Review and explain key concepts: Middleware, End-to-End Arguments in Systems Design.
Understand and explain function decomposition concerns in the design of distributed
systems.
Review, explain and apply distributed computing models, client-server computing, etc.
Readings:
Aiken, B., Strassner, J., Carpenter, B., Foster, I., Lynch, C., Mambretti, J., Moore, R.,
and Teitelbaum, B. “'Network Policy and Services: A Report of a Workshop on Middleware’, RFC
2768.,” 2000.
Gazis, A., Katsiri, E., Middleware 101, Communications of the ACM, Volume 65, Issue 9, September 2022, pp. 38-42.
J. H. Saltzer, D. P. Reed, and D. D. Clark, “End-to-end arguments in system design,”
ACM Trans. Comput. Syst., vol. 2, no. 4, pp. 277–288, Nov. 1984.
A. Carzaniga, G. Pietro Picco and G. Vigna, "Designing distributed applications with mobile
code paradigms." Proceeding ICSE '97 Proceedings of the 19th international conference on
Software
engineering, 1997.
Understand, explain and apply Cloud Computing Definitions.
Understand, explain and apply key concepts of Cloud economics.
Understand and explain the key factors of the Cloud evolution.
Understand, explain, and apply the concepts of Cloud, IAAS, PAAS, SAAS, Elasticity.
Readings:
M. Armbrust et al., “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4,
p. 50, 2010.
R. Barga, D. Gannon, and D. Reed, “The client and the cloud: Democratizing research
computing,” IEEE Internet Comput., vol. 15, no. 1, pp. 72–75, 2011.
Flexera. (2020). 2020 Flexera State of the Cloud Report. 1–19.
Armbrust, A. Fox, and R. Griffith, M. (2009). Above the clouds: A Berkeley view of cloud
computing.
In University of California, Berkeley, Tech. Rep. UCB.
https://doi.org/10.1145/1721654.1721672
Fang Liu, Jin Tong, Jian Mao, Robert Bohn, John Messina, L. B. and D. L. (2011).
NIST Cloud Computing Reference Architecture: Recommendations of the National Institute of
Standards and Technology. 35. https://doi.org/500-299
Discuss and understand trends in Cloud Computing development.
Understand and explain the current computing landscape.
Understand and explain concepts, key constraints and key challenges in Edge and Fog
computing.
Memorize and use values of key properties of distributed systems' components and their
evolution: CPU speed, network latencies, power consumption.
Readings:
M. Satyanarayanan, W. Gao, and B. Lucia, “The computing landscape of the 21st century,”
HotMobile 2019 - Proc. 20th Int. Work. Mob. Comput. Syst. Appl., pp. 45–50, 2019.
S. Zeuch, A. Chaudhary, B. Del Monte, H. Gavriilidis, D. Giouroukis, P. M. Grulich,
S. Bress, J. Traub, V. Markl, "The NebulaStream Platform: Data and Application Management
for the Internet of Things," CIDR 2020.
M. Satyanarayanan, “A Brief History of Cloud Offload: A Personal Journey from Odyssey
Through Cyber Foraging to Cloudlets,”
ACM SIGMOBILE Mob. Comput. Commun. Rev., vol. 18, no. 4, pp. 19–23, 2015.
Additional Readings (Computer Architecture and Computer Networking):
J. L. Hennessy and D. A. Patterson, “A new golden age for computer architecture,”
Commun. ACM, vol. 62, no. 2, pp. 48–60, 2019.
Limoncelli, T. A. (2012). "OpenFlow: A radical new idea in networking: An open standard that
enables software-defined networking." Queue, 10(6), 40.
https://doi.org/10.1145/2246036.2305856
A. Gupta and R. K. Jha, “A Survey of 5G Network: Architecture and Emerging Technologies,”
IEEE Access, vol. 3. Institute of Electrical and Electronics Engineers Inc., pp. 1206–1232,
2015.
N. Feamster, J. Rexford, E. Z.-A. S. Computer, and U. 2014,
“The road to SDN: an intellectual history of programmable networks,” in Dl.Acm.Org.
S. Sicari, A. Rizzardi, and A. Coen-Porisini, “5G In the internet of things era: An overview
on security and privacy challenges,” Comput. Networks, vol. 179, p. 107345, Oct. 2020.
Cloud Infrastructure Software, Workloads, and Metrics
Learning Objectives:
Understand and explain the software infrastructure building blocks of WSC.
Understand and explain the characteristics of typical workloads running on WSC.
Understand and explain the role of monitoring systems, and the concepts of blackbox
monitoring and instrumentation.
Review, understand and explain common techniques for improving the performance and
availability of WSC.
