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

Data Mining

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The tail at scale

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Clearing the clouds

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Quasar

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DBOS: A Proposal for a Data-Centric Operating SystemPreprint

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Aggregate-Driven Trace Visualizations for Performance DebuggingPreprint

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LQ-GNN: A Graph Neural Network Model for Response Time Prediction of Microservice-Based Applications in the Computing Continuum

Richart, M., Gorricho, J., Baliosián, J., Contreras, L. M., Muñiz, A., & Serrat, J. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3564214 · Cited by 4

TracePicker: Optimization-Based Trace Sampling for Microservice-Based Systems

Xie, S., Wang, J., Li, M., Chen, P., Xuan, J., & Li, B. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3729351 · Cited by 4

HardHarvest: Hardware-Supported Core Harvesting for Microservices

Stojkovic, J., Liu, C., Shahbaz, M., & Torrellas, J. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731071 · Cited by 4

CEMA: Cost Effective Multi-Layered Autoscaling for Microservice based Applications

Shafi, N., Abdullah, M., Iqbal, W., & Bukhari, F. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104266 · Cited by 4

Generating Microservice Graphs with Production Characteristics for Efficient Resource Scaling

Du, F., Shi, J., Chen, Q., Pang, P., Li, L., & Guo, M. (2025) · International Conference on Supercomputing · doi:10.1145/3721145.3725761 · Cited by 4

Fornax: A Hardware-Centric Session Management in Large Public Cloud Network

Yu, H., Wang, J., Zhao, J., Ren, K., Lin, G., Zhang, B., Guan, Y., Li, X., Yin, H., Liang, J., et al. (2025) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3718958.3750501 · Cited by 4

A Systematic Literature Review on Fault Injection Testing of Microservice Systems

Yu, S., Wu, H., Niu, X., & Nie, C. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3621564 · Cited by 4

iSafeRM: An Interpretable and Safety-Aware Resource Management Framework for Microservice Systems With SLO Guarantees

Dou, H., Li, L., Fang, W., Zhang, Y., & Zheng, Z. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3620809 · Cited by 4

Adaptive Resource Allocation for Cloud-native Microservice via Meta-learning and Hyper-heuristic Algorithms

Xia, H., Hao, J., & Cao, R. (2025) · Journal of Grid Computing · doi:10.1007/s10723-025-09814-5 · Cited by 4

Energy-Aware Service Mesh Deployment and Online Request Routing in Edge: A Hierarchical Deep Reinforcement Learning Approach

Hu, Y., Hu, J., Yang, Y., Hu, M., Peng, K., Zheng, T., Cai, C., & Xiong, Z. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3674203 · Cited by 4

A Retrieval-Augmented Log-Metric-Trace Twin for Backend Failure Diagnosis

Liu, Z., Wu, K., Dang, S., & Yang, Y. (2026) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1109/eitce70137.2026.11634314 · Cited by 5

MicroProf : Code-level Attribution of Unnecessary Data Transfer in Microservice Applications

Tariq, S. S. M., Menard, L., Su, P., & Roy, P. (2023) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3622787 · Cited by 4

TraceUpscaler: Upscaling Traces to Evaluate Systems at High Load

Sajal, S. M., Zhu, T., Urgaonkar, B., & Sen, S. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3629581 · Cited by 4

Understanding Delays in AF_XDP-based Applications

du Perron, K. C., Pacheco, D. L., & Huet, F. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622351 · Cited by 4

CausalMesh: A Causal Cache for Stateful Serverless Computing

Zhang, H., Mu, S., Angel, S., & Liu, V. (2025) · ACM SIGMOD Record · doi:10.1145/3733620.3733631 · Cited by 4

Data Access-Centered Understanding of Microservices Architectures

André, M., Rivière, É., & Cleve, A. (2025) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c65153.2025.00007 · Cited by 4

TraDE: Network and Traffic-Aware Adaptive Scheduling for Microservices Under Dynamics

Chen, M., Islam, M. T., Rodriguez, M. A., & Buyya, R. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3626424 · Cited by 4

