Reference number : GC-2017-Q2-00123, amendment number : , agreement type : G, recipient legal name : Feng, Chen (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Two Hat Security aims to develop next generation moderation tools to identify offensive images/videos,_x000D_
especially those containing child sexual abuse material (CSAM). If Two Hat is able to detect those contents as_x000D_
soon as they are uploaded to the cloud, it can help to stop the spread of CSAM online. However, this presents a_x000D_
difficult engineering challenge: After Two Hat develops a highly accurate tool to detect CSAM, how can it_x000D_
support low latency detection with ever increasing CSAM contents? The proposed project is to achieve_x000D_
sub-second detection time in a cloud environment with a large number of servers. We will make use of a recent_x000D_
breakthrough in cloud load balancing---the so-called Join-the-Idle-Queue (JIQ) strategy. First, we will conduct_x000D_
a complete theoretical analysis for JIQ and in doing so obtain important engineering insights. Second, we will_x000D_
develop various JIQ algorithms based on our insights. Finally, we will work together with software engineers at_x000D_
Two Hat to implement our JIQ algorithms on a cloud platform, such as Amazon EC2, Google Cloud, or_x000D_
Microsoft Azure., program purpose french : Two Hat Security aims to develop next generation moderation tools to identify offensive images/videos,_x000D_
especially those containing child sexual abuse material (CSAM). If Two Hat is able to detect those contents as_x000D_
soon as they are uploaded to the cloud, it can help to stop the spread of CSAM online. However, this presents a_x000D_
difficult engineering challenge: After Two Hat develops a highly accurate tool to detect CSAM, how can it_x000D_
support low latency detection with ever increasing CSAM contents? The proposed project is to achieve_x000D_
sub-second detection time in a cloud environment with a large number of servers. We will make use of a recent_x000D_
breakthrough in cloud load balancing---the so-called Join-the-Idle-Queue (JIQ) strategy. First, we will conduct_x000D_
a complete theoretical analysis for JIQ and in doing so obtain important engineering insights. Second, we will_x000D_
develop various JIQ algorithms based on our insights. Finally, we will work together with software engineers at_x000D_
Two Hat to implement our JIQ algorithms on a cloud platform, such as Amazon EC2, Google Cloud, or_x000D_
Microsoft Azure., agreement title english : Cloud load balancing for low latency applications, agreement title french : Cloud load balancing for low latency applications, agreement number : EGP, agreement value : 24900.0, agreement start date : 2017-07-12, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada