VI. CONCLUSION

- The authors considered the multi-resource management of IIoT applications, the transoceanic logistics, in integrated terrestrial-satellite networks.
- On this basis, the authors can get requirements of resources of IIoT nodes, and match it with the proper service side.
- The authors use MCPR algorithm to do preliminary matching, and then matching results are adjusted according to MCRC algorithm, which take the quantity of resource cubes of service sides and the analysis of Markov approximation into consideration.
- The total delay of the system is significantly reduced.

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### Cites methods from "Resource Cube: Multi-Virtual Resour..."

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##### Frequently Asked Questions (2)

###### Q2. What have the authors stated for future works in "Resource cube: multi-virtual resource management for integrated satellite-terrestrial industrial iot networks" ?

In the future work, the authors will continue to explore new ways to achieve more flexible allocation of multi-dimensional resources.