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Timeloops: System Call Policy Learning for Containerized Microservices.

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Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer., , , , , , и . ACM SIGOPS Oper. Syst. Rev., 53 (1): 34-39 (2019)Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices., , , , , , и . CoRR, (2019)An Open-Source Benchmark Suite for Cloud and IoT Microservices., , , , , , , , , и 14 other автор(ы). CoRR, (2019)Timeloops: System Call Policy Learning for Containerized Microservices., , , и . CoRR, (2022)MUSE: Multi-Use Error Correcting Codes., , , , , и . CoRR, (2021)Unveiling the Hardware and Software Implications of Microservices in Cloud and Edge Systems., , , , , , , , , и 14 other автор(ы). IEEE Micro, 40 (3): 10-19 (2020)Revisiting Residue Codes for Modern Memories., , , , , и . IEEE Micro, 43 (4): 53-61 (июля 2023)Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices., , , , , , и . ASPLOS, стр. 19-33. ACM, (2019)Revisiting Residue Codes for Modern Memories., , , , , и . MICRO, стр. 73-90. IEEE, (2022)An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems., , , , , , , , , и 14 other автор(ы). ASPLOS, стр. 3-18. ACM, (2019)