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SCIENTIA SINICA Informationis, Volume 47 , Issue 9 : 1129-1148(2017) https://doi.org/10.1360/N112017-00071

Collaboration environment for JointCloud computing

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  • ReceivedApr 9, 2017
  • AcceptedJun 22, 2017
  • PublishedSep 7, 2017

Abstract


Funded by

国家重点研发计划(2016YFB1000100)


Acknowledgment

在此感谢国家重点研发计划“云计算和大数据"专项总体组专家对本项目研究给予的技术指导, 感谢项目各参研单位的专家贡献的智慧, 以及国防科学技术大学彭宇行研究员、中山大学陈伟利博士等在本文撰写过程中所给予的建议和帮助, 感谢云际计算项目合作单位UCloud公司在数据交易安全屋的产品设计、实现以及产业化推广方面做出的贡献.


References

[1] Foster I, Zhao Y, Raicu I, et al. Cloud computing and grid computing 360-degree compared. In: Proceedings of IEEE Grid Computing Environments Workshop, Austin, 2009. 1--10. Google Scholar

[2] Wang H M, Shi P C, Zhang Y M. JointCloud: a cross-cloud cooperation architecture for integrated internet service customization. In: Proceedings of the 37th IEEE International Conference on Distributed Computing Systems, Atlanta, 2017. 1--10. Google Scholar

[3] 李国杰. 为构建协作共赢的云计算环境而努力. 计算机学会通讯, 2017, 13: 9--9. Google Scholar

[4] Buyya R, Ranjan R, Calheiros R N. InterCloud: utility-oriented federation of cloud computing environments for scaling of application services. In: Proceedings of the 10th International Conference on Algorithms and Architectures for Parallel Processing, Busan, 2010. 13--31. Google Scholar

[5] Efthymia T, Anastasios G, Kostas T. Multi-objective optimization of dataflows in a multi-cloud environment. In: Proceedings of the 2nd Workshop on Data Analytics in the Cloud, New York, 2013. 6--10. Google Scholar

[6] Petri I, Diaz-Montes J, Zou M. Market Models for Federated Clouds. IEEE Trans Cloud Comput, 2015, 3: 398-410 CrossRef Google Scholar

[7] Guzek M, Gniewek A, Bouvry P, et al. Cloud brokering: current practices and upcoming challenges. IEEE Cloud Comput, 2015, 2: 40--47. Google Scholar

[8] Tram Truong-Huu , Chen-Khong Tham . A Novel Model for Competition and Cooperation among Cloud Providers. IEEE Trans Cloud Comput, 2014, 2: 251-265 CrossRef Google Scholar

[9] Mei H, Liu X Z. Software techniques evolved by the Internet: current situation and future trend. Chinese Sci Bull, 2010, 55: 1214--1220. Google Scholar

[10] Lu X, Wang H, Wang J. Internet-based Virtual Computing Environment: Beyond the data center as a computer. Future Generation Comp Syst, 2013, 29: 309-322 CrossRef Google Scholar

[11] Erl T. SoA: Principles of Service Design. Upper Saddle River: Prentice Hall Press, 2007. Google Scholar

[12] Cao D G, An B, Shi P C. Providing Virtual Cloud for Special Purposes on Demand in JointCloud Computing Environment. J Comput Sci Technol, 2017, 32: 211-218 CrossRef Google Scholar

[13] Hossny E, Khattab S, Omara F A, et al. Semantic-based generation of generic-API adapters adapters for portable cloud applications. In: Proceedings of the 3rd Workshop on CrossCloud Infrastructures and Platforms, London, 2016. Google Scholar

[14] Hossny E, Khattab S, Omara F A, et al. Towards a standard PaaS implementation API: a generic cloud persistentstorage API. In: Proceedings of the 3rd International IBM Cloud Academy Conference, Budapestm, 2015. Google Scholar

