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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In-Datacenter Performance Analysis of a Tensor Processing Unit

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The tail at scale

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The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

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Large-scale cluster management at Google with Borg

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Clearing the clouds

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Quasar

Delimitrou, C., & Kozyrakis, C. (2014) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2541940.2541941 · Cited by 704

A reconfigurable fabric for accelerating large-scale datacenter services

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Graph Neural Network-Based SLO-Aware Proactive Resource Autoscaling Framework for Microservices

Park, J., Choi, B., Lee, C., & Han, D. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3393427 · Cited by 30

Experimental evaluation of architectural software performance design patterns in microservices

Meijer, W., Trubiani, C., & Aleti, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112183 · Cited by 36

Learning-Based Microservice Placement and Migration for Multi-Access Edge Computing

Ray, K., Banerjee, A., & Narendra, N. C. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3344192 · Cited by 26

Breakfast of champions

Raghavan, D., Levis, P. A., Zaharia, M., & Zhang, I. Y. (2021) · Workshop on Hot Topics in Operating Systems · doi:10.1145/3458336.3465287 · Cited by 25

Cerebros: Evading the RPC Tax in Datacenters

Pourhabibi, A., Sutherland, M., Daglis, A., & Falsafi, B. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480055 · Cited by 25

A Cloud-Based Container Microservices: A Review on Load-Balancing and Auto-Scaling Issues

Rabiu, S., Yong, C. H., & Syed-Mohamad, S. M. (2022) · International Journal on Data Science · doi:10.18517/ijods.3.2.80-92.2022 · Cited by 25

The SPEC-RG Reference Architecture for The Compute Continuum

Jansen, M., Al-Dulaimy, A., Papadopoulos, A. V., Trivedi, A., & Iosup, A. (2023) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid57682.2023.00051 · Cited by 30

An Auto-Scaling Approach for Microservices in Cloud Computing Environments

ZargarAzad, M., & Ashtiani, M. (2023) · Journal of Grid Computing · doi:10.1007/s10723-023-09713-7 · Cited by 24

Performance Characterization of Communication Protocols in Microservice Applications

Kumar, P. K., Agarwal, R., Shivaprasad, R., Sitaram, D., & Kalambur, S. (2021) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets50376.2021.9555425 · Cited by 24

Dynamic Microservice Deployment and Offloading for Things–Edge–Cloud Computing

Tian, X., Meng, H., Shen, Y., Zhang, J., Chen, Y., & Li, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3370170 · Cited by 25

Sleuth: A Trace-Based Root Cause Analysis System for Large-Scale Microservices with Graph Neural Networks

Gan, Y., Liu, G., Zhang, X., Zhou, Q., Wu, J., & Jiang, J. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3623278.3624758 · Cited by 23

Integrating System State into Spatio Temporal Graph Neural Network for Microservice Workload Prediction

Luo, Y., Gao, M., Yu, Z., Ge, H., Gao, X., Cai, T., & Chen, G. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671508 · Cited by 23

Bypassing the load balancer without regrets

Kogias, M., Iyer, R., & Bugnion, E. (2020) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3419111.3421304 · Cited by 23

EnosLib: A Library for Experiment-Driven Research in Distributed Computing

Cherrueau, R.-A., Delavergne, M., van Kempen, A., Lèbre, A., Pertin, D., Balderrama, J. R., Simonet, A., & Simonin, M. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3111159 · Cited by 22

pHPA: A Proactive Autoscaling Framework for Microservice Chain

Choi, B., Park, J., Lee, C., & Han, D. (2021) · Asia-Pacific Workshop on Networking (APNet) · doi:10.1145/3469393.3469401 · Cited by 22

AutoMan: Resource-efficient provisioning with tail latency guarantees for microservices

Cai, B., Wang, B., Yang, M., & Guo, Q. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.014 · Cited by 24

Halfmoon: Log-Optimal Fault-Tolerant Stateful Serverless Computing

Qi, S., Liu, X., & Jin, X. (2023) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3600006.3613154 · Cited by 21

