Cloud Computing Operations Research

Published Online:https://doi.org/10.1287/serv.1120.0038

References

  • Al-Daoud H, Al-Azzoni I, Down DG. Power-aware linear programming based scheduling for heterogeneous server clusters. Future Generation Comput. Systems (2012) 28(5):745–754CrossrefGoogle Scholar
  • Armbrust M, Fox A, Friffith R, Joseph AD, Katz R, Konwinskii A, Lee G, et al. A view of cloud computing. Comm. ACM (2010) 53(4):50–58CrossrefGoogle Scholar
  • Bapna R, Das S, Day R, Garfinkel R, Stallaert J. A clock-and-offer auction market for grid resources when bidders face stochastic computational needs. INFORMS J. Comput. (2011) 23(4):630–647LinkGoogle Scholar
  • Bartholdi JJ, Hackman ST. Warehouse and Distribution Science (2011) (Georgia Institute of Technology, Atlanta) . version 0.95 http://www2.isye.gatech.edu/~jjb/whGoogle Scholar
  • Bin E, Biran O, Boni O, Hadad E, Kolodner EK, Moatti Y, Lorenz DH. Guaranteeing high availability goals for virtual machine placement. Proc. 31st Internat. Conf. Distributed Comput. Systems (ICDCS) (2011) (IEEE Computer Society, Washington, DC) 700–709Google Scholar
  • Chen H, Yao DD. Fundamentals of Queueing Networks (2001) (Springer, New York) CrossrefGoogle Scholar
  • Dai JG, Lin W. Maximum pressure policies in stochastic processing networks. Oper. Res. (2005) 53(2):197–218LinkGoogle Scholar
  • Dieker AB, Shin J. From local to global stability in stochastic processing networks through quadratic Lyapunov functions. Math. Oper. Res. (2013) . ePub ahead of print March 13, DOI:10.1287/moor.2013.0588Google Scholar
  • Dieker AB, Ghosh S, Squillante MS. Optimal resource capacity management for stochastic networks. (2012) . Working paper, Georgia Institute of Technology, AtlantaGoogle Scholar
  • Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. Proc. Grid Comput. Environments Workshop (2008) Austin, TX:1–10Google Scholar
  • Gandhi A, Gupta V, Harchol-Balter M, Kozuch MA. Optimality analysis of energy-performance trade-off for server farm management. Performance Eval. (2010) 67(11):1155–1171CrossrefGoogle Scholar
  • Gentry C. Computing arbitrary functions of encrypted data. Comm. ACM (2010) 53(3):97–105CrossrefGoogle Scholar
  • Ghosh R, Naik VK, Trivedi KS. Power-performance trade-offs in IaaS cloud: A scalable analytic approach. Proc. 2011 IEEE/IFIP 41st Conf. Dependable Systems Networks Worshop (2011) (IEEE Computer Society, Washington, DC) 152–157Google Scholar
  • Greenberg A, Hamilton J, Maltz DA, Patel P. The cost of a cloud: Research problems in data center networks. ACM SIGCOMM Comput. Comm. Rev. (2009) 39(1):68–73CrossrefGoogle Scholar
  • Iyoob I, Zarifoglu E. Optimal capacity management for IaaS consumers. (2011) . Working paper, University of Texas at Austin, AustinGoogle Scholar
  • Iyoob I, Zarifoglu E, Modh M, Farooq M. Optimally sourcing services in hybrid cloud environments. (2011) . U.S. Patent 13/373,162, filed November 9, 2011Google Scholar
  • Klančnik T, Blažič BJ. Context-aware information broker for cloud computing. Internat. Rev. Comput. Software (2010) 5(1):52–58Google Scholar
  • Kwasnica AM, Ledyard JO, Porter D, DeMartini C. A new and improved design for multiobject iterative auctions. Management Sci. (2005) 51(3):419–434LinkGoogle Scholar
  • Lefèvre L, Orgerie A-C. Designing and evaluating an energy efficient cloud. J. Supercomputing (2010) 51(3):352–373CrossrefGoogle Scholar
  • Levi R, Radovanović A. Provably near-optimal LP-based policies for revenue management in systems with reusable resources. Oper. Res. (2010) 58(2):503–507LinkGoogle Scholar
  • Lin M, Wierman A, Andrew LHL, Thereska E. Online dynamic capacity provisioning in data centers. Proc. Allerton Conf. Comm. Control, Comput. (2011) Monticello, IL:1159–1163Google Scholar
  • Maguluri ST, Srikant R, Ying L. Stochastic models of load balancing and scheduling in cloud computing clusters. Proc. IEEE INFOCOM Conf. (2012) (IEEE Computer Society, Washington, DC) 702–710Google Scholar
  • Mateus CR, Gautam N. Efficient control of an M/Mt/kt queue with application to energy management in data centers. (2011) . Working paper, Texas A&M University, College StationGoogle Scholar
  • Mell P, Grance T. The NIST definition of cloud computing: Recommendations of the National Institute of Standards and Technology. (2011) . NIST Special Publication 800-145, National Institute of Standards and Technology, Gaithersburg, MDGoogle Scholar
  • Menascé DA, Ngo P. Understanding cloud computing: Experimentation and capacity planning. Proc. Comput. Measurement Group Conf. (2009) DallasGoogle Scholar
  • Meng X, Isci C, Kephart J, Zhang L, Bouillet E, Pendarakis D. Efficient resource provisioning in compute clouds via VM multiplexing. Proc. 7th Internat. Conf. Autonomic Comput. (ICAC) (2010) (ACM, New York) 11–20Google Scholar
  • Mojcilović A, Connors D, Maglio P, Kieliszewski CA, Spohrer JC. Workforce analytics for the services economy. Handbook of Service Science (2010) (Springer, New York) 437–460CrossrefGoogle Scholar
  • Morton DP, Pan F, Saeger KJ. Models for nuclear smuggling interdiction. IIE Trans. (2005) 39(1):3–14CrossrefGoogle Scholar
  • Phillips R. Pricing and Revenue Optimization (2005) (Stanford University Press, Stanford, CA) CrossrefGoogle Scholar
  • Pinedo M. Scheduling: Theory, Algorithms, and Systems (2008) (Springer, New York) Google Scholar
  • Talluri K, Van Ryzin G. The Theory and Practice of Revenue Management (2005) (Springer, New York) CrossrefGoogle Scholar
  • Tsitsiklis JN, Xu K. On the power of (even a little) resource pooling. Stochastic Systems (2012) 2:1–66LinkGoogle Scholar
  • Undheim A, Chilwan A, Heegaard P. Differentiated availability in cloud computing SLAs. Proc. 2011 IEEE/ACM Conf. Grid Comput. (2011) (IEEE Computer Society, Washington, DC) 129–136Google Scholar
  • Vishwanath KV, Nagappan N. Characterizing cloud computing hardware reliability. Proc. 1st ACM Sympos. Cloud Comput. (2010) (ACM, New York) 193–204Google Scholar
  • Zheng Y, Shroff N, Sinha P. Design of a power efficient cloud computing environment: Heavy traffic limits and QoS. (2011) . Working paper, Ohio State University, ColumbusGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.