[15] Alomari E, Barnawi A, Sakr S. Cdport: a framework of data portability in cloud platforms. In: Proceedings of the 16th International Conference on Information Integration and Web-based Applications and Services, Hanoi, 2014. 126--133. Google Scholar

[16] Rafique A, Walraven S, Lagaisse B, et al. Towards portability and interoperability support in middleware for hybrid clouds. In: Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM) CrossCloud Workshop, Toronto, 2014. 7--12. Google Scholar

[17] Kolb S, Wirtz G. Towards application portability in platform as a service. In: Proceedings of the IEEE 8th International Symposium on Service Oriented System Engineering, Washington, 2014. 218--229. Google Scholar

[18] Rafael M V, Eduardo H, Ignacio M L, et al. BEACON: a cloud network federation framework. In: Proceedings of Advances in Service-Oriented and Cloud Computing, Messina, 2016. 325--337. Google Scholar

[19] Linux foundation collaborative projects. Opendaylight — an open source community and meritocracy for softwaredefined networking. 2013. http://www.valleytalk.org/wp-content/uploads/2013/05/opendaylight_open_community_and_meritocracy_for_sdn_v3.pdf. Google Scholar

[20] Contrail White Paper. Overview of the contrail system, components and usage, 2014. http://contrail-project.eu. Google Scholar

[21] Balus F, Stiliadis D, Bitar N. Federated SDN-based controllers for NVO3, ietf-drafts, 2012. http://tools.ietf.org/html/draft-sbnvo3- sdn-federation-00. Google Scholar

[22] Xu M X, Tian W H, Rajkumar B. A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr Comput Pract Exper, 2017. Google Scholar

[23] de Oliveira D, Oca?a K A C S, Bai?o F. A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds. J Grid Computing, 2012, 10: 521-552 CrossRef Google Scholar

[24] Mann Z A. Allocation of virtual machines in cloud data centers — a survey of problem models and optimization algorithms. ACM Comput Surv, 2015, 48: 11. Google Scholar

[25] Tordsson J, Montero R S, Moreno-Vozmediano R. Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Comp Syst, 2012, 28: 358-367 CrossRef Google Scholar

[26] Simarro J L L, Moreno-Vozmediano R, Montero R S, et al. Dynamic placement of virtual machines for cost optimization in multi-cloud environments. In: Proceedings of the 2011 International Conference on High Performance Computing and Simulation (HPCS 2011), Istambul, 2011. 1--7. Google Scholar

[27] Chaisiri S, Lee B S, Niyato D. Optimal virtual machine placement across multiple cloud providers. In: Proceedings of IEEE Asia-Pacific Services Computing Conference, Singapore, 2009. 103--110. Google Scholar

[28] Breitgand D, Marashini A, Tordsson J. Policy-Driven Service Placement Optimization in Federated Clouds. Technical Report, IBM Haifa Labs. 2011. Google Scholar

[29] Nikolay G, Rajkumar B. Dynamic Selection of Virtual Machines for Application Servers in Cloud Environments. Report number: CLOUDS-TR-2016-1. 2016. Google Scholar

[30] Travostino F, Daspit P, Gommans L. Seamless live migration of virtual machines over the MAN/WAN. Future Generation Comp Syst, 2006, 22: 901-907 CrossRef Google Scholar

[31] Clark C, Fraser K, Hand S, et al. Live migration of virtual machines. In: Proceedings of the 2nd Symposium on Networked Systems Design and Implementation, Berkeley, 2005. 273--286. Google Scholar

[32] Voorsluys W, Broberg J, Venugopal S, et al. Cost of virtual machine live migration in clouds: a performance evaluation. In: Proceedings of the 1st International Conference on Cloud Computing, Beijing, 2009. 254--265. Google Scholar

[33] Amid K B, Seyyed M H. A review of workflow scheduling in cloud computing environment. Inter J Comput Sci Manage Res, 2012, 1: 348--351. Google Scholar

[34] Michael L P. Scheduling: Theory, Algorithms, and Systems. 3rd ed. Berlin: Springer, 2008. Google Scholar

