Abstract
Pervasive computing involves the placement of processing units and services close to end users to support intelligent applications that will facilitate their activities. With the advent of the Internet of Things (IoT) and the Edge Computing (EC), one can find more room for placing services at various points in the interconnection of the aforementioned infrastructures. Of significant importance is the processing of the collected data to provide analytics and knowledge. Such processing can be realized upon the EC nodes that exhibit increased computational capabilities compared to IoT devices. An ecosystem of intelligent nodes is created at the EC giving the opportunity to support cooperative models towards the provision of the desired analytics. Nodes become the hosts of geo-distributed datasets formulated by the reports of IoT devices. Upon the datasets, a number of queries/tasks can be executed either locally or remotely. Queries/tasks can be offloaded for performance reasons to deliver the most appropriate response. However, an offloading action should be carefully designed being always aligned with the data present to the hosting node. In this paper, we present a model to support the cooperative aspect in the EC infrastructure. We argue on the delivery of data synopses distributed in the ecosystem of EC nodes making them capable to take offloading decisions fully aligned with data present at every peer. Nodes exchange their data synopses to inform their peers. We propose a scheme that detects the appropriate time to distribute the calculated synopsis trying to avoid the network overloading especially when synopses are frequently extracted due to the high rates at which IoT devices report data to EC nodes. Our approach involves a deep learning model for learning the distribution of calculated synopses and estimate future trends. Upon these trends, we are able to find the appropriate time to deliver synopses to peer nodes. We provide the description of the proposed mechanism and evaluate it based on real datasets. An extensive experimentation upon various scenarios reveals the pros and cons of the approach by giving numerical results.
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References
Aggarwal, C., Han, J., Wang, J., Yu, P.: A framework for clustering evolving data streams. In: VLDB Conference, pp. 81–92 (2003)
Aggarwal, C., Han, J., Wang, J., Yu, P.: On-demand classification of data streams. In: ACM KDD Conference, pp. 503–508 (2004)
Aggarwal, C., Yu, P.: A survey of synopsis construction in data streams. In: Aggarwal, C. (ed.) Data Streams, Models and Algorithms. Springer, Heidelberg (2007)
Alon, N., Gibbons, P., Matias, Y., Szegedy, M.: Tracking joins and self joins in limited storage. In: ACM PODS Conference, pp. 10–20 (1999)
Amrutha, S., et al.: Data dissemination framework for IoT based applications. Indian J. Sci. Technol. 9(48), 1–5 (2016)
Anagnostopoulos, C., Kolomvatsos, K.: An intelligent, time-optimized monitoring scheme for edge nodes. J. Netw. Comput. Appl. 148 (2019). https://doi.org/10.1016/j.jnca.2019.102458
Anglano, C., Canonico, M., Guazzone, M.: Profit-aware resource management for edge computing systems. In: 1st International Workshop on Edge Systems, Analytics and Networking, pp. 25–30 (2018)
Babcock, B., Datar, M., Motwani, R.: Load shedding techniques for data stream systems. In: Workshop on Management and Processing of Data Streams (2003)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: PODS, pp. 1–16 (2002)
Bellavista, P., Corradi, A., Foschini, L., Scotece, D.: Differentiated service/data migration for edge services leveraging container characteristics. IEEE Access 7 (2019)
Bhardwaj, K., Agrawal, P., Gavrilovska, A., Schwan, K.: AppSachet: distributed app delivery from the edge cloud. In: 7th International Conference Mobile Computing, Applications, and Services, pp. 89–106 (2015)
Chakrabarti, K., Garofalakis, M., Rastogi, R., Shim, K.: Approximate query processing with wavelets. VLDB J. 10(2–3), 199–223 (2001)
Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: ICALP (2002)
Cherrueau, R.A., Lebre, A., Pertin, D., Wuhib, F., Soares, J.: Edge computing resource management system: a critical building block!. In: USENIX Workshop on Hot Topics in Edge Computing, Initiating the debate via OpenStack, pp. 1–6 (2018)
Chu, D., Deshpande, A., Hellerstein, J., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: 22nd International Conference on Data Engineering (ICDE 06) (2006)
Cormode, G., Muthukrishnan, S.: What’s hot and what’s not: tracking most frequent items dynamically. In: ACM PODS Conference (2005). https://doi.org/10.1145/1061318.1061325
Dobra, A., Garofalakis, M.N., Gehrke, J., Rastogi, R.: Sketch-based multi-query processing over data streams. In: EDBT Conference (2004). https://doi.org/10.1007/978-3-540-24741-8_32
Gehrke, J., Korn, F., Srivastava, D.: On computing correlated aggregates over continual data streams. In: SIGMOD Conference (2001). https://doi.org/10.1145/375663.375665
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE TFS 12 (2004). https://doi.org/10.1109/TFUZZ.2004.832538
