Polaris Scheduler: Edge Sensitive and SLO Aware Workload Scheduling in Cloud-Edge-IoT Clusters

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

doi:10.1109/cloud53861.2021.00034

Cited by 25 publications

(2 citation statements)

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“…Through a review on literature one can find three different approaches. First, there is research targeting specific topics of these tiers, such as data caching [2], [3], service deployment [4], computation offloading [5], [6], resource allocation [7], scheduling [8], [9], traffic grooming [10] or service decomposition [11], among others. Second, there is research that addresses these specific topics but they are contextualized in a specific domain.…”

Section: Related Workmentioning

confidence: 99%

On Distributed Computing Continuum Systems

Dustdar

Pujol

Donta

2023

IEEE Trans. Knowl. Data Eng.

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This article presents our vision on the need of developing new managing technologies to harness distributed "computing continuum" systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software. We present a new methodology to manage distributed "computing continuum" systems. This is based on a mathematical artifact called Markov Blanket, which sets these systems in a Markovian space, more suitable to cope with their complex characteristics. Furthermore, we develop the concept of equilibrium for these systems, providing a more flexible management framework compared with the one based on thresholds, currently in use for Internet-based distributed systems. Finally, we also link the equilibrium with the development of adaptive mechanisms. However, we are aware that developing the entire methodology requires a big effort and the use of learning techniques, therefore, we finish this article with an overview of the techniques required to develop this methodology.

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Section: Related Workmentioning

confidence: 99%

On Distributed Computing Continuum Systems

Dustdar

Pujol

Donta

2023

IEEE Trans. Knowl. Data Eng.

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“…Distributed computing has evolved from content delivery networks to become a generally accepted and commonly used edge computing paradigm that brings processing and data storage closer to the end user's location [102] [103]. Instant data that is created by the user, and only for the user, requires compute and storage on the edge, but big data always requires cloud-based storage [104].…”

Section: Edge 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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