Cited By
View all- Zhu LTamburri DCasale G(2023)RADF: Architecture decomposition for function as a serviceSoftware: Practice and Experience10.1002/spe.327654:4(566-594)Online publication date: 28-Sep-2023
Towards Modern Development of Cloud Applications
Authors: 
Sanjay Ghemawat, 
Robert Grandl, Srdjan Petrovic, Michael Whittaker, Parveen Patel, Ivan Posva, Amin VahdatAuthors Info & Claims
Sanjay Ghemawat, Robert Grandl, Srdjan Petrovic, Michael Whittaker, Parveen Patel, Ivan Posva, Amin VahdatAuthors Info & ClaimsPages 110 - 117
Abstract
When writing a distributed application, conventional wisdom says to split your application into separate services that can be rolled out independently. This approach is well-intentioned, but a microservices-based architecture like this often backfires, introducing challenges that counteract the benefits the architecture tries to achieve. Fundamentally, this is because microservices conflate logical boundaries (how code is written) with physical boundaries (how code is deployed). In this paper, we propose a different programming methodology that decouples the two in order to solve these challenges. With our approach, developers write their applications as logical monoliths, offload the decisions of how to distribute and run applications to an automated runtime, and deploy applications atomically. Our prototype implementation reduces application latency by up to 15× and reduces cost by up to 9× compared to the status quo.
References
[1]
Acorn. https://www.acorn.io/.
[2]
Actor model. https://en.wikipedia.org/wiki/Actor_model.
[3]
Akka. https://akka.io.
[4]
Amazon Web Services. https://aws.amazon.com/.
[5]
Apache avro. https://avro.apache.org/docs/1.2.0/.
[6]
Apache thrift. https://thrift.apache.org/.
[7]
AWS Cloud Map. https://aws.amazon.com/cloud-map/.
[8]
Azure Machine Learning. https://docs.microsoft.com/en-us/azure/machine-learning.
[9]
Blue/green deployments. https://tinyurl.com/3bk64ch2.
[10]
The c++ actor framework. https://www.actor-framework.org/.
[11]
Cloudflare Workers. https://workers.cloudflare.com/.
[12]
Continuous integration and delivery - circleci. https://circleci.com/.
[13]
Dapr - distributed application runtime. https://dapr.io/.
[14]
Distributed systems safety research. https://jespen.io.
[15]
Docker compose. https://docs.docker.com/compose/.
[16]
Google Cloud. https://cloud.google.com/.
[17]
Google Cloud AI Platform. https://cloud.google.com/ai-platform.
[18]
grpc. https://grpc.io/.
[19]
Helm. http://helm.sh.
[20]
Horizontal Pod Autoscaling. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/.
[21]
Istio. https://istio.io/.
[22]
Jenkins. https://www.jenkins.io/.
[23]
Json. https://www.json.org/json-en.html.
[24]
Kalix. https://www.kalix.io/.
[25]
Kubernetes. https://kubernetes.io/.
[26]
Locust. https://locust.io/.
[27]
Micro | powering the future of cloud. https://micro.dev/.
[28]
Pods. https://kubernetes.io/docs/concepts/workloads/pods/.
[29]
Postgresql. https://www.postgresql.org/.
[30]
Protocol buffers. https://developers.google.com/protocol-buffers.
[31]
RabbitMQ. https://www.rabbitmq.com/.
[32]
Remote direct memory access. https://en.wikipedia.org/wiki/Remote_direct_memory_access.
[33]
REST API. https://restfulapi.net/.
[34]
Scaling up the Prime Video audio/video monitoring service and reducing costs by 90%. https://tinyurl.com/yt6nxt63.
[35]
Skaffold. https://skaffold.dev/.
[36]
Temporal. https://temporal.io/.
[37]
Terraform. https://www.terraform.io/.
[38]
The Go programming language. https://go.dev/.
[39]
To Microservices and Back Again - Why Segment Went Back to a Monolith. https://tinyurl.com/5932ce5n.