Understand and explain the concept of tail-latency.
Become familiar and explain the challenges arising from deploying cloud computing
technologies in Warehouse Scale Computers, in particular for availability and resource
isolation.
Understand and explain the concept of "Cloud native" software.
Understand and explain the concepts of availability, replication and partitioning.
Understand, analyze and apply the concepts of MTBF, MTTR, Yield and Harvest.
Understand and analyze the CAP theorem.
Understand and explain the implications of CAP for Cloud system and application design.
"Lessons from giant-scale services," E. Brewer, IEEE Internet Comput., vol. 5, no. 4, pp.
46–55, Jul. 2001.
"CAP twelve years later: How the ‘rules’ have changed," E. Brewer,
Computer, vol. 45, no. 2, pp. 23–29, Jan. 2012.
"Harvest, yield, and scalable tolerant systems," A. Fox and E. A. Brewer, Proc. Work. Hot
Top. Oper. Syst. - HOTOS, pp. 174–178, 1999.
"The tail at scale," J. Dean and L. A. Barroso, Commun. ACM, vol. 56, no. 2, pp. 74–80, Feb.
2013.
Additional Readings:
"Attack of the killer microseconds," L. Barroso, M. Marty, D. Patterson, and P. Ranganathan,
Commun. ACM, vol. 60, no. 4, pp. 48–54, Apr. 2017.
"Failure management in Grids: The case of the EGEE infrastructure," Neocleous, Dikaiakos, Fragopoulou, & Markatos,
Parallel Processing Letters, 17(4), 2007, pp 391–410. https://doi.org/10.1142/S0129626407003113.
Module 4: Virtualization, Containers and Resource Management
Lecture 7:
Virtualization
Learning Objectives:
Review basic Operating Systems' concepts of relevance to virtualization: core abstractions,
layering, libraries, application binary interface, security and privilege management,
protection rings, running in kernel vs. user mode.
Understand and explain different virtualization types.
Examine, understand and assess the concepts of server virtualization, virtual machines, and
hypervisors.
Understand and explain techniques for implementing virtual machines: de-privileging,
primary and shadow structures, memory tracing.
Become familiar with the VMWare virtualization platforms.
Readings: Overview, Introductory Concepts, History
E. Kotsovinos, “Virtualization: Blessing or Curse?,” Commun. ACM, vol. 54, no. 1, p. 61,
Jan. 2011.
Readings: Virtualization Techniques
VMware, “Understanding Full Virtualization, ParaVirtualization, and Hardware Assist,” VMWare
White Paper, 2007.
M. Pearce, S. Zeadally, and R. Hunt, “Virtualization: Issues,
Security Threats, and Solutions,” ACM Comput. Surv., vol. 45, no. 2, pp. 1–39, 2013.
K. Adams and O. Agesen, “A comparison of software and hardware techniques for x86
virtualization,” in International Conference on Architectural Support for Programming
Languages and Operating
Systems - ASPLOS, 2006, pp. 2–13.
Examine, understand and assess techniques for using and managing virtual machines in IaaS
clouds.
Understand and describe the problem of VM allocation to physical machines.
Understand and discuss the role and functionality of Cloud Application Management
Frameworks.
Explore and explain the TOSCA standard and its usefulness.
Study, explore, and explain the architecture, functionality and benefits of CAMF and
CELAR.
Understand and explain the support of elasticity actions by Cloud Application Management
Frameworks.
Review and explain the key requirements and properties of Container Management Systems.
Become familiar and explain the main properties, techniques and characteristics of Cloud
resource management systems, like Mesos and Borg.
Become familiar and describe the Kubernetes technology.
Readings:
Z. A. Mann and Z. Adam, “Allocation of Virtual Machines in Cloud Data Centers—A Survey
of Problem Models and Optimization Algorithms,” ACM Comput. Surv., vol. 48, no. 1, pp.
1–34, Aug. 2015.
I. Pietri and R. Sakellariou, “Mapping Virtual Machines onto Physical Machines in Cloud
Computing,” ACM Comput. Surv., vol. 49, no. 3, pp. 1–30, 2016.
Loulloudes, N., Sofokleous, C., Trihinas, D., Dikaiakos, M. D., & Pallis, G. (2015).
Enabling Interoperable
Cloud Application Management through an Open Source Ecosystem. IEEE Internet Computing,
19(3), 54–59.
https://doi.org/10.1109/MIC.2015.60.
