An approach to design and develop generic integrated architecture for autonomic software system


The continuous increase in the system’s software management and run-time exception problems in system’s software leads to the development of self-managed autonomic software systems. The idea of developing autonomic software system was proposed by IBM in 2001. The approach involves prevention of security threats; system or any of the system’s software failures, and will promise high performance software system. Autonomic computing approach is inspired by the autonomic human nervous system. The human nervous system is self-capable and sends instant control messages to the brain to control body temperature, to take unconscious decision, heal body wound, prevent body from danger, etc. The human body nervous system takes decision itself without the consciousness of a human. Similar approach was tried to develop in software system by IBM which followed by different IT industries such as Microsoft, HP, Oracle etc. The autonomic computing approach makes the decision-making process more reliable and responsive. With the development in the field of autonomic computing, run-time exception handling task, security threats, system failures related issues have been resolved at some extent but it compromises software systems’ performance. The reason of low system’s performance is the implementation of the autonomic features for different software systems’ functionalities which makes the software system more complex. To manage system’s software complexity, there is a need to define high-level administrative policies for system’s software management. Several autonomic software systems’ architecture ideas have been proposed by the researchers so far for system’s software management. However, the existing architectures are designed and developed in such a way that it will retain the system’s complexity due to which software systems’ performance degrade. Also, the existing architectures are not designed with complete autonomic features. Compared to existing architectures, the authors in the present paper proposed architecture to reduce the software management complexity and designed a self-managed policy system. This will in result improve software systems’ performance. The exceptions are categorized in two: autonomic and non-autonomic exceptions. To improve the performance of the proposed architecture, mapping of exceptions will be performed in terms of fully-matched, partially matched and not matched. The proposed work on self-management properties has been implemented using case study which gives promising results and confidence for the same in real-time complex applications also.

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  1. Berekmeri M, Serrano D, Bouchenak S, Marchand N, Robu B (2016) Feedback autonomic provisioning for guaranteeing performance in mapreduce systems. IEEE Trans Cloud Comput 6(4):1004–1016

    Article  Google Scholar 

  2. Chen S, Liu Y, Gorton I, Liu A (2005) Performance prediction of component-based applications. J Syst Softw 74(1):35–43

    Article  Google Scholar 

  3. Dehraj P, Sharma A (2015) Complexity based maintenance assessment for autonomic agent. In: Zhuang X (ed) Advances in Computer Science, Italy, pp 221-231, WSEAS-Conference, Rome, Italy

  4. Dehraj P, Sharma A (2019) Complexity assessment for autonomic systems by using neuro-fuzzy approach advances in intelligent systems and computing, vol 731. Springer, Singapore, pp 541–549

    Google Scholar 

  5. Dehraj P, Sharma A, Grover PS (2018) Incorporating autonomicity and trustworthiness aspects for assessing software quality IJET 7(1.1):421–425

    Google Scholar 

  6. Dehraj P, Sharma A, Grover PS (2019) Maintenance assessment guidelines for autonomic system using ANP approach. J Stat Manage Syst Taylor Francis 22(2):289–300

    Google Scholar 

  7. González JMN, Jiménez JA, López JCD (2018) Optimizing failure prediction time windows through genetic algorithms and random forests. IEEE Access 6:58307–58323

    Article  Google Scholar 

  8. Guerrero-Contreras G, Garrido JL, Balderas-Diaz S, Rodríguez-Domínguez C (2017) A context-aware architecture supporting service availability in mobile cloud computing. IEEE Trans Serv Comput 10(6):956–968

    Article  Google Scholar 

  9. Horn P (2001) Autonomic computing: IBM’s Perspective on the State of Information Technology

  10. Hossain MS et al (2018) Cloud-assisted secure video transmission and sharing framework for smart cities. Future Gener Comput Syst 83:596–606.

    Article  Google Scholar 

  11. IBM (2006) An architectural blueprint for autonomic computing, IBM White Paper

  12. Kephart J, Kephart J, Chess D, Boutilier C, Das R, Kephart JO, Walsh WE (2003) An architectural blueprint for autonomic computing. IBM White paper, 2–10

  13. Khalid A, Haye MA, Khan MJ, Shamail S (2009) Survey of frameworks, architectures and techniques in autonomic computing. In: 5th international conference autonomic and autonomous systems, 2009. ICAS’09, pp 220–225. IEEE

  14. Kumar M, Sharma A (2017) An integrated framework for software vulnerability detection, analysis and mitigation: an autonomic system. Sādhanā 42(9):1481–1493

    Article  Google Scholar 

  15. Levitin G, Xing L, Dai Y (2018) Optimizing computational mission operation by periodic backups and preventive replacements. IEEE Trans Syst Man Cybern Syst 48(9):1505–1520

    Article  Google Scholar 

  16. Li T et al (2018) Socially-conforming cooperative computation in cloud networks. J Parallel Distrib Comput. 117:274–280.

