Graphless: Toward Serverless Graph Processing

Toader, Lucian; Uta, Alexandru; Musaafir, Ahmed; Iosup, Alexandru

doi:10.1109/ispdc.2019.00012

Cited by 17 publications

(4 citation statements)

References 26 publications

(38 reference statements)

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“…Your development and security teams should meet face to face and have a conversation about the goals of each function and the precautions they need to take to ensure its safety. This is the best practice [73][74][75][76].…”

Section: Serverless Best Practices For Any Cloudmentioning

confidence: 99%

The Rise of Serverless Architectures: Security Challenges and Best Practices

Cinar

2023

AJRCoS

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The field of serverless computing has had significant growth and recognition in the past decade. This emerging area has garnered attention because to its notable impact on cost reduction, latency reduction, scalability improvement, and elimination of server-side management, among other benefits. Nevertheless, there is still a dearth of comprehensive study that would facilitate developers and academics in gaining a more profound comprehension of the importance of serverless computing in many scenarios. Therefore, it is imperative to provide scholarly study data that has been published within this particular field. This study conducted a comprehensive analysis of 275 scholarly articles retrieved from reputable literature sources, with the aim of extracting valuable insights pertaining to serverless computing. Subsequently, the acquired data underwent analysis in order to address many study inquiries pertaining to the contemporary advancements in serverless computing, encompassing its fundamental principles, available platforms, and patterns of utilization, among other relevant aspects. In addition, we analyze the current obstacles confronting serverless computing and explore potential avenues for future research to facilitate its deployment and utilization.

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Section: Serverless Best Practices For Any Cloudmentioning

confidence: 99%

The Rise of Serverless Architectures: Security Challenges and Best Practices

Cinar

2023

AJRCoS

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“…Second, as serverless computing becomes increasingly relevant for data-intensive workloads-e.g., in graph processing [9] and object storage [8]-additional experiments targeting these kinds workloads will be needed. A benchmark will need to evaluate the (possible) interplay between the storage and execution platforms, and explore how data-intensive serverless applications are designed.…”

Section: Ideas For the Longer-term Futurementioning

confidence: 99%

“…We evaluated the serverless platforms of the three major cloud providers (Azure, AWS, and Google), and a state-of-the-art, opensource FaaS platform, called Fission. 8 As a baseline, we additionally include SimFaaS, 9 which is a simple FaaS simulator.…”

Section: Function Runtime Overheadmentioning

confidence: 99%

Beyond Microbenchmarks: The SPEC-RG Vision for a Comprehensive Serverless Benchmark

Eyk

Scheuner

Eismann

et al. 2020

Companion of the ACM/SPEC International Conference on Performance Engineering

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Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open-and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms.

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“…Worse still, fine-tuning hyperparameters and identifying suitable training schemes for self-supervised models can be both expensive and resource-intensive, particularly for large-scale graphs with dense connections. To this end, when a GNN is making a prediction, one naturally raises a question: is it possible to effectively simplify or reduce the graph to not only accelerate graph algorithms, including GNNs, but also aid in storage, visualization, and retrieval for associated graph data analysis tasks (Jin et al 2022b,a;Toader et al 2019;Zhang et al 2021)?…”

Section: Introductionmentioning

confidence: 99%