Benchmarking Web API Quality – Revisited

Bermbach, D., & Wittern, E. (2020) · Journal of Web Engineering · doi:10.13052/jwe1540-9589.19563 · Cited by 21

NeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering

Zhou, Z., Chen, Y., Zhang, T., Wang, Y., Shu, R., Xu, S., Cheng, P., Qu, L., Xiong, Y., Zhang, J., et al. (2024) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro61859.2024.00111 · Cited by 20

Towards Modern Development of Cloud Applications

Ghemawat, S., Grandl, R., Petrovic, S., Whittaker, M., Patel, P., Posva, I., & Vahdat, A. M. (2023) · Workshop on Hot Topics in Operating Systems · doi:10.1145/3593856.3595909 · Cited by 20

MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice Applications

Chen, H., Chen, P., Yu, G., Li, X., & He, Z. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3363902 · Cited by 19

Horizontal Auto-Scaling for Multi-Access Edge Computing Using Safe Reinforcement Learning

Ray, K., & Banerjee, A. (2021) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3475991 · Cited by 19

Performance optimization for cloud computing systems in the microservice era: state-of-the-art and research opportunities

Zeng, R., Hou, X., Zhang, L., Li, C., Zheng, W., & Guo, M. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-020-0072-3 · Cited by 21

Microservices Configurations and the Impact on the Performance in Cloud Native Environments

Mekki, M., Toumi, N., & Ksentini, A. (2022) · Conference on Local Computer Networks · doi:10.1109/lcn53696.2022.9843385 · Cited by 19

Cloud Computing and Internet of Things: Recent Trends and Directions

Goudarzi, M., Ilager, S., & Buyya, R. (2022) · Internet of things · doi:10.1007/978-3-031-05528-7_1 · Cited by 23

Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning

Lyu, N., Wang, Y., Cheng, Z., Zhang, Q., & Chen, F. (2025) · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP) · doi:10.1145/3778534.3778668 · Cited by 18

Executing Microservice Applications on Serverless, Correctly

Καλλάς, Κ., Zhang, H., Rajeev Alur, A., Angel, S., & Liu, V. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3571206 · Cited by 18

BeeHive: Sub-second Elasticity for Web Services with Semi-FaaS Execution

Zhao, Z., Wu, M., Tang, J., Zang, B., Wang, Z., & Chen, H. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575752 · Cited by 20

An Evaluation of Service Mesh Frameworks for Edge Systems

Elkhatib, Y., & Poyato, J. P. (2023) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3578354.3592867 · Cited by 18

HyperPlane: A Scalable Low-Latency Notification Accelerator for Software Data Planes

Mirhosseini, A., Golestani, H. B., & Wenisch, T. F. (2020) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro50266.2020.00074 · Cited by 18

AIBench Scenario: Scenario-Distilling AI Benchmarking

Gao, W., Tang, F., Zhan, J., Wen, X., Wang, L., Cao, Z., Lan, C., Luo, C., Liu, X., & Jiang, Z. (2021) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1109/pact52795.2021.00018 · Cited by 18

Characterizing a Memory Allocator at Warehouse Scale

Zhou, Z., Gogte, V., Vaish, N., Kennelly, C., Xia, P. J., Kanev, S., Moseley, T., Delimitrou, C., & Ranganathan, P. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651350 · Cited by 17

Lessons learned from migrating complex stateful applications onto serverless platforms

Jin, Z., Zhu, Y., Zhu, J., Yu, D., Li, C., Chen, R., Akkuş, İ. E., & Xu, Y. (2021) · ACM SIGOPS Asia-Pacific Workshop on Systems (APSys) · doi:10.1145/3476886.3477510 · Cited by 17

Automated performance prediction of microservice applications using simulation

Courageux-Sudan, C., Orgerie, A., & Quinson, M. (2021) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots53633.2021.9614260 · Cited by 17

Characterizing and Optimizing End-to-End Systems for Private Inference

Garimella, K., Ghodsi, Z., Jha, N. K., Garg, S., & Reagen, B. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3582016.3582065 · Cited by 17

