Building AI Agents for Autonomous Clouds: Challenges and Design Principles
Authors: 
Manish Shetty, Yinfang Chen, Gagan Somashekar, Minghua Ma, 
Yogesh Simmhan, Xuchao Zhang, Jonathan Mace, Dax Vandevoorde, Pedro Las-Casas, Shachee Mishra Gupta, Suman Nath, Chetan Bansal, Saravan RajmohanAuthors Info & Claims
Pages 99 - 110
Abstract
The rapid growth in the use of Large Language Models (LLMs) and AI Agents as part of software development and deployment is revolutionizing the information technology landscape. While code generation receives significant attention, a higher-impact application lies in using agents for the operational resilience of cloud services, which currently require significant human effort and domain knowledge. There is a growing interest in AI for IT Operations (AIOps), which aims to automate complex operational tasks, like fault localization and root cause analysis, reducing human intervention and customer impact. However, achieving the vision of autonomous and self-healing clouds through AIOps is hampered by the lack of standardized frameworks for building, evaluating, and improving AIOps agents. This vision paper lays the groundwork for such a framework by framing the requirements and then discussing design decisions that satisfy them. We also propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults. We report promising results and lay the groundwork to build a modular and robust framework for building, evaluating, and improving agents for autonomous clouds.
References
[1]
2022. AWS Fault Injection Simulator. https://aws.amazon.com/fis/.
[2]
2022. Jepsen. https://jepsen.io/.
[3]
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
[4]
AgentOps-AI. 2024. agentops. https://github.com/AgentOps-AI/agentops.
[5]
Toufique Ahmed, Supriyo Ghosh, Chetan Bansal, Thomas Zimmermann, Xuchao Zhang, and Saravan Rajmohan. 2023. Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models. In Proceedings of the 45th International Conference on Software Engineering (ICSE'23).
[6]
Aider. 2024. How aider scored SOTA 26.3% on SWE Bench Lite. https://aider.chat/2024/05/22/swe-bench-lite.html.
[7]
Ramnatthan Alagappan, Aishwarya Ganesan, Yuvraj Patel, Thanumalayan Sankaranarayana Pillai, Andrea C. Arpaci-Dusseau, and Remzi H. Arpaci-Dusseau. 2016. Correlated Crash Vulnerabilities. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI'16).
[8]
Ahmed Alquraan, Hatem Takruri, Mohammed Alfatafta, and Samer Al-Kiswany. 2018. An Analysis of Network-Partitioning Failures in Cloud Systems. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI'18).
[9]
George Amvrosiadis, Jun Woo Park, Gregory R. Ganger, Garth A. Gibson, Elisabeth Baseman, and Nathan DeBardeleben. 2018. On the diversity of cluster workloads and its impact on research results. In 2018 USENIX Annual Technical Conference (USENIX ATC 18). USENIX Association, Boston, MA, 533--546. https://www.usenix.org/conference/atc18/presentation/amvrosiadis
[10]
Vaastav Anand, Deepak Garg, Antoine Kaufmann, and Jonathan Mace. 2023. Blueprint: A Toolchain for Highly-Reconfigurable Microservice Applications. In Proceedings of the 29th Symposium on Operating Systems Principles (Koblenz, Germany) (SOSP '23). Association for Computing Machinery, New York, NY, USA, 482--497. https://doi.org/10.1145/3600006.3613138
[11]
Shrey Baheti, Shreyas Badiger, and Yogesh Simmhan. 2021. VIoLET: An Emulation Environment for Validating IoT Deployments at Large Scales. ACM Trans. Cyber-Phys. Syst. 5, 3, Article 25 (jul 2021), 39 pages. https://doi.org/10.1145/3446346
[12]
Radu Banabic and George Candea. 2012. Fast Black-Box Testing of System Recovery Code. In Proceedings of the 7th European Conference on Computer Systems (EuroSys'12).
[13]
Chetan Bansal, Sundararajan Renganathan, Ashima Asudani, Olivier Midy, and Mathru Janakiraman. 2020. Decaf: Diagnosing and triaging performance issues in large-scale cloud services. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice.
[14]
Rodrigo N Calheiros, Rajiv Ranjan, Anton Beloglazov, César AF De Rose, and Rajkumar Buyya. 2011. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41, 1 (2011), 23--50.
