S. Biookaghazadeh, M. Zhao, and F. Ren, Are fpgas suitable for edge computing?" in HotEdge, vol.18, pp.1-6

A. Toshniwal, S. Taneja, A. Shukla, K. Ramasamy, J. M. Patel et al., , pp.147-156

A. Katsifodimos and S. Schelter, Apache flink: Stream analytics at scale, IC2EW'16, pp.193-193

, Spark streaming, 2010.

S. Sakr, Z. Maamar, A. Awad, B. Benatallah, W. M. Van-der et al., Business process analytics and big data systems: A roadmap to bridge the gap, IEEE Access, vol.6, pp.77-308, 2018.

S. Yi, Z. Hao, Q. Zhang, Q. Zhang, W. Shi et al., Lavea: Latency-aware video analytics on edge computing platform, ICDCS'17, pp.2573-2574

G. Ananthanarayanan, P. Bahl, P. Bodk, K. Chintalapudi, M. Philipose et al., Real-time video analytics: The killer app for edge computing, Computer, vol.50, issue.10, pp.58-67, 2017.

N. Mohamed, J. Al-jaroodi, I. Jawhar, S. Lazarova-molnar, and S. Mahmoud, Smartcityware: A serviceoriented middleware for cloud and fog enabled smart city services, IEEE Access, vol.5, pp.17-576, 2017.

E. G. Renart, J. Diaz-montes, and M. Parashar, Datadriven stream processing at the edge, ICFEC'17, pp.31-40

M. Chao, C. Yang, Y. Zeng, and R. Stoleru, F-mstorm: Feedback-based online distributed mobile stream processing, SEC'18, pp.273-285

, Apache edgent

&. Minifi,

H. P. Sajjad, K. Danniswara, A. Al-shishtawy, and V. Vlassov, Spanedge: Towards unifying stream processing over central and near-the-edge data centers, SEC'16, pp.168-178

P. Ravindra, A. Khochare, S. Reddy, S. Sharma, P. Varshney et al., ECHO: an adaptive orchestration platform for hybrid dataflows across cloud and edge, CoRR, 2017.

S. Wu, M. Liu, S. Ibrahim, H. Jin, L. Gu et al., Turbostream: Towards low-latency data stream processing, ICDCS'18, pp.983-993

M. Najafi, K. Zhang, M. Sadoghi, and H. Jacobsen, Hardware acceleration landscape for distributed realtime analytics: Virtues and limitations, ICDCS'17, pp.1938-1948

C. Kachris and D. Soudris, A survey on reconfigurable accelerators for cloud computing, FPL'16, pp.1-10

J. Cong, Z. Fang, M. Lo, H. Wang, J. Xu et al., Understanding performance differences of fpgas and gpus, FCCM'18, pp.93-96

A. Putnam, A. Caulfield, E. Chung, D. Chiou, K. Constantinides et al., A reconfigurable fabric for accelerating largescale datacenter services, pp.13-24

G. Chrysos, P. Dagritzikos, I. Papaefstathiou, and A. Dollas, Hc-cart: A parallel system implementation of data mining classification and regression tree (cart) algorithm on a multi-fpga system, ACM Trans. Archit. Code Optim, vol.9, issue.4, p.25, 2013.

G. Dai, T. Huang, Y. Chi, N. Xu, Y. Wang et al., Foregraph: Exploring large-scale graph processing on multi-fpga architecture, FPGA'17, pp.217-226

N. Suda, V. Chandra, G. Dasika, A. Mohanty, Y. Ma et al., Throughputoptimized opencl-based fpga accelerator for large-scale convolutional neural networks, FPGA'16, pp.16-25

C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao et al., Optimizing fpga-based accelerator design for deep convolutional neural networks, FPGA'15, pp.161-170

A. Nydriotis, P. Malakonakis, N. Pavlakis, G. Chrysos, E. Ioannou et al., Leveraging reconfigurable computing in distributed real-time computation systems, EDBT/ICDT Workshops, pp.1-6, 2016.

K. Nakamura, A. Hayashi, and H. Matsutani, An fpgabased low-latency network processing for spark streaming, BigData'17, pp.2410-2415

M. Huang, D. Wu, C. H. Yu, Z. Fang, M. Interlandi et al., Programming and runtime support to blaze fpga accelerator deployment at datacenter scale, pp.456-469

M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, The case for vm-based cloudlets in mobile computing, IEEE Pervasive Computing, vol.8, issue.4, pp.14-23, 2009.

