An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems

Data Mining

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Large-Scale Machine Learning with Stochastic Gradient Descent

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The tail at scale

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Clearing the clouds

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Quasar

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A reconfigurable fabric for accelerating large-scale datacenter services

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Data Management in Microservices: State of the Practice, Challenges, and Research DirectionsPreprint

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Heuristic and Reinforcement Learning Algorithms for Dynamic Service Placement on Mobile Edge CloudPreprint

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A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing

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Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms

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Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives

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Mind the Gap

Liu, L., Wang, H., Wang, A., Xiao, M., Cheng, Y., & Chen, S. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486997 · Cited by 10

Characterizing and synthesizing the workflow structure of microservices in ByteDance Cloud

Wen, Y., Cheng, G., Deng, S., & Yin, J. (2022) · Journal of Software Evolution and Process · doi:10.1002/smr.2467 · Cited by 10

Back to the future: N-Versioning of Microservices

Espinoza, A. M., Wood, R., Forrest, S., & Tiwari, M. (2022) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn53405.2022.00049 · Cited by 10

MicroLens: A Performance Analysis Framework for Microservices Using Hidden Metrics With BPF

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Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Liang, M., Fu, W., Feng, L., Lin, Z., Panakanti, P., Zheng, S., Sridharan, S., & Delimitrou, C. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589072 · Cited by 11

POBO: Safe and optimal resource management for cloud microservices

Guo, H., Cao, H., He, J., Liu, X., & Shi, Y. (2023) · Performance Evaluation · doi:10.1016/j.peva.2023.102376 · Cited by 10

μOpt: An Efficient Optimal Autoscaler for Microservice Applications

Incerto, E., Pizziol, R., & Tribastone, M. (2023) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) · doi:10.1109/acsos58161.2023.00024 · Cited by 10

Unveiling the Hardware and Software Implications of Microservices in Cloud and Edge Systems

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PLASMA

Sang, B., Roman, P.-L., Eugster, P. T., Lu, H., Ravi, S., & Petri, G. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387553 · Cited by 10

Characterizing the Scale-Up Performance of Microservices using TeaStore

Caculo, S., Lahiri, K., & Kalambur, S. (2020) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc50251.2020.00014 · Cited by 10

Automating instrumentation choices for performance problems in distributed applications with VAIF

Toslali, M., Ateş, E., Ellis, A., Zhang, Z., Huye, D., Liu, L., Puterman, S., Coskun, A. K., & Sambasivan, R. R. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3487000 · Cited by 10

Transactions across serverless functions leveraging stateful dataflows

de Heus, M., Psarakis, K., Fragkoulis, M., & Katsifodimos, A. (2022) · Information Systems · doi:10.1016/j.is.2022.102015 · Cited by 10

Reducing the Tail Latency of Microservices Applications via Optimal Configuration Tuning

Somashekar, G., Suresh, A., Tyagi, S., Dhyani, V., Donkada, K., Pradhan, A., & Gandhi, A. (2022) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) · doi:10.1109/acsos55765.2022.00029 · Cited by 10

Ditto: End-to-End Application Cloning for Networked Cloud Services

Liang, M., Gan, Y., Li, Y., Torres, C., Dhanotia, A., Ketkar, M., & Delimitrou, C. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575751 · Cited by 10

Quantitative Analysis of Cloud Function Evolution in the AWS Serverless Application RepositoryPreprint

Spillner, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04800 · Cited by 10

EdgeBench: A Workflow-Based Benchmark for Edge Computing

Yang, Q., Jin, R., Gandhi, N., Ge, X., Khouzani, H. A., & Zhao, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-54053-0_12 · Cited by 14

LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

Gu, L., Huang, J., Huang, S., Zeng, D., Li, B., & Jin, H. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229072 · Cited by 9

A self-stabilizing and auto-provisioning orchestration for microservices in edge-cloud continuum

Cai, B., Wang, X., Wang, B., Yang, M., Guo, Y., & Guo, Q. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110279 · Cited by 10

Derm: SLA-aware Resource Management for Highly Dynamic Microservices

Chen, L., Luo, S., Lin, C.-Y., Mo, Z., Xu, H., Ye, K., & Xu, C. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00039 · Cited by 10

Process-as-a-Service: Unifying Elastic and Stateful Clouds with Serverless Processes

Copik, M., Calotoiu, A., Réthy, G., Boehringer, R., Bruno, R., & Hoefler, T. (2024) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3698038.3698567 · Cited by 9

DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter Workloads

Su, W., Dhanotia, A., Torres, C., Gandhi, J., Gholkar, N., Kanaujia, S., Naumov, M., Subramanian, K., Andrei, V., Yuan, Y., et al. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731411 · Cited by 9

BLOC: Balancing Load with Overload Control In the Microservices Architecture

Bhattacharya, R., & Wood, T. (2022) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) · doi:10.1109/acsos55765.2022.00027 · Cited by 9

μConAdapter

Liu, J., Zhang, S., & Wang, Q. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624980 · Cited by 9

An Overview of Microservice-Based Systems Used for Evaluation in Testing and Monitoring: A Systematic Mapping Study

Fischer, S., Urbanke, P., Ramler, R., Steidl, M., & Felderer, M. (2024) · doi:10.1145/3644032.3644445 · Cited by 10

Online Marketplace: A Benchmark for Data Management in Microservices

Laigner, R., Zhang, Z., Liu, Y., Gomes, L. F., & Zhou, Y. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709653 · Cited by 9

Microservice Dynamic Architecture-Level Deployment Orchestration

Bacchiani, L., Bravetti, M., Giallorenzo, S., Mauro, J., Talevi, I., & Zavattaro, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78142-2_16 · Cited by 9

Detecting Layered Bottlenecks in Microservices

Inagaki, T., Ueda, Y., Ohara, M., Choochotkaew, S., Amaral, M., Trent, S., Chiba, T., & Zhang, Q. (2022) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud55607.2022.00062 · Cited by 9

Escra: Event-driven, Sub-second Container Resource Allocation

Cusack, G., Nazari, M., Goodarzy, S., Hunhoff, E., Oberai, P., Keller, E. R., Rozner, E., & Han, R. Y. (2022) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs54860.2022.00038 · Cited by 9

Enhancing the Configuration Tuning Pipeline of Large-Scale Distributed Applications Using Large Language Models (Idea Paper)

Somashekar, G., & Kumar, R. (2023) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3578245.3585032 · Cited by 9

Dynamically Balancing Load with Overload Control for Microservices

Bhattacharya, R., Gao, Y., & Wood, T. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3676167 · Cited by 8

XFBench: A Cross-Cloud Benchmark Suite for Evaluating FaaS Workflow Platforms

Kulkarni, V., Reddy, N., Khare, T., Mohan, H., Murali, J., Mohith, A., Ragul, B., Balajee, S., Sanjjit, S., Swathika, D., et al. (2024) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid59990.2024.00067 · Cited by 8

Virtuoso: Enabling Fast and Accurate Virtual Memory Research via an Imitation-based Operating System Simulation Methodology

Kanellopoulos, K., Sgouras, K., Bostanci, F. N., Kakolyris, A. K., Konar, B. K., Bera, R., Sadrosadati, M., Kumar, R., Vijaykumar, N., & Mutlu, O. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3676641.3716027 · Cited by 13

Blueprint: A Toolchain for Highly-Reconfigurable Microservice Applications

Anand, V., Garg, D., Kaufmann, A., & Mace, J. (2023) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3600006.3613138 · Cited by 8

Rethinking Deployment for Serverless Functions: A Performance-First Perspective

Li, Y., Zhao, L., Yang, Y., & Qu, W. (2023) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3581784.3613211 · Cited by 8

Memento: Architectural Support for Ephemeral Memory Management in Serverless Environments

Wang, Z., Zhao, K., Li, P., Jacob, A., Kozuch, M. A., Mowry, T. C., & Skarlatos, D. (2023) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3613424.3623795 · Cited by 8

Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices

Chow, K.-H., Deshpande, U., Deenadhayalan, V., Seshadri, S., & Liu, L. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3629587 · Cited by 8

Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer

Gan, Y., Zhang, Y., Hu, K., Cheng, D., He, Y., Pancholi, M., & Delimitrou, C. (2019) · ACM SIGOPS Operating Systems Review · doi:10.1145/3352020.3352026 · Cited by 8

PMNet: In-Network Data Persistence

Seemakhupt, K., Liu, S., Senevirathne, Y., Shahbaz, M., & Khan, S. (2021) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca52012.2021.00068 · Cited by 8

Resource scheduling techniques in cloud from a view of coordination: a holistic survey

Wang, Y., Yu, J., & Yu, Z. (2023) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2100298 · Cited by 8

DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services

Savasci, M., Ali-Eldin, A., Eker, J., Robertsson, A., & Shenoy, P. J. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583437 · Cited by 8

Sync-Millibottleneck Attack on Microservices Cloud Architecture

Gu, X., Wang, Q., Yan, Q., Liu, J., & Pu, C. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3644991 · Cited by 8

