SLO Script: A Novel Language for Implementing Complex Cloud-Native Elasticity-Driven SLOs

Pusztai, Thomas; Morichetta, Andrea; Pujol, Víctor Casamayor; Dustdar, Schahram; Nastić, Stefan; Ding, Xiaoning; Vij, Deepak; Xiong, Ying

doi:10.1109/icws53863.2021.00017

Cited by 22 publications

(2 citation statements)

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“…In turn, several tools such as Datadog [30] and AppDynamics [31] are now performing application-level instrumentation and offering Monitoring-as-a-Service. Moreover, initiatives from the monitoring research community focus on monitoring agent placement [32], low-cost and adaptive metric collection [33], and advanced traceability and SLO configuration [34].…”

Section: Related Workmentioning

confidence: 99%

FedMon: A Federated Learning Monitoring Toolkit

Symeonides,

Trihinas,

Nikolaidis

2024

IoT

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Federated learning (FL) is rapidly shaping into a key enabler for large-scale Artificial Intelligence (AI) where models are trained in a distributed fashion by several clients without sharing local and possibly sensitive data. For edge computing, sharing the computational load across multiple clients is ideal, especially when the underlying IoT and edge nodes encompass limited resource capacity. Despite its wide applicability, monitoring FL deployments comes with significant challenges. AI practitioners are required to invest a vast amount of time (and labor) in manually configuring state-of-the-art monitoring tools. This entails addressing the unique characteristics of the FL training process, including the extraction of FL-specific and system-level metrics, aligning metrics to training rounds, pinpointing performance inefficiencies, and comparing current to previous deployments. This work introduces FedMon, a toolkit designed to ease the burden of monitoring FL deployments by seamlessly integrating the probing interface with the FL deployment, automating the metric extraction, providing a rich set of system, dataset, model, and experiment-level metrics, and providing the analytic means to assess trade-offs and compare different model and training configurations.

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Section: Related Workmentioning

confidence: 99%

FedMon: A Federated Learning Monitoring Toolkit

Symeonides,

Trihinas,

Nikolaidis

2024

IoT

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“…It is becoming increasingly evident that the rise of cloud computing and the rise of AI are mutually reinforcing. As a result, using AI in the cloud can improve the cloud's performance, efficiency, and digital transformation [42]. AI in the cloud computing environment is a crucial key to enabling organisations to become more efficient, strategic and insight-driven, while at the same time providing greater flexibility, agility and cost savings [43].…”

Section: Cloud Computingmentioning

confidence: 99%

AI for Next Generation Computing: Emerging Trends and Future Directions

Gill,

Xu,

Ottaviani

et al. 2022

Preprint

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Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

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