S. Yi, Z. Hao, Z. Qin, and Q. Li, Fog computing: Platform and applications, pp.73-78

I. Kuon, R. Tessier, and J. Rose, Fpga architecture: Survey and challenges, Foundations and Trends in Electronic Design Automation, vol.2, issue.2, pp.135-253, 2008.

H. Okuhata, R. Imai, M. Ise, R. Y. Omaki, H. Nakamura et al., Implementation of dynamicrange enhancement and super-resolution algorithms for medical image processing, pp.181-184

Q. Y. Tang and M. A. Khalid, Acceleration of kmeans algorithm using altera sdk for opencl, ACM Trans. Reconfigurable Technol. Syst, vol.10, issue.1, pp.1-6, 2016.

, Opencl overview

, Intel fpga sdk for opencl

Q. Ning, C. Chen, R. Stoleru, and C. Chen, Mobile storm: Distributed real-time stream processing for mobile clouds, pp.139-145

J. Morales, E. Rosas, and N. Hidalgo, Symbiosis: Sharing mobile resources for stream processing, ISCC'14, pp.1-6

H. Wang and L. Peh, Mobistreams: A reliable distributed stream processing system for mobile devices, IPDPS'14, pp.51-60

R. B. Das, G. D. Bernardo, and H. Bal, Large scale stream analytics using a resource-constrained edge, pp.135-139

J. Choi, J. Park, H. D. Park, and O. Min, Dart: Fast and efficient distributed stream processing framework for internet of things, ETRI Journal, vol.39, issue.2, pp.202-212, 2017.

Y. Xiong, D. Zhuo, S. Moon, M. Xie, I. Ackerman et al., Amino -a distributed runtime for applications running dynamically across device, pp.361-366

M. Habib-ur-rehman, P. P. Jayaraman, S. U. Malik, A. U. Khan, and M. M. Gaber, Rededge: A novel architecture for big data processing in mobile edge computing environments, Journal of Sensor and Actuator Networks, vol.6, issue.3, pp.1-17, 2017.

B. Theeten and N. Janssens, Chive: Bandwidth optimized continuous querying in distributed clouds, IEEE Transactions on Cloud Computing, vol.3, issue.2, pp.219-232, 2015.

A. D. Veith, M. D. De-assunão, and L. Lefèvre, Latency-Aware Placement of Data Stream Analytics on Edge Computing, ICSOC'18, pp.215-229
URL : https://hal.archives-ouvertes.fr/hal-01875936

G. Amarasinghe, M. D. De-assuno, A. Harwood, and S. Karunasekera, A data stream processing optimisation framework for edge computing applications, ISOR-C'18, pp.91-98
URL : https://hal.archives-ouvertes.fr/hal-01862063

R. Appuswamy, C. Gkantsidis, D. Narayanan, O. Hodson, and A. Rowstron, Scale-up vs scale-out for hadoop: Time to rethink?, vol.20, pp.1-20

Y. Shan, B. Wang, J. Yan, Y. Wang, N. Xu et al., Fpmr: Mapreduce framework on fpga, FPGA'10, pp.93-102

K. Neshatpour, M. Malik, and H. Homayoun, Accelerating machine learning kernel in hadoop using fpgas, CCGrid'15, pp.1151-1154

Z. Wang, S. Zhang, B. He, and W. Zhang, Melia: A mapreduce framework on opencl-based fpgas, IEEE Transactions on Parallel and Distributed Systems, vol.27, issue.12, pp.3547-3560, 2016.

Y. Chen, J. Cong, Z. Fang, J. Lei, and P. Wei, When apache spark meets fpgas: A case study for next-generation dna sequencing acceleration, Hot-Cloud'16, pp.64-70

E. Ghasemi and P. Chow, Accelerating apache spark with fpgas, Concurrency and Computation Practice and Experience, vol.31, issue.2, p.4222, 2017.

J. He, Y. Chen, T. Z. Fu, X. Long, M. Winslett et al., Haas: Cloud-based real-time data analytics with heterogeneity-aware scheduling, ICDCS'18, pp.1017-1028

, Apache hadoop yarn

, Apache mesos

J. Cong, P. Wei, and C. H. Yu, From JVM to FP-GA: Bridging abstraction hierarchy via optimized deep pipelining, pp.1-6

S. Venkataraman, E. Bodzsar, I. Roy, A. Auyoung, and R. S. Schreiber, Presto: Distributed machine learning and graph processing with sparse matrices, pp.197-210