Time: Friday, February 1, 2:30-3:30 pm
Place: Carleton University, Systems and Computer Engineering
Maker Lab, across room 4460 in Mackenzie Building. See map.


Speaker: Dr. Jalal Khamse-Ashari, Research Associate, Systems and Computer Engineering, Carleton University

ABSTRACT: Cloud computing has become increasingly popular as it provides a cost-effective alternative to proprietary high performance computing systems. As the workloads to data-centers housing cloud computing platforms are intensively growing, developing an efficient and fair resource allocation mechanism which guarantees quality-of-service for different users has become increasingly important. Fair resource management in such a shared computing system is particularly challenging because of (a) the presence of multiple types of resources, (b) diversity in the users’ resource demands, (c) heterogeneity of servers, and (d) placement constraints. To achieve efficient (in terms of utilization) and fair allocation of resources in a private cloud setting in the presence of the above mentioned complexities, we propose a new multi-resource allocation mechanism wherein each server allocates resources to different users based on a per-server metric. The proposed server-based approach not only results in a fully distributed implementation, but also is shown to satisfy several fairness-related properties which are generally deemed desirable. Finally, we extend the proposed approach to achieve cost-efficient and fair allocation of resources in a public cloud setting.

BIO: Jalal Khamse-Ashari received his BSc degree from Isfahan University of Technology, Isfahan, Iran, in 2010, and the MSc degree from Sharif University of Technology, Tehran, Iran, in 2012, both in electrical engineering. He joined the department of systems and computer engineering, Carleton University in 2015 to pursue his PhD studies. He is currently a post-doctoral fellow at SCE department, Carleton University. His research interests include performance evaluation of communications networks, resource allocation and scheduling, algorithm design, optimization and game theory with application to cloud computing and next generation mobile networks.

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