An experiment-driven performance model of stream processing operators in Fog computing environments, ACM/SIGAPP Symp. On Applied Computing (SAC 2019, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02394396
Scheduling linear chain streaming applications on heterogeneous systems with failures, Future Gener. Comput. Syst, vol.29, issue.5, pp.1140-1151, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00926146
GASP: genetic algorithms for service placement in fog computing systems, Algorithms, vol.12, issue.10, p.201, 2019. ,
Optimal operator deployment and replication for elastic distributed data stream processing. Concurrency and Computation: Practice and Experience, vol.30, p.4334, 2018. ,
Cost-aware streaming workflow allocation on geodistributed data centers, IEEE Transactions on Computers, 2017. ,
Geelytics: Enabling on-demand edge analytics over scoped data sources, IEEE Int. Cong. on BigData, 2016. ,
Elastic scaling for data stream processing, IEEE Tr. on Parallel and Distributed Systems, vol.25, issue.6, pp.1447-1463, 2013. ,
Optimal placement of stream processing operators in the fog, 2019 IEEE 3rd Int. Conf. on Fog and Edge Computing (ICFEC), pp.1-10, 2019. ,
Quantifying the impact of edge computing on mobile applications, Proc. of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, p.5, 2016. ,
Performance-oriented deployment of streaming applications on cloud, IEEE Tr. on Big Data, vol.5, issue.1, pp.46-59, 2019. ,
Placement and chaining for run-time IoT service deployment in edge-cloud, IEEE Transactions on Network and Service Management, pp.1-1, 2019. ,
Joint operator scaling and placement for distributed stream processing applications in edge computing, Int. Conf. on Service-Oriented Computing, pp.461-476, 2019. ,
Secure and sustainable load balancing of edge data centers in fog computing, IEEE Communications Magazine, vol.56, issue.5, pp.60-65, 2018. ,
Spanedge: Towards unifying stream processing over central and near-the-edge data centers, IEEE/ACM Symp. on Edge Comp, 2016. ,
Riotbench: A real-time iot benchmark for distributed stream processing platforms, 2017. ,
An optimal model for optimizing the placement and parallelism of data stream processing applications on cloud-edge computing, 32nd IEEE Int. Symp. on Computer Architecture and High Performance Computing. IEEE (2020) ,
URL : https://hal.archives-ouvertes.fr/hal-02926459
Resource aware placement of iot application modules in fogcloud computing paradigm, IFIP/IEEE Symp. on Integrated Net. and Service Mgmt (IM), 2017. ,
Analyzing efficient stream processing on modern hardware, Proc. VLDB Endow, vol.12, issue.5, pp.516-530, 2019. ,
Latency-aware deployment of iot services in a cloud-edge environment, Int. Conf. on Service-Oriented Computing, pp.231-236, 2019. ,