SpecFaaS: Accelerating Serverless Applications with Speculative Function Execution

Stojkovic, J., Xu, T., Franke, H., & Torrellas, J. (2023) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca56546.2023.10071120 · Cited by 17

μManycore: A Cloud-Native CPU for Tail at Scale

Stojkovic, J., Liu, C., Shahbaz, M., & Torrellas, J. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589068 · Cited by 17

Sora

Liu, J., Wang, Q., Zhang, S., Hu, L., & Silva, D. D. (2023) · International Middleware Conference · doi:10.1145/3590140.3592851 · Cited by 17

SLO-Aware Function Placement for Serverless Workflows With Layer-Wise Memory Sharing

Cheng, D., Yan, K., Cai, X. T., Gong, Y., & Hu, C. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3391858 · Cited by 18

On Adaptive Edge Microservice Placement: A Reinforcement Learning Approach Endowed With Graph Comprehension

Chen, L., Bai, Y., Zhou, P., Li, Y., Qu, Z., & Xu, J. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3396510 · Cited by 17

Adaptive QoS-Aware Microservice Deployment With Excessive Loads via Intra- and Inter-Datacenter Scheduling

Shi, J., Fu, K., Wang, J., Chen, Q., Zeng, D., & Guo, M. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3425931 · Cited by 17

Robust System Instance Clustering for Large-Scale Web Services

Zhang, S., Li, D., Zhong, Z., Zhu, J., Liang, M., Luo, J., Sun, Y., Su, Y., Xia, S., Hu, Z., et al. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511983 · Cited by 17

Dagger: Towards Efficient RPCs in Cloud Microservices With Near-Memory Reconfigurable NICs

Lazarev, N., Adit, N., Xiang, S., Zhang, Z., & Delimitrou, C. (2020) · IEEE Computer Architecture Letters · doi:10.1109/lca.2020.3020064 · Cited by 17

RIBBON

Li, B., Roy, R. B., Patel, T., Gadepally, V. N., Gettings, K., & Tiwari, D. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3476168 · Cited by 17

PACED: Provenance-based Automated Container Escape Detection

Abbas, M., Khan, S., Monum, A., Zaffar, F., Tahir, R., Eyers, D. M., Irshad, H., Gehani, A., Yegneswaran, V., & Pasquier, T. (2022) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e55432.2022.00035 · Cited by 22

Murphy: Performance Diagnosis of Distributed Cloud Applications

Harsh, V., Zhou, W., Ashok, S., Mysore, R. N., Godfrey, P. B., & Banerjee, S. (2023) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3603269.3604877 · Cited by 21

A Novel Approach on Micro Sponges Drug Delivery System: Method of Preparations, Application, and its Future Prospective

Mandal, S. K., Bhumika, B. K., Kumar, M., Hak, J., Vishvakarma, P., & Sharma, U. K. (2023) · Indian Journal of Pharmaceutical Education and Research · doi:10.5530/ijper.58.1.5 · Cited by 16

GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications

Somashekar, G., Dutt, A., Adak, M., Lorido-Botrán, T., & Gandhi, A. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645665 · Cited by 17

StatuScale: Status-aware and Elastic Scaling Strategy for Microservice Applications

Wen, L., Xu, M., Gill, S. S., Hilman, M. H., Srirama, S. N., Ye, K., & Xu, C. (2025) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3686253 · Cited by 16

Speedo

Daw, N., Bellur, U., & Kulkarni, P. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486982 · Cited by 16

Unleashing the Scalability Potential of Power-Constrained Data Center in the Microservice Era

Hou, X., Liu, J., Li, C., & Guo, M. (2019) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3337821.3337857 · Cited by 16

A search-based identification of variable microservices for enterprise SaaS

Khoshnevis, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-022-1390-4 · Cited by 15

KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices

Hua, Q., Yang, D., Qian, S., Hu, H., Cao, J., & Xue, G. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583288 · Cited by 15

A Deep Reinforcement Learning-Based Deception Asset Selection Algorithm in Differential Games

He, W., Tan, J., Guo, Y., Shang, K., & Zhang, H. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3451189 · Cited by 15

