Luo Mai

PhD Student
e-mail: luo[dot]mai11{at}imperial[dot]ac[dot]uk

About me
I am a 3rd year PhD student at the Department of Computing of Imperial College London under the supervision of Dr. Paolo Costa and Prof. Alexander L. Wolf. My PhD is kindly sponsored by a 2012 - 2015 Google PhD Fellowship in Cloud Computing. I received my Master of Research (MRes) degree at Imperial College at 2012, and Bachelor of Engineering (BEng) degree from Xidian University at 2011.

  • MLNet is accepted to present at HotCloud'15. Check it here - May 2015
  • I am going to join in the Microsoft Cloud and Information Service Lab as a summer research intern - April 2015
  • ICCSW'15 is online now! - January 2015
  • I did a 4-month research intern in the Wireless and Networking group at MSR - August 2014
  • I was in the organization commitee of the Imperial College Computing Student Workshop (ICCSW'14) - Feburary 2014
  • GearBox was featured in The Register and Imperial College's website - November 2013
  • I was awarded a 3-year Google fellowship! See more details here - October 2012

Research Interests

My research interests lie on the intersection of networking and distributed systems, with particular emphasis on data centre networking and large-scale data analytics frameworks including MapReduce-like batch processing clusters and distirbuted machine learning systems. I validate my ideas by developing experimental systems as well as analytical models to explore, understand and verify observations.

  • NaaS: Network-as-a-Service in the Cloud (MLNet @ HotCloud'15, NetAgg @ CoNEXT'14)
    NaaS integrates current cloud computing offerings with direct yet secure access to the network infrastructure by tenants. With Naas, tenants can easily deploy advanced application-specific network services, such as custom routing, in-network data aggregation, redundancy elimination and smart caching. We believe that NaaS has the potential to revolutionise the current cloud offerings because it would increase the performance of tenants' applications through efficient forwarding and in-network operations but also reduce development complexity by combining (distributed) computation and network in a single, coherent, abstraction.

  • Arbitrated Resource Planning in Multi-Tenant Clouds (GearBox @ LADIS'13, Press: The Register)
    Current IaaS Cloud providers often delegate the decisions of resource allocation to the the tenants themselves. Although this model ensures certain performance guarantees to the clients, it severely limits the flexibility of resource utilisation in the Cloud itself. In this project, we aim to design, develop and evaluate novel resource planning algorithms for multi- tenant Clouds to increase Cloud resource utilisation and to provide flexible cost models. At the core of this project lies the development of novel models that accurately capture and access the trade-offs among resource allocation, resource costing and application performance metrics.

  • Energy-efficient Mobile Data Harvesting in Sensor Networks (MASS'11, IJDSN'12)
    In this project, I proposed and developed a light-weight distributed protocol to facilitate rangers to achieve long-lasting data harvesting in a wireless sensor network that is deployed in the vast forest.

Google scholar profile page