[15]
Marco Canini, Daniele Venzano, Peter Perešíni, Dejan Kostić, and Jennifer Rexford. 2012. A NICE Way to Test OpenFlow Applications. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI'12).
[16]
Chaos Mesh Community. [n. d.]. ChaosMesh. https://chaos-mesh.org/. Accessed: 2024-07-08.
[17]
ChaosBlade Team. [n. d.]. ChaosBlade. https://github.com/chaosblade-io/chaosblade. Accessed: 2024-07-08.
[18]
Haicheng Chen, Wensheng Dou, Dong Wang, and Feng Qin. 2020. CoFI: Consistency-Guided Fault Injection for Cloud Systems. In Proceedings of the 35th ACM/IEEE International Conference on Automated Software Engineering (ASE'20).
[19]
Junjie Chen, Xiaoting He, Qingwei Lin, Yong Xu, Hongyu Zhang, Dan Hao, Feng Gao, Zhangwei Xu, Yingnong Dang, and Dongmei Zhang. 2019. An empirical investigation of incident triage for online service systems. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP'19).
[20]
Junjie Chen, Xiaoting He, Qingwei Lin, Hongyu Zhang, Dan Hao, Feng Gao, Zhangwei Xu, Yingnong Dang, and Dongmei Zhang. 2019. Continuous incident triage for large-scale online service systems. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19).
[21]
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021).
[22]
Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, Jun Zeng, Supriyo Ghosh, Xuchao Zhang, Chaoyun Zhang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, and Tianyin Xu. 2024. Automatic Root Cause Analysis via Large Language Models for Cloud Incidents. In Proceedings of the Nineteenth European Conference on Computer Systems, EuroSys 2024, Athens, Greece, April 22-25, 2024. ACM, 674--688. https://doi.org/10.1145/3627703.3629553
[23]
Maria Christakis, Patrick Emmisberger, Patrice Godefroid, and Peter Müller. 2017. A General Framework for Dynamic Stub Injection. In Proceedings of the 39th International Conference on Software Engineering (ICSE'17).
[24]
Yuanshun Dai, Yanping Xiang, and Gewei Zhang. 2009. Self-healing and Hybrid Diagnosis in Cloud Computing. In Cloud Computing, Martin Gilje Jaatun, Gansen Zhao, and Chunming Rong (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg.
[25]
Andrea Detti, Ludovico Funari, and Luca Petrucci. 2023. μBench: An Open-Source Factory of Benchmark Microservice Applications. IEEE Transactions on Parallel and Distributed Systems 34, 3 (2023), 968--980. https://doi.org/10.1109/TPDS.2023.3236447
[26]
Envoy Docs. 2022. Envoy Fault Injection. https://www.envoyproxy.io/docs/envoy/latest/api-v3/extensions/filters/http/fault/v3/fault.proto.
[27]
Michael Ferdman, Almutaz Adileh, Onur Kocberber, Stavros Volos, Mohammad Alisafaee, Djordje Jevdjic, Cansu Kaynak, Adrian Daniel Popescu, Anastasia Ailamaki, and Babak Falsafi. 2012. Clearing the clouds: a study of emerging scale-out workloads on modern hardware. Acm sigplan notices 47, 4 (2012), 37--48.
[28]
Yu Gan, Yanqi Zhang, Dailun Cheng, Ankitha Shetty, Priyal Rathi, Nayan Katarki, Ariana Bruno, Justin Hu, Brian Ritchken, Brendon Jackson, et al. 2019. An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. 3--18.
[29]
Vaibhav Ganatra, Anjaly Parayil, Supriyo Ghosh, Yu Kang, Minghua Ma, Chetan Bansal, Suman Nath, and Jonathan Mace. 2023. Detection Is Better Than Cure: A Cloud Incidents Perspective. In Proceedings of the 31st Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE).
[30]
Peter Garraghan, Renyu Yang, Zhenyu Wen, Alexander Romanovsky, Jie Xu, Rajkumar Buyya, and Rajiv Ranjan. 2018. Emergent Failures: Rethinking Cloud Reliability at Scale. IEEE Cloud Computing 5, 5 (2018), 12--21. https://doi.org/10.1109/MCC.2018.053711662
[31]
Supriyo Ghosh, Manish Shetty, Chetan Bansal, and Suman Nath. 2022. How to fight production incidents? an empirical study on a large-scale cloud service. In Proceedings of the 13th Symposium on Cloud Computing. 126--141.
