Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework

Cordingly, Robert; Heydari, Navid; Yu, Hanfei; Hoang, Varik; Sadeghi, Zohreh; Lloyd, Wes

doi:10.1145/3447545.3451173

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…We allocate 4vCPUs for every memory configuration in GCR except for 512MB where we allocate 2vCPUs. To reduce variance in performance measurements for the serverless workloads due to cold starts [10], we set the maximum number of concurrent instances for all services to one. Furthermore, we set the maximum number of concurrent requests that can be handled by a container in GCR to one.…”

Section: Methodsmentioning

confidence: 99%

“…Evaluating the general performance of FaaS platforms and improving the performance of FaaS functions has also been actively researched [8,10]. In [10], the authors present the Serverless Application Analytics Framework (SAAF), to improve observability on the performance of FaaS functions on commercial FaaS platforms. SAAF currently supports multiple FaaS platforms and several different programming languages.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards Demystifying Intra-Function Parallelism in Serverless Computing

Kiener¹,

Chadha²,

Gerndt³

2021

Preprint

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Serverless computing offers a pay-per-use model with high elasticity and automatic scaling for a wide range of applications. Since cloud providers abstract most of the underlying infrastructure, these services work similarly to black-boxes. As a result, users can influence the resources allocated to their functions, but might not be aware that they have to parallelize them to profit from the additionally allocated virtual CPUs (vCPUs). In this paper, we analyze the impact of parallelization within a single function and container instance for AWS Lambda, Google Cloud Functions (GCF), and Google Cloud Run (GCR). We focus on compute-intensive workloads since they benefit greatly from parallelization. Furthermore, we investigate the correlation between the number of allocated CPU cores and vCPUs in serverless environments. Our results show that the number of available cores to a function/container instance does not always equal the number of allocated vCPUs. By parallelizing serverless workloads, we observed cost savings up to 81% for AWS Lambda, 49% for GCF, and 69.8% for GCR.CCS Concepts: • Computer systems organization → Cloud computing.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards Demystifying Intra-Function Parallelism in Serverless Computing

Kiener¹,

Chadha²,

Gerndt³

2021

Preprint

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“…However, these dashboards offer limited features such as visualizing the workload, reports on resource utilization, and run-time logs. As the root access to the underlying operating system is not provided in a FaaS offering, installation and configuration of any additional observability tool on them is not possible [69].…”

Section: Cloud Native Observability and Compliance Challengesmentioning

confidence: 99%

A Reference Architecture for Observability and Compliance of Cloud Native Applications

Pourmajidi¹,

Zhang²,

Steinbacher³

et al. 2023

Preprint

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The evolution of Cloud computing led to a novel breed of applications known as Cloud-Native Applications (CNAs). However, observing and monitoring these applications can be challenging, especially if a CNA is bound by compliance requirements.To address this challenge, we explore the characteristics of CNAs and how they affect CNAs' observability and compliance. We then construct a reference architecture for observability and compliance pipelines for CNAs. Furthermore, we sketch instances of this reference architecture for single-and multi-cloud deployments.The proposed architecture embeds observability and compliance into the CNA architecture and adopts a "battery-included" mindset. This architecture can be applied to small and large CNA deployments in regulated and non-regulated industries. It allows Cloud practitioners to focus on what is critical, namely building their products, without being burdened by observability and compliance requirements. This work may also interest academics as it provides a building block for generic CNA architectures.

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“…Serverless platforms, typically furnish basic dashboards to monitor ongoing processes. However, these dashboards have limited capabilities, such as workload visualization and resource utilization reports, as they lack root access to the underlying operating system, making installation and configuration of additional observability tools impossible [25].…”

Section: Serverlessmentioning

confidence: 99%

Traditional Techniques and Emerging Technologies in Observability

Subburaman

2022

J Arti Inte & Cloud Comp

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Identity and access management is the bedrock of cybersecurity. Identity access to digital resources is governed by its techniques, procedures, and rules, which also define the breadth of identity permission over those resources. Some new cyberattack or data breach pops up in the news every week. Many data breaches occur due to inadequate security measures, software flaws, human mistake, malevolent insiders, or the abuse of access and privileges. An improved access control system is possible with the use of AI methods. In order for organisations to better handle authentication and access control in order to reduce cyber risks and other IAM difficulties, studies into artificial intelligence in IAM are necessary. With an eye towards AI’s potential uses in identity and access management - more especially in the areas of privilege monitoring, administration, and control - this research investigates the nature of the connection between AMIS and AI. To better understand how AI works in minimising recognised IAM issues, this study aimed to present evidence from the relevant literature. This study’s results show how AI reinforces identity and access management, which helps with automating procedures, keeping up with technology advances, and reducing the prevalence of cyber threats. One way to accomplish this is by using a binary classification system for security access control, which takes the PDP problem and turns it into a yes/no question. In order to create a distributed, effective, and accurate policy decision point (PDP), a vector decision classifier is also built using the supervised machine learning technique. Kaggle-Amazon access control policy dataset evaluated performance by comparing the proposed mechanism to previous research standards in terms of performance, duration, and flexibility. Given that the PDP is not in direct contact with the PAP, the proposed approach accomplishes a high level of secrecy in relation to access control requirements. In conclusion, PDP-based ML can manage massive access requests, execute many major policies simultaneously, and have a 95% accuracy rate, all without policy conflicts, with a response time of about 0.15 s. The security of access control can be enhanced by making it more responsive, flexible, dynamic, and dispersed.

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Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework

Cited by 8 publications

References 4 publications

Towards Demystifying Intra-Function Parallelism in Serverless Computing

Towards Demystifying Intra-Function Parallelism in Serverless Computing

A Reference Architecture for Observability and Compliance of Cloud Native Applications

Traditional Techniques and Emerging Technologies in Observability

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