IntelliStore: An Intelligent AI Agent Framework for Autonomous Storage and Database Optimization in Cloud-Native Microservices

Mukkolakkal, M. R. C. (2024) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v3i12.1024 · Cited by 4

NetClone: Fast, Scalable, and Dynamic Request Cloning for Microsecond-Scale RPCs

Kim, G. (2023) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3603269.3604820 · Cited by 4

Assessment of Aging and Rejuvenation for Resiliency in Heterogeneous Network Clusters

Avritzer, A., Janes, A., Marin, A., van Hoorn, A., Camilli, M., Trubiani, C., & Menasché, D. S. (2023) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew60843.2023.00074 · Cited by 4

Slower is Greener: Acceptance of Eco-feedback Interventions on Carbon Heavy Internet Services

Kim, H., Young, S., Chen, X., Gupta, U., & Hester, J. D. (2025) · ACM Journal on Computing and Sustainable Societies · doi:10.1145/3723038 · Cited by 4

Decentralized and Policy-Aware Serverless Orchestration for the Federated Web

Zhang, Y., & Jacobsen, H. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715573 · Cited by 4

CNSBench: A Cloud Native Storage Benchmark

Merenstein, A., Tarasov, V., Anwar, A., Bhagwat, D., Lee, J. A., Rupprecht, L., Skourtis, D., Yang, Y., & Zadok, E. (2021) · File and Storage Technologies · Cited by 4

General and Fast Inter-Process Communication via Bypassing Privileged Software

Mi, Z., Zhuang, H., Zang, B., & Chen, H. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3130751 · Cited by 4

System-level Implications of Serverless: Workload Characterizing and Performance Understanding

Deng, D., Lin, Y., & Ye, K. (2022) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00049 · Cited by 3

Detecting Anomalies Through Sequential Performance Analysis in Virtualized Environments

Gonçalves, C. F., Menasché, D. S., Avritzer, A., Antunes, N., & Vieira, M. P. A. (2023) · IEEE Access · doi:10.1109/access.2023.3293643 · Cited by 4

Topology-Aware Self-Adaptive Resource Provisioning for Microservices

Zeng, H., Wang, T., Li, A., Wu, Y., Wu, H., & Zhang, W. (2023) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws60048.2023.00016 · Cited by 4

SlackVM: Packing Virtual Machines in Oversubscribed Cloud Infrastructures

Jacquet, P., Ledoux, T., & Rouvoy, R. (2024) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster59578.2024.00024 · Cited by 4

Cache Characterization of Workloads in a Microservice Environment

Athlur, S., Sondhi, N., Batra, S., Kalambur, S., & Sitaram, D. (2019) · IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) · doi:10.1109/ccem48484.2019.00010 · Cited by 4

Wise Toolkit: Enabling Microservice-Based System Performance Experiments

Lima, R. A., Kimball, J., Ferreira, J. E., & Pu, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59635-4_11 · Cited by 4

Serpens: A High Performance FaaS Platform for Network Functions

Yu, H., Zhang, H., Shen, J., Geng, Y., Wang, J., Miao, C., & Xu, M. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3263272 · Cited by 4

Proceedings of the 6th International Workshop on Edge Systems, Analytics and Networking

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Pattern learning for scheduling microservice workflow to cloud containers

Li, W., Li, X., & Chen, L. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02115-5 · Cited by 3

Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning

Wang, Y., Mäntylä, M., Demeyer, S., Beyazıt, M., Kisaakye, J., & Nyyssölä, J. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3715742 · Cited by 3

SampleHST-X: A Point and Collective Anomaly-Aware Trace Sampling Pipeline with Approximate Half Space Trees

Gias, A. U., Gao, Y., Sheldon, M., Perusquía, J. A., O’Brien, O., & Casale, G. (2024) · Journal of Network and Systems Management · doi:10.1007/s10922-024-09818-8 · Cited by 3

GenesisRM: A state-driven approach to resource management for distributed JVM web applications

Xu, D., Liu, F., Wang, B., Tang, X., Zeng, D., Gao, H., Chen, R., & Wu, Q. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107539 · Cited by 3

