Quality of service and loss evaluation method for multi-variant system

doi:10.11772/j.issn.1001-9081.2022010119

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 876-884.DOI: 10.11772/j.issn.1001-9081.2022010119

• Computer software technology • Previous Articles Next Articles

Quality of service and loss evaluation method for multi-variant system

Yumei CHEN(), Hongchao HU, Yawen WANG

Information Technology Research Institute，Information Engineering University，Zhengzhou Henan 450001，China

Received:2022-02-08 Revised:2022-03-24 Accepted:2022-04-06 Online:2022-04-24 Published:2023-03-10
Contact: Yumei CHEN
About author:HU Hongchao， born in 1982， Ph. D.， professor. His research interests include mimic defense， active cyber defense.
WANG Yawen， born in 1990， Ph. D.， assistant research fellow. His research interests include multi-variant execution environment， software diversification.
Supported by:
National Key Research and Development Program of China(2021YFB1006200);National Natural Science Foundation of China(62072467)

多变体系统服务质量及损耗评估方法

陈玉枚(), 扈红超, 王亚文

战略支援部队信息工程大学信息技术研究所，郑州 450001

通讯作者: 陈玉枚
作者简介:陈玉枚（1997—），女（土家族），重庆人，硕士研究生，主要研究方向：多变体系统、拟态防御
扈红超（1982—），男，河南商丘人，教授，博士，主要研究方向：拟态防御、网络主动防御
王亚文（1990—），男，河南郑州人，助理研究员，博士，主要研究方向：多变体执行环境、软件多样化。
基金资助:
国家重点研发计划项目(2021YFB1006200);国家自然科学基金资助项目(62072467)

Abstract

Abstract:

Multi-variant system uses diversified technology and dynamic redundancy strategy to achieve high security and high reliability of the system from the architecture level. However， the existing researches are rarely related to the quantitative evaluation of Quality of Service （QoS） for multi-variant system. For this problem， a QoS and loss evaluation method for multi-variant system was proposed. Firstly， on the basis of formal modeling of multi-variant system architecture， the QoS evaluation model and process were proposed based on QoS attribute and weight matrices， and the importances of QoS attributes were evaluated by information entropy method. Secondly， some typical examples of multi-variant system were constructed， and QoS attributes that affect the performance and security of the system were designed and selected. Finally， QoS attribute weight， QoS value， QoS difference and loss of the system were evaluated quantitatively， in which the average loss of performance attributes is 15.86 percentage points and the gain of safety attribute is 4.98 percentage points. Evaluation results show that the multi-variant mechanism has QoS loss， but its security attribute brings a certain degree of QoS gain， which provides a reference for constructing a multi-variant system with high QoS and low QoS loss.

Key words: multi-variant system, Quality of Service (QoS), QoS loss, QoS attribute, quantitative evaluation

摘要：

多变体系统利用多样化技术和动态冗余策略从架构层面实现系统的高安全性和高可靠性，然而现有研究很少涉及多变体系统服务质量（QoS）的量化评估。针对以上问题，提出一种多变体系统QoS及损耗评估方法。首先，在多变体系统架构形式化建模的基础上，基于QoS属性及权重矩阵提出QoS评估模型和流程，并使用信息熵法则评估QoS属性的重要程度；然后，构造典型的多变体系统测试实例，设计并选取影响系统性能和安全性的QoS属性；最后，量化评估系统的QoS属性权重、QoS值、QoS差异和损耗，其中，性能属性平均损耗15.86个百分点，安全属性增益4.98个百分点。评估结果表明多变体机制存在QoS损耗，但它的安全属性带来了一定程度的QoS增益，可为构造高QoS以及低QoS损耗的多变体系统提供一些参考。

关键词: 多变体系统, 服务质量（QoS）, 服务质量损耗, 服务质量属性, 量化评估

CLC Number:

TP311

Yumei CHEN, Hongchao HU, Yawen WANG. Quality of service and loss evaluation method for multi-variant system[J]. Journal of Computer Applications, 2023, 43(3): 876-884.

陈玉枚, 扈红超, 王亚文. 多变体系统服务质量及损耗评估方法[J]. 《计算机应用》唯一官方网站, 2023, 43(3): 876-884.

