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检索条件"任意字段=2023 IEEE/ACM International Workshop on Cloud Intelligence and AIOps, AIOps 2023"

共 12 条记录，以下是1-10 订阅

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Proceedings - 2023 ieee/acm international workshop on cloud intelligence and aiops, aiops 2023

Proceedings - 2023 IEEE/ACM International Workshop on Cloud ...

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2023 ieee/acm international workshop on cloud intelligence and aiops, aiops 2023

The proceedings contain 3 papers. The topics discussed include: knowledge-based intelligent system for IT incident DevOps;SoK: machine learning for continuous integration;and monitoring workload performance in noisy n...

ISBN: (纸本)9798350323740

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Message from cloud intelligence / aiops workshop 2025 Chairs

Message from Cloud Intelligence / AIOps Workshop 2025 Chairs

引用

cloud intelligence & aiops (aiops), ieee/acm international workshop on

作者： Jian Zhang Qingwei Lin Xin Peng Microsoft Redmond USA Microsoft Beijing China Fudan University Shanghai China

ISBN: (数字)9798331526382

ISBN: (纸本)9798331526399

The 6th international workshop on cloud intelligence / aiops is a forum for researchers, scientists, engineers, and practitioners to share and learn AI/ML powered DevOps solutions for cloud services. We envision that with the advance of AI/ML and related technologies like Large Language Models (LLMs), the cloud industry can make significant progress in its efficiency and reliability while keeping up the sustained and exponential growth of the cloud.

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aiops: Analysing cloud Failure Detection Approaches for Enhanced Operational Efficency 1

AIOps: Analysing Cloud Failure Detection Approaches for Enha...

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1st ieee international Conference on Applied intelligence and Sustainable Computing, ICAISC 2023

作者： Kumar, Prashanth Shreyas Kumar, Hp University of Newcastle School of Information and Physical Sciences College of Engineering Science and Environment Callaghan Australia SC Ventures by Standard Chartered Department of Financial Services Bengaluru India

ISBN: (纸本)9798350323795

In recent years, Modern society is progressively moving in the direction of complex and distributed computing structures. Because of the high complexity of the organization, management teams execute regular checking and resolving processes to extend the consistency and reliability of modern system applications. Hence, the concept of automated and intelligent systems is receiving more attention from Information Technology (IT), manufacturing, and academic fields. In the modern day, IT organizations and institutions are growing faster to reduce human supervision because it is more problematic to handle. Therefore, in this research, Artificial intelligence for IT Operations (aiops) is analyzed to overcome modern IT management difficulties due to AI and Big Data. Even though, the research progress on aiops is still unstructured and unknown, because of lacking principles in classifying impacts for data necessities, target goals, and factors. This review work provides an in-depth analysis to gather and establish several contributions to aiops in a unique reference index. This research is focused to minimize cloud service interruption and ensure high system accessibility by utilizing AI methods. This comprehensive research supports researchers in accomplishing a better solution for cloud failure detection. © 2023 ieee.

关键词： Human resource management

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Partial Granger causality-based feature selection algorithm for workload prediction in cloud systems 14

Partial Granger causality-based feature selection algorithm ...

引用

14th international Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Changhoon Ko, Eunsoo Song, Minjae Yun, Hoyeong Kim, Wooju Artificial Intelligence Lab Okestro Seoul Korea Republic of Yonsei University Dept. of Industrial Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

The development of the cloud technology led to the rising interest in multi/hybrid cloud and emergence of artificial intelligence for IT operations(aiops). The core elements of aiops involve predicting how each metric of the data center will change in the future. Compared to general multivariate time-series prediction problems, causal relationships between each variable have a significant impact on cloud metric prediction. This paper focuses on the causality between variables, which partially arises when a specific event occurs in a cloud data center to achieve good predictive performance. The proposed model detects partial causality and sums it up again to extract key variables that explain the target variable well. Through this, variables with higher predictive performance than existing methods were found. We also propose a structure that improves the performance of the prediction model and minimizes inference time through a variable selection technique based on partial causality. By applying this to the actual operating cloud environment, it was proved that it can be effectively applied to the real world. © 2023 ieee.