    Article  Google Scholar 

  17. Long X, Gong X, Que X, Wang W, Liu B, Jiang S, Kong N (2017) Autonomic networking: architecture design and standardization. IEEE Internet Comput 21(5):48–53

    Article  Google Scholar 

  18. Mattmann CA, Crichton DJ, Hughes SJ, Kelly SC, Paul M (2004) Software architecture for large-scale, distributed, data-intensive systems. In: Proceedings of the 4th working IEEE/IFIP conference on software architecture (WICSA 2004), pp 255–264. IEEE

  19. Midya S, Roy A, Majumder K, Phadikar S (2018) Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture: a hybrid adaptive nature inspired approach. J Netw Comput Appl 103:58–84

    Article  Google Scholar 

  20. Nikolaychuk OA, Pavlov AI, Stolbov AB (2018) The software platform architecture for the component-oriented development of knowledge-based systems. In: 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO), pp 1064–1069. IEEE

  21. Patel DT (2019) Distributed computing for internet of things (IoT). In: Computational intelligence in the internet of things, pp. 84–109. IGI Global

  22. Petritsch H (2006) Service-oriented architecture (SOA) versus component based architecture. Vienna University of Technology, Vienna, 18

  23. Pintea C-M (2014) Advances in bio-inspired computing for combinatorial optimization problem, Springer ISBN 978-3-642-40178-7

  24. Portela AER, Perdomo J G (2011) Survey: Termites system with self-healing based on autonomic computing. In: 2011 6th colombian computing congress (CCC), pp 1–6. IEEE

  25. Russell LW, Morgan SP, Chron EG (2003) Clockwork: a new movement in autonomic systems. IBM Syst J 42(1):77–84

    Article  Google Scholar 

  26. Salehie M, Tahvildari L (2005) Autonomic computing: emerging trends and open problems. In: ACM SIGSOFT software engineering notes (Vol. 30, No. 4, pp. 1-7). ACM

  27. Shaheen N, Raza B, Malik AK (2018). A CBR model for workload characterization in autonomic database management system. In: 2018 14th international conference on emerging technologies (ICET), pp 1–6. IEEE

  28. Stergiou C et al (2018) Secure integration of IoT and cloud computing. Future Gener Comput Syst 78(3):964–975.

    Article  Google Scholar 

  29. Tesauro G, Chess DM, Walsh WE, Das R, Segal A, Whalley I, White SR (2004) A multi-agent systems approach to autonomic computing. In: Proceedings of the 3rd international joint conference on autonomous agents and multiagent systems-volume 1, pp 464–471. IEEE Computer Society

  30. Tveit A (2001) A survey of agent-oriented software engineering. In: NTNU computer science graduate student conference, norwegian university of science and technology

  31. Wang G, Fung CK (2004). Architecture paradigms and their influences and impacts on component-based software systems. In: 37th annual hawaii international conference on system sciences, 2004, (pp. 10-pp). IEEE

  32. Wooldridgey M, Ciancarini P (2000). Agent-oriented software engineering: the state of the art. In: International workshop on agent-oriented software engineering, pp 1–28. Springer, Berlin

  33. Yang XS, Cui ZH, Xiao RB, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier, Amsterdam

    Google Scholar 

  34. Yaseen Q, Aldwairi M, Jararweh Y et al (2018) Collusion attacks mitigation in internet of things: a fog based model. Multimed Tools Appl 77(14):18249–18268.

    Article  Google Scholar 

  35. Zhang Y, Liu A, Qu W (2004) Software architecture design of an autonomic system. In: Proceedings of the 5th Australasian workshop on software and system architectures

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Dehraj, P., Sharma, A. An approach to design and develop generic integrated architecture for autonomic software system. Int J Syst Assur Eng Manag 11, 690–703 (2020).

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  • Autonomic and non-autonomic computing agent
  • Dynamic decision maker
  • Performance analyzer
  • Domain data