Acceleration-as-a-µService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs

Diamantopoulos, D., Polig, R., Ringlein, B., Purandare, M., Weiss, B., Hagleitner, C., Lantz, M. A., & Abel, F. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00096 · Cited by 8

Smart Healthcare System in Server-Less Environment: Concepts, Architecture, Challenges, Future Directions

Deka, R. K., Ghosh, A., Nanda, S., Barik, R. K., & Saikia, M. J. (2024) · Computers · doi:10.3390/computers13040105 · Cited by 7

Software Aging Detection and Rejuvenation Assessment in Heterogeneous Virtual Networks

Avritzer, A., Janes, A., Marin, A., Trubiani, C., van Hoorn, A., Camilli, M., Menasché, D. S., & Bondi, A. B. (2025) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2025.3547612 · Cited by 7

Fuzzing Microservice Resilience under Uncertainty with LLM-Guided Scenario Generation

Xue, F., Ying, T., Hu, P., Wang, L., Qing, Z., & Ding, T. (2026) · International Conference on Electronic Engineering and Informatics (EEI) · doi:10.1109/eei70303.2026.11640489 · Cited by 7

[SoK] Identifying Mismatches Between Microservice Testbeds and Industrial Perceptions of Microservices

Seshagiri, V., Huye, D., Liu, L., Wildani, A., & Sambasivan, R. R. (2022) · Journal of Systems Research · doi:10.5070/sr32157839 · Cited by 7

QoS-Aware Irregular Collaborative Inference for Improving Throughput of DNN Services

Fu, K., Shi, J., Chen, Q., Zheng, N., Zhang, W., Zeng, D., & Guo, M. (2022) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41404.2022.00074 · Cited by 7

Continuous Certification of Non-functional Properties Across System Changes

Anisetti, M., Ardagna, C. A., & Bena, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48421-6_1 · Cited by 7

An artificial intelligence strategy for the deployment of future microservice-based applications in 6G networks

Ssemakula, J. B., Gorricho, J., Kibalya, G., & Serrat, J. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09643-9 · Cited by 7

Erlang: Application-Aware Autoscaling for Cloud Microservices

Sachidananda, V., & Sivaraman, A. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3650084 · Cited by 7

Data Orchestration in Deep Learning Accelerators

Krishna, T., Kwon, H., Parashar, A., Pellauer, M., & Samajdar, A. (2020) · Synthesis lectures on computer architecture · doi:10.2200/s01015ed1v01y202005cac052 · Cited by 19

PRESTO: a latency-aware power-capping orchestrator for cloud-native microservices

Brondolin, R., & Santambrogio, M. D. (2020) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) · doi:10.1109/acsos49614.2020.00021 · Cited by 7

Experience: Implementation of Edge-Cloud for Autonomous Navigation Applications

Makkena, Y. C., Tella, R. R., Parekh, N., Saraf, P. K., Annu, A., Shukla, H., Matathammal, A., Danda, S. C. S., Chandrahas, P., Jadhav, A. R., et al. (2023) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets56262.2023.10041370 · Cited by 7

Grunt Attack: Exploiting Execution Dependencies in Microservices

Gu, X., Wang, Q., Liu, J., & Wei, J. (2024) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58291.2024.00025 · Cited by 7

HashCache: Accelerating Serverless Computing by Skipping Duplicated Function Execution

Wu, Z., Deng, Y., Zhou, Y. X., Cui, L., & Qin, X. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3323330 · Cited by 6

QueueFlower: Orchestrating Microservice Workflows via Dynamic Queue Balancing

Cao, H., Liu, X., Guo, H., He, J., & Liu, X. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00155 · Cited by 6

RpcNIC: Enabling Efficient Datacenter RPC Offloading on PCIe-attached SmartNICs

Zhang, J., Huang, H., Chen, X., Li, X., Zhao, J., Liu, M., & Wang, Z. (2025) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00104 · Cited by 6

Bridge the Islands: Pointer Analysis for Microservice Systems

Zhang, T., Liang, Y., Li, G., Tan, T. H., Xu, C., & Li, Y. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728896 · Cited by 6

Towards Performance and Energy Aware Kubernetes Scheduler

Dong, H., Singh, P., Awad, Y., George, F., Narayanam, K., Arora, S., & Appavoo, J. (2025) · ACM SIGEnergy Energy Informatics Review · doi:10.1145/3757892.3757902 · Cited by 6

Time-Varying Microservice Orchestration With Routing for Dynamic Call Graphs via Multi-Scale Deep Reinforcement Learning