关键词： aiops Feature Selection Granger Causality Prediction

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Orchestrating Cross-Layer Anomaly Detection and Mitigation to Address Gray Failures in Large-Scale cloud Infrastructure

Orchestrating Cross-Layer Anomaly Detection and Mitigation t...

引用

cloud intelligence & aiops (aiops), ieee/acm international workshop on

作者： Ze Li Chang Lou Vignatha Yenugutala Vivek Ramamurthy Eion Blanchard Minghua Ma Murali Chintalapati Microsoft Azure Microsoft USA University of Virginia USA Microsoft USA

ISBN: (数字)9798331526382

ISBN: (纸本)9798331526399

cloud infrastructure in production constantly experiences gray failures: a degraded state in which failures go undetected by system mechanisms, yet adversely affect end-users. Addressing the underlying anomalies on host nodes is crucial for such gray failures. However, current approaches suffer from two key limitations: first, existing detection relies solely on singulardimension signals from hosts, thus often suffering from biased views due to differential observability; second, existing mitigation actions are often insufficient, primarily consisting of host-level operations such as reboots, which leave most production issues to manual intervention. This paper presents Panacea, a holistic framework to automatically detect and mitigate host anomalies, addressing gray failures in production cloud infrastructure. PANACEA expands beyond host-level scope: it aggregates and correlates insights from VMs and application layers to bridge the detection gap, and orchestrates fine-grained and safe mitigation across all levels. Panacea is versatile, designed to support a wide range of anomalies. It has been deployed in production at millions of hosts.

关键词： Bridges Cross layer design Prevention and mitigation Aggregates Production Reliability Observability Anomaly detection

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Automated Bug Discovery in cloud Infrastructure-as-Code Updates with LLM Agents

Automated Bug Discovery in Cloud Infrastructure-as-Code Upda...

引用

cloud intelligence & aiops (aiops), ieee/acm international workshop on

作者： Yiming Xiang Zhenning Yang Jingjia Peng Hermann Bauer Patrick Tser Jern Kon Yiming Qiu Ang Chen University of Michigan

ISBN: (数字)9798331526382

ISBN: (纸本)9798331526399

cloud environments are increasingly managed by Infrastructure-as-Code (IaC) platforms (e.g., Terraform), which allow developers to define their desired infrastructure as a configuration program that describes cloud resources and their dependencies. This shields developers from low-level operations for creating and maintaining resources, since they are automatically performed by IaC platforms when compiling and deploying the configuration. However, while IaC platforms are rigorously tested for initial deployments, they exhibit myriad errors for runtime updates, e.g., adding/removing resources and dependencies. IaC updates are common because cloud infrastructures are long-lived but user requirements fluctuate over time. Unfortunately, our experience shows that updates often introduce subtle yet impactful bugs. The update logic in IaC frameworks is hard to test due to the vast and evolving search space, which includes diverse infrastructure setups and a wide range of provided resources with new ones frequently added. We introduce TerraFault, an automated, efficient, LLM-guided system for discovering update bugs, and report our findings with an initial prototype. TerraFault incorporates various optimizations to navigate the large search space efficiently and employs techniques to accelerate the testing process. Our prototype has successfully identified bugs even in simple IaC updates, showing early promise in systematically identifying update bugs in today's IaC frameworks to increase their reliability.

关键词： Software testing Runtime Navigation Computer bugs Prototypes Life estimation Debugging Software reliability Logic Optimization

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Automated Lifting for cloud Infrastructure-as-Code Programs

Automated Lifting for Cloud Infrastructure-as-Code Programs

引用

cloud intelligence & aiops (aiops), ieee/acm international workshop on

作者： Jingjia Peng Yiming Qiu Patrick Tser Jern Kon Pinhan Zhao Yibo Huang Zheng Guo Xinyu Wang Ang Chen University of Michigan Ann Arbor

