Service-Oriented Computing – ICSOC 2017 Workshops

Elastic behaviors enable Cloud Systems to auto-adapt to their incoming workloads, by provisioning and releasing computing resources, in a way to ensure a controlled compromise between performance and cost-saving requirements. However, due to the highly fluctuating workloads tendencies, it makes it difficult to predict how a cloud system would behave and to provide precise auto-adaptation action plans. In this paper, we propose a BRS (short for Bigraphical Reactive Systems) based approach to provide a formal description for cloud systems structures and their elastic behaviors using bigraphs and bigraphical reaction rules. In addition, elasticity strategies are introduced to encode cloud systems’ auto-adaptation policies. Proposed approach is illustrated and evaluated through an example.

[1]  Juan Enrique Ramos,et al.  Using TF-IDF to Determine Word Relevance in Document Queries , 2003 .

[2]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[3]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[4]  T. H. Tse,et al.  Where to adapt dynamic service compositions , 2009, WWW '09.

[5]  Jinpeng Huai,et al.  An Adaptive Web Services Selection Method Based on the QoS Prediction Mechanism , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[6]  Valérie Issarny,et al.  QoS-Aware Service Composition in Dynamic Service Oriented Environments , 2009, Middleware.

[7]  Zibin Zheng,et al.  Collaborative reliability prediction of service-oriented systems , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

[8]  Klaus Marius Hansen,et al.  Service Composition Issues in Pervasive Computing , 2010, IEEE Pervasive Computing.

[9]  Zibin Zheng,et al.  Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing , 2011, 2011 IEEE 30th International Symposium on Reliable Distributed Systems.

[10]  Rodrigo Roman,et al.  Securing the Internet of Things , 2017, Smart Cards, Tokens, Security and Applications, 2nd Ed..

[11]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[12]  Radu Calinescu,et al.  Dynamic QoS Management and Optimization in Service-Based Systems , 2011, IEEE Transactions on Software Engineering.

[13]  Annapaola Marconi,et al.  Research challenges on online service quality prediction for proactive adaptation , 2012, 2012 First International Workshop on European Software Services and Systems Research - Results and Challenges (S-Cube).

[14]  Lars Grunske,et al.  An automated approach to forecasting QoS attributes based on linear and non-linear time series modeling , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.

[15]  Chen Wang,et al.  A Two-Phase Online Prediction Approach for Accurate and Timely Adaptation Decision , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[16]  Zhaohui Wu,et al.  An Extended Matrix Factorization Approach for QoS Prediction in Service Selection , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[17]  Lars Grunske,et al.  An Approach to Forecasting QoS Attributes of Web Services Based on ARIMA and GARCH Models , 2012, 2012 IEEE 19th International Conference on Web Services.

[18]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.

[19]  Paolo Bellavista,et al.  Convergence of MANET and WSN in IoT Urban Scenarios , 2013, IEEE Sensors Journal.

[20]  Mohan Kumar,et al.  Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Middleware for Pervasive Computing: a Survey , 2022 .

[21]  H. Bauer,et al.  The Internet of Things: Sizing up the opportunity , 2014 .

[22]  Zibin Zheng,et al.  Investigating QoS of Real-World Web Services , 2014, IEEE Transactions on Services Computing.

[23]  Johan A. K. Suykens,et al.  QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset , 2014, Inf. Sci..

[24]  Luigi Alfredo Grieco,et al.  Security, privacy and trust in Internet of Things: The road ahead , 2015, Comput. Networks.

[25]  Marios D. Dikaiakos,et al.  AdaM: An adaptive monitoring framework for sampling and filtering on IoT devices , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[26]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[27]  Hai Dong,et al.  Long-Term QoS-Aware Cloud Service Composition Using Multivariate Time Series Analysis , 2016, IEEE Transactions on Services Computing.

[28]  Amit Sharma,et al.  A Review of Text Mining Techniques & Applications , 2017 .

[29]  Zibin Zheng,et al.  Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix Factorization , 2017, IEEE Transactions on Parallel and Distributed Systems.

[30]  Siobhán Clarke,et al.  Middleware for Internet of Things: A quantitative evaluation in small scale , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[31]  Fan Li,et al.  Implementing heterogeneous, autonomous, and resilient services in IoT: An experience report , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[32]  Mohamed Mohamed,et al.  An Optimization Approach for Adaptive Monitoring in IoT Environments , 2017, 2017 IEEE International Conference on Services Computing (SCC).

[33]  Siobhán Clarke,et al.  Quality of service approaches in IoT: A systematic mapping , 2017, J. Syst. Softw..

[34]  Siobhán Clarke,et al.  Quantitative Evaluation of QoS Prediction in IoT , 2017, 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W).

[35]  Siobhán Clarke,et al.  Goal-Driven Service Composition in Mobile and Pervasive Computing , 2018, IEEE Transactions on Services Computing.