Arjun Singhvi
ABSTRACT
Computing/infrastructure as a service continues to evolve with bare metal, virtual machines, containers and now serverless granular computing service offerings. Granular computing enables developers to decompose their applications into smaller logical units or functions, and run them on small, low cost and short lived computation containers without having to worry about setting up servers - hence the term serverless computing. While serverless environments can be used very cost effectively for large scale parallel processing data analytics applications, it is less clear if network intensive packet processing applications can also benefit from these new computing services as they do not share the same characteristics. This paper examines the architectural constraints as well as current serverless implementations to develop a position on this topic and influence the next generation of computing services. We support our position through measurement and experimentation on Amazon's AWS Lambda service with a few popular network functions.
Supplemental Material
- 2017. AWS Lambda. https://aws.amazon.com/lambda. (2017).Google Scholar
- 2017. Azure Functions. https://functions.azure.com. (2017).Google Scholar
- 2017. Berkeley Extensible Software Switch (BESS). http://span.cs.berkeley.edu/bess.html. (2017).Google Scholar
- 2017. Data Plane Development Kit (DPDK). http://dpdk.org. (2017).Google Scholar
- 2017. Google Cloud Functions. https://cloud.google.com/functions. (2017).Google Scholar
- 2017. IBM Bluemix Openwhisk. https://www.ibm.com/cloud-computing/bluemix/openwhisk. (2017).Google Scholar
- 2017. Iperf. Documentation. http://software.es.net/iperf/. (2017).Google Scholar
- 2017. IronFunctions. https://github.com/iron-io/functions. (2017).Google Scholar
- 2017. Network Functions Virtualisation - Introductory White Paper. https://portal.etsi.org/NFV/NFV_White_Paper.pdf. (2017).Google Scholar
- 2017. OpenLambda. https://open-lambda.org. (2017).Google Scholar
- Anat Bremler-Barr, Yotam Harchol, and David Hay. 2016. OpenBox: a software-defined framework for developing, deploying, and managing network functions. In Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference. ACM, 511--524. Google ScholarDigital Library
- Sadjad Fouladi, Riad S. Wahby, Brennan Shacklett, Karthikeyan Vasuki Balasubramaniam, William Zeng, Rahul Bhalerao, Anirudh Sivaraman, George Porter, and Keith Winstein. 2017. Encoding, Fast and Slow: Low-Latency Video Processing Using Thousands of Tiny Threads. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA, 363--376. https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/fouladi Google ScholarDigital Library
- Aaron Gember-Jacobson, Raajay Viswanathan, Chaithan Prakash, Robert Grandl, Junaid Khalid, Sourav Das, and Aditya Akella. 2014. OpenNF: Enabling Innovation in Network Function Control. In Proceedings of the 2014 ACM Conference on SIGCOMM (SIGCOMM '14). ACM, New York, NY, USA, 163--174. Google ScholarDigital Library
- Jinho Hwang, K. K. Ramakrishnan, and Timothy Wood. 2014. NetVM: High Performance and Flexible Networking Using Virtualization on Commodity Platforms. In 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14). USENIX Association, Seattle, WA, 445--458. https://www.usenix.org/conference/nsdi14/technical-sessions/presentation/hwang Google ScholarDigital Library
- Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, and Benjamin Recht. 2017. Occupy the Cloud: Distributed Computing for the 99%. In Proceedings of the 2017 Symposium on Cloud Computing (SoCC '17). ACM, New York, NY, USA, 445--451. Google ScholarDigital Library
- Murad Kablan, Azzam Alsudais, Eric Keller, and Franck Le. 2017. Stateless Network Functions: Breaking the Tight Coupling of State and Processing. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA, 97--112. https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/kablan Google ScholarDigital Library
- Eddie Kohler, Robert Morris, Benjie Chen, John Jannotti, and M Frans Kaashoek. 2000. The Click modular router. ACM Transactions on Computer Systems (TOCS) 18, 3 (2000), 263--297. Google ScholarDigital Library
- Aurojit Panda, Sangjin Han, Keon Jang, Melvin Walls, Sylvia Ratnasamy, and Scott Shenker. 2016. NetBricks: Taking the V out of NFV. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, GA, 203--216. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/panda Google ScholarDigital Library
- Aurojit Panda, Ori Lahav, Katerina Argyraki, Mooly Sagiv, and Scott Shenker. 2017. Verifying Reachability in Networks with Mutable Datapaths. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA, 699--718. https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/panda-mutable-datapaths Google ScholarDigital Library
- Vern Paxson. 1999. Bro: a system for detecting network intruders in real-time. Computer networks 31, 23, 2435--2463. Google ScholarDigital Library
- Martin Roesch et al. 1999. Snort: Lightweight intrusion detection for networks.Google Scholar
- Brendan Tschaen, Ying Zhang, Theo Benson, Sujata Banerjee, Jeongkeun Lee, and Joon-Myung Kang. 2016. SFC-Checker: Checking the correct forwarding behavior of Service Function chaining. In Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE Conference on. IEEE, 134--140.Google Scholar
- Wei Zhang, Jinho Hwang, Shriram Rajagopalan, K.K. Ramakrishnan, and Timothy Wood. 2016. Flurries: Countless Fine-Grained NFs for Flexible Per-Flow Customization. In Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies (CoNEXT '16). ACM, New York, NY, USA, 3--17. Google ScholarDigital Library
Index Terms
- Granular Computing and Network Intensive Applications: Friends or Foes?
Recommendations
Performance Evaluation of Data Intensive Computing In the Cloud
Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing ...
Comments