Figures/Tables 10

References 48

1	VOLCKAERT S. Advanced techniques for multi-variant execution［D］. Ghent： Ghent University， 2015： 1-12.
2	VOLCKAERT S， DE SUTTER B， DE BAETS T， et al. GHUMVEE： efficient， effective， and flexible replication［C］// Proceedings of the 2012 International Symposium on Foundations and Practice of Security， LNCS 7743. Berlin： Springer， 2013： 261-277.
3	VOLCKAERT S， COPPENS B， DE SUTTER B， et al. Taming parallelism in a multi-variant execution environment［C］// Proceedings of the 12th European Conference on Computer Systems. New York： ACM， 2017： 270-285. 10.1145/3064176.3064178
4	COX B， EVANS D， FILIPI A， et al. N-variant systems： a secretless framework for security through diversity［C］// Proceedings of the 15th USENIX Security Symposium. Berkeley： USENIX Association， 2006： 105-120.
5	VOULIMENEAS A， SONG D， PARZEFALL F， et al. Distributed heterogeneous N-variant execution［C］// Proceedings of the 2020 International Conference on Detection of Intrusions and Malware， and Vulnerability Assessment， LNCS 12223. Cham： Springer， 2020： 217-237.
6	GARCIA M， NEVES N， BESSANI A. SieveQ： a layered BFT protection system for critical services［J］. IEEE Transactions on Dependable and Secure Computing， 2018， 15（3）： 511-525. 10.1109/tdsc.2016.2593442
7	GARCIA M， BESSANI A， NEVES N. Lazarus： automatic management of diversity in BFT systems［C］// Proceedings of the 20th International Middleware Conference. New York： ACM， 2019： 241-254. 10.1145/3361525.3361550
8	HU H C， WU J X， WANG Z P， et al. Mimic defense： a designed-in cybersecurity defense framework［J］. IET Information Security， 2018， 12（3）： 226-237. 10.1049/iet-ifs.2017.0086
9	WANG W， LI G， GAI K， et al. Modelization and analysis of dynamic heterogeneous redundant system［J］. Concurrency Computation： Practice and Experience， 2022， 34（12）： No.e6035. 10.1002/cpe.6035
10	张杰鑫，庞建民，张铮. 拟态构造的Web服务器异构性量化方法［J］.软件学报， 2020， 31（2）：564-577.
	ZHANG J X， PANG J M， ZHANG Z. Quantification method for heterogeneity on Web server with mimic construction［J］. Journal of Software， 2020， 31（2）： 564-577.
11	齐超. 拟态网络操作系统架构及关键技术研究［D］. 郑州：战略支援部队信息工程大学， 2018： 1-118.
	QI C. Research on the key technologies of mimic network operating system architecture［D］. Zhengzhou： Information Engineering University， 2018： 21-23.
12	LEI C， ZHANG H Q， TAN J L， et al. Moving target defense techniques： a survey［J］. Security and Communication Networks， 2018， 2018： No.3759626. 10.1155/2018/3759626
13	KRITIKOS K， PLEXOUSAKIS D. Requirements for QoS-based web service description and discovery［J］. IEEE Transactions on Services Computing， 2009， 2（4）：320-337. 10.1109/tsc.2009.26
14	LIU Y T， NGU A H， ZENG L Z. QoS computation and policing in dynamic web service selection［C］// Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers and Posters. New York： ACM， 2004： 66-73. 10.1145/1013367.1013379
15	仝青，张铮，张为华，等. 拟态防御Web服务器设计与实现［J］. 软件学报， 2017， 28（4）：883-897.
	TONG Q， ZHANG Z， ZHANG W H， et al. Design and implementation of mimic defense Web server［J］. Journal of Software， 2017， 28（4）： 883-897.
16	张铮，马博林，邬江兴. web服务器拟态防御原理验证系统测试与分析［J］. 信息安全学报， 2017， 2（1）：13-28.
	ZHANG Z， MA B L， WU J X. The test and analysis of prototype of mimic defense in web servers［J］. Journal of Cyber Security， 2017， 2（1）： 13-28.
17	PETRIE C J. Web Service Composition［M］. Cham： Springer， 2016： 48-103. 10.1007/978-3-319-32833-1_3
18	KUMAR S， MISHRA R B. A hybrid model for service selection in semantic web service composition［J］. International Journal of Intelligent Information Technologies， 2008， 4（4）： 55-69. 10.4018/jiit.2008100104
