Latency-aware placement of data stream analytics on edge computing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 11236 LNCS, Issue , 2018, Pages 215-229

  da Silva Veith, Alexandre ^a   de Assunção, Marcos Dias ^a   Lefèvre, Laurent ^a  

^a UNIV LYON   (France)

Author keywords

Cloud computing; Data stream processing; Edge computing; Resource management; Scheduling; Series parallel graphs

Indexed keywords

CLOUD COMPUTING; EDGE COMPUTING; INFORMATION MANAGEMENT; SCHEDULING; TOPOLOGY;

APPLICATION REQUIREMENTS; DATA STREAM PROCESSING; NETWORK CAPABILITY; OPERATOR PLACEMENTS; RESOURCE MANAGEMENT; SERIES-PARALLEL GRAPH; STREAM PROCESSING; TIME CONSTRAINTS;

DATA HANDLING;

EID: 85056882385     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-030-03596-9_14     Document Type: Conference Paper

References (21)

1. de Assunção, M.D., da Silva Veith, A., Buyya, R.: Distributed data stream processing and edge computing: a survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103, 1–17 (2018)
2. Benoit, A., Dobrila, A., Nicod, J.M., Philippe, L.: Scheduling linear chain streaming applications on heterogeneous systems with failures. Future Gener. Comput. Syst. 29(5), 1140–1151 (2013)
3. Buddhika, T., Pallickara, S.: Neptune: real time stream processing for internet of things and sensing environments. In: IEEE International Parallel and Distributed Processing Symposium, pp. 1143–1152, May 2016
4. Cardellini, V., Grassi, V., Lo Presti, F., Nardelli, M.: Optimal operator placement for distributed stream processing applications. In: 10th ACM International Conference on Distributed Event-Based Systems, pp. 69–80. ACM, New York (2016)
5. Cardellini, V., Grassi, V., Presti, F.L., Nardelli, M.: Distributed QoS-aware scheduling in Storm. In: 9th ACM International Conference on Distributed Event-Based Systems, DEBS 2015, pp. 344–347. ACM, New York (2015)
6. Cheng, B., Papageorgiou, A., Bauer, M.: Geelytics: enabling on-demand edge analytics over scoped data sources. In: IEEE International Congress on BigData, pp. 101–108 (2016)
7. Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: a decentralized network coordinate system. SIGCOMM Comput. Commun. Rev. 34(4), 15–26 (2004)
8. Eidenbenz, R., Locher, T.: Task allocation for distributed stream processing. In: IEEE INFOCOM 2016, pp. 1–9, April 2016
9. Elbamby, M.S., Bennis, M., Saad, W.: Proactive edge computing in latency-constrained fog networks. In: European Conference on Networks and Communications, pp. 1–6, June 2017. https://doi.org/10.1109/EuCNC.2017.7980678
10. Ghosh, R., Simmhan, Y.: Distributed scheduling of event analytics across edge and cloud. ACM Trans. Cyber-Phys. Syst. 2(4), 24 (2017, to Appear)
11. Ha, K., et al.: The impact of mobile multimedia applications on data center consolidation. In: IEEE International Conference on Cloud Engineering (IC2E), pp. 166–176, March 2013
12. Hochreiner, C., Vogler, M., Waibel, P., Dustdar, S.: VISP: an ecosystem for elastic data stream processing for the internet of things. In: 20th IEEE International Enterprise Distributed Object Computing Conference, pp. 1–11, September 2016
13. Hu, W., et al.: Quantifying the impact of edge computing on mobile applications. In: 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016, pp. 5:1–5:8. ACM, New York (2016)
14. Kaur, N., Sood, S.K.: Efficient resource management system based on 4VS of big data streams. Big Data Res. 9, 98–106 (2017)
15. Morales, J., Rosas, E., Hidalgo, N.: Symbiosis: sharing mobile resources for stream processing. In: IEEE Symposium on Computers and Communications Workshop, pp. 1–6, June 2014
16. Ni, L., Zhang, J., Jiang, C., Yan, C., Yu, K.: Resource allocation strategy in fog computing based on priced timed petri nets. IEEE IoT J. 4(5), 1216–1228 (2017)
17. Peng, B., Hosseini, M., Hong, Z., Farivar, R., Campbell, R.: R-storm: resource-aware scheduling in storm. In: 16th Annual Middleware Conference, Middleware 2015, pp. 149–161. ACM, New York (2015)
18. Ravindra, P., Khochare, A., Reddy, S.P., Sharma, S., Varshney, P., Simmhan, Y.: Echo: an adaptive orchestration platform for hybrid dataflows across cloud and edge. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-69035-3 28
19. Sajjad, H.P., Danniswara, K., Al-Shishtawy, A., Vlassov, V.: Spanedge: towards unifying stream processing over central and near-the-edge data centers. In: 2016 IEEE/ACM Symposium on Edge Computing, pp. 168–178, October 2016
20. Shukla, A., Chaturvedi, S., Simmhan, Y.: Riotbench: an IoT benchmark for distributed stream processing systems. Concurr. Comput.: Pract. Exp. 29(21), e4257 (2017)
21. Taneja, M., Davy, A.: Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 1222–1228, May 2017