FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

Mokhtari, Ali; Hossen, Md Abir; Jamshidi, Pooyan; Salehi, Mohsen Amini

doi:10.1109/cloud55607.2022.00069

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…This way, researchers can apply practical use of E2C in order to help design and compare their own real world distributed systems or clusters. 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%

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

Mokhtari¹,

Drake²,

Huynh³

et al. 2023

Preprint

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“…An example of semantic reusing can be in an application that provides ambient perception for blind and visually impaired users by detecting obstacles in their environment (e.g., References 106,107). These users often visit repeated locations and interact with objects they have previously encountered during their day‐to‐day activities.…”

Section: Reusing Opportunities In the Serverless Cloudsmentioning

confidence: 99%

Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

Denninnart,

Chanikaphon,

Amini Salehi

2023

Softw Pract Exp

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SummaryServerless computing along with Function‐as‐a‐Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single‐purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems—both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient.

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Joint Optimization of Caching, Computing, and Trajectory Planning in Aerial Mobile Edge Computing Networks: An MADDPG Approach

Sun,

Zhou,

Zhang

et al. 2024

IEEE Internet Things J.

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FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

Cited by 6 publications

References 32 publications

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

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

Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

Joint Optimization of Caching, Computing, and Trajectory Planning in Aerial Mobile Edge Computing Networks: An MADDPG Approach

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