| Spanedge: Towards unifying stream processing over central and near-the-edge data centers HP Sajjad, K Danniswara, A Al-Shishtawy, V Vlassov 2016 IEEE/ACM Symposium on Edge Computing (SEC), 168-178, 2016 | 143 | 2016 |
| Efficient representation learning using random walks for dynamic graphs HP Sajjad, A Docherty, Y Tyshetskiy arXiv preprint arXiv:1901.01346, 2019 | 40 | 2019 |
| Boosting vertex-cut partitioning for streaming graphs HP Sajjad, AH Payberah, F Rahimian, V Vlassov, S Haridi 2016 IEEE International Congress on Big Data (BigData Congress), 1-8, 2016 | 38 | 2016 |
| Scaling hdfs with a strongly consistent relational model for metadata K Hakimzadeh, H Peiro Sajjad, J Dowling Distributed Applications and Interoperable Systems: 14th IFIP WG 6.1 …, 2014 | 22 | 2014 |
| Smart partitioning of geo-distributed resources to improve cloud network performance HP Sajjad, F Rahimian, V Vlassov 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), 112-118, 2015 | 12 | 2015 |
| Stream processing in community network clouds K Danniswara, HP Sajjad, A Al-Shishtawy, V Vlassov 2015 3rd International Conference on Future Internet of Things and Cloud …, 2015 | 10 | 2015 |
| Optimizing windowed aggregation over geo-distributed data streams HP Sajjad, Y Liu, V Vlassov 2018 IEEE International Conference on Edge Computing (EDGE), 33-41, 2018 | 6 | 2018 |
| Reproducible distributed clusters with mutable containers: To minimize cost and provisioning time HP Sajjad, K Hakimzadeh, S Perera Proceedings of the Workshop on Hot Topics in Container Networking and …, 2017 | 2 | 2017 |
| Maintaining Strong Consistency Semantics in a Horizontally Scalable and Highly Available Implementation of HDFS HP Sajjad, MH Harirbaf Master Thesis, KTH Royal Institute of Technology, 2013 | 2 | 2013 |
| Scalable streaming graph partitioning S Khamoushi, S Mohammadreza | 1 | 2017 |
| Efficient Representation Learning Using RandomWalks for Dynamic Graphs H Peiro Sajjad, A Docherty, Y Tyshetskiy | | 2019 |
| Methods and Algorithms for Data-Intensive Computing: Streams, Graphs, and Geo-Distribution H Peiro Sajjad KTH Royal Institute of Technology, 2019 | | 2019 |
| Efficient and Cost-effective Workflow Based on Containers for Distributed Reproducible Experiments S Perera | | 2016 |
| Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers H Peiro Sajjad KTH Royal Institute of Technology, 2016 | | 2016 |
| Smart Partitioning of Geo-Distributed Resources to Improve Cloud Network Performance H Peiro Sajjad, F Rahimian, V Vlassov The 2015 4th IEEE International Conference on Cloud Networking (IEEE …, 2015 | | 2015 |
| Topology-Aware Placement of Stream Processing Components on Geographically Distributed Virtualized Environments K Danniswara | | 2015 |
| Distributed Applications and Interoperable Systems K Magoutis, P Pietzuch, M Macías, J Guitart, S Brenner, B Garbers, ... Springer Berlin Heidelberg, 2014 | | 2014 |
| Maintaining Strong Consistency Semantics in a Horizontally Scalable and Highly Available Implementation of HDFS H Peiro Sajjad, M Hakimzadeh Harirbaf | | 2013 |
Vladimir VlassovProfessor in Computer Systems, KTH Royal Institute of Technology在 kth.se 的电子邮件经过验证