19	JIANG W， HU S L， LIU Z Y. Top k query for QoS-aware automatic service composition［J］. IEEE Transactions on Services Computing， 2014， 7（4）： 681-695. 10.1109/tsc.2013.41
20	HWANG S Y， HSU C C， LEE C H. Service selection for web services with probabilistic QoS［J］. IEEE Transactions on Services Computing， 2015， 8（3）： 467-480. 10.1109/tsc.2014.2338851
21	ZENG L Z， BENATALLAH B， NGU A H H， et al. QoS-aware middleware for web services composition［J］. IEEE Transactions on Software Engineering， 2004， 30（5）： 311-327. 10.1109/tse.2004.11
22	ZENG L F， XU S J， WANG Y， et al. Toward cost-effective replica placements in cloud storage systems with QoS awareness［J］. Software： Practice and Experience， 2017， 47（6）： 813-829. 10.1002/spe.2441
23	CHEN J C， ZHOU H B， ZHANG N， et al. Service-oriented dynamic connection management for software-defined internet of vehicles［J］. IEEE Transactions on Intelligent Transportation Systems， 2017， 18（10）： 2826-2837. 10.1109/tits.2017.2705978
24	DING Z J， LIU J J， SUN Y Q， et al. A transaction and QoS-aware service selection approach based on genetic algorithm［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2015， 45（7）： 1035-1046. 10.1109/tsmc.2015.2396001
25	SILVA A S DA， MA H， ZHANG M J. A graph-based particle swarm optimisation approach to QoS-aware Web service composition and selection［C］// Proceedings of the 2014 IEEE Congress on Evolutionary Computation. Piscataway： IEEE， 2014： 3127-3134. 10.1109/cec.2014.6900404
26	LI G Y， BOUKHATEM L， WU J S. Adaptive quality-of-service-based routing for vehicular ad hoc networks with ant colony optimization［J］. IEEE Transactions on Vehicular Technology， 2017， 66（4）： 3249-3264. 10.1109/tvt.2016.2586382
27	LIU C Y， ZOU C M， WU P. A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing［C］// Proceedings of the 13th International Symposium on Distributed Computing and Applications to Business， Engineering and Science. Piscataway： IEEE， 2014： 68-72. 10.1109/dcabes.2014.18
28	MA H， WANG A Q， ZHANG M J. A hybrid approach using genetic programming and greedy search for QoS-aware web service composition［M］// HAMEURLAIN A， KÜNG J， WAGNER R， et al. Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII： Special Issue on Database- and Expert-Systems Applications， LNCS 8980. Berlin： Springe， 2015： 180-205.
29	张杰鑫，庞建民，张铮，等. 拟态构造Web服务器的服务质量量化方法［J］. 计算机科学， 2019， 46（11）：109-118. 10.11896/jsjkx.181001922
	ZHANG J X， PANG J M， ZHANG Z， et al. QoS quantification method for Web server with mimic construction［J］. Computer Science， 2018， 46（11）： 109-118. 10.11896/jsjkx.181001922
30	HAN Q， LI W M， SONG Y F， et al. A new method for MAGDM based on improved TOPSIS and a novel Pythagorean fuzzy soft entropy［J］. Symmetry， 2019， 11（7）： No.905. 10.3390/sym11070905
31	MALEKI H， VALIZADEH S， KOCH W， et al. Markov modeling of moving target defense games［C］// Proceedings of the 2016 ACM Workshop on Moving Target Defense. New York： ACM， 2016： 81-92. 10.1145/2995272.2995273
32	OKHRAVI H， RABE M A， MAYBERRY T J， et al. Survey of cyber moving target techniques： TR-1166［R］. Cambridge： MIT Lexington Lincoln Laboratory， 2013. 10.21236/ada591804
33	HWANG D， SHIN J， KIM J， et al. Data randomization for multi-variant execution environment［C］// Proceedings of the 2019 International SoC Design Conference. Piscataway： IEEE， 2019： 291-292. 10.1109/isocc47750.2019.9027747
