Management of Cloud Infastructures: Policy-Based Revenue Optimization

Competition on global markets forces many enterprises to make use of new applications, reduce process times and at the same time cut the costs of their IT-infrastructure. To achieve this, it is necessary to maintain a high degree of flexibility with respect to the IT-infrastructure. Facing this challenge the idea of Cloud computing has been gaining interest lately. Cloud services can be accessed on demand without knowledge of the underlying infrastructure and have already succeeded in helping companies deploy products faster. Using Cloud services the New York Times managed to convert scanned images containing 11 million articles into PDF within 24 hours at a cost of merely 240 US-$. However Cloud providers will only offer their services, if they can realize sufficient benefit. To achieve this, the efficiency of Cloud infrastructure management must be increased. To this end we propose the use of concepts from revenue management and further enhancements.

[1]  N. Carr.  The end of corporate computing , 2005 .

[2]  Oscar H. Ibarra,et al.  Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems , 1975, JACM.

[3]  Patrick Martin,et al.  Workload class importance policy in autonomic database management systems , 2006, Seventh IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY'06).

[4]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[5]  Michael A. Rappa,et al.  The utility business model and the future of computing services , 2004, IBM Syst. J..

[6]  Peter R. Wurman,et al.  Market structure and multidimensional auction design for computational economies , 1999 .

[7]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[8]  Ian Miguel,et al.  The Temporal Knapsack Problem and Its Solution , 2005, CPAIOR.

[9]  Segev Wasserkrug,et al.  Autonomic self-optimization according to business objectives , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[10]  Klara Nahrstedt,et al.  A distributed resource management architecture that supports advance reservations and co-allocation , 1999, 1999 Seventh International Workshop on Quality of Service. IWQoS'99. (Cat. No.98EX354).

[11]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[12]  K. Keahey,et al.  Trading Grid services within the UK e-science Grid , 2004 .

[13]  Deeparnab Chakrabarty,et al.  Knapsack Problems , 2008 .

[14]  Rajkumar Buyya,et al.  Economic-based Distributed Resource Management and Scheduling for Grid Computing , 2002, ArXiv.

[15]  Suresh K. Nair,et al.  An application of yield management for Internet Service Providers , 2001 .

[16]  Jordi Torres,et al.  Web Customer Modeling for Automated Session Prioritization on High Traffic Sites , 2007, User Modeling.

[17]  Yezekael Hayel,et al.  Yield management for IT resources on demand: Analysis and validation of a new paradigm for managing computing centres , 2005 .

[18]  Rajkumar Buyya,et al.  Pricing for Utility-Driven Resource Management and Allocation in Clusters , 2007, Int. J. High Perform. Comput. Appl..

[19]  Jordi Torres,et al.  Building Online Performance Models of Grid Middleware with Fine-Grained Load-Balancing: A Globus Toolkit Case Study , 2007, EPEW.

[20]  Donald F. Ferguson,et al.  Economic models for allocating resources in computer systems , 1996 .

[21]  Ioana Popescu,et al.  Revenue Management in a Dynamic Network Environment , 2003, Transp. Sci..

[22]  Prashant J. Shenoy,et al.  Resource overbooking and application profiling in shared hosting platforms , 2002, OSDI '02.

[23]  Jordi Torres,et al.  Should the grid middleware look to self-managing capabilities? , 2007, Eighth International Symposium on Autonomous Decentralized Systems (ISADS'07).

[24]  Robert Klein.  Network capacity control using self-adjusting bid-prices , 2007, OR Spectr..

[25]  Werner Römisch,et al.  Airline network revenue management by multistage stochastic programming , 2008, Comput. Manag. Sci..

[26]  D. E. Campbell.  Resource Allocation Mechanisms , 1987 .

[27]  Jordi Torres,et al.  Autonomic QoS-Aware resource management in grid computing using online performance models , 2007, ValueTools '07.

[28]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[29]  Dirk Neumann,et al.  Economically Enhanced Resource Management for Internet Service Utilities , 2007, WISE.

[30]  Odej Kao,et al.  Introducing Risk Management into the Grid , 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

[31]  Rajkumar Buyya,et al.  Managing Cancellations and No-Shows of Reservations with Overbooking to Increase Resource Revenue , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[32]  Jordi Torres,et al.  Self-adaptive utility-based web session management , 2009, Comput. Networks.

[33]  Chris M. Kenyon,et al.  Grid resource commercialization: economic engineering and delivery scenarios , 2004 .

[34]  W. Lieberman.  The Theory and Practice of Revenue Management , 2005 .