[40]
WebSocket. https://en.wikipedia.org/wiki/WebSocket.
[41]
Online boutique. https://github.com/GoogleCloudPlatform/microservices-demo, 2023.
[42]
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. Tensorflow: A system for large-scale machine learning. In OSDI, 2016.
[43]
A. Adya, R. Grandl, D. Myers, and H. Qin. Fast key-value stores: An idea whose time has come and gone. In HotOS, 2019.
[44]
A. Adya, D. Myers, J. Howell, J. Elson, C. Meek, V. Khemani, S. Fulger, P. Gu, L. Bhuvanagiri, J. Hunter, R. Peon, L. Kai, A. Shraer, A. Merchant, and K. Lev-Ari. Slicer: Auto-sharding for datacenter applications. In OSDI, 2016.
[45]
D. Ardelean, A. Diwan, and C. Erdman. Performance analysis of cloud applications. In NSDI, 2018.
[46]
S. Ashok, P. B. Godfrey, and R. Mittal. Leveraging service meshes as a new network layer. In HotNets, 2021.
[47]
A. Basiri, N. Behnam, R. De Rooij, L. Hochstein, L. Kosewski, J. Reynolds, and C. Rosenthal. Chaos engineering. In IEEE Software, 2016.
[48]
B. Bloom, J. Field, N. Nystrom, J. Östlund, G. Richards, R. Strniša, J. Vitek, and T. Wrigstad. Thorn: Robust, concurrent, extensible scripting on the jvm. In OOPSLA, 2009.
[49]
J. C. Corbett, J. Dean, M. Epstein, A. Fikes, C. Frost, J. J. Furman, S. Ghemawat, A. Gubarev, C. Heiser, P. Hochschild, W. Hsieh, S. Kanthak, E. Kogan, H. Li, A. Lloyd, S. Melnik, D. Mwaura, D. Nagle, S. Quinlan, R. Rao, L. Rolig, Y. Saito, M. Szymaniak, C. Taylor, R. Wang, and D. Woodford. Spanner: Google's globally-distributed database. In OSDI, 2012.
[50]
D. Crankshaw, X. Wang, G. Zhou, M. J. Franklin, J. E. Gonzalez, and I. Stoica. Clipper: A low-latency online prediction serving system. In NSDI, 2017.
[51]
J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. In OSDI, 2004.
[52]
J. Eker, J. Janneck, E. Lee, J. Liu, X. Liu, J. Ludvig, S. Neuendorffer, S. Sachs, and Y. Xiong. Taming heterogeneity - the ptolemy approach. In Proceedings of the IEEE, 2003.
[53]
M. J. Fischer, N. A. Lynch, and M. S. Paterson. Impossibility of distributed consensus with one faulty process. In ACM Journal, 1985.
[54]
Y. Gan, M. Liang, S. Dev, D. Lo, and C. Delimitrou. Sage: Practical and Scalable ML-Driven Performance Debugging in Microservices. In ASPLOS, 2021.
[55]
Y. Gan, Y. Zhang, D. Cheng, A. Shetty, P. Rathi, N. Katarki, A. Bruno, J. Hu, B. Ritchken, B. Jackson, et al. An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems. In ASPLOS, 2019.
[56]
Y. Gan, Y. Zhang, K. Hu, Y. He, M. Pancholi, D. Cheng, and C. Delimitrou. Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices. In ASPLOS, 2019.
[57]
R. Grandl, G. Ananthanarayanan, S. Kandula, S. Rao, and A. Akella. Multi-resource packing for cluster schedulers. In SIGCOMM, 2014.
[58]
M. Henning. The rise and fall of corba: There's a lot we can learn from corba's mistakes. In Queue, 2006.
[59]
M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: Distributed data-parallel programs from sequential building blocks. In Eurosys, 2007.