Binz, T., Breitenbücher, U., Kopp, O., & Leymann, F. (2014). TOSCA: Portable automated
deployment
and management of cloud applications. Advanced Web Services, 9781461475, 527–549.
https://doi.org/10.1007/978-1-4614-7535-4_22
Bellendorf, J., & Mann, Z. Á. (2020). Specification of cloud topologies and
orchestration using TOSCA: a survey. Computing, 102(8), 1793–1815.
https://doi.org/10.1007/s00607-019-00750-3.
B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes,
“Borg, Omega, and Kubernetes - Lessons Learned from Three Container-Management Systems over
a Decade,” ACM Queue, vol. 14, no. February, pp. 70–93, 2016.
B . Hindman et al., “Mesos: A platform for fine-grained
resource sharing in the data center,”
in Proceedings of NSDI 2011: 8th USENIX Symposium on Networked Systems Design and
Implementation, 2011,
pp. 295–308.
A. Verma, L. Pedrosa, M. R. Korupolu, D. Oppenheimer, E. Tune,
and J. Wilkes,
“Large-scale cluster management at Google with Borg,” in Proceedings of the European
Conference on Computer Systems (EuroSys), 2015.
Additional Readings
Sofokleous, C., Loulloudes, N., Trihinas, D., Pallis, G., & Dikaiakos, M. D. (2014).
C-Eclipse: An open-source management framework for cloud applications. Lecture Notes in
Computer
Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics),
8632 LNCS, 38–49. https://doi.org/10.1007/978-3-319-09873-9_4.
Soldani, J., Binz, T., Breitenbücher, U., Leymann, F., & Brogi, A. (2016). ToscaMart: A
method for adapting and reusing cloud applications. Journal of Systems and Software, 113,
395–406.
https://doi.org/10.1016/j.jss.2015.12.025
Marko Lukša, "Kubernetes in Action," Manning, 2018.
J. Nickoloff and S. Kuenzli (2019), Ch. 11-13, "Docker in Action" 2nd Edition, Manning.
R. Peinl, F. Holzschuher, and F. Pfitzer, “Docker Cluster Management for the Cloud -
Survey Results and Own Solution,” J. Grid Comput., vol. 14, no. 2, pp. 265–282, 2016.
Understand and explain concepts and challenges related to storage in Cloud computing
systems.
Understand, explain and apply the concepts of availability, consistency and replication in
storage systems.
Review the challenges and capabilities of Cloud storage services.
Understand and explain concepts and challenges related to storage in Cloud computing
systems.
Understand, explain and apply the concepts of availability, consistency and replication in
storage systems.
Review the challenges and capabilities of Cloud storage services.
Readings:
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003).
"The Google file system." Proceedings of the Nineteenth ACM Symposium on Operating Systems
Principles - SOSP ’03, 29. https://doi.org/10.1145/945445.945450
A. Lakshman and P. Malik, “Cassandra - A decentralized structured storage system,” in
Operating Systems
Review (ACM), 2010, vol. 44, no. 2, pp. 35–40.
K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The Hadoop distributed file system,”
in 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010, 2010.
Additional Readings:
Bresnahan, J., Keahey, K., LaBissoniere, D., & Freeman, T. (2011). "Cumulus: An open source
storage cloud for science." ScienceCloud’11 - Proceedings of the 2nd International Workshop
on Scientific Cloud Computing, 25–31. https://doi.org/10.1145/1996109.1996115
Calder, B.et al. (2011).
"Windows azure storage: A highly available cloud storage service with strong consistency."
SOSP’11 -
Proceedings of the 23rd ACM Symposium on Operating Systems Principles, 143–157.
https://doi.org/10.1145/2043556.2043571
Cecchet, E., Candea, G., & Ailamaki, A. (2008).
"Middleware-based database replication: The gaps between theory and practice." Proceedings
of the ACM SIGMOD International Conference on Management of Data, 739–752.
https://doi.org/10.1145/1376616.1376691
Brantner, M., Florescu, D., Graf, D., Kossmann, D., & Kraska, T. (2008).
"Building a database on S3." Proceedings of the ACM SIGMOD International Conference on
Management of Data, 3, 251–263. https://doi.org/10.1145/1376616.1376645
Kurra, H., Al-Nashif, Y., & Hariri, S. (2013). Resilient cloud data storage services.
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference.
https://doi.org/10.1145/2494621.2494634
Han, J., Haihong, E., Le, G., & Du, J. (2011). "Survey on NoSQL database." Proceedings -
2011 6th International Conference on Pervasive Computing and Applications, ICPCA 2011,
363–366. https://doi.org/10.1109/ICPCA.2011.6106531
Explore and become familiar with the serverless Cloud computing paradigm.
Understand and explain the main drivers and key concepts behind serverless computing and the
Function-as-a-Service model.
Understand the key characteristics of FaaS and its differences from PaaS, SaaS and IaaS.