[35] Tsamoura E, Gounaris A, Tsichlas K. Multi-objective optimization of dataflows in a multi-cloud environment. In: Proceedings of the 2nd Workshop on Data Analytics in the Cloud, New York, 2013. 6--10. Google Scholar

[36] Petri I, Diaz-Montes J, Zou M. Market Models for Federated Clouds. IEEE Trans Cloud Comput, 2015, 3: 398-410 CrossRef Google Scholar

[37] Kondo D, Javadi B, Malecot P, et al. Cost-benefit analysis of cloud computing versus desktop grids. In: Proceedings of the 2009 IEEE International Symposium on Parallel and Distributed Processing, Washington, 2009. 1--12. Google Scholar

[38] Douglas G, Drawert B, Krintz C, et al. CloudTracker: using execution provenance to optimize the cost of cloud use. In: Proceedings of the 11th International Conference on Grid Economics and Business Models, Cardiff, 2014. 99--113. Google Scholar

[39] Chaisiri S, Lee B S, Niyato D. Optimization of resource provisioning cost in cloud computing. IEEE Trans Serv Comput, 2012, 5: 164-177 CrossRef Google Scholar

[40] Guzek M, Gniewek A, Bouvry P, et al. Cloud brokering: current practices and upcoming challenges. IEEE Cloud Comput, 2015, 2: 40--47. Google Scholar

[41] Elmroth E, Marquez F G, Henriksson D, et al. Accounting and billing for federated cloud infrastructures. In: Proceedings of the 8th International Conference on Grid and Cooperative Computing, Lanzhou, 2009. 268--275. Google Scholar

[42] Rochwerger B, Breitgand D, Levy E, et al. The reservoir model and architecture for open federated cloud computing. IBM J Res Dev. 2009, 53: 1--11. Google Scholar

[43] Riedel M, Wittenburg P, Reetz J. A data infrastructure reference model with applications: towards realization of a ScienceTube vision with a data replication service. J Internet Serv Appl, 2013, 4: 1-16 CrossRef Google Scholar

[44] Poon J, Dryja T. The bitcoin lightning network: scalable off-chain instant payments. 2015. https://lightning.network/lightningnetwork-paper.pdf (visited on 2016-04-19). Google Scholar

[45] Miller A, Bentov I, Kumaresan R, et al. Sprites: payment channels that go faster than lightning,. arXiv Google Scholar

[46] Anthony T. Network topology and routing. Lightning Network Development Discussion, 2015. http://lists.linuxfoundation.org/pipermail/lightning-dev/2015-September/000188.html. Google Scholar

[47] Rusty R. Ionization protocol: flood routing. Lightning Network Development Discussion, 2015. http://lists.linuxfoundation.org/pipermail/lightning-dev/2015-September/000199.html. Google Scholar

[48] Amos B. Ionization protocol: flood routing. Lightning Network Development Discussion, 2015. http://lists.linuxfoundation.org/pipermail/lightning-dev/2015-September/000212.html. Google Scholar

[49] Prihodko P, Zhigulin S, Sahno M, et al. Flare: an approach to routing in lightning network. White Paper. 2016. http://bitfury.com/content/5-white-papers-research/whitepaper_flare_an_approach_to_routing_in_lightning_network_7_7_2016.pdf. Google Scholar

[50] Michelfeit J. Security and routing in the ripple payment network. Dissertation for M.S. Degree. Brno Ripple: Masaryk University, 2011. Google Scholar

[51] Nakamoto S. Bitcoin: a peer-to-peer electronic cash system. Consulted, 2008. Google Scholar

[52] Gervais A, Karame G O, Glykantzis V, et al. On the security and performance of proof of work blockchains. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, New York, 2016. 3--16. Google Scholar

[53] Ittay E, Adem E G, Emin G S, et al. Bitcoin-NG: a scalable blockchain protocol. In: Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation, Santa Clara, 2015. 45--59. Google Scholar