[60]
K. Jay, N. Neha, and R. Jun. Kafka : a distributed messaging system for log processing. In NetDB, 2011.
[61]
A. Joe. Erlang. In Communications of the ACM, 2010.
[62]
L. Lamport. The temporal logic of actions. In ACM TOPLS, 1994.
[63]
A. Langley, A. Riddoch, A. Wilk, A. Vicente, C. Krasic, D. Zhang, F. Yang, F. Kouranov, I. Swett, J. Iyengar, J. Bailey, J. Dorfman, J. Roskind, J. Kulik, P. Westin, R. Tenneti, R. Shade, R. Hamilton, V. Vasiliev, W.-T. Chang, and Z. Shi. The quic transport protocol: Design and internet-scale deployment. In SIGCOMM, 2017.
[64]
N. Lazarev, N. Adit, S. Xiang, Z. Zhang, and C. Delimitrou. Dagger: Towards Efficient RPCs in Cloud Microservices with Near-Memory Reconfigurable NICs. In ASPLOS, 2021.
[65]
S. Lee, Z. Guo, O. Sunercan, J. Ying, T. Kooburat, S. Biswal, J. Chen, K. Huang, Y. Cheung, Y. Zhou, K. Veeraraghavan, B. Damani, P. M. Ruiz, V. Mehta, and C. Tang. Shard manager: A generic shard management framework for geo-distributed applications. In SOSP, 2021.
[66]
B. Livshits and E. Kiciman. Doloto: Code splitting for network-bound web 2.0 applications. In FSE, 2008.
[67]
S. Luo, H. Xu, C. Lu, K. Ye, G. Xu, L. Zhang, Y. Ding, J. He, and C. Xu. Characterizing microservice dependency and performance: Alibaba trace analysis. In SOCC, 2021.
[68]
N. A. Lynch. Distributed algorithms. In Morgan Kaufmann Publishers Inc., 1996.
[69]
S. McClure, S. Ratnasamy, D. Bansal, and J. Padhye. Rethinking networking abstractions for cloud tenants. In HotOS, 2021.
[70]
P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, M. Elibol, Z. Yang, W. Paul, M. I. Jordan, and I. Stoica. Ray: A distributed framework for emerging ai applications. In OSDI, 2018.
[71]
H. Qiu, S. S. Banerjee, S. Jha, Z. T. Kalbarczyk, and R. K. Iyer. FIRM: An intelligent fine-grained resource management framework for SLO-Oriented microservices. In OSDI, 2020.
[72]
D. Raghavan, P. Levis, M. Zaharia, and I. Zhang. Breakfast of champions: towards zero-copy serialization with nic scatter-gather. In HotOS, 2021.
[73]
F. Romero, Q. Li, N. J. Yadwadkar, and C. Kozyrakis. Infaas: Automated model-less inference serving. In ATC, 2021.
[74]
B. Sergey, G. Allan, K. Gabriel, L. James, P. Ravi, and T. Jorgen. Orleans: Cloud computing for everyong. In SOCC, 2011.
[75]
M. Waseem, P. Liang, G. Márquez, and A. D. Salle. Testing microservices architecture-based applications: A systematic mapping study. In APSEC, 2020.
[76]
Wikipedia contributors. Fallacies of distributed computing.
[77]
M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M. J. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI, 2012.
[78]
Y. Zhang, J. Yang, Z. Jin, U. Sethi, K. Rodrigues, S. Lu, and D. Yuan. Understanding and detecting software upgrade failures in distributed systems. In SOSP, 2021.
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June 2023
247 pages
ISBN:9798400701955
DOI:10.1145/3593856
- General Chair:
- Malte Schwarzkopf,
- Program Chairs:
- Andrew Baumann,
- Natacha Crooks
Copyright © 2023 Owner/Author(s).
This work is licensed under a Creative Commons Attribution International 4.0 License.
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View all- Zhu LTamburri DCasale G(2023)RADF: Architecture decomposition for function as a serviceSoftware: Practice and Experience10.1002/spe.327654:4(566-594)Online publication date: 28-Sep-2023
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