Karanika, A., Oikonomou, P., Kolomvatsos, K., Loukopoulos, T.: A demand-driven, proactive tasks management model at the edge. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2020)
Kolomvatsos, K.: A proactive uncertainty driven model for data synopses management in pervasive applications. In: 6th IEEE International Conference on Data Science and Systems (DSS), Fiji, 14–16 December (2020)
Kolomvatsos, K.: An intelligent scheme for assigning queries. Appl. Intell. 48(9), 2730–2745 (2017). https://doi.org/10.1007/s10489-017-1099-5
Kolomvatsos, K.: A distributed, proactive intelligent scheme for securing quality in large scale data processing. Computing 101, 1687–1710 (2019)
Kolomvatsos, K., Anagnostopoulos, C.: An intelligent edge-centric queries allocation scheme based on ensemble models. ACM Trans. Internet Technol. (2020). https://doi.org/10.1145/3417297
Kolomvatsos, K., Anagnostopoulos, C.: A probabilistic model for assigning queries at the edge. Computing 102, 865–892 (2020)
Kolomvatsos, K., Anagnostopoulos, C.: Multi-criteria optimal task allocation at the edge. Futur. Gener. Comput. Syst. 93, 358–372 (2019)
Kolomvatsos, K., Anagnostopoulos, C., Hadjiefthymiades, S.: Data fusion & type-2 fuzzy inference in contextual data stream monitoring. IEEE Trans. Syst. Man Cybern.: Syst. PP(99), 1–15 (2016)
Kolomvatsos, K., Anagnostopoulos, C., Koziri, M., Loukopoulos, T.: Proactive & Time-Optimized Data Synopsis Management at the Edge. IEEE Trans. Knowl. Data Eng. (IEEE TKDE) (2020). https://doi.org/10.1109/TKDE.2020.3021377
Kolomvatsos, K., Anagnostopoulos, C., Marnerides, A., Ni, Q., Hadjiefthymiades, S., Pezaros, D.: Uncertainty-driven ensemble forecasting of QoS in software defined networks. In: 22nd IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece (2017)
Lakshmi, K.P., Reddy, C.R.K.: A survey on different trends in data streams. In: IEEE International Conference on Networking and Information Technology (2010). https://doi.org/10.1109/ICNIT.2010.5508473
Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: VLDB Conference (2002)
Martin, R., Vahdat, A., Culler, D., Anderson, T.: Effects of communication latency, overhead, and bandwidth in a cluster architecture. In: 4th Annual International Symposium on Computer Architecture (1997). https://doi.org/10.1145/384286.264146
Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. IEEE Comput. Intell. Mag. 2(1) (2007). https://doi.org/10.1109/MCI.2007.380672
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)
Mesiar, R., Kolesarova, A., Calvo, T., Komornikova, M.: A review of aggregation functions. Studies in Fuzziness and Soft Computing (2008). https://doi.org/10.1007/978-3-540-73723-0_7
Muthukrishnan, S.: Data streams: algorithms and applications. In: 14th Annual ACM-SIAM Symposium on Discrete Algorithms (2003)
Najam, S., Gilani, S., Ahmed, E., Yaqoob, I., Imran, M.: The role of edge computing in Internet of Things. IEEE Commun. Mag. (2018). https://doi.org/10.1109/MCOM.2018.1700906
Novák, V., Perfilieva, I., Močkoř, J.: Mathematical Principles of Fuzzy Logic. Kluwer Academic, Dordrecht (1999)
Sardellitti, S., Scutari, G., Barbarossa, S.: Joint optimisation of radio and computational resources for multicell mobile-edge computing. IEEE Trans. Signal Inf. Process. Netw. 1(2), 89–103 (2015)
Savolainen, P., et al.: Spaceify: a client-edge-server ecosystem for mobile computing in smart spaces. In: International Conference on Mobile Computing & Networking, pp. 211–214 (2013)
Schweller, R., Gupta, A., Parsons, E., Chen, Y.: Reversible sketches for efficient and accurate change detection over network data streams. In: Internet Measurement Conference Proceedings, pp. 207–212 (2004)
Shekhar, S., Gokhale, A.: Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (2017)
Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Satyanarayanan, M.: Scalable crowd-sourcing of video from mobile devices. In: 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 139–152 (2013)
Tatbul, N., Zdonik, S.: A subset-based load shedding approach for aggregation queries over data streams. In: 32nd International Conference on Very Large Data Bases, Seoul, Korea (2006)
Vandeput, N.: Data Science for Supply Chain Forecast (2018). Independently Published
Wang, N., Varghese, B., Matthaiou, M., Nikolopoulos, D.: ENORM: a framework for edge node resource management. IEEE Trans. Serv. Comput. (2017). https://doi.org/10.1109/TSC.2017.2753775
Yao, Y., Cao, Q., Vasilakos, A.V.: EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In: IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 182–190 (2013)
Zhou, A., Wang, S., Li, J., Sun, Q., Yang, F.: Optimal mobile device selection for mobile cloud service providing. J. Supercomput. 72(8), 3222–3235 (2016)
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Fountas, P., Kolomvatsos, K., Anagnostopoulos, C. (2021). A Deep Learning Model for Data Synopses Management in Pervasive Computing Applications. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_44
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