Fair Scheduling in Cloud Datacenters with Multiple Resource Types

Speaker:        Wei Wang
                Department of Electrical and Computer Engineering
                University of Toronto

Title:          "Fair Scheduling in Cloud Datacenters with Multiple
                 Resource Types"

Date:           Monday, 23 February 2015

Time:           4:00pm - 5:00pm

Venue:          Lecture Theatre F (near lifts 25/26), HKUST

Abstract:

In the era of big data, it has been the norm for cloud datacenters to run
data analytic applications at a large scale. Yet, as multiple applications
share resources in these datacenters, it is important to design scheduling
disciplines for datacenter resources to be shared in a fair and efficient
manner. In this talk, I will present a new class of scheduling disciplines
that are specifically designed for sharing multiple resource types in
cloud datacenters. I will first discuss how multiple resource types are to
be shared in space, and present a new design that allocates resources to
applications by scheduling their computing tasks onto datacenter nodes. I
will then focus on the problem of sharing resources over time, and present
a new scheduler to multiplex application flows in software routers,
sharing both CPU and link bandwidth. In both contexts, I will open with
examples, explore their theoretical underpinnings related to fairness and
efficiency, and conclude with challenges and observations in real-world
implementations.

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Biography:

Wei Wang is a Ph.D. candidate in the Department of Electrical and Computer
Engineering at the University of Toronto, working with Prof. Baochun Li
and Prof. Ben Liang. He received the B.Engr. and M.Engr. degrees from the
Department of Electrical Engineering at Shanghai Jiao Tong University in
2007 and 2010, respectively.

Wei's research interests cover the broad area of distributed systems, with
specific emphasis on cloud computing, computer networks, and data analytic
systems. His thesis research focused on designing fundamental resource
sharing and scheduling policies for large clusters, data analytic systems,
and network appliances that are widely deployed in cloud datacenters. He
is also interested in problems at the intersection of cloud computing and
economics. His work has been selected as the Best Paper Finalist by the
USENIX ICAC 2013.