Towards an efficient distributed cloud computing architecture

Cloud computing is an emerging field in computer science. Users are utilizing less of their own existing resources, while increasing usage of cloud resources. There are many advantages of distributed computing over centralized architecture. With increase in number of unused storage and computing resources and advantages of distributed computing resulted in distributed cloud computing. In the distributed cloud environment that we propose, resource providers (RP) compete to provide resources to the users. In the distributed cloud all the cloud computing and storage services are offered by distributed resources. In this architecture resources are used and provided by the users in a peer to peer fashion. We propose using multi-valued distributed hash tables for efficient resource discovery. Leveraging the fact that there are many users providing resources such as CPU and memory, we define these resources under one key to easily locate devices with equivalent resources. We have evaluated the performance of resource discovery mechanisms for the distributed cloud and distributed cloud storage and compared the results with existing DHTs, peer to peer clients such as VUZE [1] and explored the feasibility and efficiency of the proposed schemes in terms of resource/service discovery and allocation. We use a simultaneous Auction mechanism and select a set of winners once we receive all contributions or bids. In a real world scenario, users request resources with multiple capabilities, and in order to find such resources we use a contribution mechanism where service providers will provide a contribution price to users for providing a resource. Users use our proposed auction mechanism to select the resources from the set of resource providers. We show that Nash equilibrium can be achieved and how we can avoid the problem of free riders in the distributed cloud.

[1]  David P. Anderson,et al.  BOINC: a system for public-resource computing and storage , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[2]  Rajkumar Buyya,et al.  Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

[3]  Hao Li,et al.  The Research on Cloud Resource Pricing # Strategies Based on Cournot Equilibrium , 2012 .

[4]  David R. Karger,et al.  Chord: a scalable peer-to-peer lookup protocol for internet applications , 2003, TNET.

[5]  Kenneth Ward Church,et al.  On Delivering Embarrassingly Distributed Cloud Services , 2008, HotNets.

[6]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[7]  Lei Yang,et al.  A Novel P2P Network Model for Cloud Computing Based on Game Theory , 2012, 2012 International Conference on Computer Science and Service System.

[8]  Divyakant Agrawal,et al.  Approximate Range Selection Queries in Peer-to-Peer Systems , 2003, CIDR.

[9]  M. Frans Kaashoek,et al.  Vivaldi: a decentralized network coordinate system , 2004, SIGCOMM 2004.

[10]  David Fernández-Baca,et al.  Allocating Modules to Processors in a Distributed System , 1989, IEEE Trans. Software Eng..

[11]  Armando Fox,et al.  Cloud Computing—What's in It for Me as a Scientist? , 2011, Science.

[12]  George H. L. Fletcher,et al.  Unstructured Peer-to-Peer Networks: Topological Properties and Search Performance , 2004, AP2PC.

[13]  John J. Liu,et al.  Competitive pricing of mixed retail and e-tail distribution channels , 2005 .

[14]  David Mazières,et al.  Kademlia: A Peer-to-Peer Information System Based on the XOR Metric , 2002, IPTPS.

[15]  Ian Lumb,et al.  A Taxonomy and Survey of Cloud Computing Systems , 2009, 2009 Fifth International Joint Conference on INC, IMS and IDC.

[16]  Patrick Valduriez,et al.  Survey of data replication in P2P systems , 2006 .

[17]  Hagit Attiya,et al.  Distributed Computing: Fundamentals, Simulations and Advanced Topics , 1998 .

[18]  Manish Parashar,et al.  Squid: Enabling search in DHT-based systems , 2008, J. Parallel Distributed Comput..

[19]  Nicholas Hopper,et al.  Secure latency estimation with treeple , 2010, CCS '10.

[20]  Andrew Warfield,et al.  Xen and the art of virtualization , 2003, SOSP '03.

[21]  Peter J. H. King,et al.  Querying multi-dimensional data indexed using the Hilbert space-filling curve , 2001, SGMD.

[22]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[23]  Kyungyong Lee,et al.  MatchTree: Flexible, scalable, and fault-tolerant wide-area resource discovery with distributed matchmaking and aggregation , 2013, Future Gener. Comput. Syst..

[24]  Judith Kelner,et al.  Resource allocation for distributed cloud: concepts and research challenges , 2011, IEEE Network.

[25]  Hong Liu,et al.  Introducing a Distributed Cloud Architecture with Efficient Resource Discovery and Optimal Resource Allocation , 2013, 2013 IEEE Ninth World Congress on Services.

[26]  Antonio Puliafito,et al.  Cloud@Home: Bridging the Gap between Volunteer and Cloud Computing , 2009, ICIC.

[27]  Nicholas Hopper,et al.  Accurate and Provably Secure Latency Estimation with Treeple , 2011, NDSS.

[28]  Naixue Xiong,et al.  A game-theoretic method of fair resource allocation for cloud computing services , 2010, The Journal of Supercomputing.

[29]  Meina Song,et al.  A Cloud Computing Platform Based on P2P , 2009, 2009 IEEE International Symposium on IT in Medicine & Education.

[30]  Özalp Babaoglu,et al.  Design and implementation of a P2P Cloud system , 2012, SAC '12.

[31]  Beng Chin Ooi,et al.  Supporting multi-dimensional range queries in peer-to-peer systems , 2005, Fifth IEEE International Conference on Peer-to-Peer Computing (P2P'05).

[32]  Fei Teng,et al.  A New Game Theoretical Resource Allocation Algorithm for Cloud Computing , 2010, GPC.