[54] Sompolinsky Y, Zohar A. Secure high-rate transaction processing in bitcoin. In: Proceedings of International Conference on Financial Cryptography and Data Security. Berlin: Springer, 2015. Google Scholar

[55] Croman K, Decker C, Eyal I, et al. On scaling decentralized blockchains. In: Proceedings of International Conference on Financial Cryptography and Data Security. Berlin: Springer, 2016. Google Scholar

[56] Andresen G. Increase maximum block size. 2015. https://github.com/bitcoin/bips/blob/master/bip-0101.mediawiki. Google Scholar

[57] Garzik J. Making decentralized economic policy. 2015. http://gtf.org/garzik/bitcoin/BIP100-blocksizechangeproposal.pdf. Google Scholar

[58] Jeff G. Block size increase to 2mb. 2015. https://github.com/bitcoin/bips/blob/master/bip-0102.mediawiki. Google Scholar

[59] Larimer D. Delegated proof-of-stake white paper. 2014. http://www.bts.hk/dpos-baipishu.html. Google Scholar

[60] Castro M, Liskov B. Practical byzantine fault tolerance. In: Proceedings of the 3rd Symposium on Operating Systems Design and Implementation, Cambridge, 1999. 99: 173--186. Google Scholar

[61] Zheng Z B, Xie S A, Dai H N, et al. An overview of blockchain technology: architecture, consensus, and future trends. In: Proceedings of 6th IEEE International Congress on Big Data, Beijing, 2017. 557--564. Google Scholar

[62] Abraham I, Malkhi D. BVP: byzantine vertical paxos. 2016. https://www.zurich.ibm.com/dccl/papers/abraham_dccl.pdf. Google Scholar

[63] Pass R, Shi E. FruitChains: a fair blockchain. IACR Cryptology ePrint Archive, 2016, 2016: 916. Google Scholar

[64] Bentov I, Pass R, Shi E. The sleepy model of consensus. IACR Cryptology ePrint Archive, 2016, 2016: 918. Google Scholar

[65] Pass R, Shi E. Hybrid consensus: efficient consensus in the permissionless model. IACR Cryptology ePrint Archive, 2016, 2016: 917. Google Scholar

[66] Ittai A, Dahlia M, Kartik N, et al. Solidus: an incentive-compatible cryptocurrency based on permissionless byzantine consensus,. arXiv Google Scholar

[67] David S, Noah Y, Arthur B. The ripple protocol consensus algorithm. Ripple Labs Inc, 2014. https://ripple.com/files/ripple_consensus_whitepaper.pdf. Google Scholar

[68] David M. The stellar consensus protocol: a federated model for internet-level consensus. Stellar Dev Found, 2016. https://www.stellar.org/papers/stellar-consensus-protocol.pdf. Google Scholar

[69] Thomas S, Schwartz E. A protocol for interledger payments. https://interledger.org/interledger.pdf, 2015. Google Scholar

[70] Hope-Bailie A, Thomas S. Interledger: creating a standard for payments. In: Proceedings of the 25th International Conference Companion on World Wide Web, Montréal, 2016. Google Scholar

[71] Schwartz E. A payment protocol of the Web, for the Web: or, finally enabling Web micropayments with the interledger protocol. In: Proceedings of the 25th International Conference Companion on World Wide Web, Montréal, 2016. Google Scholar

[72] Gavin W. Polkadot: vision for a heterogeneous multi-chain framework. Draft, 2016. http://www.the-blockchain.com/docs/Gavin. Google Scholar

[73] 众安信息技术服务有限公司. 安链云链网络白皮书, 2017. http://static.zhongan.com/website/tech/whitepaper.zip. Google Scholar

[74] Zackary H, Yanislav M, Jack P. Eternity blockchain: the trustless, decentralized and purely functional oracle machine. Whitepaper, 2017. https://blockchain.aeternity.com/. Google Scholar