[32]
Jiawei Tyler Gu, Xudong Sun, Wentao Zhang, Yuxuan Jiang, Chen Wang, Mandana Vaziri, Owolabi Legunsen, and Tianyin Xu. 2023. Acto: Automatic End-to-End Testing for Operation Correctness of Cloud System Management. In Proceedings of the 29th ACM Symposium on Operating Systems Principles (SOSP'23).
[33]
Haryadi S. Gunawi, Thanh Do, Pallavi Joshi, Peter Alvaro, Joseph M. Hellerstein, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Koushik Sen, and Dhruba Borthakur. 2011. Fate and Destini: A Framework for Cloud Recovery Testing. In Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI'11).
[34]
Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao. 2022. Adbench: Anomaly detection benchmark. Advances in Neural Information Processing Systems 35 (2022), 32142--32159.
[35]
Johann Hauswald, Michael A Laurenzano, Yunqi Zhang, Cheng Li, Austin Rovinski, Arjun Khurana, Ronald G Dreslinski, Trevor Mudge, Vinicius Petrucci, Lingjia Tang, et al. 2015. Sirius: An open end-to-end voice and vision personal assistant and its implications for future warehouse scale computers. In Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems. 223--238.
[36]
Shilin He, Botao Feng, Liqun Li, Xu Zhang, Yu Kang, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang. 2023. STEAM: Observability-Preserving Trace Sampling. Association for Computing Machinery, New York, NY, USA, 1750--1761. https://doi.org/10.1145/3611643.3613881
[37]
Shilin He, Xu Zhang, Pinjia He, Yong Xu, Liqun Li, Yu Kang, Minghua Ma, Yining Wei, Yingnong Dang, Saravanakumar Rajmohan, et al. 2022. An empirical study of log analysis at Microsoft. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE).
[38]
Helm. 2024. Helm: The package manager for Kubernetes.
[39]
Victor Heorhiadi, Shriram Rajagopalan, Hani Jamjoom, Michael K Reiter, and Vyas Sekar. 2016. Gremlin: Systematic Resilience Testing of Microservices. In Proceedings of the IEEE 36th International Conference on Distributed Computing Systems (ICDCS'16).
[40]
Galen Hunt and Doug Brubacher. 1999. Detours: Binary Interception of Win32 Functions. In Proceedings of the 3rd USENIX Windows NT Symposium.
[41]
Istio Docs. 2022. Istio Fault Injection. https://istio.io/latest/docs/tasks/traffic-management/fault-injection/.
[42]
Vincent Jacob, Fei Song, Arnaud Stiegler, Yanlei Diao, and Nesime Tatbul. 2020. Anomalybench: An open benchmark for explainable anomaly detection. CoRR abs/2010.05073 (2020).
[43]
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. 2024. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code. arXiv preprint arXiv:2403.07974 (2024).
[44]
Naman Jain, Manish Shetty, Tianjun Zhang, King Han, Koushik Sen, and Ion Stoica. 2024. R2E: Turning any Github Repository into a Programming Agent Environment. In Forty-first International Conference on Machine Learning.
[45]
Jiajun Jiang, Weihai Lu, Junjie Chen, Qingwei Lin, Pu Zhao, Yu Kang, Hongyu Zhang, Yingfei Xiong, Feng Gao, Zhangwei Xu, et al. 2020. How to mitigate the incident? an effective troubleshooting guide recommendation technique for online service systems. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE'20).
[46]
Zu-Ming Jiang, Jia-Ju Bai, Kangjie Lu, and Shi-Min Hu. 2020. Fuzzing Error Handling Code using Context-Sensitive Software Fault Injection. In Proceedings of the 29th USENIX Security Symposium.
[47]
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. 2024. SWE-bench: Can Language Models Resolve Real-world Github Issues?. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=VTF8yNQM66
[48]
Pengxiang Jin, Shenglin Zhang, Minghua Ma, Haozhe Li, Yu Kang, Liqun Li, Yudong Liu, Bo Qiao, Chaoyun Zhang, Pu Zhao, et al. 2023. Assess and Summarize: Improve Outage Understanding with Large Language Models. In Proceedings of the Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE).
[49]
Xiaoen Ju, Livio Soares, Kang G. Shin, Kyung Dong Ryu, and Dilma Da Silva. 2013. On Fault Resilience of OpenStack. In Proceedings of the 12th ACM Symposium on Cloud Computing (SOCC'13).
[50]
Sayash Kapoor, Benedikt Stroebl, Zachary S Siegel, Nitya Nadgir, and Arvind Narayanan. 2024. AI Agents That Matter. arXiv preprint arXiv:2407.01502 (2024).
[51]
Harshad Kasture and Daniel Sanchez. 2016. Tailbench: a benchmark suite and evaluation methodology for latency-critical applications. In 2016 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 1--10.
[52]
Nane Kratzke and Peter-Christian Quint.2016. ppbench-A Visualizing Network Benchmark for Microservices. In CLOSER (2). 223--231.
[53]
Varad Kulkarni et al. 2024. XFBench: A Cross-Cloud Benchmark Suite for Evaluating FaaS Workflow Platforms. In 24th IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGRID).
[54]
Tanakorn Leesatapornwongsa, Mingzhe Hao, Pallavi Joshi, Jeffrey F. Lukman, and Haryadi S. Gunawi. 2014. SAMC: Semantic-Aware Model Checking for Fast Discovery of Deep Bugs in Cloud Systems. In Proceedings of the 11th USENIX Conference on Operating Systems Design and Implementation (OSDI'14).
[55]
Liqun Li, Xu Zhang, Xin Zhao, Hongyu Zhang, Yu Kang, Pu Zhao, Bo Qiao, Shilin He, Pochian Lee, Jeffrey Sun, et al. 2021. Fighting the fog of war: Automated incident detection for cloud systems. In 2021 USENIX Annual Technical Conference (USENIX ATC 21). 131--146.
[56]
Wenrui Li, Pengcheng Zhang, and Zhongxue Yang. 2012. A Framework for Self-Healing Service Compositions in Cloud Computing Environments. In 2012 IEEE 19th International Conference on Web Services. https://doi.org/10.1109/ICWS.2012.109
[57]
Zeyan Li, Junjie Chen, Rui Jiao, Nengwen Zhao, Zhijun Wang, Shuwei Zhang, Yanjun Wu, Long Jiang, Leiqin Yan, Zikai Wang, et al. 2021. Practical root cause localization for microservice systems via trace analysis. In 2021 IEEE/ACM 29th International Symposium on Quality of Service.
[58]
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics 12 (2024), 157--173.
[59]
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al. 2023. Agent-bench: Evaluating llms as agents. arXiv preprint arXiv:2308.03688 (2023).
[60]
Yuhe Liu, Changhua Pei, Longlong Xu, Bohan Chen, Mingze Sun, Zhirui Zhang, Yongqian Sun, Shenglin Zhang, Kun Wang, Haiming Zhang, et al. 2023. OpsEval: A Comprehensive Task-Oriented AIOps Benchmark for Large Language Models. arXiv preprint arXiv:2310.07637 (2023).
[61]
Jie Lu, Chen Liu, Lian Li, Xiaobing Feng, Feng Tan, Jun Yang, and Liang You. 2019. CrashTuner: Detecting Crash-Recovery Bugs in Cloud Systems via Meta-Info Analysis. In Proceedings of the 26th ACM Symposium on Operating System Principles (SOSP'19).
[62]
Minghua Ma, Zheng Yin, Shenglin Zhang, Sheng Wang, Christopher Zheng, Xinhao Jiang, Hanwen Hu, Cheng Luo, Yilin Li, Nengjun Qiu, et al. 2020. Diagnosing root causes of intermittent slow queries in cloud databases. Proceedings of the VLDB Endowment (VLDB'20) (2020).
[63]
Minghua Ma, Shenglin Zhang, Junjie Chen, Jim Xu, Haozhe Li, Yongliang Lin, Xiaohui Nie, Bo Zhou, Yong Wang, and Dan Pei. 2021. Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems. In 2021 USENIX Annual Technical Conference (ATC'21).
[64]
Minghua Ma, Shenglin Zhang, Dan Pei, Xin Huang, and Hongwei Dai. 2018. Robust and rapid adaption for concept drift in software system anomaly detection. In 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE'18).
[65]
Rupak Majumdar and Filip Niksic. 2018. Why is Random Testing Effective for Partition Tolerance Bugs?. In Proceedings of the 45th ACM SIGPLAN Symposium on Principles of Programming Languages (POPL'18).
