research-article

Architectural Principles for Cloud Software

Authors:

Pooyan Jamshidi,

Olaf ZimmermannAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 18, Issue 2

Article No.: 17, Pages 1 - 23

https://doi.org/10.1145/3104028

Published: 02 February 2018 Publication History

Abstract

A cloud is a distributed Internet-based software system providing resources as tiered services. Through service-orientation and virtualization for resource provisioning, cloud applications can be deployed and managed dynamically. We discuss the building blocks of an architectural style for cloud-based software systems. We capture style-defining architectural principles and patterns for control-theoretic, model-based architectures for cloud software. While service orientation is agreed on in the form of service-oriented architecture and microservices, challenges resulting from multi-tiered, distributed and heterogeneous cloud architectures cause uncertainty that has not been sufficiently addressed. We define principles and patterns needed for effective development and operation of adaptive cloud-native systems.

References

[1]

A. Ahmad, P. Jamshidi, and C. Pahl. 2014. Classification and comparison of architecture evolution reuse knowledge – A systematic review. J. Softw.: Evol. Process 26, 7 (2014), 654--691.

Digital Library

[2]

N. Antonopoulos and L. Gillam. 2010. Cloud Computing: Principles, Systems and Applications. Springer.

Digital Library

[3]

H. Arabnejad, P. Jamshidi, G. Estrada, N. El Ioini, and C. Pahl. 2016. An auto-scaling cloud controller using fuzzy Q-learning—Implementation in openstack. In Proceedings of the European Conference on Service-Oriented and Cloud Computing (ESOCC’16).

[4]

H. Arabnejad, C. Pahl, P. Jamshidi, and G. Estrada. 2017. A comparison of reinforcement learning techniques for fuzzy cloud auto-scaling. In Proceedings of the International Symposium on Cluster, Cloud and Grid Computing (CCGrid’17).

Digital Library

[5]

A. Balalaie, A. Heydarnoori, and P. Jamshidi. 2016. Microservices architecture enables DevOps: Migration to a cloud-native architecture. IEEE Softw. 33, 3 (May 2016), 42--52.

Digital Library

[6]

L. Baresi and C. Ghezzi. 2013. A journey through SMScom: Self-managing situational computing. Comput. Sci.-Res. Dev. 28, 4 (2013), 267--277.

Digital Library

[7]

A. Brunnert, A. van Hoorn, F. Willnecker, A. Danciu, W. Hasselbring, C. Heger, N. Herbst, P. Jamshidi, R. Jung, J. von Kistowski, A. Koziolek, J. Kross, S. Spinner, C. Vögele, J. Walter, and A. Wert. 2015. Performance-oriented DevOps: A research agenda. Technical Report SPEC-RG-2015-01, SPEC Research Group (RG) DevOps Performance. https://arxiv.org/abs/1508.04752.

[8]

P. Cedillo, J. Jimenez-Gomez, S. Abrahao, and E. Insfran. 2015. Towards a monitoring middleware for cloud services. In Proceedings of the International Conference on Services Computing (SCC’15). 451--458.

Digital Library

[9]

K. Chan, I. Poernomo, H. Schmidt, and J. Jayaputera. 2005. A model-oriented framework for runtime monitoring of nonfunctional properties. In Proceedings Conference on Quality of Software Architectures and Software Quality. Lecture Notes in Computer Science, vol. 3712. Springer.

Digital Library

[10]

B. H. C. Cheng, R. De Lemos, H. Giese, P. Inverardi, J. Magee, J. Andersson, B. Becker, N. Bencomo, Y. Brun, B. Cukic, and others. 2009. Software engineering for self-adaptive systems: A research roadmap. In Software Engineering for Self-adaptive Systems. Springer.

Digital Library

[11]

K. Czarnecki and S. Helsen. 2003. Classification of model transformation approaches. In Workshop Generative Techniques in the Context of the Model Driven Architecture. 1--17.

[12]

R. De Lemos, H. Giese, H. A. Müller, M. Shaw, J. Andersson, M. Litoiu, B. Schmerl, G. Tamura, N. M. Villegas, T. Vogel, and others. 2013. Software engineering for self-adaptive systems: A second research roadmap. In Software Engineering for Self-Adaptive Systems II. Springer, 1--32.

[13]

T. Erl. 2005. Service-oriented Architecture: Concepts, Technology, and Design. Pearson Education.

Digital Library

[14]

N. Esfahani and S. Malek. 2013. Uncertainty in self-adaptive software systems. In Software Engineering for Self-Adaptive Systems II. Springer, 214--238.

[15]

S. Farokhi, P. Jamshidi, I. Brandic, and E. Elmroth. 2015. Self-adaptation challenges for cloud-based applications: A control theoretic perspective. In Proceedings of the 10th International Workshop on Feedback Computing.