Hu, Y., Hou, L., Hu, J., Ren, M., Hu, M., Cai, C., & Peng, K. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3597631 · Cited by 6

Transformer-based performance prediction and proactive resource allocation for cloud-native microservices

Chen, Y., Hao, J., Peng, Y., & Xia, H. (2025) · Cluster Computing · doi:10.1007/s10586-025-05237-9 · Cited by 6

An LLM-Augmented Predictive Auto-Scaling Framework for Latency-Sensitive Microservice Backends

Hsieh, C.-Y., Xiao, Z., Liu, X., Wang, Z., Liu, Y., & Li, X. (2026) · International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) · doi:10.1109/icbase70763.2026.11619406 · Cited by 6

Invenio

Sheoran, A., Fahmy, S., Sharma, P. K., & Modi, N. (2021) · ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS) · doi:10.1145/3493425.3502750 · Cited by 6

High performance microservice communication technology based on modified remote procedure call

Zhang, L., Pang, K., Xu, J., & Niu, B. (2023) · Scientific Reports · doi:10.1038/s41598-023-39355-4 · Cited by 6

A Profiling-Based Benchmark Suite for Warehouse-Scale Computers

Abel, A., Li, Y., O'Grady, R., Kennelly, C., & Gove, D. (2024) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass61541.2024.00046 · Cited by 6

Performance Modeling of Distributed Data Processing in Microservice Applications

Gao, Y., Casale, G., & Singhal, R. (2024) · ACM Transactions on Modeling and Performance Evaluation of Computing Systems · doi:10.1145/3687265 · Cited by 6

Mosaic: Harnessing the Micro-Architectural Resources of Servers in Serverless Environments

Stojkovic, J., Choukse, E., Saurez, E., Goiri, Í., & Torrellas, J. (2024) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro61859.2024.00103 · Cited by 6

LSRAM: A Lightweight Autoscaling and SLO Resource Allocation Framework for Microservices Based on Gradient Descent

Hu, K., Xu, M., Ye, K., & Xu, C. (2024) · Software Practice and Experience · doi:10.1002/spe.3395 · Cited by 6

ACE

Byrne, A., Nadgowda, S. J., & Coskun, A. K. (2020) · International Workshop on Serverless Computing (WoSC) · doi:10.1145/3429880.3430098 · Cited by 6

Practical and Scalable ML-Driven Cloud Performance Debugging With Sage

Gan, Y., Liang, M., Dev, S., Lo, D., & Delimitrou, C. (2022) · IEEE Micro · doi:10.1109/mm.2022.3169445 · Cited by 6

InviCloak

Lin, S., Xin, R., Goel, A., & Yang, X. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3559336 · Cited by 6

Cooperative Concurrency Control for Write-Intensive Key-Value Workloads

Sutherland, M., Falsafi, B., & Daglis, A. (2022) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3567955.3567957 · Cited by 6

Accelerating Data Serialization/Deserialization Protocols with In-Network Compute

Cao, S., Girolamo, S. D., & Hoefler, T. (2022) · doi:10.1109/exampi56604.2022.00008 · Cited by 6

Antipode: Enforcing Cross-Service Causal Consistency in Distributed Applications

Loff, J. F., Porto, D. C. F., Garcia, J. C., Mace, J., & Rodrigues, R. (2023) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3600006.3613176 · Cited by 6

Ambience: an operating system for IoT microservices

Bakir, F., Wang, S., Ekaireb, T., Pearson, J., Krintz, C., & Wolski, R. (2024) · Journal of Edge Computing · doi:10.55056/jec.786 · Cited by 7

Scheduling Microservice Containers on Large Core Machines Through Placement and Coalescing

Rao, V. S., Singh, V., Goutham, K. S., Kempaiah, B. U., Mampilli, R. J., Kalambur, S., & Sitaram, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88224-2_5 · Cited by 6

A Review on Database and Transaction Models in Different Cloud Application Architectures

Priya, N., & Punithavathy, E. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-7657-4_65 · Cited by 6

Enabling Containerized, Parametric and Distributed Database Deployment and Benchmarking as a Service

Kousiouris, G., & Kyriazis, D. (2021) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3447545.3451188 · Cited by 6

Fifer: Tackling Underutilization in the Serverless EraPreprint

Gunasekaran, J. R., Thinakaran, P., Chidambaram, N., Kandemir, M., & Das, C. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12819 · Cited by 6

DmRPC: Disaggregated Memory-aware Datacenter RPC for Data-intensive Applications

Zhang, J., Chen, X., Zhang⋆, Y., & Wang, Z. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00291 · Cited by 5