Understand the main limitations of current FaaS offerings.
Explore and describe examples of structuring Cloud applications following the serverless
paradigm.
Understand and describe limitations that current Cloud services pose to serverless
applications.
Understand and explain the concepts of stateless vs. stateful functions; explain how
stateful
applications
can be implemented on top of stateless functions.
Readings:
P. Castro, V. Ishakian, V. Muthusamy, and A. Slominski, “The rise of serverless computing,”
Commun. ACM, vol. 62, no. 12, pp. 44–54, Nov. 2019.
E. Jonas et al., “Cloud Programming Simplified: A Berkeley View on Serverless Computing,”
Feb. 2019.
J. M. Hellerstein et al., “Serverless Computing: One Step Forward, Two Steps Back,” in
Conference on Innovative Data Systems Research (CIDR), 2019.
Optional Additional Readings:
I. E. Akkus et al., “SAND: Towards High-performance Serverless
Computing,” in Proceedings of the 2018 USENIX Conference on Usenix Annual
Technical Conference, 2018, pp. 923–935.
Oakes, E., Yang, L., Zhou, D., Houck, K., Harter, T., Arpaci-Dusseau, A. C., &
Arpaci-Dusseau, R. H.
(2020).
SOCK: Rapid task provisioning with serverless-optimized containers. Proceedings of the
2018 USENIX Annual Technical Conference, USENIX ATC 2018, 57–69,
https://www.usenix.org/conference/atc18/presentation/oakes.
A. Agache et al., “Firecracker: Lightweight virtualization for serverless applications,”
in Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation,
NSDI 2020, 2020, pp. 419–434.
Wang, L., Li, M., Zhang, Y., Ristenpart, T., & Swift, M. (2018). Peeking Behind the
Curtains of Serverless Platforms. 2018 USENIX Annual Technical Conference (USENIX ATC 18),
133–146.
(slides).
Y. Gan et al. (2019). “An Open-Source Benchmark Suite for Microservices and Their
Hardware-Software
Implications for Cloud & Edge Systems,” in Proceedings of the Twenty-Fourth International
Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS
’19,
2019, pp. 3–18.
D. Ardelean, A. Diwan, C. Erdman (2018).
“Performance Analysis of Cloud Applications,” in Proceedings of the 15th USENIX Symposium
on Networked Systems Design and Implementation (NSDI ’18), pp. 405–417.
M.G. Kakivaya et al., “Service Fabric: A Distributed
Platform for Building Microservices in the Cloud,” Proc. 13th
EuroSys Conf. EuroSys 2018, vol. 2018.
Y. Gan and C. Delimitrou, “The Architectural Implications of
Cloud Microservices,” IEEE Comput. Archit. Lett., vol. 17, no. 2, pp. 155–158, Jul. 2018.
E. Fadda, P. Plebani, M. Vitali. "Monitoring-aware Optimal Deployment
for Applications based on Microservices," IEEE Transactions on Services
Computing, 2019. https://doi.org/10.1109/tsc.2019.2910069
L. Leite, C. Rocha, F. Kon, D. Milojicic, and P. Meirelles, “A survey of DevOps concepts and
challenges,”
ACM Comput. Surv., vol. 52, no. 6, 2019.
A. Wiedemann, H. Gewald, H. Krcmar, and M. Wiesche, “The DevOps Phenomenon - An executive
crash course,”
ACM Queue, 2019.
D. Trihinas, A. Tryfonos, M. D. Dikaiakos, and G. Pallis, “DevOps as a Service: Pushing the
Boundaries of Microservice Adoption,” IEEE Internet Comput., vol. 22, no. 3, pp. 65–71, May
2018.
N. Dragoni et al., “Microservices: Yesterday, today, and tomorrow,” in Present and Ulterior
Software Engineering, Springer International Publishing, 2017, pp. 195–216.
O. Zimmermann, “Microservices tenets: Agile approach to service development and deployment,”
Comput.
Sci. - Res. Dev., vol. 32, no. 3–4, pp. 301–310, Jul. 2017.
Explore and understand the key concepts behind popular Big-data processing platforms.
Become familiar with Hadoop's programming model.
Explore and familiarize with Apache SPARK's ecosystem.
Readings:
J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,”
in OSDI 2004 - 6th Symposium on Operating Systems Design and Implementation, 2004, pp.
137–149.
S. Tang, B. He, C. Yu, Y. Li, and K. Li, “A Survey on Spark Ecosystem for Big Data
Processing,” arXiv:1811.08834, 2018.
M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark:
Cluster computing with working sets,” 2nd USENIX Work. Hot Top. Cloud Comput. HotCloud 2010,
2010.
