Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.

[1]  James J. Jiang,et al.  Discrepancy Theory Models of Satisfaction in IS Research , 2012 .

[2]  M Smith,et al.  Predictors of Family Satisfaction with an Australian Palliative Home Care Service: A Test of Discrepancy Theory , 1999, Journal of palliative care.

[3]  Marin Litoiu,et al.  Resource provisioning for cloud computing , 2009, CASCON.

[4]  Subhajyoti Bandyopadhyay,et al.  Cloud computing - The business perspective , 2011, Decis. Support Syst..

[5]  Bu-Sung Lee,et al.  Profit maximization model for cloud provider based on Windows Azure platform , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[6]  David Blaauw,et al.  Theoretical and practical limits of dynamic voltage scaling , 2004, Proceedings. 41st Design Automation Conference, 2004..

[7]  Leonard Kleinrock,et al.  Queueing Systems: Volume I-Theory , 1975 .

[8]  Alex Delis,et al.  Flexible use of cloud resources through profit maximization and price discrimination , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[9]  Richard N. Cardozo An Experimental Study of Customer Effort, Expectation, and Satisfaction , 1965 .

[10]  Keqin Li Optimal Load Distribution for Multiple Heterogeneous Blade Servers in a Cloud Computing Environment , 2011, IPDPS Workshops.

[11]  Albert Y. Zomaya,et al.  Profit-Driven Service Request Scheduling in Clouds , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[12]  G. Haines,et al.  The Theory of Buyer Behavior. , 1970 .

[13]  Yurdaer N. Doganata,et al.  Selecting Optimum Cloud Availability Zones by Learning User Satisfaction Levels , 2015, IEEE Transactions on Services Computing.

[14]  Dongdong Zhang,et al.  A Cloud Task Scheduling Algorithm Based on Users' Satisfaction , 2013, 2013 Fourth International Conference on Networking and Distributed Computing.

[15]  David E. Irwin,et al.  Balancing risk and reward in a market-based task service , 2004, Proceedings. 13th IEEE International Symposium on High performance Distributed Computing, 2004..

[16]  Kenli Li,et al.  Energy-aware preemptive scheduling algorithm for sporadic tasks on DVS platform , 2013, Microprocess. Microsystems.

[17]  S. Wittevrongel,et al.  Queueing Systems , 2019, Introduction to Stochastic Processes and Simulation.

[18]  David Abramson,et al.  Economic models for resource management and scheduling in Grid computing , 2002, Concurr. Comput. Pract. Exp..

[19]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[20]  A. Parasuraman,et al.  Reassessment of expectations as a comparison standard in measuring service quality: Implications , 1994 .

[21]  Kenli Li,et al.  A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Computing , 2015, IEEE Transactions on Computers.

[22]  Dave Durkee,et al.  Why Cloud Computing Will Never Be Free , 2010, ACM Queue.

[23]  Bharadwaj Veeravalli,et al.  On the Design of Mutually Aware OptimalPricing and Load Balancing Strategiesfor Grid Computing Systems , 2014, IEEE Transactions on Computers.

[24]  Rajkumar Buyya,et al.  A taxonomy of market‐based resource management systems for utility‐driven cluster computing , 2006, Softw. Pract. Exp..

[25]  John Wilkes,et al.  Profitable services in an uncertain world , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[26]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[27]  Kurt Matzler,et al.  THE KANO MODEL: HOW TO DELIGHT YOUR CUSTOMERS , 1996 .

[28]  Ivan Stojmenovic,et al.  Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multicore Server Processors across Clouds and Data Centers , 2014, IEEE Transactions on Computers.

[29]  B. Snoj,et al.  Marketing aspects of service quality. , 2001 .

[30]  Keqin Li Optimal configuration of a multicore server processor for managing the power and performance tradeoff , 2011, The Journal of Supercomputing.

[31]  Rajkumar Buyya,et al.  Libra: a computational economy‐based job scheduling system for clusters , 2004, Softw. Pract. Exp..

[32]  Albert Y. Zomaya,et al.  Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud , 2011, HPDC '11.

[33]  Gilbert A. Churchill,et al.  An Investigation into the Determinants of Customer Satisfaction , 1982 .

[34]  Anantha P. Chandrakasan,et al.  Low-power CMOS digital design , 1992 .

[35]  C. Grönroos A Service Quality Model and its Marketing Implications , 1984 .

[36]  Massoud Pedram,et al.  Maximizing Profit in Cloud Computing System via Resource Allocation , 2011, 2011 31st International Conference on Distributed Computing Systems Workshops.

[37]  Fengju Kang,et al.  Cloud Simulation Resource Scheduling Algorithm Based on Multi-dimension Quality of Service , 2012 .

[38]  Dong Seong Kim,et al.  End-to-End Performability Analysis for Infrastructure-as-a-Service Cloud: An Interacting Stochastic Models Approach , 2010, 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing.

[39]  Ming Zhao,et al.  Profit Aware Load Balancing for Distributed Cloud Data Centers , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

[40]  P. Wilton,et al.  Models of Consumer Satisfaction Formation : An Extension , 1988 .

[41]  Hewijin Christine Jiau,et al.  Improving Consumer Satisfaction through Building an Allocation Cloud , 2012 .

[42]  Rajkumar Buyya,et al.  SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments , 2012, J. Comput. Syst. Sci..

[43]  Dmytro Dyachuk,et al.  Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[44]  David E. Culler,et al.  User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[45]  Ben H. H. Juurlink,et al.  Leakage-Aware Multiprocessor Scheduling , 2009, J. Signal Process. Syst..

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

[47]  Mohammad Reza Meybodi,et al.  Decreasing Impact of SLA Violations:A Proactive Resource Allocation Approachfor Cloud Computing Environments , 2014, IEEE Transactions on Cloud Computing.

[48]  Minglu Li,et al.  Customer Satisfaction-Aware Scheduling for Utility Maximization on Geo-distributed Cloud Data Centers , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[49]  Jelena V. Misic,et al.  Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[50]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[51]  Steven Skiena,et al.  The Algorithm Design Manual , 2020, Texts in Computer Science.

[52]  N. Kano,et al.  Attractive Quality and Must-Be Quality , 1984 .