Delay Optimality in Load-Balancing Systems

We are in the midst of a major data revolution. The total data generated
by humans from the dawn of civilization until the turn of the
new millennium is now being generated every other day. Driven by
a wide range of data-intensive devices and applications, this growth
is expected to continue its astonishing march, and fuel the development
of new and larger data centers. In order to exploit the lowcost
services offered by these resource-rich data centers, application
developers are pushing computing and storage away from the
end-devices and instead deeper into the data-centers. Hence, the end-users' experience is now dependent on
the performance of the algorithms used for data retrieval, and job scheduling within the data-centers. In particular,
providing low-latency services are critically important to the end-user experience for a wide variety of applications.
Our goal has been to develop the analytical foundations and practical methodologies to enable solutions that
result in low-latency services. In this talk, I will focus on our efforts on reducing the latency through load balancing
in large-scale data center systems. We will develop simple implementable schemes that achieve the optimal
delay performance when the load of the network is very large. In particular we will show that very simple schemes
that use an adaptive threshold for load balancing can achieve excellent delay performance even with minimum
message overhead. We will begin our discussion that focuses on a single load balancer and then extend the
work to a multi-load balancer scenario, where each load balancer needs to operate independently of the others
to minimize the communication between them. In this setting we will show that estimation errors can actually be
used to our advantage to prevent local hot spots. We will conclude with a list of interesting open questions that
merit future investigations.