This talk focuses on the central problem of coordinating computation and caching in networks, using some recent results in stochastic geometry and information theory. Our goal is to provide a FAST, RELIABLE, and CHEAP design for 5G mobile networks. The first part of the talk focuses on decentralized caching by utilizing the redundancy across multiple, geo-dispersed, and mobile sources of data. In order to leverage proximity-based communications such as peer-to-peer systems or device-to-device communications, we exploited the spatial diversity of the content and the topology as a proxy for optimizing cache placement. We proposed novel decentralized and spatial exclusion-based cache placement policies. These policies promote diversity and reciprocation (FAST); provide guarantees on the cache hit probability (RELIABLE); and offload traffic from congested base stations, and are promising for proximity-based applications (CHEAP). The second part of the talk concerns with the limits of reliability with imperfect feedback when coding, and development of scalable and robust routing solutions for connectivity in wireless mesh networks. This approach utilizes coding for optimizing the tradeoff between in-order delivery delay and throughput, which is promising for computing systems such as the Internet of things, and ultra-reliable and low-latency communications e.g. mission-critical communications, and connected vehicles in 5G networks (FAST). It also provides robustness and delay guarantees (RELIABLE); and has very low complexity in terms of coding overhead, and is cost effective via the use of multi-hop WiFi links (CHEAP). Finally, this talk describes a new perspective on cloud/fog computing, by coordinating caching and computation in order to handle the large volume of data with growing computational demand. Our goal is to devise coding techniques for functional compression, and coordinating computation and caching in networks, by employing the concepts of graph entropy and function surjectivity. These techniques suit different applications such as caching, classification, federated learning, quantization, and compressed sensing. Our unified insights suggest to cache at the edge (FAST); distribute storage by exploiting geographic diversity and paths (RELIABLE); and distribute computation by making use of underlying redundancy both in data and functions, in order to recover a sparse representation, or labeling (CHEAP).
Derya Malak is a Postdoctoral Associate at the Massachusetts Institute of Technology and Northeastern University, where she has been working with Prof. Muriel Médard and Prof. Edmund Yeh, respectively. She received a Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin under the supervision of Prof. Jeffrey G. Andrews, in August 2017, where she was affiliated with the Wireless Networking & Communications Group (WNCG). Previously, she received an M.S. degree in Electrical and Electronics Engineering at Koc University, Istanbul, Turkey, in February 2013. She received a B.S. in Electrical and Electronics Engineering (with minor in Physics) at Middle East Technical University, Ankara, Turkey, in June 2010. Derya has held summer internships at Huawei Technologies, Plano, TX, and Bell Laboratories, Murray Hill, NJ. She was awarded the Graduate School fellowship by the University of Texas at Austin between 2013-2017. She was selected to participate in the Rising Stars Workshop for women in EECS, MIT, in October 2018.