Fast and Efficient Scaling for Microservices with SurgeGuard

Ghosh, A., Yadwadkar, N. J., & Erez, M. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00103 · Cited by 3

Enhancing Workload Predictions Using Service Interactions in Cloud-Native Microservices

Mahajan, I., & Nadig, D. (2024) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet62863.2024.10815917 · Cited by 3

Performance prediction and resource adaptive adjustment for cloud-native microservices

Peng, Y., Hao, J., & Chen, Y. (2025) · Cluster Computing · doi:10.1007/s10586-025-05437-3 · Cited by 3

How to Evaluate Distributed Coordination Systems?–A Survey and Analysis

Turkkan, B. O., Rodrigues, E., Kosar, T., Charapko, A., Ailijiang, A., & Demirbaş, M. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3631614 · Cited by 3

Benchmarking the Overhead of Distributed Tracing Agents

Reichelt, D. G., Yang, S., Hanson, M., & Hasselbring, W. (2026) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3777884.3797004 · Cited by 3

Better Orchestration for SLO-Oriented Cross-site Microservices in Multi-tenant Cloud/Edge Continuum

Cao, Y. (2023) · International Middleware Conference · doi:10.1145/3626564.3629091 · Cited by 3

PULSE: Using Mixed-Quality Models for Reducing Serverless Keep-Alive Cost

Sankaranarayanan, K., Roy, R. B., & Tiwari, D. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00021 · Cited by 3

CARL: Cost-Optimized Online Container Placement on VMs Using Adversarial Reinforcement Learning

Vinayak, P. S., Mallick, S., Jagarlamudi, L., Chakraborty, A., & Simmhan, Y. L. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3528446 · Cited by 3

Copper and Wire: Bridging Expressiveness and Performance for Service Mesh Policies

Saxena, D., Zhang, W., Pailoor, S., Dillig, I., & Akella, A. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3669940.3707257 · Cited by 3

Full stack optimization of microservice architecture: systematic review and research opportunity

Yang, M., Wang, X., Cai, B., Wang, Y., & Guo, Y. (2025) · Cluster Computing · doi:10.1007/s10586-025-05690-6 · Cited by 3

The Case for Benchmarking Control Operations in Cloud Native Storage

Merenstein, A., Tarasov, V., Anwar, A., Bhagwat, D., Rupprecht, L., Skourtis, D., & Zadok, E. (2020) · Cited by 3

Sizing and Coordinated Scaling of Microservices

Agarwal, P., & Lakshmi, J. (2020) · IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) · doi:10.1109/ccem50674.2020.00013 · Cited by 3

On language-agnostic abstract-syntax trees

Curtis, J. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3506962 · Cited by 3

Towards More Effective and Explainable Fault Management Using Cross-Layer Service Topology

Mathews, D. R., Verma, M., Lakshmi, J., & Aggarwal, P. (2022) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud55607.2022.00026 · Cited by 3

A case for using cache line deltas for high frequency VM snapshotting

Waddington, D., Hershcovitch, M., Sundararaman, S., & Dickey, C. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563481 · Cited by 3

MicroBlend: An Automated Service-Blending Framework for Microservice-Based Cloud Applications

Son, M., Mohanty, S., Gunasekaran, J. R., & Kandemir, M. (2023) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud60044.2023.00062 · Cited by 3

Serverless? RISC more!

Starc, R., Kuchler, T., Giardino, M. J., & Klimovic, A. (2024) · Workshop on SErverless Systems, Applications and MEthodologies (SESAME) · doi:10.1145/3642977.3652095 · Cited by 3

MINOS: Distributed Consistency and Persistency Protocol Implementation & Offloading to SmartNICs

Psistakis, A., Chaix, F., & Torrellas, J. (2024) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca57654.2024.00076 · Cited by 3

CoolDC: A Cost-Effective Immersion-Cooled Datacenter with Workload-Aware Temperature Scaling

Min, D., Byun, I., Lee, G.-H., & Kim, J. (2024) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3664925 · Cited by 3

Towards Sustainable Deployment of Microservices over the Cloud-IoT Continuum, with FREEDA