µqSim: Enabling Accurate and Scalable Simulation for Interactive Microservices

Zhang, Y., Gan, Y., & Delimitrou, C. (2019) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass.2019.00034 · Cited by 15

Characterization and modeling of an edge computing mixed reality workload

Toczé, K., Lindqvist, J., & Nadjm‐Tehrani, S. (2020) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-020-00190-x · Cited by 16

Towards Optimal Configuration of Microservices

Somashekar, G., & Gandhi, A. (2021) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3437984.3458828 · Cited by 15

Exploring Efficient Microservice Level Parallelism

Wang, X., Li, C., Zhang, L., Hou, X., Chen, Q., & Guo, M. (2022) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps53621.2022.00030 · Cited by 15

TraceWeaver: Distributed Request Tracing for Microservices Without Application Modification

Ashok, S., Harsh, V., Godfrey, P. B., Mittal, R., Parthasarathy, S., & Shwartz, L. (2024) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3651890.3672254 · Cited by 14

TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework for Microservice-based Systems with Multimodal Data

Xie, S., Wang, J., He, H., Wang, Z., Zhao, Y., Zhang, N., & Li, B. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3734868 · Cited by 22

IoT Serverless Computing at the Edge: Open Issues and Research Direction

Kjorveziroski, V., Bernad, C., Roig, P. J., Gilly, K., Mishev, A., Trajkovik, V., & Filiposka, S. (2021) · Transactions on Networks and Communications · doi:10.14738/tnc.94.11231 · Cited by 14

LibPreemptible: Enabling Fast, Adaptive, and Hardware-Assisted User-Space Scheduling

Li, Y., Lazarev, N., Koufaty, D. A., Yin, T., Anderson, A., Zhang, Z., Suh, G. E., Kaffes, K., & Delimitrou, C. (2024) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca57654.2024.00075 · Cited by 15

Ursa: Lightweight Resource Management for Cloud-Native Microservices

Zhang, Y., Zhou, Z., Elnikety, S., & Delimitrou, C. (2024) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca57654.2024.00077 · Cited by 18

Unifying serverless and microservice workloads with SigmaOS

Szekely, A., Belay, A., Morris, R., & Kaashoek, M. F. (2024) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3694715.3695947 · Cited by 14

Benchmarking the performance of microservice applications

Grambow, M., Wittern, E., & Bermbach, D. (2020) · ACM SIGAPP Applied Computing Review · doi:10.1145/3429204.3429206 · Cited by 14

HydraGen: A Microservice Benchmark Generator

Sedghpour, M. R. S., Duque, A. O., Cai, X., Skubic, B., Elmroth, E., Klein, C., & Tordsson, J. (2023) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud60044.2023.00030 · Cited by 14

MicroSplit: Efficient Splitting of Microservices on Edge Clouds

Rahmanian, A. A., Ali-Eldin, A., Skubic, B., & Elmroth, E. (2022) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec54971.2022.00027 · Cited by 13

Microservices From Cloud to Edge: An Analytical Discussion on Risks, Opportunities and Enablers

Detti, A. (2023) · IEEE Access · doi:10.1109/access.2023.3277759 · Cited by 13

LatenSeer

Zhang, Y., Isaacs, R., Yue, Y., Yang, J., Zhang, L., & Vigfússon, Ý. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624787 · Cited by 16

Warming Up a Cold Front-End with Ignite

Schall, D., Sandberg, A., & Grot, B. (2023) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3613424.3614258 · Cited by 13

Evaluating intrusion detection for microservice applications: Benchmark, dataset, and case studies

Flora, J., & Antunes, N. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112142 · Cited by 13

The Tale of Errors in Microservices

Lee, I.-T. A., Zhang, Z., Parwal, A., & Chabbi, M. (2024) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3700436 · Cited by 13

GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

Yuan, H., Wang, T., Fu, M., & Shi, Y. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3547339 · Cited by 14

Improving the mean-field fluid model of processor sharing queueing networks for dynamic performance models in cloud computing

