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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

JointCloud computing is a new generation of cloud computing mode based on the cooperation among multicloud service entities. Through the deep integration of multicloud resources, it facilitates developers to customize cloud services by the method of “software definition", and to create cloud value. Achieving successful cloud service transactions is the most important challenge that JointCloud faces. This paper originally puts forward a supporting environment of cross-stakeholders' peer-to-peer transactions, including resources and services, named JointCloud Cooperation Environment (JCCE). The core of JCCE architecture involves distributed cloud transaction, distributed cloud community, distributed cloud monitoring, and the blockchain-based distributed ledger system. In such primary services, the related information exchange techniques, especially value exchange techniques, are conducive to breaking the information asymmetry among the participants in JointCloud, creating win-win opportunities for each party, and forming strong support for the JointCloud computing business innovation. From the perspective of cloud service consumers, cloud service providers, and cloud service brokers, JCCE's consumption service mode, supply service mode, and consultation service mode are put forward to guide the transaction participants to apply the value-exchange service of JCCE efficiently and conveniently. Finally, with regard to the support service of value exchange, the operation mechanism of JCCE is expounded in different scenarios including cloud resource transaction, data transaction, and custom software service transaction.


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

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