Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge

Zobaed, SM; Mokhtari, Ali; Champati, Jaya Prakash; Kourouma, Mathieu; Salehi, Mohsen Amini

doi:10.1109/ucc56403.2022.00012

Cited by 4 publications

(1 citation statement)

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“…As an example, in [15], we have used E2C to examine energy efficiency and fairness of scheduling methods on a heterogeneous edge. Also, in [22], we extended E2C to simulate the memory allocation policies of multi-tenant applications on a homogeneous edge computing system.…”

Section: Simulating a Heterogeneous Comput-ing System Via E2cmentioning

confidence: 99%

E2C: A Visual Simulator to Reinforce Education of Heterogeneous Computing Systems

Mokhtari¹,

Drake²,

Huynh³

et al. 2023

Preprint

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Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. In particular, with the increasing popularity of accelerator technologies (e.g., GPUs and TPUs) and the emergence of domain-specific computing via ASICs and FPGA, the matter of heterogeneity and understanding its ramifications on the system performance has become more critical than ever before. However, it is challenging to effectively educate students about the potential impacts of heterogeneity on: (a) the performance of distributed systems; and (b) the logic of resource allocation methods to efficiently utilize the resources. Making use of the real infrastructure (such as those offered by the public cloud providers) for benchmarking the performance of heterogeneous machines, for different applications, with respect to different objectives, and under various workload intensities is cost-and time-prohibitive. Moreover, not all students (globally and nationally) have access or can afford such real infrastructure. To reinforce the quality of learning about various dimensions of heterogeneity, and to decrease the widening gap in education, we develop an open-source simulation tool, called E2C , that can help students researchers and practitioners to study any type of heterogeneous (or homogeneous) computing system and measure its performance under various system configurations. To make the learning curve shallow, E2C is equipped with an intuitive graphical user interface (GUI) that enables its users to easily examine system-level solutions (scheduling, load balancing, scalability, etc.) in a controlled environment within a short time and at no cost. In particular, E2C is a discrete event simulator that offers the following features: (i) simulating a heterogeneous computing system; (ii) implementing a newly developed scheduling method and plugging it into the system, (iii) measuring energy consumption and other output-related metrics; and (iv) powerful visual aspects to ease the learning curve for students. We used E2C as an assignment in the Distributed and Cloud Computing course. Our anonymous survey study indicates that students rated E2C with the score of 8.7 out of 10 for its usefulness in understanding the concepts of scheduling in heterogeneous computing. Moreover, our pre-and post-evaluations indicate that E2C has improved the students' understanding of heterogeneous computing systems by around 18%.

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Section: Simulating a Heterogeneous Comput-ing System Via E2cmentioning

confidence: 99%