Efficient Multivariate Time Series Anomaly Detection through Transfer Learning for Large-Scale Software Systems

Sun, Y., Liang, M., Zhang, S., Che, Z., Luo, Z., Li, D., Zhang, Y., Pei, D., Pan, L., & Hou, L. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3702984 · Cited by 6

Grad: Intelligent Microservice Scaling by Harnessing Resource Fungibility

Chen, L., Lin, C., Luo, S., Xu, H., & Xu, C. (2025) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00044 · Cited by 5

Concord: Rethinking Distributed Coherence for Software Caches in Serverless Environments

Stojkovic, J., Alverti, C., Andrade, A., Iliakopoulou, N., Franke, H., Xu, T., & Torrellas, J. (2025) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00043 · Cited by 5

Can We Trust the Metrics? Safe Remediation under Adversarial and Noisy Observability

Chen, B., Chen, F., Zhan, Y., Ren, L., & Tang, Y. (2026) · International Conference on Image Processing and Artificial Intelligence (ICIPAI) · doi:10.1109/icipai70034.2026.11605678 · Cited by 5

Synthetic to Real-World: Insights on Microservices-Based Application Benchmarks

Rashmi, M., Vismaya, R., Patil, A., Rani, G. R., Borkade, T. N., & Auradkar, P. K. (2024) · IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) · doi:10.1109/iciteics61368.2024.10625221 · Cited by 5

Automated Synthesis of Effect Graph Policies for Microservice-Aware Stateful System Call Specialization

Blair, W., Araujo, F., Taylor, T., & Jang, J. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00064 · Cited by 5

EXIST: Enabling Extremely Efficient Intra-Service Tracing Observability in Datacenters

Wang, X., Hou, X., Li, C., Li, Y., Liu, D., Xu, G., Yang, G., Zhang, L., Wu, Y., Yuan, X., et al. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3676641.3716283 · Cited by 5

Snicket

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A Proactive Cloud Application Auto-Scaler using Reinforcement Learning

Heimerson, A., Eker, J., & Årzén, K.-E. (2022) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00040 · Cited by 5

iTCRL: Causal-Intervention-Based Trace Contrastive Representation Learning for Microservice Systems

Tian, X., Ying, S., Li, T., Yuan, M., Wang, R., Zhao, Y., & Shang, J. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3446532 · Cited by 5

Data Distribution and Exploitation in a Global Microservice Artefact Observatory

Gkikopoulos, P. (2019) · IEEE World Congress on Services (SERVICES) · doi:10.1109/services.2019.00089 · Cited by 5

Load balancers need in-band feedback control

Shobhana, B. V., Narayana, S., & Nath, B. (2022) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3563766.3564094 · Cited by 5

VECROsim: A Versatile Metric-oriented Microservice Fault Simulation System (Tools and Artifact Track)

Bi, T., Pan, Y., Jiang, X., Ma, M., & Wang, P. (2022) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre55969.2022.00037 · Cited by 6

CredsCache: Making OverlayFS scalable for containerized services

Choi, K., Seo, H., Han, H., Ryu, M., & Kang, S. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.04.027 · Cited by 5

SweetspotVM: Oversubscribing CPU without Sacrificing VM Performance

Jacquet, P., Ledoux, T., & Rouvoy, R. (2024) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid59990.2024.00026 · Cited by 5

An Open-Source Benchmark Suite for Cloud and IoT MicroservicesPreprint

Gan, Y., Yanqi, Z., Cheng, D., Shetty, A., Rathi, P., Katarki, N., Bruno, A., Hu, J., Ritchken, B., Jackson, B., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11055 · Cited by 2

The Architectural Implications of Microservices in the CloudPreprint

Gan, Y., & Delimitrou, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10351 · Cited by 4

The Architectural Implications of Cloud Microservices

Gan, Y., & Delimitrou, C. (2018) · IEEE Computer Architecture Letters · doi:10.1109/lca.2018.2839189 · Cited by 121

Sinan: ML-based and QoS-aware resource management for cloud microservices

Zhang, Y., Hua, W., Zhou, Z., Suh, G. E., & Delimitrou, C. (2021) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3445814.3446693 · Cited by 230

Unveiling the Hardware and Software Implications of Microservices in Cloud and Edge Systems

Gan, Y., Zhang, Y., Cheng, D., Shetty, A., Rathi, P., Katarki, N., Bruno, A., Hu, J., Ritchken, B., Jackson, B., et al. (2020) · IEEE Micro · doi:10.1109/mm.2020.2985960 · Cited by 10