ISBN: (数字)9798331526382

ISBN: (纸本)9798331526399

Infrastructure-as-code (IaC) is reshaping how cloud resources are managed. IaC users write high-level programs to define their desired infrastructure, and the underlying IaC platforms automatically deploy the constituent resources into the cloud. While proven powerful at creating greenfield deployments (i.e., deployments from scratch), existing IaC platforms provide limited support for managing brownfield infrastructure (i.e., existing, non-IaC deployments). This hampers migration from legacy management approaches to an IaC workflow and hinders wider IaC adoption. Managing brownfield deployments requires techniques to lift low-level cloud states and translate them into corresponding IaC programs—the reversal of the regular deployment process. Existing tools rely heavily on rule-based reverse engineering, which suffers from the lack of automation, limited resource coverage, and prevalence of errors. In this work, we lay out the vision for Lilac, an approach that frees IaC lifting from extensive manual engineering. Lilac brings the best of both worlds: leveraging LLMs to automate lifting rule extraction, coupled with symbolic methods to provide correctness assurance. We envision that Lilac would enable the construction of an automated and provider-agnostic lifting tool with high coverage and accuracy. A prototype of LILAC is available here.

关键词： Automation Accuracy Reverse engineering Prototypes Manuals Resource management

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Incorporating Startup Delay into Collaborative Edge Computing for Superior Task Efficiency 32

Incorporating Startup Delay into Collaborative Edge Computin...

引用

ieee/acm 32nd international Symposium on Quality of Service (IWQoS)

作者： Xu, Changfu Guo, Jianxiong Zeng, Jiandian Lie, Yupeng Cao, Jiannong Wang, Tian BNU HKBU United Int Coll Guangdong Prov Key Lab IRADS Zhuhai Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai Peoples R China

ISBN: (纸本)9798350350128

Collaborative edge computing enables low service delay fir many delay-sensitive Internet of Things applications through edge-edge and edge-cloud collaborations. Due to the limited edge resources and varying task demands, optimizing Joint Service Placement and Task Offloading (JSPTO) becomes crucial in minimizing overall processing delays. However, existing,JSPTO methods overlook the impact of service startup delay, which may undermine total latency reduction, especially in scenarios with large startup delays. This paper introduces an online JSPTO method that integrates the consideration of service startup delay to enhance task offloading efficiency. However, a significant challenge is ensuring timely service response with large startup delays. We formulate this problem as an integer linear programming problem, aiming to minimize the total service startup and task processing delay. We propose a novel algorithm called SD-JSPTO, which performs online JSPTO in the presence of large startup delays. Theoretical performance analyses reveal that SD-JSPTO attains a near-optimal solution within polynomial time, demonstrating a competitive ratio of 1+A(2)/VTopt, Experimental evaluations demonstrate that our method significantly reduces the total delay by no less than 18.72% compared to state-of-the-art baseline methods while preserving system stability.

关键词： Collaborative edge computing Startup delay-aware Superior task efficiency Service placement

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How Far Have Edge clouds Gone? A Spatial-Temporal Analysis of Edge Network Latency In the Wild 31

How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis o...

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31st ieee/acm international Symposium on Quality of Service, IWQoS 2023

作者： Zhang, Heng Huang, Shaoyuan Xu, Mengwei Guo, Deke Wang, Xiaofei Leung, Victor C.M. Wang, Wenyu College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada Co. Ltd Shanghai China

ISBN: (纸本)9798350399738

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds. © 2023 ieee.

关键词： Edge computing

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A Holistic QoS View of Crowdsourced Edge cloud Platform 31

A Holistic QoS View of Crowdsourced Edge Cloud Platform

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31st ieee/acm international Symposium on Quality of Service, IWQoS 2023

作者： Shen, Shihao Feng, Yicheng Xu, Mengwei Zhang, Cheng Wang, Xiaofei Wang, Wenyu Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China Institute of Technology Tianjin University of Finance and Economics Tianjin China Co. Ltd. Shanghai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350399738

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: (1) First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, and what are the major abnormal behaviors. (2) Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. (3) Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users. © 2023 ieee.

关键词： Edge computing

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