[16]

C. Fehling, F. Leymann, R. Retter, W. Schupeck, and P. Arbitter. 2014. Cloud computing patterns. Springer-Verlag Wien.

[17]

A. Filieri, M. Maggio, K. Angelopoulos, N. D’Ippolito, I. Gerostathopoulos, A. Hempel, H. Hoffmann, P. Jamshidi, E. Kalyvianaki, C. Klein, F. Krikava, S. Misailovic, A. V. Papadopoulos, S. Ray, A. M. Sharifloo, S. Shevtsov, M. Ujma, and T. Vogel. 2015. Software engineering meets control theory. In Proceedings of the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS’2015).

Digital Library

[18]

B. Fitzgerald and K.-J. Stol. 2014. Continuous software engineering and beyond: Trends and challenges. In Proceedings of the 1st International Workshop on Rapid Continuous Software Engineering (RCoSE’14).

Digital Library

[19]

D. Garlan and M. Shaw. 1994. An Introduction to Software Architecture. Vol. 1. World Scientific.

[20]

C. Ghezzi, L. S. Pinto, P. Spoletini, and G. Tamburrelli. 2013. Managing non-functional uncertainty via model-driven adaptivity. In Proceedings of the 2013 International Conference on Software Engineering. 33--42.

Digital Library

[21]

S. He, L. Guo, Y. Guo, C. Wu, M. Ghanem, and R. Han. 2012. Elastic application container: A lightweight approach for cloud resource provisioning. In Proceedings of the International Conference on Advances in Information Networking and Applications.

Digital Library

[22]

R. Heinrich, E. Schmieders, R. Jung, K. Rostami, A. Metzger, W. Hasselbring, R. Reussner, and K. Pohl. 2014. Integrating run-time observations and design component models for cloud system analysis. In Proceedings of the 9th Workshop on [email protected]. 41–46. http://ceur-ws.org/Vol-1270/.

[23]

Z.-S. Hou and J.-X. Xu. 2007. New feedback-feedforward configuration for the iterative learning control of a class of discrete-time systems. Acta Automatica Sinica 33, 3 (2007), 323–326.

[24]

J. Hu, J. Gu, G. Sun, and T. Zhao. 2010. A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In Proceedings of the International Symposium on Parallel Architectures, Algorithms and Programming.

Digital Library

[25]

P. Jamshidi, A. Ahmad, and C. Pahl. 2014. Autonomic resource provisioning for cloud-based software. In Proceedings of the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS’14).

Digital Library

[26]

P. Jamshidi, C. Pahl, S. Chinenyeze, and X. Liu. 2015. Cloud migration patterns: A multi-cloud service architecture perspective. In Proceeings of the Service-Oriented Computing Workshops (ICSOC’14). 6--19.

[27]

P. Jamshidi, A. Sharifloo, C. Pahl, H. Arabnejad, A. Metzger, and G. Estrada. 2016. Fuzzy self-learning controllers for elasticity management in dynamic cloud architectures. In Proceedings of the 2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA’16). IEEE, 70--79.

[28]

S. Kounev. 2011. Self-aware software and systems engineering: A vision and research roadmap. GI Softwaretech.-Trends 31, 4 (2011), 21--25.

[29]

S. Kounev, F. Brosig, and N. Huber. 2014. The Descartes Modeling Language. Department of Comp Science, University of Wuerzburg, Tech. Rep (2014).

[30]

N. Kratzke and R. Peinl. 2016. ClouNS - A cloud-native application reference model for enterprise architects. In Proceedings of the International Enterprise Distributed Object Computing Workshop (EDOCW’16). 1--10.

[31]

P. B. Kruchten. 1995. The 4+1 view model of architecture. IEEE Softw. 12, 6 (1995), 42--50.

Digital Library

[32]

J. Lewis and M. Fowler. 2014. Microservices. Retrieved January 7, 2018 from http://martinfowler.com/articles/microservices.html.

[33]

P. Mell and T. Grance. 2011. The NIST definition of cloud computing. Technical Report SP 800-145. Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, US.

Digital Library

[34]

Microsoft. 2009. Software Architecture and Design. Retrieved January 7, 2018 from https://msdn.microsoft.com/en-us/library/ee658093.aspx.

[35]

S. Newman. 2015. Building Microservices. O’Reilly.

Digital Library

[36]

C. Pahl. 2015. Containerization and the PaaS cloud. IEEE Cloud Comput. 2, 3 (2015), 24--31.

[37]

C. Pahl, A. Brogi, J. Soldani, and P. Jamshidi. 2017a. Cloud container technologies: A state-of-the-art review. IEEE Transactions on Cloud Computing. Published online at https://ieeexplore.ieee.org/document/7922500/.

[38]

C. Pahl and P. Jamshidi. 2015. Software architecture for the cloud - A roadmap towards control-theoretic, model-based cloud architecture. In Proceedings of the European Conference on Software Architecture (ECSA’15).

