1 Digital Twin-Empowered Network Planning for Multi-Tier Computing

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Digital Twin-Empowered Network Planning for

Multi-Tier Computing

Conghao Zhou, Jie Gao, Mushu Li, Xuemin (Sherman) Shen, Weihua Zhuang

Abstract

In this paper, we design a resource management scheme to support stateful applications, which will

be prevalent in 6G networks. Different from stateless applications, stateful applications require context

data while executing computing tasks from user terminals (UTs). Using a multi-tier computing paradigm

with servers deployed at the core network, gateways, and base stations to support stateful applications, we

aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage,

and communication resources and the cost from reconﬁguring resource reservation. The coupling among

different resources and the impact of UT mobility create challenges in resource management. To address

the challenges, we develop digital twin (DT) empowered network planning with two elements, i.e., multi-

resource reservation and resource reservation reconﬁguration. First, DTs are designed for collecting

UT status data, based on which UTs are grouped according to their mobility patterns. Second, an

algorithm is proposed to customize resource reservation for different groups to satisfy their different

resource demands. Last, a Meta-learning-based approach is developed to reconﬁgure resource reservation

for balancing the network resource usage and the reconﬁguration cost. Simulation results demonstrate

that the proposed DT-empowered network planning outperforms benchmark frameworks by using less

resources and incurring lower reconﬁguration costs.

Index Terms

6G, digital twin, network planning, multi-tier computing, Meta learning.

C. Zhou, X. Shen, and W. Zhuang are with the Department of Electrical and Computer Engineering, University of Waterloo,

Waterloo, ON, N2L 3G1, Canada (e-mail: c89zhou@uwaterloo.ca; sshen@uwaterloo.ca; wzhuang@uwaterloo.ca).

J. Gao is with the School of Information Technology, Carleton University, Ottawa, ON, K1S 5B6, Canada

(email: jie.gao6@carleton.ca ).

M. Li is with the Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto,

ON, M5B 2K3, Canada (e-mail: mushu1.li@ryerson.ca)

arXiv:2210.02616v2 [cs.NI] 6 Dec 2022

I. INTRODUCTION

The sixth generation (6G) networks are expected to support a wide range of computing

applications [1]. A large portion of these applications are stateful, meaning that context data

is required to execute computing tasks [2], [3]. For example, augmented reality applications

require volumetric media objects or holograms, as the context data, to process video segments

for user terminals (UTs). The prevalent mobile edge computing (MEC) paradigm provides a

solution to supporting computing applications with low ofﬂoading delay but has limitations in

supporting stateful applications [4]. Speciﬁcally, edge servers close to UTs generally have limited

storage capacity to store all context data of stateful applications. Moreover, even if the context

data could be fully stored at an edge server, limited communication coverage and a relatively

small number of UTs served by the edge server would degrade storage resource utilization.

To address the above limitations, both the industry and the academia have started looking

into the collaboration of servers [5], [6]. Extending from MEC, multi-tier computing integrates

multiple servers deployed at the core network, gateways, access points, and other locations in the

network for executing computing tasks from UTs. Servers at different tiers have diverse features

in terms of resource capacity and service coverage [7]. Speciﬁcally, servers deployed at the core

network and gateways have larger service coverage and more abundant resources than servers

deployed at access points. Through coordinating servers at different tiers, multi-tier computing

can exploit different features of servers to support computing applications, especially the stateful

ones, in the era of 6G.

Network planning, as an important part of network management, can facilitate the coordination

of servers at different tiers. Network planning consists of resource reservation and resource reser-

vation reconﬁguration [1]. Resource reservation refers to proactively reserving network resources

for satisfying the upcoming resource demands from UTs. Resource reservation reconﬁguration

refers to timely updating resource reservation decisions to adapt to time-varying resource de-

mands and dynamic network environments. Network planning for supporting stateful applications

faces four challenges. First, the reservation of computing, storage, and communication resources

for stateful applications is tightly coupled, yielding existing resource reservation solutions for

supporting stateless applications inapplicable. Second, the requests for context data may vary

across a network, rendering both computing task assignment and storage resource reservation

dependent on speciﬁc servers and UT mobility patterns. Third, information regarding individual

UT status, e.g., UT mobility, is unavailable at the time of network planning, yet such UT-level

information can be useful for accurately calculating the amount of network resources needed

for supporting stateful applications [8]. Fourth, reconﬁguring computing and storage resource

reservation for stateful applications in a dynamic network environment yields additional costs due

to computing service interruption, which complicates resource reservation reconﬁguration [9].

Addressing the above challenges is important to accurate and adaptive network planning for

supporting stateful applications in 6G.

Recently, the digital twin paradigm has started attracting attention as a potential solution to

advancing network management for 6G [10]. The concept of digital twins (DTs) originates from

product life-cycle management in industry, where a DT is a synchronized virtual replica of

a physical object [11], [12]. For 6G networks, DTs can be introduced to represent individual

UTs. Each DT consists of a UT data proﬁle that describes the corresponding UT, including the

UT’s mobility, service demands, and quality of service (QoS) satisfaction, and DT functions for

data acquisition, processing, and analysis [10]. The introduction of DTs brings three beneﬁts

to network planning. First, historical data contained in DTs can be used to predict UT status

in the upcoming time interval, which can, in turn, facilitate customized resource reservation

for highly diversiﬁed UTs and enable ﬁne-grained network planning. Second, data indicating the

performance of network planning can be collected based on DTs, which can provide a foundation

for resource reservation reconﬁguration in network planning to adapt to a highly dynamic network

environment. Third, DTs should acquire extensive and well-organized data that can be used

to explore and exploit hidden network characteristics, thereby facilitating effective data-driven

network planning approaches to enhancing network performance. Due to the above beneﬁts, DTs

can be exploited and designed to improve the granularity, adaptivity, and intelligence of network

planning in 6G.