[66]
Paul D. Marinescu, Radu Banabic, and George Candea. 2010. An Extensible Technique for High-Precision Testing of Recovery Code. In Proceedings of the 2010 USENIX Annual Technical Conference (USENIX ATC'10).
[67]
Paul D Marinescu and George Candea. 2009. LFI: A Practical and General Library-Level Fault Injector. In Proceedings of the 39th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'09).
[68]
Paul D. Marinescu and George Candea. 2011. Efficient Testing of Recovery Code Using Fault Injection. ACM Transactions on Computer Systems (TOCS) 29, 4 (Dec. 2011), 1--38.
[69]
Christopher S. Meiklejohn, Andrea Estrada, Yiwen Song, Heather Miller, and Rohan Padhye. 2021. Service-Level Fault Injection Testing. In Proceedings of the 2013 ACM Symposium on Cloud Computing (SOCC'21).
[70]
Jayashree Mohan, Ashlie Martinez, Soujanya Ponnapalli, Pandian Raju, and Vijay Chidambaram. 2018. Finding Crash-Consistency Bugs with Bounded Black-Box Crash Testing. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI'18).
[71]
Netflix. [n.d.]. ChaosMonkey. https://github.com/Netflix/chaosmonkey. Accessed: 2024-07-08.
[72]
Paolo Notaro, Jorge Cardoso, and Michael Gerndt. 2021. A survey of aiops methods for failure management. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 6 (2021), 1--45.
[73]
Thanumalayan Sankaranarayana Pillai, Vijay Chidambaram, Samer Al Kiswany, Andrea C. Arpaci-Dusseau, and Remzi H. Arpaci-Dusseau. 2014. All File Systems Are Not Created Equal: On the Complexity of Crafting Crash-Consistent Applications. In Proceedings of the 11th USENIX Conference on Operating Systems Design and Implementation (OSDI'14).
[74]
Reuters. 2023. Microsoft cloud outage hits users around the world. (2023). https://www.cnn.com/2023/01/25/tech/microsoft-cloud-outage-worldwide-trnd/index.html
[75]
Patrick Reynolds, Charles Killian, Janet L. Wiener, Jeffrey C. Mogul, Mehul A. Shah, and Amin Vahdat. 2006. Pip: Detecting the Unexpected in Distributed Systems. In Proceedings of the 3rd USENIX Symposium on Networked Systems Design and Implementation (NSDI'06).
[76]
Manish Shetty, Chetan Bansal, Suman Nath, Sean Bowles, Henry Wang, Ozgur Arman, and Siamak Ahari. 2022. DeepAnalyze: learning to localize crashes at scale. In Proceedings of the 44th International Conference on Software Engineering. 549--560.
[77]
Manish Shetty, Chetan Bansal, Sai Pramod Upadhyayula, Arjun Radhakrishna, and Anurag Gupta. 2022. AutoTSG: learning and synthesis for incident troubleshooting. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE'22).
[78]
Jesper Simonsson, Long Zhang, Brice Morin, Benoit Baudry, and Martin Monperrus. 2021. Observability and chaos engineering on system calls for containerized applications in docker. Future Generation Computer Systems 122 (2021), 117--129.
[79]
Vikramank Y. Singh, Kapil Vaidya, Vinayshekhar Bannihatti Kumar, Sopan Khosla, Balakrishnan Narayanaswamy, Rashmi Gangadharaiah, and Tim Kraska. 2024. Panda: Performance Debugging for Databases using LLM Agents. In 14th Conference on Innovative Data Systems Research, CIDR 2024, Chaminade, HI, USA, January 14-17, 2024.
[80]
Amith Singhee and Praveen Jayachandran. 2023. From Clouds to Hybrid Clouds. ACM India Minigraphs (2023).
[81]
Gagan Somashekar, Anurag Dutt, Mainak Adak, Tania Lorido Botran, and Anshul Gandhi. 2024. GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications. In Proceedings of the ACM on Web Conference 2024 (Singapore, Singapore) (WWW '24). Association for Computing Machinery, New York, NY, USA, 3085--3095. https://doi.org/10.1145/3589334.3645665
[82]
Gagan Somashekar, Anurag Dutt, Rohith Vaddavalli, Sai Bhargav Varanasi, and Anshul Gandhi. 2022. B-MEG: Bottlenecked-Microservices Extraction Using Graph Neural Networks. In Companion of the 2022 ACM/SPEC International Conference on Performance Engineering (Bejing, China) (ICPE '22). Association for Computing Machinery, New York, NY, USA, 7--11. https://doi.org/10.1145/3491204.3527494
[83]
Akshitha Sriraman and Thomas F Wenisch. 2018. μ suite: a benchmark suite for microservices. In 2018 ieee international symposium on workload characterization (iiswc). IEEE, 1--12.