[39]

C. Pahl, P. Jamshidi, and D. Weyns. 2017b. Cloud architecture continuity: Change models and change rules for sustainable cloud software architectures. J. Softw.: Evol. Process 29, 2 (2017).

[40]

C. Pahl and B. Lee. 2015. Containers and clusters for edge cloud architectures - A technology review. In Proceedings of the 3rd International Conference on Future Internet of Things and Cloud (FiCloud’15). IEEE.

Digital Library

[41]

C. Pautasso, O. Zimmermann, M. Amundsen, J. Lewis, and N. Josuttis. 2017. Microservices in practice, part 1: Reality check and service design. IEEE Softw. 34, 1 (2017), 91--98.

Digital Library

[42]

R. Perrey and M. Lycett. 2003. Service-oriented architecture. In Proceedings of the Symposium on Applications and the Internet Workshops 2003. 116--119.

Digital Library

[43]

P. Sawyer, N. Bencomo, J. Whittle, E. Letier, and A. Finkelstein. 2010. Requirements-aware systems: A research agenda for re for self-adaptive systems. In Proceedings 18th IEEE International Requirements Engineering Conference (RE). 95–103.

Digital Library

[44]

T. Stahl, M. Voelter, and K. Czarnecki. 2006. Model-driven Software Development: Technology, Engineering, Management. Wiley 8 Sons.

Digital Library

[45]

V. Stantchev and C. Schräpfer. 2009. Negotiating and enforcing QoS and SLAs in grid and cloud computing. In Proceedings of the Advances in Grid and Pervasive Computing. Vol. 5529. 25--35.

Digital Library

[46]

A. Van Deursen, P. Klint, and J. Visser. 2000. Domain-specific languages: An annotated bibliography. Sigplan Not. 35, 6 (2000), 26--36.

Digital Library

[47]

A. Van Hoorn, M. Rohr, A. Gul, and W. Hasselbring. 2009. An adaptation framework enabling resource-efficient operation of software systems. In Proceedings of the Warm Up Workshop for the ACM/IEEE International Conference on Software Engineering (ICSE’10).

Digital Library

[48]

J. Varia. 2011. Architecting for the cloud: Best practices. Technical Report. Amazon Web Services. https://media.amazonwebservices.com/AWS_Cloud_Best_Practices.pdf.

[49]

T. Vogel and H. Giese. 2014. Model-driven engineering of self-adaptive software with EUREMA. ACM Trans. Auton. Adapt. Syst. 8, 4 (2014), 18.

Digital Library

[50]

S. Wätzoldt and H. Giese. 2014. Classifying distributed self-* Systems based on runtime models and their coupling. In Proceedings of the 9th Workshop on [email protected]. 11–20. http://ceur-ws.org/Vol-1270/.

[51]

R. Wieringa and A. Moralı. 2012. Technical action research as a validation method in information systems design science. In Proceedings of the International Conference on Design Science Research in Information Systems. 220--238.

Digital Library

[52]

H. Xiong, F. Fowley, and C. Pahl. 2015. An architecture pattern for multi-cloud high availability and disaster recovery. In Proceedings of the Workshop on Federated Cloud Networking (FedCloudNet’15).

[53]

L. Zhang, Y. Zhang, P. Jamshidi, L. Xu, and C. Pahl. 2014. Workload patterns for quality-driven dynamic cloud service configuration and auto-scaling. In Proceedings of the IEEE/ACM International Conference on Utility and Cloud Computing (UCC’14). 156--165.

Digital Library

[54]

O. Zimmermann. 2009. An Architectural Decision Modeling Framework for Service Oriented Architecture Design. Ph.D. Dissertation. Universität Stuttgart.

[55]

O. Zimmermann. 2016. Mircroservices tenets: Agile approach to service development and deployment. In Proceedings of the Symposium/Summer School on Service-Oriented Computing.

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Index Terms

Architectural Principles for Cloud Software
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
  2. Software organization and properties
    1. Contextual software domains
      1. Software infrastructure
    2. Software system structures
      1. Distributed systems organizing principles
        Cloud computing
      2. Software architectures

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 18, Issue 2

Special Issue on Internetware and Devops and Regular Papers

May 2018

294 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3182619

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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Copyright © 2018 ACM.

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Publication History

Published: 02 February 2018

Accepted: 01 May 2017

Revised: 01 May 2017

Received: 01 September 2016

Published in TOIT Volume 18, Issue 2

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Zafar IAdriano CGiese HBalsamo SKnottenbelt WAbad CShang W(2024)STIGS: Spatio-Temporal Interference Graph Simulator for Self-Configurable Multi-Tenant Cloud SystemsCompanion of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629527.3653664(52-56)Online publication date: 7-May-2024
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