Soldani, J., Amadini, R., Antonio Brogi, A., Forti, S., Giallorenzo, S., Plebani, P., Vitali, M., & Zavattaro, G. (2024) · Workshop on Flexible Resource and Application Management on the Edge (FRAME) · doi:10.1145/3659994.3660311 · Cited by 3

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation

Cabrera, C., Paleyes, A., & Lawrence, N. D. (2024) · doi:10.1145/3643657.3643910 · Cited by 4

Graph-based Anti-Pattern Detection in Microservice Applications

Røhne, A. L., Pronk, B., & Åkesson, B. (2024) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa64295.2024.00059 · Cited by 3

Styx: Transactional Stateful Functions on Streaming Dataflows

Psarakis, K., Christodoulou, G., Siachamis, G., Fragkoulis, M., & Katsifodimos, A. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725363 · Cited by 3

AIBench: Scenario-distilling AI Benchmarking.

Gao, W., Tang, F., Zhan, J., Wen, X., Wang, L., Cao, Z., Lan, C., Luo, C., & Jiang, Z. (2020) · Cited by 3

A Delta-Debugging Approach to Assessing the Resilience of Actor Programs through Run-time Test Perturbations

Bleser, J. D., Nucci, D. D., & Roover, C. D. (2020) · doi:10.1145/3387903.3389303 · Cited by 3

Leveraging Queueing Theory and OS Profiling to Reduce Application Latency

Gandhi, A., & Suresh, A. (2019) · doi:10.1145/3366625.3368853 · Cited by 3

Sage: Using Unsupervised Learning for Scalable Performance Debugging in MicroservicesPreprint

Gan, Y., Liang, M., Dev, S., Lo, D., & Delimitrou, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00267 · Cited by 3

Towards Offloadable and Migratable Microservices on Disaggregated Architectures: Vision, Challenges, and Research RoadmapPreprint

Lu, X., & Kashyap, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11272 · Cited by 3

Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future DirectionsPreprint

Ilager, S., & Buyya, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02342 · Cited by 3

FedApp: a Research Sandbox for Application Orchestration in Federated Clouds using OpenStackPreprint

Ruuskanen, J., Peng, H., Åkesson, A., Larsson, L., & Kihl, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01480 · Cited by 3

Characterization and Reclamation of Frozen Garbage in Managed FaaS Workloads

Zhao, Z., Wu, M., Chen, H., & Zang, B. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3629579 · Cited by 3

Context-Aware Fault Classification for Multi-Access Edge Computing

Ray, K. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3438828 · Cited by 2

Boki: Towards Data Consistency and Fault Tolerance with Shared Logs in Stateful Serverless Computing

Jia, Z., & Witchel, E. (2024) · ACM Transactions on Computer Systems · doi:10.1145/3653072 · Cited by 2

SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice

Ren, R., Yang, J., Yang, L., Gu, X., & Sun, L. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3694984 · Cited by 2

Toward Data-Centric Service Composition

Fu, S. D., Zhang, H., Teoh, R., Priadka, T., & Ratnasamy, S. (2024) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3696348.3702013 · Cited by 2

Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs

Wang, Y., & Birman, K. (2025) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3721146.3721958 · Cited by 3

Generating Executable Microservice Applications for Performance Benchmarking

Lubas, Y., Straesser, M., Bauer, A., & Kounev, S. (2025) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3676151.3719361 · Cited by 2

Dynamic QoS-Driven Framework for Co-Scheduling of Distributed Long-Running Applications on Shared Clusters

Zhu, J., Wang, H., Su, P., Wang, Y., & Pan, W. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3571098 · Cited by 2

Leveraging Petri Nets for Workflow Anomaly Detection in Microservice Architectures

Kamboj, P., Artho, C. V., Guanciale, R., Jabbarvand, R., & Godfrey, P. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-94634-9_11 · Cited by 2

Transactional Cloud Applications: Status Quo, Challenges, and Opportunities

Laigner, R., Christodoulou, G., Psarakis, K., Katsifodimos, A., & Zhou, Y. (2025) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3722212.3725635 · Cited by 2