[84]
Laura Stevens. 2017. Amazon Finds the Cause of Its AWS Outage: A Typo. (2017). https://www.wsj.com/articles/amazon-finds-the-cause-of-its-aws-outage-a-typo-1488490506
[85]
Xudong Sun, Wenqing Luo, Jiawei Tyler Gu, Aishwarya Ganesan, Ramnatthan Alagappan, Michael Gasch, Lalith Suresh, and Tianyin Xu. 2022. Automatic Reliability Testing for Cluster Management Controllers. In Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI'22).
[86]
The Overleaf Team. 2024. Overleaf: An open-source online real-time collaborative LaTeX editor. https://github.com/overleaf/overleaf.
[87]
Takanori Ueda, Takuya Nakaike, and Moriyoshi Ohara. 2016. Workload characterization for microservices. In 2016 IEEE international symposium on workload characterization (IISWC). IEEE, 1--10.
[88]
Laurens Versluis, Roland Mathá, Sacheendra Talluri, Tim Hegeman, Radu Prodan, Ewa Deelman, and Alexandru Iosup. 2019. The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures - Technical Report. CoRR abs/1906.07471 (2019). arXiv:1906.07471 http://arxiv.org/abs/1906.07471
[89]
Jóakim von Kistowski, Simon Eismann, Norbert Schmitt, André Bauer, Johannes Grohmann, and Samuel Kounev. 2018. TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research. In Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (Milwaukee, WI, USA) (MASCOTS '18).
[90]
Lei Wang, Jianfeng Zhan, Chunjie Luo, Yuqing Zhu, Qiang Yang, Yongqiang He, Wanling Gao, Zhen Jia, Yingjie Shi, Shujie Zhang, et al. 2014. Bigdatabench: A big data benchmark suite from internet services. In 2014 IEEE 20th international symposium on high performance computer architecture (HPCA). IEEE, 488--499.
[91]
Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Lunting Fan, Lingfei Wu, and Qingsong Wen. 2023. Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. arXiv preprint arXiv:2310.16340 (2023).
[92]
Sean Wolfe. 2018. Amazon's one hour of downtime on Prime Day may have cost it up to $100 million in lost sales. (2018). https://www.businessinsider.com/amazon-prime-day-website-issues-cost-it-millions-in-lost-sales-2018-7
[93]
Zhe Xie, Haowen Xu, Wenxiao Chen, Wanxue Li, Huai Jiang, Liangfei Su, Hanzhang Wang, and Dan Pei. 2023. Unsupervised Anomaly Detection on Microservice Traces through Graph VAE. In Proceedings of the ACM Web Conference 2023.
[94]
Xiaohan Yan, Ken Hsieh, Yasitha Liyanage, Minghua Ma, Murali Chintalapati, Qingwei Lin, Yingnong Dang, and Dongmei Zhang. 2023. Aegis: Attribution of Control Plane Change Impact across Layers and Components for Cloud Systems. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP'23).
[95]
John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024. Swe-agent: Agent-computer interfaces enable automated software engineering. arXiv preprint arXiv:2405.15793 (2024).
[96]
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR).
[97]
Tianyi Yu, Qingyuan Liu, Dong Du, Yubin Xia, Binyu Zang, Ziqian Lu, Pingchao Yang, Chenggang Qin, and Haibo Chen. 2020. Characterizing serverless platforms with serverlessbench. In Proceedings of the 11th ACM Symposium on Cloud Computing (Virtual Event, USA) (SoCC '20). Association for Computing Machinery, New York, NY, USA, 30--44. https://doi.org/10.1145/3419111.3421280
[98]
Jun Zeng, Zheng Leong Chua, Yinfang Chen, Kaihang Ji, Zhenkai Liang, and Jian Mao. 2021. WATSON: Abstracting Behaviors from Audit Logs via Aggregation of Contextual Semantics. In Network and Distributed System Security Symposium (NDSS'21).
