Today's era of cloud computing is powered by massive data centers. A data center network enables the exchange of data in the form of packets among the servers within these data centers. Given the size of today's data centers, it is desirable to design low-complexity scheduling algorithms which result in a fixed average packet delay, independent of the size of the data center. We consider the scheduling problem in an input-queued switch, which is a good abstraction for a data center network.
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Combinatorial design theory has its roots in recreational mathematics and is concerned with the arrangement of the elements of a finite set into subsets, such that the collection of subsets has certain “nice” properties. In this talk we shall demonstrate that interpreting designs in the right manner yields improved solutions for distributed storage and content caching and novel impossibility results for distributed function computation.
The Internet of Things (IoT) is the network of physical objects ‘things’. The connectivity requirements of the things depend heavily on the application. In this talk, we focus on the use cases that require low power consumption, long battery life, and are characterized by low duty cycle and massive number of low cost devices. This talk is divided into two parts. In the first part, we focus on Narrowband IoT system for low power cellular connectivity, and in the second part, we discuss ambient re-scatter communications that allow extreme low power short range connectivity.
Everyone has some experience of solving jigsaw puzzles. When facing ambiguities of assembling a pair of pieces, a common strategy we use is to look at clues from additional pieces and make decisions among all relevant pieces together. In this talk, I will show how to apply this common practice to develop data-driven algorithms that significantly outperform pair-wise algorithms. I will start with describing a computation framework for the joint inference of correspondences among shape/image collections.