Posco Tso | 曹鳳波

@conference{cui2017heterogeneouscenters,
title = {Heterogeneous Network Policy Enforcement in Data Centers},
author = {Cui, L and Tso, F and Jia, W},
url = {http://poscotso.com/wp-content/uploads/2017/04/hope-2017.pdf},
year = {2017},
date = {2017-05-08},
booktitle = {IFIP/IEEE International Symposium on Integrated Network Management},
organization = {Lisbon, Portugal},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{alfoudi2017trafficnetworks,
title = {Traffic Management in LTE-WiFi Slicing Networks},
author = {Alfoudi, A and Dighriri, M and Lee, GM and Pereira, R and Tso, F},
url = {http://poscotso.com/wp-content/uploads/2017/04/icin-2017.pdf},
year = {2017},
date = {2017-03-07},
booktitle = {The 20th Conference on Innovations in Clouds, Internet and Networks (ICIN)},
organization = {Paris, France},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{kecskemeti2017modellingsimulation,
title = {Modelling Low Power Compute Clusters for Cloud Simulation},
author = {Kecskemeti, G and Hajji, W and Tso, F},
url = {http://poscotso.com/wp-content/uploads/2017/04/rpi-pdp17.pdf},
year = {2017},
date = {2017-03-06},
booktitle = {The 25th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2017)},
organization = {St. Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@article{Cui2016Plan,
title = {PLAN: Joint Policy- and Network-Aware VM Management for Cloud Data Centers},
author = {Lin Cui , Fung Po Tso, Dimitrios P. Pezaros, Weijia Jia, Wei Zhao},
url = {http://www.poscotso.com/wp-content/uploads/2016/09/policy_aware_vm_migration.pdf},
issn = {1045-9219},
year = {2016},
date = {2016-08-19},
journal = {IEEE Transactions on Parallel and Distributed Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Tso2016b,
title = {Network and Server Resource Management Strategies for Data Centre Infrastructures: A Survey},
author = {Fung Po Tso and Simon Jouet and Dimitrios P. Pezaros},
url = {http://www.poscotso.com/wp-content/uploads/2016/07/dc_control_comnet.pdf},
doi = {10.1016/j.comnet.2016.07.002},
issn = {1389-1286},
year = {2016},
date = {2016-07-06},
journal = {Journal of Computer Networks},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Hajji2016,
title = {Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data},
author = {Hajji, Wajdi and Tso, Fung Po},
url = {http://www.poscotso.com/wp-content/uploads/2016/06/electronics-05-00029.pdf},
doi = {10.3390/electronics5020029},
issn = {2079-9292},
year = {2016},
date = {2016-06-06},
journal = {Electronics},
volume = {5},
number = {2},
abstract = {Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{Cui2016,
title = {Synergistic Policy and Virtual Machine Consolidation in Cloud Data Centers},
author = {Lin Cui and Richard Cziva and Fung Po Tso and Dimitrios P. Pezaros},
url = {http://www.poscotso.com/wp-content/uploads/2016/03/sync_cui.pdf},
year = {2016},
date = {2016-04-09},
booktitle = {The 34th IEEE International Conference on Computer Communications (INFOCOM)},
journal = {The 34th IEEE International Conference on Computer Communications (INFOCOM 2016)},
note = {Acceptance Ratio: 18.25%},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@techreport{Tso2016,
title = {Understanding The Network I/O Performance of Heterogenous Virtualisation in Cloud Data Centres},
author = {Fung Po Tso},
url = {http://www.poscotso.com/wp-content/uploads/2016/03/tso2016vmperf.pdf},
year = {2016},
date = {2016-03-08},
institution = {Department of Computer Science, Liverpool John Moores University},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}

@article{Cziva2016,
title = {SDN-based Virtual Machine Management for Cloud Data Centers},
author = {Richard Cziva and Simon Jouet and David Stapleton and Fung Po Tso and Dimitrios Pezaros},
url = {http://www.poscotso.com/wp-content/uploads/2016/03/cziva2016sdn.pdf},
doi = {10.1109/TNSM.2016.2528220},
issn = {1932-4537},
year = {2016},
date = {2016-02-23},
journal = {IEEE Transactions on Network and Service Management},
keywords = {},
pubstate = {published},
tppubtype = {article}
}