Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Motivation
The global IP traffic is growing rapidly due to increased demand for online services such as cloud storage, video on demand, live streaming, and file sharing. These online services, which are responsible for the bulk of the Internet traffic, heavily rely on distributed storage systems (DSS). Hence, around the world, the number and size of DSSs have rapidly increased in recent years. In fact, DSSs are already among the top contributors of carbon emission in the modern world. In the US, for example, they are only second to the airline industry and expected to be the top contributor by 2020. Thus, research on efficient data storage techniques for DSSs has recently attracted a lot of interest both in academia and in industry.
Background
Hardware and software failures in a DSS result in data loss and unavailability. Naturally, many service providers keep multiple copies of data for reliability and availability. The downside, of course, is the huge storage overhead (typically 200%) and the associated maintenance costs. As a solution, recently, error correction coding is suggested for DSSs. Unfortunately, most existing error correction codes cannot be used in these large-scale applications, because the repair traffic that they create is beyond the network capacity of the DSSs. Hence, a strong need for more efficient codes, tailored for large-scale applications, is felt.
Goals
In this research program, using our past knowledge of modern coding and our recent activities on coding for DSSs, we will develop efficient and scalable error correction coding solutions for distributed storage systems. We will also seek theoretical results on the fundamental relations among different performance measures (such as coding overhead, code reliability, repair traffic, update complexity, etc.).
There are a large number of high-impact open problems in this area all suitable for training HQPs.
Impact
This research proposal follows several goals that are exceedingly important to both practitioners and theorists in the area of coding and information theory. Improving the efficiency of large-scale data storage not only improves our online experience, but also significantly reduces the carbon footprint of DSSs. We hope our results throughout this work and our contribution with other Canadian researchers turn Canada into a major scientific center in this field, which can technically and scientifically support any data center located in Canada. In fact, since cooling is a major cost in data centers, Canada because of its colder climate and its safety is considered as an ideal place to host data centers.
In the past decade, the world has seen a significant increase in online activity. As a result, large-scale data storage/communication currently is and will continue to be a big engineering challenge. Our activity in this field will train HQPs that will be in high demand and involved in very high impact engineering activities.