A User Relinquishment-based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers

In a cloud federation, by using the pay-as-you-go billing model users can relinquish their services at any point in time and pay accordingly. Therefore, this thesis aims to study the resource assignment problem in the situation where the user relinquishment impacts the net profit of a cloud service provider. As a solution, our study 1) proposes a tool to calculate the net profit which includes income, electricity expenses, and relinquishment loss; 2) compares different ways to predict the user behavior and deduce a better prediction technique based on linear regression; and 3) proposes a relinquishment-aware resource optimization model to estimate the amount of resources based upon the predicted user behavior. Simulations were performed with the CloudSim framework. The results show that instead of blindly assigning resources to users, a cloud service provider with a finite resource pool can gain more by estimating the resources using better prediction techniques.

[1]  Pascal Bouvry,et al.  Amazon Elastic Compute Cloud (EC2) vs. In-House HPC Platform: A Cost Analysis , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[2]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[3]  Bu-Sung Lee,et al.  Towards Economic Fairness for Big Data Processing in Pay-as-You-Go Cloud Computing , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[4]  Eui-nam Huh,et al.  Broker as a Service (BaaS) Pricing and Resource Estimation Model , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[5]  Lisandro Zambenedetti Granville,et al.  Data Center Network Virtualization: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[6]  Tao Li,et al.  Optimization of Resource Allocation and Energy Efficiency in Heterogeneous Cloud Data Centers , 2015, 2015 44th International Conference on Parallel Processing.

[7]  Bin Tang,et al.  Profit-based file replication in data intensive cloud data centers , 2017, 2017 IEEE International Conference on Communications (ICC).

[8]  Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making , 2012 .

[9]  Achim Streit,et al.  Simulation-based Evaluation of an Intercloud Service Broker , 2012, CLOUD 2012.

[10]  Anna Melekhova,et al.  CPU utilization prediction methods overview , 2015, CEE-SECR '15.

[11]  Marzia Zaman,et al.  A Framework for Automatic Resource Provisioning for Private Clouds , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[12]  Shikharesh Majumdar,et al.  Engineering resource management middleware for optimizing the performance of clouds processing mapreduce jobs with deadlines , 2014, ICPE.

[13]  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 .

[14]  Yi-Cheng Zhang,et al.  Statistical Mechanics of Competitive Resource Allocation , 2013, 1305.2121.

[15]  Rajkumar Buyya,et al.  Cost-Effective Provisioning and Scheduling of Deadline-Constrained Applications in Hybrid Clouds , 2012, WISE.

[16]  Patrizio Dazzi,et al.  QBROKAGE: A Genetic Approach for QoS Cloud Brokering , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[17]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[18]  Hai Jin,et al.  Flexible Instance: Meeting Deadlines of Delay Tolerant Jobs in the Cloud with Dynamic Pricing , 2016, 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS).

[19]  Subhajyoti Bandyopadhyay,et al.  Cloud Computing - The Business Perspective , 2011, 2011 44th Hawaii International Conference on System Sciences.

[20]  Baochun Li,et al.  Maximizing revenue with dynamic cloud pricing: The infinite horizon case , 2012, 2012 IEEE International Conference on Communications (ICC).

[21]  Junliang Chen,et al.  Workload Predicting-Based Automatic Scaling in Service Clouds , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[22]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[23]  Kento Aida,et al.  Towards Understanding the Usage Behavior of Google Cloud Users: The Mice and Elephants Phenomenon , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[24]  David Hilley,et al.  Cloud Computing: A Taxonomy of Platform and Infrastructure-level Offerings , 2009 .

[25]  Jie Xu,et al.  An Approach for Characterizing Workloads in Google Cloud to Derive Realistic Resource Utilization Models , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[26]  Tram Truong-Huu,et al.  Handling Uncertainty and Diversity in Cloud Bandwidth Demands for Revenue Maximization , 2015, 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI).

[27]  Apostolos Papageorgiou,et al.  Maximizing Cloud Provider Profit from Equilibrium Price Auctions , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[28]  Jie Li,et al.  Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs , 2015, IEEE Transactions on Smart Grid.

