AIOps: Predictive Analytics &amp; Machine Learning in Operations

Masood, Adnan; Hashmi, Adnan

doi:10.1007/978-1-4842-4106-6_7

Cited by 39 publications

(17 citation statements)

References 0 publications

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“…Cloud is like a video game, which emits an enormous quantity of operating data and telemetry, much like a Tesla electric vehicle [45]. As a result, AI-based cloud computing is basically AI Ops, which uses algorithms to make sense of all this data rather than relying on humans [46,47]. In the post-COVID future, cloud-computing investment increased by 37 percent to $29 billion in the first quarter of 2020 compared to the first quarter of 2019 [48].…”

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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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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“…However, such intelligent mechanisms can only take place with the appropriate visibility and reaction over performance data across all disparate Cloud-to-Edge resources. AIOps [6] has recently been proposed as an effective paradigm to exploit AI/ML techniques towards IT operations automation, by correlating data across different interdependent environments and providing real-time, actionable insights over system behaviors, as well as recommendations and (semi-)automated corrective actions. AIOps services provide timely awareness and proactive actions over service quality degradation, resource utilization changes and system mis-configurations, using event management mechanisms combined with application logic to identify root causes and to trigger appropriate restorative management workflows.…”

Section: Aiops For Elastic Cloud To Edge Intelligencementioning

confidence: 99%

MEDAL: An AI-driven Data Fabric Concept for Elastic Cloud-to-Edge Intelligence

Theodorou¹,

Gerostathopoulos²,

Alshabani³

et al. 2021

Preprint

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Current Cloud solutions for Edge Computing are inefficient for datacentric applications, as they focus on the IaaS/PaaS level and they miss the data modeling and operations perspective. Consequently, Edge Computing opportunities are lost due to cumbersome and data assets-agnostic processes for end-to-end deployment over the Cloud-to-Edge continuum. In this paper, we introduce MEDALan intelligent Cloud-to-Edge Data Fabric to support Data Operations (DataOps) across the continuum and to automate management and orchestration operations over a combined view of the data and the resource layer. MEDAL facilitates building and managing data workflows on top of existing flexible and composable data services, seamlessly exploiting and federating IaaS/PaaS/SaaS resources across different Cloud and Edge environments. We describe the MEDAL Platform as a usable tool for Data Scientists and Engineers, encompassing our concept and we illustrate its application though a connected cars use case

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“…As the deluge of data generated by IoT continues to increase, and as demands from new use cases increasingly require edge deployments, e.g. vCDN, the ability of cloud service providers and cloud carriers to respond quickly to demands on infrastructure, service incidents, and improve on key metrics decreases (Masood and Hashmi 2019). Increasingly, enterprises are looking to AI for IT Operations (or AIOps).…”

Section: Machine Learning and Ai For It Operationsmentioning

confidence: 99%

“…1. automating and enhancing routine IT operations so that expensive and scarce IT staff have more time to focus on high value tasks, 2. predicting and recognising anomalies, serious issues, and outages more quickly and with greater accuracy than humanly possible thereby reducing mean time to detect (MTTD) and increasing mean time to failure (MTTF), and 3. suggesting intelligent remediation that reduces mean time to repair (MTTR) (IBM 2019;Masood and Hashmi 2019).…”

Section: Machine Learning and Ai For It Operationsmentioning

confidence: 99%