Efficient and Reliable Communications in Distributed Networks

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The applicant's long-term research objective is to make communication (wireless or wired) more efficient (in terms of consuming resources such as power), faster, and more reliable. Three out of four proposed short-term research plans are focused on broadcasting, an important communication primitive, while the fourth short-term research plan aims to improve efficiency and reliability of distributed storage systems, where we view storage as communication in time with erasures (storage node failures).******Billions of devices that use radio for communication have limited power supplies (e.g., they run on batteries). Those devices can benefit from saving in energy consumption in communication primitives such as broadcast. ***Also, faster and more reliable communication improves quality of service, an important criteria particularly in real time applications. Our first short-term objective is to analyze the gain of energy accumulation in reducing energy consumption of broadcast. The second short-term objective is to improve quality of service of broadcast for real time applications such as real time video streaming.******Distributed wired networks can also benefit from faster broadcast. In computing clusters and data centers, many-to-many data transfer (a generalization of broadcast), can take a large percentage of applications execution time. For example, in a MapReduce framework, data transfers between mappers and reducers modeled as a many-to-many transfer, can account for more than 50% of the total running time. It is, therefore, highly desirable to devise effective algorithms to alleviate the data transfer bottleneck such as those widely reported in MapReduce.******Similar techniques and ideas used for addressing erasures in wireless and wired networks have been used to analyze and handle erasures in distributed storage systems. Considering the scale of today's data centers, algorithms and codes with even slightly lower storage, bandwidth and computation requirements than the existing ones can free-up significant amount of resources, resulting in, for example, saving in maintenance cost and electricity usage, and can indirectly protect our environment. The last short-term objective of the proposed research is to reduce computational complexity of coding/encoding by finding optimal binary codes within a class of erasure codes called locally repairable codes, attractive for their simplicity.**