Model-Based Operator Placement for Data Processing in IoT Environments

The advances of the Internet of Things (IoT) lead to further challenges for data processing. Besides deriving meaningful information from a high amount of raw data, processing data in a timely manner is required as well, in order to enable the development of reactive IoT applications. Usually, the processing of IoT data is done in cloud-based infrastructures, which provide on-demand resources to process the data as needed. However, this affects timely processing, since sending data to off-premise cloud infrastructures increases latency and network traffic. In this paper, we propose a method to process data streams primarily on-premise in IoT environments, i.e., data is processed near to their data sources and the processing power already provided by IoT devices in the environment is explored.

[1]  Qiang Chen,et al.  Aurora : a new model and architecture for data stream management ) , 2006 .

[2]  Frank Dürr,et al.  Solving the Multi-Operator Placement Problem in Large-Scale Operator Networks , 2010, 2010 Proceedings of 19th International Conference on Computer Communications and Networks.

[3]  Ying Xing,et al.  Providing resiliency to load variations in distributed stream processing , 2006, VLDB.

[4]  Victor W. Marek,et al.  An Edge-Focused Model for Distributed Streaming Data Applications , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[5]  Tamara Dean,et al.  Network+ Guide to Networks , 1999 .

[6]  Bernhard Mitschang,et al.  Customization and provisioning of complex event processing using TOSCA , 2018, Computer Science - Research and Development.

[7]  Bernhard Mitschang,et al.  M-TOP: multi-target operator placement of query graphs for data streams , 2011, IDEAS '11.

[8]  Albert Y. Zomaya,et al.  The Next Grand Challenges: Integrating the Internet of Things and Data Science , 2018, IEEE Cloud Computing.

[9]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[10]  Jesfis Peral,et al.  Heuristics -- intelligent search strategies for computer problem solving , 1984 .

[11]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[12]  Navendu Jain,et al.  Adaptive Control of Extreme-scale Stream Processing Systems , 2006, 26th IEEE International Conference on Distributed Computing Systems (ICDCS'06).

[13]  Pascal Hirmer,et al.  FlexMash 2.0 - Flexible Modeling and Execution of Data Mashups , 2016, RMC.

[14]  María Bermúdez-Edo,et al.  IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics , 2016, Personal and Ubiquitous Computing.

[15]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[16]  Adrian McEwen,et al.  Designing the Internet of Things , 2013 .

[17]  Bernhard Mitschang,et al.  An Approach for CEP Query Shipping to Support Distributed IoT Environments , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).