Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System
Abstract
:1. Introduction
- (1)
- More heterogeneous end systems enlarge the attack surface [5] of the RPSS, which leads to a decrease in the reliability of the RPSS. Due to the increasing number and variety of end systems, the attack surface of the RPSS is also increasing, thus increasing the wider range of system failures, which further causes maintenance delays leading to a decrease in the overall reliability of the RPSS.
- (2)
- More and more unknown and targeted attacks enlarge the risk of expanding computing services from station-end systems to regional centers. Some APT attack programs and malicious destructive programs implanted by exploiting the vulnerabilities of the station-end system may affect the normal computation of the regional center service program by destroying the terminal data, thus leading to the failure of the regional node service, especially in the fields of transportation [6,7,8,9].
- (a)
- We propose a double-layer DHR (DDHR) architecture to adapt to the cloud-edge architecture of the RPSS. The DDHR architecture contains a data flow processing algorithm and a weighted mimetic ruling algorithm. Among them, the data processing algorithm can be fully adapted to the distributed deployment conditions of the redundant executor sets, and the upper layer of the redundant executor set hidden characteristics, which in turn reduces the similarity of the system and improves the dynamics of the system; the weighted mimetic ruling algorithms can be used to increase the upper limit of the number of redundant executives for voting effectiveness of the double-layer redundant executors set, under the condition of minimizing the complexity of the voting algorithms.
- (b)
- We propose a reliability indicator, called dynamic available similarity, which is a special metric for the DDHR architecture to evaluate the available heterogeneity of a double-layer redundant set of systems against a single attack process under random interaction conditions. The dynamic available similarity is different from the traditional heterogeneity metrics under the DHR architecture, which dynamically determine the heterogeneity of a single attack in real time against the access restriction characteristics of the upper and lower redundancy layers of the DDHR architecture.
- (c)
- We propose a reliability indicator, called scheduling cycle under dual-layer similarity threshold conditions, which is another special metric to evaluate the dynamic performance of DDHR architecture in practical engineering applications when redundant entities are cleaned and scheduled offline. This metric expands on the traditional DHR architecture dynamic metrics by adding similarity limits between the same layer and different layers, to simulate the impact of redundancy offline, cleaning, and other situations on system reliability in real engineering scenarios.
- (d)
- We design a prototype laboratory system for the DDHR architecture. By deploying heterogeneous systems and applications in a multi-virtual machine environment on a trial basis in the lab, we have realized the construction of the DDHR architecture, validation of its effectiveness, and verification of its multi-task concurrent performance.
- (1)
- Related Work: Section 2 provides an introduction to the relevant references and prior studies in the field.
- (2)
- DDHR Architecture and Process Design: in Section 3, the RPSS with DDHR architecture is modeled, and the design of the process and algorithm are presented through a pseudo-code display.
- (3)
- Numerical Modeling and Comparison: Section 4 focuses on performing numerical modeling of indicators specific to the DDHR architecture. Furthermore, a comparative analysis is conducted, comparing the results with those obtained from the DHR architecture.
- (4)
- Experimental Simulation: Section 5 presents the details of the experimental simulations carried out to validate the proposed approach.
- (5)
- Conclusion and Future Work: Section 6 concludes the article by summarizing the findings and contributions. Additionally, it outlines potential directions for future research.
2. Related Work
- (1)
- Regarding the construction of mimic defense architecture.
- (2)
- In the context of mimic defense dynamic scheduling and voting strategy analysis and optimization.
3. DDHR Architecture Design
3.1. DDHR Architecture Design Idea
- (1)
- Layering of redundant resources to improve system dynamics under resource-limited conditions.
- (2)
- Access Hiding to Upper Redundant Resources to Improve System Heterogeneity under Two-layer Computing Conditions.
3.2. Design of DDHR Logical Architecture for RPSS
- (1)
- Processing Module:
- (2)
- Scheduling Gateway:
- (3)
- Scheduling Module:
- (4)
- Redundant executors pool:
- (5)
- Input Module:
- (6)
- Output Module:
3.3. Engineering Architecture Design
- (1)
- The Line Center Node transformation
- (2)
- The Regional center Node transformation
3.4. DDHR Architecture Data Processing Design
- (1)
- System Initialization
- (2)
- Message Request Submission by the Client Agent
- (3)
- Execution by the Lower-layer Redundant executors
- (4)
- Operation of the Scheduling Gateway
- (5)
- Execution by the Upper-layer Redundant Executors
- (6)
- Mimic Voting
Algorithms 1. Data processing algorithm simulation code |
(1) Initialization Input: redundant executors pool, upper-layer redundant executors set, lower-layer redundant executors set, redundant executors routing link list GList(), scheduling algorithm F Function Init U,D, U,D F(A) GList() F(U,D) End (2) Business process Input: message inf, upper-layer redundant executors set, upper-layer isomer set redundancy n, lower-layer isomer set D, lower-layer isomer set redundancy m, scheduling gateway storage message set G, upper and lower isomer link list GList (), data processing result set K, V Output: Mimic verdict result rs Function mimicjob Init() for i in D do k[i]= D[i].job(inf) //The lower redundant executors process the message inf to obtain k if k[i]!=null then G GList(k[i],inf) D k[i] endif endfor for j in U do // The upper redundant executors process K to obtain <k, v> <k[j],v[j]>= U[j].job(D) KV <k[j],v[j]> // The upper redundant executors process the message inf and obtains <k′, v′> <k′[j],v′[j]>= U[j].job(G) K′V′ <k′[j],v′[j]> endfor if count(KVD)==n then rs KV else // The upper and lower-layer redundant executors output results then rs then rs KV else rs endif endif return rs end |
4. DDHR Architecture Analysis
4.1. System Heterogeneity Analysis
4.2. System Dynamics Analysis
4.3. System Failure Probability Analysis
5. Experiment Simulation
5.1. Available Similarity Simulation Experiments
5.2. Scheduling Cycle Simulation Experiment under Double-Layer Similarity Threshold Conditions
- (1)
- Without considering the similarity threshold:
- (2)
- Considering the similarity threshold:
- (1)
- DDHR Architecture Scheduling Cycle.