In this paper, we design a network planning scheme for supporting a stateful application in

the scenario of multi-tier computing. Our research objective is to ﬁnd out the minimum amount

of network resources (including computing, storage, and communication) needed for supporting

the application and also balance the resource usage and the cost from resource reservation

reconﬁguration in a dynamic network environment. To achieve this objective, we propose a DT-

empowered network planning framework with the following two elements: group-based multi-

resource reservation and closed-loop resource reservation reconﬁguration. First, we design DTs

for individual UTs to characterize their status and group them based on their mobility patterns.

We propose an algorithm based on matching theory and particle swarm optimization to address

the coupling relation among computing, storage, and communication in resource reservation.

The proposed method enables customized resource reservation for satisfying different resource

demands of UT groups with different mobility patterns. Second, we develop a Meta-learning-

based approach for resource reservation reconﬁguration to cope with the dynamic network

environment. The main contributions of this paper are the followings:

•We propose a novel network planning framework to facilitate ﬁne-grained resource reser-

vation based on UT data contained in DTs;

•We address a challenging multi-resource reservation problem for supporting stateful appli-

cations in multi-tier computing;

•We develop an automated closed-loop approach to reconﬁgure resource reservation in a

dynamic network environment for balancing the network resource usage and the cost from

reconﬁguring resource reservation.

The remainder of this paper is organized as follows. Section II provides an overview of related

studies. Section III describes the considered network scenario, the proposed DT-empowered

framework, and the system model. Section IV formulates the network planning problem for

multi-tier computing. Sections V and VI introduce the proposed solutions for resource reservation

and resource reservation reconﬁguration, respectively. Section VII presents the simulation results,

followed by the conclusion and future work in Section VIII.

II. RELATED WORK

Network resource management is conducted in both short-term and long-term [1]. Short-term

resource allocation in the operation stage relies on real-time information on individual UTs, such

as UT locations, and targets real-time UT satisfaction. By contrast, long-term resource reservation

in the planning stage is based on aggregated information of UTs, such as the number of UTs

covered by an access point, and focuses on network performance, such as resource utilization

over a relatively long time period, ranging from several minutes to hours [13], [14].

Most works on real-time resource allocation focus on computing task ofﬂoading and ser-

vice placement, among which many consider one-tier computing such as cloud computing

or MEC [15]–[19]. Based on the real-time computing task arrival of each UT, decentralized

and centralized communication and computing resource allocation approaches are proposed to

minimize the delay of computing task ofﬂoading and executing in cloud computing and MEC,

respectively [15], [16]. Service placement is studied in MEC based on the real-time UT location

and the type of service required by each UT to maximize the number of UTs that can be severed

under each edge server’s resource capacity [17], [18]. Some works focus on resource allocation

for multi-tier computing [20]–[24]. Computing resources on fog nodes and the cloud server

are allocated to UTs at different locations to satisfy the delay requirements of their computing

tasks [20]. Li et al. investigate a service placement approach for cloud and edge computing to

satisfy each UT’s computing demands [21]. Given that the same type of computing tasks can

share computing results, Yu et al. study joint computing task ofﬂoading and service placement in

multi-tier computing to reduce the delay of executing computing tasks [22]. Considering space-

ground integrated networks, the authors in [23], [24] investigate how to allocate computing and

communication resources available at satellites, terrestrial access points, and UTs to minimize

the delay of executing computing tasks from UTs.

There are less studies on long-term resource reservation for computing task ofﬂoading. Based

on the aggregated computing demands from all access points, a proactive computing resource

reservation approach in MEC is designed to minimize the delay of executing computing tasks [25]

and maximizing resource utilization in computing task execution [26], respectively. Yin et

al. study edge server placement to minimize the network resource usage based on statistical

computing demands [27]. There are limited works on long-term resource reservation in multi-tier

computing [8], [28]. Considering edge and cloud computing, Zhou et al. propose a computing

resource reservation approach to minimize network resource usage while satisfying different

delay requirements of two applications [28]. For servers located at different tiers of space-

ground integrated networks, joint communication and computing resource reservation is studied

to minimize the long-term cost of delay requirement violation and network reconﬁguration [8].

While allocating resources to UTs to satisfy their real-time computing demands is important,

proactively reserving resources on servers is also essential. In this work, we focus on network

planning in the presence of the coupling relation among computing, storage, and communication

resources and the impact of UT mobility in long-term resource reservation to support stateful

applications. We leverage DTs to improve the granularity and effectiveness of network planning

as compared to conventional resource reservation.

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1DigitalTwin-EmpoweredNetworkPlanningforMulti-TierComputingConghaoZhou,JieGao,MushuLi,Xuemin(Sherman)Shen,WeihuaZhuangAbstractInthispaper,wedesignaresourcemanagementschemetosupportstatefulapplications,whichwillbeprevalentin6Gnetworks.Differentfromstatelessapplications,statefulapplicationsrequirecont...

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