Researcher:
Sonbol, Karim

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Master Student

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Karim

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Sonbol

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Sonbol, Karim
Sonbol, Karim Mohamed Abdelazim Abouelmaati

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2019 - 20203

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Researcher Of Publications: search.filters.isAuthorOfPublication.8f2cc473-2653-4136-8dcc-9c88303a3494

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    Review of RDMA-enabled consensus protocols
    (IEEE, 2019) N/A; Department of Computer Engineering; Sonbol, Karim; Özkasap, Öznur; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    Several cloud computing applications use Replicated State Machines (RSM) to provide fault-tolerant services, ensuring consistency with consensus protocols. However, these protocols often come with a high latency cost, sometimes even forcing system designers to sacrifice consistency for availability. This latency is due, in part, to unnecessary data copies in the kernel TCP/IP layers. Remote Direct Memory Access (RDMA) bypasses the kernel to provide faster communication and lower CPU overhead through zero-copy data transfer. Recent works have utilized RDMA primitives to improve the performance of consensus protocols. However, integrating RDMA into such protocols and utilizing it efficiently can be a complex task. In this paper, we address this problem by presenting a systematic review of the state-of-the-art approaches for implementing RDMA-based consensus protocols.
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    EdgeKV: distributed key-value store for the network edge
    (IEEE, 2020) Al Oqily, Ibrahim; Aloqaily, Moayad; N/A; Department of Computer Engineering; Sonbol, Karim; Özkasap, Öznur; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    With improvements in computation and storage resources, data access through the network becomes the bottleneck for several cloud applications. Even with high-speed networks, the high latency of the cloud access makes it unfeasible or unfavourable for latency-sensitive applications such as autonomous driving, smart factories, and video streaming. Edge computing provides a solution by utilizing the network edge resources that are closer to the end users. Nevertheless, it is a non-trivial task to design a large-scale edge-capable system that is stable, fault-tolerant, and efficient [1]. In this paper, present the design of EdgeKV: a novel general-purpose distributed key-value store for the network edge. We demonstrate the features of EdgeKV for achieving high efficiency and scalability while providing flexibility, ease of use, and data privacy. We evaluated our prototype on the Grid'5000 framework with multiple realistic Yahoo! Cloud Serving Benchmark (YCSB) workloads. Our initial results show that EdgeKV achieves 72% higher throughput and 47% lower latency on average than centralized cloud storage, for read-dominated workloads.
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    PublicationOpen Access
    EdgeKV: decentralized, scalable, and consistent storage for the edge
    (Elsevier, 2020) Al-Oqily, Ibrahim; Aloqaily, Moayad; N/A; Department of Computer Engineering; Sonbol, Karim; Özkasap, Öznur; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    Edge computing moves the computation closer to the data and the data closer to the user to overcome the high latency communication of cloud computing. Storage at the edge allows data access with high speeds that enable latency-sensitive applications in areas such as autonomous driving and smart grid. However, several distributed services are typically designed for the cloud and building an efficient edge-enabled storage system is challenging because of the distributed and heterogeneous nature of the edge and its limited resources. In this paper, we propose EdgeKV, a decentralized storage system designed for the network edge. EdgeKV offers fast and reliable storage, utilizing data replication with strong consistency guarantees. With a location-transparent and interface-based design, EdgeKV can scale with a heterogeneous system of edge nodes. We implement a prototype of the EdgeKV modules in Golang and evaluate it in both the edge and cloud settings on the Grid’5000 testbed. We utilize the Yahoo! Cloud Serving Benchmark (YCSB) to analyze the system's performance under realistic workloads. Our evaluation results show that EdgeKV outperforms the cloud storage setting with both local and global data access with an average write response time and throughput improvements of 26% and 19% respectively under the same settings. Our evaluations also show that EdgeKV can scale with the number of clients, without sacrificing performance. Finally, we discuss the energy efficiency improvement when utilizing edge resources with EdgeKV instead of a centralized cloud.