Nebula: Distributed Edge Cloud for Data Intensive Computing

IEEE Transactions on Parallel and Distributed Systems

Volumn 28, Issue 11, 2017, Pages 3229-3242

  Jonathan, Albert ^a   Ryden, Mathew ^a   Oh, Kwangsung ^a   Chandra, Abhishek ^a   Weissman, Jon ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

cloud computing; data intensive computing; Distributed Systems; edge cloud

Indexed keywords

CLOUD COMPUTING; DIGITAL STORAGE;

CLOUD INFRASTRUCTURES; DATA-INTENSIVE COMPUTING; DISTRIBUTED SYSTEMS; EDGE CLOUDS; EXISTING SYSTEMS; GEOGRAPHICAL LOCATIONS; OPTIMIZATION TECHNIQUES; POPULAR PLATFORM;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 85021835257     PISSN: 10459219     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPDS.2017.2717883     Document Type: Article

References (41)

1. Amazon web services. [Online]. Available: http://aws.amazon. com
2. Microsoft azure. [Online]. Available: http://azure.microsoft. com/
3. Amazon data center location. [Online]. Available: http://docs. Aws.amazon.com/general/latest/gr/rande.html
4. Microsoft data center location. [Online]. Available: https://www. microsoft.com/en-us/cloud-platform/global-datacenters
5. Google data center location. [Online]. Available: https://www. google.com/about/datacenters/inside/locations/
6. Z. Wu, M. Butkiewicz, D. Perkins, E. Katz-Bassett, and H. V. Madhyastha, "Spanstore: Cost-effective geo-replicated storage spanning multiple cloud services," in Proc. 24th ACM Symp. Operating Syst. Principles, 2013, pp. 292-308.
7. J. C. Corbett, et al., "Spanner: Googles globally distributed database," ACM Trans. Comput. Syst., vol. 31, no. 3, 2013, Art, no. 8.
8. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, "The case for VM-based cloudlets in mobile computing," IEEE Pervasive Comput., vol. 8, no. 4, pp. 14-23, Oct.-Dec. 2009.
9. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," in Proc. 1st Edition MCC Workshop Mobile Cloud Comput., 2012, pp. 13-16.
10. D. P. Anderson, "Boinc: A system for public-resource computing and storage," in Proc. 5th IEEE/ACM Int. Workshop Grid Comput., 2004, pp. 4-10.
11. B. Cohen, "Incentives build robustness in bittorrent," in Proc. Workshop Economics Peer-to-Peer Syst., 2003, vol. 6, pp. 68-72.
12. J. Dean and S. Ghemawat, "MapReduce: Simplified data processing on large clusters," Commun. ACM, vol. 51, no. 1, pp. 107-113, 2008.
13. B. Chun, et al.,"Planetlab: An overlay testbed for broad-coverage services," ACM SIGCOMM Comput. Commun. Rev., vol. 33, no. 3, pp. 3-12, Jul. 2003.
14. F. Costa, L. Silva, and M. Dahlin, "Volunteer cloud computing: MapReduce over the internet," in Proc. IEEE Int. Symp. Parallel Distrib. Process. Workshops Phd Forum, 2011, pp. 1855-1862.
15. B. Yee, et al., "Native client: A sandbox for portable, untrusted x86 native code," in Proc. 30th IEEE Symp. Security Privacy, 2009, pp. 79-93.
16. F. Dabek, R. Cox, F. Kaashoek, and R. Morris, "Vivaldi: A decentralized network coordinate system,' ACM SIGCOMM Comput. Commun. Rev., vol. 34, no. 4, 2004, pp. 15-26.
17. R. Cox, F. Dabek, F. Kaashoek, J. Li, and R. Morris, "Practical, distributed network coordinates," ACM SIGCOMM Comput. Commun. Rev., vol. 34, no. 1, pp. 113-118, 2004.
18. A. Rabkin, M. Arye, S. Sen, V. S. Pai, and M. J. Freedman, "Aggregation and degradation in jetstream: Streaming analytics in the wide area," in Proc. 11th USENIX Symp. Netw. Syst. Des. Implementation, 2014, pp. 275-288.
19. A. Vulimiri, C. Curino, B. Godfrey, K. Karanasos, and G. Varghese, "WANalytics: Analytics for a geo-distributed dataintensive world," in Proc. ACM SIGMOD Int. Conf. Manag. Data, 2015, 1087-1092.
20. R. Viswanathan, G. Ananthanarayanan, and A. Akella, "Clarinet: Wan-aware optimization for analytics queries," in Proc. 12th USENIX Symp. Operating Syst. Des. Implementation, 2016, pp. 435-450.
