Self-organising Clusters in Edge Computing

Computation-intensive applications generally require a high computing capacity for data processing and storage that cannot be easily offered by a single Internet of Things (IoT) device. Such limitations can be successfully addressed by offloading processing and storage from resource-constrained devices to more powerful ones. In this context, edge computing is emerging as a valuable approach, since it allows data to be stored and processed closer to where it is created instead of sending it across long routes to data centres or clouds.

[1]  Kang G. Shin,et al.  QoS negotiation in real-time systems and its application to automated flight control , 1997, Proceedings Third IEEE Real-Time Technology and Applications Symposium.

[2]  Luís Nogueira,et al.  A capacity sharing and stealing strategy for open real-time systems , 2010, J. Syst. Archit..

[3]  Vuong Xuan Tran.  WS-QoSOnto: A QoS Ontology for Web Services , 2008, 2008 IEEE International Symposium on Service-Oriented System Engineering.

[4]  Christian Baun,et al.  A Taxonomy Study on Cloud Computing Systems and Technologies , 2011 .

[5]  Daniel P. Siewiorek,et al.  A resource allocation model for QoS management , 1997, Proceedings Real-Time Systems Symposium.

[6]  Riccardo Bettati,et al.  Imprecise computations , 1994, Proc. IEEE.

[7]  Tony Q. S. Quek,et al.  Adaptive Computation Scaling and Task Offloading in Mobile Edge Computing , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[8]  Massimiliano Pierobon,et al.  Guest Editorial Special Issue on the Internet of Nano Things , 2016, IEEE Internet Things J..

[9]  Naoki Wakamiya,et al.  QoS Mapping between User’s Preference and Bandwidth Control for Video Transport , 1997 .

[10]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[11]  Sherali Zeadally,et al.  Mobile cloud computing: Challenges and future research directions , 2018, J. Netw. Comput. Appl..

[12]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[13]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[14]  Jorge J. Moré,et al.  Digital Object Identifier (DOI) 10.1007/s101070100263 , 2001 .

[15]  Daniel P. Siewiorek,et al.  A scalable solution to the multi-resource QoS problem , 1999, Proceedings 20th IEEE Real-Time Systems Symposium (Cat. No.99CB37054).

[16]  Joerg Evermann,et al.  MOQ: Web services ontologies for QoS and general quality evaluations , 2007, Int. J. Metadata Semant. Ontologies.

[17]  Wolfgang Lehner,et al.  Integrated resource management for data stream systems , 2005, SAC '05.

[18]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[19]  Minhaj Ahmad Khan,et al.  A survey of computation offloading strategies for performance improvement of applications running on mobile devices , 2015, J. Netw. Comput. Appl..

[20]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[21]  Thomas Plagemann,et al.  Mapping user-level QoS to system-level QoS and resources in a distributed lecture-on-demand system , 1999, Proceedings 7th IEEE Workshop on Future Trends of Distributed Computing Systems.

[22]  Rajkumar Buyya,et al.  A Taxonomy of QoS Management and Service Selection Methodologies for Cloud Computing , 2011 .

[23]  Layuan Li,et al.  Utility-based QoS optimisation strategy for multi-criteria scheduling on the grid , 2007, J. Parallel Distributed Comput..

[24]  Frank Slomka,et al.  Efficient feasibility analysis for real-time systems with EDF scheduling , 2005, Design, Automation and Test in Europe.

[25]  Cheng Wang,et al.  Parametric analysis for adaptive computation offloading , 2004, PLDI '04.

[26]  Ian Sommerville,et al.  QoSOnt: a QoS ontology for service-centric systems , 2005 .

[27]  Nobuyuki Yamasaki,et al.  Practical Imprecise Computation Model: Theory and Practice , 2014, 2014 IEEE 17th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing.

[28]  Rongxing Lu,et al.  Securing the Internet of Things in a Quantum World , 2017, IEEE Communications Magazine.

[29]  Sandeep Kumar,et al.  A Survey of Mobile Computation Offloading: Applications, Approaches and Challenges , 2018, 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE).

[30]  Luís Nogueira,et al.  Iterative Refinement Approach for QOS-Aware Service Configuration , 2006, DIPES.