34	SALAMAT B， GAL A， FRANZ M. Reverse stack execution in a multi-variant execution environment［C/OL］// Proceedings of the 2008 Workshop on Compiler and Architectural Techniques for Application Reliability and Security. ［2021-12-17］.. 10.1109/cisis.2008.136
35	VOLCKAERT S， COPPENS B， DE SUTTER B. Cloning your gadgets： complete ROP attack immunity with multi-variant execution［J］. IEEE Transactions on Dependable and Secure Computing， 2016， 13（4）： 437-450. 10.1109/tdsc.2015.2411254
36	SALAMAT B， JACKSON T， WAGNER G， et al. Runtime defense against code injection attacks using replicated execution［J］. IEEE Transactions on Dependable and Secure Computing， 2011， 8（4）： 588-601. 10.1109/tdsc.2011.18
37	JAFARIAN J H， AL-SHAER E， DUAN Q. Openflow random host mutation： transparent moving target defense using software defined networking［C］// Proceedings of the 1st ACM International Workshop on Hot Topics in Software Defined Networks. New York： ACM， 2012： 127-132. 10.1145/2342441.2342467
38	李凌书. 拟态SaaS云安全架构及关键技术研究［D］. 郑州：战略支援部队信息工程大学， 2021： 1-125. 10.36909/jer.v9i1.8111
	LI L S. Research on key technologies of mimic SaaS cloud security architecture［D］. Zhengzhou： Information Engineering University， 2021： 35-43. 10.36909/jer.v9i1.8111
39	BESSANI A， SOUSA J， ALCHIERI E E P. State machine replication for the masses with BFT-SMART［C］// Proceedings of the 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. Piscataway： IEEE， 2014： 355-362. 10.1109/dsn.2014.43
40	HOSEK P， CADAR C. VARAN the unbelievable： an efficient n-version execution framework［C］// Proceedings of the 20th International Conference on Architectural Support for Programming Languages and Operating Systems. New York： ACM， 2015： 339-353. 10.1145/2694344.2694390
41	WU J X. Cyberspace Mimic Defense： Generalized Robust Control and Endogenous Security［M］. Cham： Springer， 2020： 4-7. 10.1007/978-3-030-29844-9_9
42	CASADO R， TUYA J， YOUNAS M. A framework to test advanced web services transactions［C］// Proceedings of the 4th IEEE International Conference on Software Testing， Verification and Validation. Piscataway： IEEE， 2011： 443-446. 10.1109/icst.2011.44
43	HICKMAN B， NEWMAN D， TADJUDIN S， et al. Benchmarking methodology for firewall performance： RFC 3511 ［S］. Reston， VA： Internet Society， 2003-04. 10.17487/rfc3511
44	QUINTERO A， SANS F， GAMESS E. Performance evaluation of IPv4/IPv6 transition mechanisms［J］. International Journal of Computer Network and Information Security， 2016， 8（2）： 1-14. 10.5815/ijcnis.2016.02.01
45	MENASE D A. Response-time analysis of composite web services［J］. IEEE Internet Computing， 2004， 8（1）： 90-92. 10.1109/mic.2004.1260710
46	邓仲华，汪宣晟，李志芳，等. 信息资源云服务的质量评价指标研究［J］. 图书与情报， 2012（4）： 12-15. 10.3969/j.issn.1003-6938.2012.04.003
	DEN Z H， WANG X S， LI Z F， et al. Research on quality indicator of information resource cloud service［J］. Library and Information， 2012（4）： 12-15. 10.3969/j.issn.1003-6938.2012.04.003
47	魏丽坤. Kano模型和服务质量差距模型的比较研究［J］. 世界标准化与质量管理， 2006（9）： 10-13. 10.3969/j.issn.1674-5698.2006.09.003
	WEI L K. A comparative study on Kano model and Gaps model of service quality［J］. World Standardization and Quality Management， 2006（9）： 10-13. 10.3969/j.issn.1674-5698.2006.09.003
48	曾威. 拟态云裁决机制与资源调度关键技术研究［D］. 郑州：战略支援部队信息工程大学， 2022： 1-75. 10.1109/ispds54097.2021.00020
	ZENG W. Research on key technologies of mimic cloud ruling mechanism and resource scheduling［D］. Zhengzhou： Information Engineering University， 2022： 39-51. 10.1109/ispds54097.2021.00020