Ruuskanen, J., Berner, T., Årzén, K.-E., & Cervin, A. (2021) · Performance Evaluation · doi:10.1016/j.peva.2021.102231 · Cited by 13

Serverless Architecture for Healthcare Management Systems

Kumari, A., & Sahoo, B. (2022) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-6684-4580-8.ch011 · Cited by 13

A Case for Performance-Aware Deployment of Containers

Boza, E. F., Abad, C. L., Narayanan, S. P., Balasubramanian, B., & Jang, M. (2019) · International Workshop on Container Technologies and Container Clouds (WoC) · doi:10.1145/3366615.3368355 · Cited by 13

AI-Driven Provisioning in the 5G Core

Sheoran, A., Fahmy, S., Cao, L., & Sharma, P. K. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3056230 · Cited by 13

A Case for Function-as-a-Service with Disaggregated FPGAs

Ringlein, B., Abel, F., Diamantopoulos, D., Weiss, B., Hagleitner, C., Reichenbach, M., & Fey, D. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00047 · Cited by 13

Express-Lane Scheduling and Multithreading to Minimize the Tail Latency of Microservices

Mirhosseini, A., West, B. L., Blake, G. W., & Wenisch, T. F. (2019) · IEEE International Conference on Autonomic Computing (ICAC) · doi:10.1109/icac.2019.00031 · Cited by 13

HiveMind: A Scalable and Serverless Coordination Control Platform for UAV SwarmsPreprint

Hu, J., Bruno, A., Ritchken, B., Jackson, B., Espinosa, M., Shah, A., & Delimitrou, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.01419 · Cited by 13

Grace: Interpretable Root Cause Analysis by Graph Convolutional Network for Microservices

Ren, R., Wang, Y., Liu, F., Li, Z., Gareth Tyson, A., Miao, T., & Xie, G. (2023) · International Workshop on Quality of Service · doi:10.1109/iwqos57198.2023.10188728 · Cited by 12

On Optimizing Traffic Scheduling for Multi-replica Containerized Microservices

Zhu, X., Li, Y., Yao, L., Qi, Z., Xu, Y., Wang, P., Wang, W., & Zhu, X. (2023) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3605573.3605646 · Cited by 12

SmartOClock: Workload- and Risk-Aware Overclocking in the Cloud

Stojkovic, J., Misra, P. A., Goiri, Í., Whitlock, S., Choukse, E., Das, M., Bansal, C., Lee, J., Sun, Z., Qiu, H., et al. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00040 · Cited by 12

An Open-Source Experimentation Framework for the Edge Cloud Continuum

Koukis, G., Skaperas, S., Kapetanidou, I. A., Tsaoussidis, V., & Mamatas, L. (2024) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps61880.2024.10620846 · Cited by 12

L3: Latency-aware Load Balancing in Multi-Cluster Service Mesh

Michaelis, O., Schmid, S., & Mostafaei, H. (2024) · International Middleware Conference · doi:10.1145/3652892.3654793 · Cited by 12

μSteal

Mirhosseini, A., & Wenisch, T. F. (2021) · International Conference on Supercomputing · doi:10.1145/3447818.3463529 · Cited by 12

FPGA and GPU-based acceleration of ML workloads on Amazon cloud - A case study using gradient boosted decision tree library

Shepovalov, M., & Akella, V. (2019) · Integration · doi:10.1016/j.vlsi.2019.09.007 · Cited by 12

Ripple: A Practical Declarative Programming Framework for Serverless ComputePreprint

Joyner, S., MacCoss, M. J., Delimitrou, C., & Weatherspoon, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00222 · Cited by 18

Guarding Serverless Applications with SecLambdaPreprint

Jegan, D. S., Wang, L., Bhagat, S., Ristenpart, T., & Swift, M. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05322 · Cited by 12

PIMCloud: QoS-Aware Resource Management of Latency-Critical Applications in Clouds with Processing-in-Memory

Chen, S., Jiang, Y., Delimitrou, C., & Martínez, J. F. (2022) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca53966.2022.00083 · Cited by 11

Lifting the Fog of Uncertainties

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