21. B. Hindman, et al., "Mesos: A platform for fine-grained resource sharing in the data center," in Proc. 8th USENIX Conf. Netw. Syst. Des. Implementation, 2011, vol. 11, pp. 22-22.
22. V. K. Vavilapalli, et al., "Apache hadoop yarn: Yet another resource negotiator," in Proc. 4th Annu. Symp. Cloud Comput., 2013, Art. no. 5.
23. M. Schwarzkopf, A. Konwinski, M. Abd-El-Malek, and J. Wilkes, "Omega: Flexible, scalable schedulers for large compute clusters," in Proc. 8th ACM Eur. Conf. Comput. Syst., 2013, pp. 351-364.
24. A. Verma, L. Pedrosa, M. Korupolu, D. Oppenheimer, E. Tune, and J. Wilkes, "Large-scale cluster management at google with borg," in Proc. 10th Eur. Conf. Comput. Syst., 2015, Art. no. 18.
25. A. Jonathan, A. Chandra, and J. Weissman, "Awan: Localityaware resource manager for geo-distributed data-intensive applications," in Proc. IEEE Int. Conf. Cloud Eng., 2016, pp. 32-41.
26. S. Saroiu, K. P. Gummadi, R. J. Dunn, S. D. Gribble, and H. M. Levy, "An analysis of internet content delivery systems," ACMSIGOPS Operating Syst. Rev., vol. 36, no. SI, pp. 315-327, 2002.
27. I. Foster, C. Kesselman, J. M. Nick, and S. Tuecke, "The physiology of the grid," Grid Comput.: Making Global Infrastructure Reality, pp. 217-249, 2003.
28. M. J. Litzkow, M. Livny, and M. W. Mutka, "Condor-a hunter of idle workstations," in Proc. 8th Int. Conf. Distrib. Comput. Syst., 1988, pp. 104-111.
29. D. P. Anderson, J. Cobb, E. Korpela, M. Lebofsky, and D. Werthimer, "Seti@ home: An experiment in public-resource computing," Commun. ACM, vol. 45, no. 11, pp. 56-61, 2002.
30. Q. Pu, et al., "Low latency geo-distributed data analytics," in Proc. ACM Conf. Special Interest Group Data Commun., 2015, pp. 421-434.
31. K. Kloudas, M. Mamede, N. Preguiça, and R. Rodrigues, "Pixida: Optimizing data parallel jobs in wide-area data analytics," in Proc. VLDB Endowment, 2015, vol. 9, no. 2, pp. 72-83.
32. P. Pietzuch, J. Ledlie, J. Shneidman, M. Roussopoulos, M. Welsh, and M. Seltzer, "Network-aware operator placement for streamprocessing systems," in Proc. 22nd Int. Conf. Data Eng., 2006, pp. 49-49.
33. S. Kannan, A. Gavrilovska, and K. Schwan, "Cloud4home-enhancing data services with@ home clouds," in Proc. 31st Int. Conf. Distrib. Comput. Syst., 2011, pp. 539-548.
34. H. Lin, X. Ma, J. Archuleta, W.-C. Feng, M. Gardner, and Z. Zhang, "Moon: MapReduce on opportunistic environments," in Proc. 19th ACM Int. Symp. High Performance Distrib. Comput., 2010, pp. 95-106.
35. Y. Luo and B. Plale, "Hierarchical MapReduce programming model and scheduling algorithms," in Proc. 12th IEEE/ACM Int. Symp. Cluster Cloud Grid Comput., 2012, pp. 769-774.
36. B. Heintz, A. Chandra, and J. Weissman, "Cross-phase optimization in MapReduce," in Proc. Cloud Comput. Data-Intensive Appl., 2014, pp. 277-302.
37. C. Jayalath, J. Stephen, and P. Eugster, "From the cloud to the atmosphere: Running MapReduce across data centers," IEEE Trans. Comput., vol. 63, no. 1, pp. 74-87, Jan. 2014.
38. J. Kubiatowicz, et al., "Oceanstore: An architecture for global-scale persistent storage," ACM Sigplan Notices, vol. 35, no. 11, pp. 190-201, 2000.
39. A. Haeberlen, A. Mislove, and P. Druschel, "Glacier: Highly durable, decentralized storage despite massive correlated failures," in Proc. 2nd Conf. Symp. Netw. Syst. Des. Implementation, 2005, pp. 143-158.
40. R. Bhagwan, K. Tati, Y. Cheng, S. Savage, and G. M. Voelker, "Total recall: System support for automated availability management," in Proc. 1st Conf. Symp. Netw. Syst. Des. Implementation, 2004, vol. 4, pp. 25-25.
41. K. Budati, J. Sonnek, A. Chandra, and J. Weissman, "Ridge: Combining reliability and performance in open grid platforms," in Proc. 16th Int. Symp. High Performance Distrib. Comput., 2007, pp. 55-64.