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Evolve or Die

Ramesh Govindan, Ina Minei, Mahesh Kallahalla, Bikash Koley, Amin Vahdat
2016 Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference - SIGCOMM '16  
Maintaining the highest levels of availability for content providers is challenging in the face of scale, network evolution, and complexity.  ...  From a detailed analysis of over 100 high-impact failure events within Google's network, encompassing many data centers and two WANs, we quantify several dimensions of availability failures.  ...  Finally, this work would not have been possible without the painstaking work of Google's network operations, SRE, and development teams that design, manage and run our global network and carefully document  ... 
doi:10.1145/2934872.2934891 dblp:conf/sigcomm/GovindanMKKV16 fatcat:ehwd36ddgvewvkfzsdsodxsibm

Video communication for teleconferencing using edge computing

Kouichi Genda, Mitsuru Abe, Shohei Kamamura
2020 IEICE Communications Express  
In the current video communication architecture, the key component, the multi-point control unit (MCU), is deployed in the central cloud server, and its bandwidth consumption in the backbone network becomes  ...  This paper proposes a backbone network resource optimization algorithm for video communications of teleconferencing that use edge computing.  ...  Ong, and A. Vahdat, "B4 and after: managing hierarchy, partitioning, and asymmetry for availability and scale in Google's software-defined WAN," Proc. ACM SIGCOMM, pp. 74-87, 2018.  ... 
doi:10.1587/comex.2020xbl0123 fatcat:2lnwakhcgvdd7bggll2uteez2a

A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks [article]

Sanaa Hamid Mohamed, Taisir E.H. El-Gorashi, Jaafar M.H. Elmirghani
2019 arXiv   pre-print
as virtualization, and software-defined networking that increasingly support big data systems.  ...  This survey article reviews the challenges associated with deploying and optimizing big data applications and machine learning algorithms in cloud data centers and networks.  ...  All data are provided in full in the results section of this paper.  ... 
arXiv:1910.00731v1 fatcat:kvi3br4iwzg3bi7fifpgyly7m4

Live in the Express Lane

Patrick Jahnke, Vincent Riesop, Pierre-Louis Roman, Pavel Chuprikov, Patrick Eugster
2021 USENIX Annual Technical Conference  
In ACM Transac- ter: Managing Hierarchy, Partitioning, and Asymmetry tions on Programming Languages and Systems, pages for Availability and Scale in Google’s Software-Defined 254  ...  B4: Experi- and Application-Level Sources of Tail Latency. In Pro- ence with a Globally-deployed Software Defined WAN.  ... 
dblp:conf/usenix/JahnkeRRCE21 fatcat:aoi3x635yjdl5bjnv5m5qo6atm

A Novel Cloud Broker-based Resource Elasticity Management and Pricing for Big Data Streaming Applications [article]

Olubisi A. Runsewe, University, My, University, My
In parallel, recent advances in technology have made it easier to collect, process and store these data streams in the cloud.  ...  hidden Markov model (LMD-HMM) framework for managing time-bounded BDSAs and a layered multi-dimensional hidden semi-Markov model (LMD-HSMM) to address unbounded BDSAs.  ...  Acknowledgements This dissertation would not have been possible without the invaluable support of many great personalities instrumental in shaping my PhD program.  ... 
doi:10.20381/ruor-23499 fatcat:ihq36w7fw5c5jfzv3rxht3yimm