[99]
Jun Zeng, Xiang Wang, Jiahao Liu, Yinfang Chen, Zhenkai Liang, Tat-Seng Chua, and Zheng Leong Chua. 2022. Shadewatcher: Recommendation-guided cyber threat analysis using system audit records. In 2022 IEEE Symposium on Security and Privacy (S&P'22).
[100]
Zhengran Zeng, Yuqun Zhang, Yong Xu, Minghua Ma, Bo Qiao, Wentao Zou, Qingjun Chen, Meng Zhang, Xu Zhang, Hongyu Zhang, Xuedong Gao, Hao Fan, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. 2023. TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems. In To appear in Proc. of ICSE.
[101]
Pingyu Zhang and Sebastian Elbaum. 2012. Amplifying Tests to Validate Exception Handling Code. In Proceedings of the 34th International Conference on Software Engineering (ICSE'12).
[102]
Qiao Zhang, Guo Yu, Chuanxiong Guo, Yingnong Dang, Nick Swanson, Xinsheng Yang, Randolph Yao, Murali Chintalapati, Arvind Krishnamurthy, and Thomas Anderson. 2018. Deepview: Virtual Disk Failure Diagnosis and Pattern Detection for Azure. In Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI'18).
[103]
Xuchao Zhang, Supriyo Ghosh, Chetan Bansal, Rujia Wang, Minghua Ma, Yu Kang, and Saravan Rajmohan. 2024. Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering (Porto de Galinhas, Brazil) (FSE 2024). Association for Computing Machinery, New York, NY, USA, 266--277. https://doi.org/10.1145/3663529.3663846
[104]
Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024. Autocoderover: Autonomous program improvement. arXiv preprint arXiv:2404.05427 (2024).
[105]
Chenyu Zhao, Minghua Ma, Zhenyu Zhong, Shenglin Zhang, Zhiyuan Tan, Xiao Xiong, LuLu Yu, Jiayi Feng, Yongqian Sun, Yuzhi Zhang, et al. 2023. Robust Multimodal Failure Detection for Microservice Systems. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
[106]
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2024. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems 36 (2024).
[107]
Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, et al. 2023. WebArena: A Realistic Web Environment for Building Autonomous Agents. arXiv preprint arXiv:2307.13854 (2023). https://webarena.dev
[108]
Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chenjie Xu, Chao Ji, and Wenyun Zhao. 2018. Benchmarking microservice systems for software engineering research. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings. 323--324.
[109]
Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chenjie Xu, Chao Ji, and Wenyun Zhao. 2018. Benchmarking microservice systems for software engineering research. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, ICSE 2018, Gothenburg, Sweden, May 27-June 03, 2018, Michel Chaudron, Ivica Crnkovic, Marsha Chechik, and Mark Harman (Eds.). ACM, 323--324. https://doi.org/10.1145/3183440.3194991
[110]
Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, and Michael R Lyu. 2019. Tools and benchmarks for automated log parsing. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 121--130.
Index Terms
- Building AI Agents for Autonomous Clouds: Challenges and Design Principles
Recommendations
Clouds Meet Agents: Toward Intelligent Cloud Services
Cloud computing systems provide large-scale infrastructures for high-performance computing that can adapt to user and application needs. Multi-agent systems (MASs) comprise interacting agents capable of intelligent behavior. Integrating these two ...
A hybrid agent architecture for modeling autonomous agents in SAGE
IDEAL'05: Proceedings of the 6th international conference on Intelligent Data Engineering and Automated LearningThis paper highlights the Hybrid agent construction model being developed that allows the description and development of autonomous agents in SAGE (Scalable, fault Tolerant Agent Grooming Environment) – a second generation FIPA-Compliant Multi-Agent ...
Autonomy of autonomous agents
PRICAI'00: Proceedings of the 6th Pacific Rim international conference on Artificial intelligenceAutonomy is one of the most notable attributes of agency, and this paper presents a formal framework for modelhng it. By representing the mental states of agents, we provide an analysis of some attributes of autonomy. In particular, we define three ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2024
1062 pages
ISBN:9798400712869
DOI:10.1145/3698038
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 November 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
Acceptance Rates
Overall Acceptance Rate 169 of 722 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 166Total Downloads
- Downloads (Last 12 months)166
- Downloads (Last 6 weeks)166
Reflects downloads up to 27 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in