[1]	WU Renbiao, ZHANG Zhenchi, JIA Yunfei, QIAO Han. Adaptive scheduling strategy based on deadline under cloud platform [J]. Journal of Computer Applications, 2023, 43(1): 176-184.
[2]	Xuemin XU, Xiuguo ZHANG, Yuanyuan XIAO, Zhiying CAO. Large-scale Web service composition based on optimized grey wolf optimizer [J]. Journal of Computer Applications, 2022, 42(10): 3162-3169.
[3]	ZHOU Shuo, QIU Runhe, TANG Minjun. Power allocation algorithm for CR-NOMA system based on tabu search and Q-learning [J]. Journal of Computer Applications, 2021, 41(7): 2026-2032.
[4]	Shuicong LIAO, Peng SUN, Xingchen LIU, Yun ZHONG. Service composition optimization based on improved krill herd algorithm [J]. Journal of Computer Applications, 2021, 41(12): 3652-3657.
[5]	Ying LEI, Wanbo ZHENG, Wei WEI, Yunni XIA, Xiaobo LI, Chengwu LIU, Hong XIE. Task offloading method based on probabilistic performance awareness and evolutionary game strategy in “cloud + edge” hybrid environment [J]. Journal of Computer Applications, 2021, 41(11): 3302-3308.
[6]	GUO Shujie, LI Zhihua, LIN Kaiqing. Fuzzy membership degree based virtual machine placement algorithmin cloud environment [J]. Journal of Computer Applications, 2020, 40(5): 1374-1381.
[7]	LUO Chiwei, QU Tao, DENG Dexiang. Rate adaption algorithm for embedded multi-channel wireless video transmission [J]. Journal of Computer Applications, 2020, 40(4): 1119-1126.
[8]	SUN Tianqi, HU Jianpeng, HUANG Juan, FAN Ying. Bandwidth resource prediction and management of Web applications hosted on cloud [J]. Journal of Computer Applications, 2020, 40(1): 181-187.
[9]	WANG Yan, MA Xiurong, SHAN Yunlong. Downlink resource scheduling based on weighted average delay in long term evolution system [J]. Journal of Computer Applications, 2019, 39(5): 1429-1433.
[10]	LI Lei, XUE Yang, LYU Nianling, FENG Min. Online task and resource scheduling designing for container cloud queue based on Lyapunov optimization method [J]. Journal of Computer Applications, 2019, 39(2): 494-500.
[11]	GUO Mian, LI Qiqi. Computation offloading policy for delay-sensitive Internet of things applications [J]. Journal of Computer Applications, 2019, 39(12): 3590-3596.
[12]	WU Muyang, LIU Zheng, WANG Yang, LI Yun, LI Tao. Quality evaluation model of network operation and maintenance based on correlation analysis [J]. Journal of Computer Applications, 2018, 38(9): 2535-2542.
[13]	WANG Zewu, SUN Lei, GUO Songhui, SUN Ruichen. Scheduling method of virtual cipher machine based on entropy weight evaluation in cryptography cloud [J]. Journal of Computer Applications, 2018, 38(5): 1353-1359.
[14]	LI Kunlun, GUAN Liwei, GUO Changlong. Cloud task scheduling strategy based on clustering and improved symbiotic organisms search algorithm [J]. Journal of Computer Applications, 2018, 38(3): 707-714.
[15]	YU Xinrong, LI Zhihua, YAN Chengyu, LI Shuangli. High efficient virtual machines consolidation method in cloud data center [J]. Journal of Computer Applications, 2018, 38(2): 550-556.

Quality of service and loss evaluation method for multi-variant system

多变体系统服务质量及损耗评估方法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 48

Related Articles 15

Recommended Articles

Metrics