- (2)
- DHR Architecture Scheduling Cycle
- (3)
- Comparison of DDHR and DHR architectures with the changing trend of thresholds.
5.3. System Failure Probability Simulation Experiment
5.4. Comparative Analysis of System Model Complexity and System Overheads
5.5. Laboratory Simulation Experiment
- (1)
- Setting up the experimental environment for the prototype system
- (2)
- Validity experiments
- Master-slave mode redundancy
- DHR and DDHR Architecture
- (3)
- Concurrency Performance Experiment
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Optimization Direction | Scheduling Constraints | Heterogeneity | Dynamic | Reliability | Other Indicators | ||
---|---|---|---|---|---|---|---|
[19] | Voting strategy | √ | Voting heterogeneous weights | ||||
[20] | Voting/Scheduling strategy | √ | √ | Historical credibility decision-making | |||
[21] | Scheduling strategy | √ | √ | √ | |||
[22] | Scheduling strategy | √ | √ | Redundancy security | |||
[23] | Scheduling strategy | √ | √ | AL/AT | |||
[24,25] | Scheduling strategy | √ | √ | √ | attack cost/defensive cost | ||
[26,27] | Scheduling strategy | √ | Time, task threshold | ||||
[28,29] | Scheduling strategy | √ | √ | High-order heterogeneity, historical confidence | |||
This paper | Voting/Scheduling strategy | √ | √ | √ | √ | Available Similarity and Redundancy Weights |
Symbol | Definition |
---|---|
A | Heterogeneous redundant system |
a | Redundant executor |
U | Upper-layer redundant executors set |
D | Lower-layer redundant executors set |
r, m, n | Redundancy |
l | Heterogeneous feature vectors |
The i-th upper redundant executor | |
The i-th lower redundant executor | |
Scheduling scheme | |
s | Similarity threshold |
Slim | Same layer redundancy similarity threshold |
Slimud | Different layer redundancy similarity threshold |
Sfi | |
T | Scheduling cycle |
P | Average system failure probability |
p(ui) | Average failure probability of Upper-layer redundant executors set |
p(di) | Average failure probability of Lower-layer redundant executors set |
Redundancy Rate r | DHR-Average Available Similarity | DDHR-Average Available Similarity |
---|---|---|
r = 3 | 0.2540 | -- |
r = 4 | 0.2444 | -- |
r = 6 | 0.24514 | 0.1175 |
r = 8 | 0.2498 | 0.1161 |
Scheduling Cycle T | r = 6 | r = 8 |
---|---|---|
DHR/DDHR | 933.74 | 480.74 |
DDHR Scheduling Cycle T | DHR Scheduling Cycle T | ||||||
---|---|---|---|---|---|---|---|
slim = 0.4 | slimud = 0.35 | slimud = 0.30 | slim = 0.4 | slim = 0.35 | slim = 0.30 | ||
r = 6 | 546.58 | 476.88 | 213.51 | 540.02 | 353.85 | 130.48 | |
r = 8 | 143.86 | 136.52 | 86.27 | 139.19 | 56.84 | 20.26 |
Probability of System Failure (p) | r = 6 | r = 8 |
---|---|---|
Traditional redundancy | ||
DHR architecture | ||
DDHR architecture |
DHR | DDHR | |
---|---|---|
Time complexity |
Classification | Name and Configuration | Environment |
---|---|---|
Lower-layer | (D)VM1(4C,8G) | JAVA_1.8&Linux_RedHat8 |
(D)VM2(4C,8G) | c#&Winserver_2018 | |
(D)VM3(4C,8G) | Python_3.9&Windows_11 | |
Upper-layer | (U)VM4(4C,8G) | JAVA_1.8&Kirin_V10 |
(U)VM5(4C,8G) | Asp.Netcore&Linux_centos8 | |
(U)VM6(4C,8G) | C++&Unicon |
Number of Concurrent Users | DHR | DDHR |
---|---|---|
100 | 0.176 s | 0.252 s |
200 | 0.288 s | 0.426 s |
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Share and Cite
Wu, X.; Wang, M.; Shen, J.; Gong, Y. Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System. Electronics 2024, 13, 3592. https://doi.org/10.3390/electronics13183592
Wu X, Wang M, Shen J, Gong Y. Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System. Electronics. 2024; 13(18):3592. https://doi.org/10.3390/electronics13183592
Chicago/Turabian StyleWu, Xinghua, Mingzhe Wang, Jinsheng Shen, and Yanwei Gong. 2024. "Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System" Electronics 13, no. 18: 3592. https://doi.org/10.3390/electronics13183592