I am a University Research Associate at SFU, working with working with Prof. Mohamed Hefeeda at the Network Systems Lab (NSL). I have received my PhD from SFU in 2019. My research interests are in computer networking. Specifically, I work on multimedia networking, distribution networks, and cloud computing. My recent research focuses on building efficient networking protocols, e.g., multicast, in data center and ISP networks. Before joining SFU, I worked as a Research Assistant at Qatar Computing Research Institute in the Distributed Systems group. I worked as a Software Development Engineer at Mentor Graphics Egypt. I got my BSc degree in Computer Engineering from Cairo University, my BSc graduation thesis is data mining on GPUs.
- Multimedia networking
- Datacenter networking
- Cloud computing
- Distributed systems
MASH: Rate adaptation for Multiview video streaming over HTTP
|MASH is a novel rate adaptation algorithm for multiview videos. Streaming multiview videos is more user centric than single-view videos, because it heavily depends on how users interact with the different views. To efficiently support this interactivity, MASH constructs probabilistic view switching models that capture the switching behavior of the user in the current session, as well as the aggregate switching behavior across all previous sessions of the same video. MASH then utilizes these models to dynamically assign relative importance to different views. Furthermore, MASH uses a new buffer-based approach to request video segments of various views at different qualities, such that the quality of the streamed videos is maximized while the network bandwidth is not wasted.|
Anahita: 3D Streaming System
|The goal is to build an efficient 3D streaming system, while preserving a good 3D experience on most of the 3D displays (TVs, tablets, mobile phones etc...). Anahita manages server side efficiently by optimize the storage usage and processing resources.|
gCloud: GPU-enabled Cloud System
|To design and implement a scalable framework to provide GPUs as on-demand computing resources to cloud users. gCloud allows on-demand access to local and remote GPUs to cloud users only when the target GPU kernel is ready for execution. In order to improve the utilization of GPUs, the framework efficiently shares the GPU resources between concurrent applications from different cloud users. Moreover, gCloud reduces the inter-application interference of concurrent kernels for GPU resources by considering the local and global memory, number of threads, and the number of thread blocks requirements of each kernel. It schedules concurrent kernels on available GPUs such that the overall inter-application interference is minimal.|
- Mohamed Hegazy, Khaled Diab, Mehdi Saeedi, Boris Ivanovic, Ihab Amer, Yang Liu, Gabor Sines, and Mohamed Hefeeda. Content-aware Video Encoding for Cloud Gaming. ACM Multimedia Systems, 2019.
- Khaled Diab and Mohamed Hefeeda. Joint Content Distribution and Traffic Engineering of Adaptive Videos in Telco-CDNs. IEEE INFOCOM, 2019 (Acceptance rate=19.7%).
- Khaled Diab and Mohamed Hefeeda. MASH: A Rate Adaptation Algorithm for Multiview Video Streaming over HTTP. IEEE INFOCOM, 2017 (Acceptance rate=21%).
- Kiana Calagari, Tarek Elgamal, Khaled Diab, Krzysztof Templin, Piotr Didyk, Wojciech Matusik, Mohamed Hefeeda. Depth Personalization and Streaming of Stereoscopic Sports Videos. ACM TOMCCAP 12(3): 41 (2016).
- Kiana Calagari, Krzysztof Templin, Tarek Elgamal, Khaled Diab, Piotr Didyk, Wojciech Matusik, Mohamed Hefeeda. Anahita: A System for 3D Video Streaming with Depth Customization. ACM Multimedia 2014: 337-346 (Acceptance rate=19%).
- Khaled Diab, Tarek Elgamal, Kiana Calagari, Mohamed Hefeeda. Storage optimization for 3d streaming systems. ACM Multimedia Systems 2014 (Acceptance rate=26%).
- Khaled Diab, M. Mustafa Rafique, and Mohamed Hefeeda. Dynamic sharing of gpus in cloud systems. IEEE IPDPS Workshops 2013: 947-954.
- Systems and Network Security, Spring 2020
- Multimedia Systems, Fall 2019