Single-Address-Space FaaS with Jord

Li, Y., Bhattacharyya, A., Kumar, M., Bhattacharjee, A., Etsion, Y., Falsafi, B., Kashyap, S., & Payer, M. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731108 · Cited by 2

Enhancing Microservices Anomaly Detection via Multimodal Data Fusion in the Wavelet Domain and Spatiotemporal Graph-based Diffusion Probabilistic Model

Ding, K., Ma, Y., Song, Z., & Bian, K. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736938 · Cited by 2

Sdser: Online Deployment and Scheduling of Dynamic DAG Functions with Bayesian Prediction in Serverless Edge Computing

Liu, W., Zhou, R., & Ma, Y. (2025) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs63083.2025.00091 · Cited by 2

AccelFlow: Orchestrating an On-Package Ensemble of Fine-Grained Accelerators for Microservices

Stojkovic, J., Farrell, A., Gong, Z., Hughes, C. J., & Torrellas, J. (2026) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca68181.2026.11408580 · Cited by 2

Dorado: Clustered Hardware Cache Coherence for 1,000+ Cores

Stojkovic, J., Farrell, A., Gerogiannis, G., Gong, Z., Hughes, C. J., & Torrellas, J. (2026) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca66397.2026.00049 · Cited by 2

SDCBench: A Benchmark Suite for Workload Colocation and Evaluation in Datacenters

Yang, Y., Kong, X., Zhao, L., Li, Y., Zhang, H., Li, J., Qi, H., & Li, K. (2022) · Intelligent Computing · doi:10.34133/2022/9810691 · Cited by 2

Invited Paper: Towards Efficient Microservice Communication

Saxena, D., Zhang, W., Tummala, M., Goel, S., & Akella, A. (2023) · Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for Distributed Systems (ApPLIED) · doi:10.1145/3584684.3597267 · Cited by 2

Flock: Towards Multitasking Virtual Machines for Function-as-a-Service

Zhao, Z., Wu, M., Cao, X., Chen, H., & Zang, B. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3288751 · Cited by 2

Microarchitectural Analysis and Characterization of Performance Overheads in Service Meshes with Kubernetes

Kurpad, S., Smruthi, B. T., Vijaykumar, S., Jain, S., & Kalambur, S. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270428 · Cited by 2

Recurrent Neural Network and Convolutional Neural Network for Detection of Denial of Service Attack in Microservices

Lee, W., Choe, Y. R., & Ghosh, R. S. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00219 · Cited by 2

Microless: Cost-Efficient Hybrid Deployment of Microservices on IaaS VMs and Serverless

Cheng, J., Zhao, Y., Li, Z., Chen, Q., Cui, W., & Guo, M. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00309 · Cited by 2

Exploring Imbalances among Microservice Containers in Large Cloud Platforms

Lin, C.-Y., Luo, S., & Xu, H. (2023) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00064 · Cited by 2

Studying the end-to-end performance, energy consumption and carbon footprint of fog applications

Courageux-Sudan, C., Orgerie, A., & Quinson, M. (2024) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc61673.2024.10733735 · Cited by 2

Zero+: Monitoring Large-Scale Cloud-Native Infrastructure Using One-Sided RDMA

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DeepMRA: An Efficient Microservices Resource Allocation Framework with Deep Reinforcement Learning in the Cloud

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A Novel Approach for Security Analysis in Microservices Using Graph Neural Networks

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Hexagon: Generating Asynchronous Microservices Benchmarks for the Evaluation of Anomaly Propagation Resistance

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Jingle: IoT-Informed Autoscaling for Efficient Resource Management in Edge Computing

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Mozart: Taming Taxes and Composing Accelerators with Shared-Memory

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Everything Everywhere All At Once: Efficient Cross-Service Program Analysis with OverSeer

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LEAD: Latency-Efficient Application Deployment for Microservices Architecture

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BASS: A Resource Orchestrator to Account for Vagaries in Network Conditions in Community Wi-Fi Mesh

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The Architectural Implications of Cloud Microservices

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

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

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