[29]  Jing Xu,et al.  QoS-Driven Cloud Resource Management through Fuzzy Model Predictive Control , 2015, 2015 IEEE International Conference on Autonomic Computing.

[30]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[31]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[32]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[33]  Mohamed Othman,et al.  Energy-Efficient Algorithms for Dynamic Virtual Machine Consolidation in Cloud Data Centers , 2017, IEEE Access.

[34]  Mohammed Samaka,et al.  Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments , 2017, 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud).

[35]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[36]  Pasi Liljeberg,et al.  LiRCUP: Linear Regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers , 2013, 2013 39th Euromicro Conference on Software Engineering and Advanced Applications.

[37]  Rami G. Melhem,et al.  Shadows on the Cloud: An Energy-aware, Profit Maximizing Resilience Framework for Cloud Computing , 2014, CLOSER.

[38]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[39]  Holger Wache,et al.  Cloud Broker: Bringing Intelligence into the Cloud , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[40]  Yonggang Wen,et al.  Energy efficiency and server virtualization in data centers: An empirical investigation , 2012, 2012 Proceedings IEEE INFOCOM Workshops.

[41]  L. Sjaastad The Costs and Returns of Human Migration , 1962 .

[42]  Yusep Rosmansyah,et al.  Gradient descent and normal equations on cost function minimization for online predictive using linear regression with multiple variables , 2014, 2014 International Conference on ICT For Smart Society (ICISS).

[43]  Wen-De Zhong,et al.  Energy efficiency aware load distribution and electricity cost volatility control for cloud service providers , 2016, J. Netw. Comput. Appl..

[44]  Djamal Zeghlache,et al.  Mathematical Programming Approach for Revenue Maximization in Cloud Federations , 2017, IEEE Transactions on Cloud Computing.

[45]  Ohad Shamir,et al.  On-demand, Spot, or Both: Dynamic Resource Allocation for Executing Batch Jobs in the Cloud , 2014, ICAC.

[46]  M. Lloret,et al.  Maximising revenue in cloud computing markets by means of economically enhanced SLA management , 2010 .

[47]  Jie Qiu,et al.  The Method and Tool of Cost Analysis for Cloud Computing , 2009, 2009 IEEE International Conference on Cloud Computing.

[48]  Rajkumar Buyya,et al.  Contention management in federated virtualized distributed systems: implementation and evaluation , 2014, Softw. Pract. Exp..

[49]  A. Kuo Opportunities and Challenges of Cloud Computing to Improve Health Care Services , 2011, Journal of medical Internet research.

[50]  Mufajjul Ali,et al.  Green Cloud on the Horizon , 2009, CloudCom.

[51]  Seema Bawa,et al.  A review on energy aware VM placement and consolidation techniques , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[52]  Amip J. Shah,et al.  Cost Model for Planning, Development and Operation of a Data Center , 2005 .

[53]  Daniel Grosu,et al.  Cloud Federations in the Sky: Formation Game and Mechanism , 2015, IEEE Transactions on Cloud Computing.

[54]  Murali S. Kodialam,et al.  Scheduling in mapreduce-like systems for fast completion time , 2011, 2011 Proceedings IEEE INFOCOM.

[55]  Fabricia Roos-Frantz,et al.  Price modeling of laaS providers using multiple regression , 2017, 2017 12th Iberian Conference on Information Systems and Technologies (CISTI).

[56]  Rita Cucchiara,et al.  Intelligent Video Surveillance as a Service , 2013, Intelligent Multimedia Surveillance.

[57]  ZhiHui Lv,et al.  RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[58]  Lieven Eeckhout,et al.  Trends in Server Energy Proportionality , 2011, Computer.

[59]  Shawon Rahman,et al.  Cloud Computing Avoids Downfall of Application Service Providers , 2015, ArXiv.

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

[61]  B. B. P. Rao,et al.  Cloud computing for Internet of Things & sensing based applications , 2012, 2012 Sixth International Conference on Sensing Technology (ICST).

[62]  Thanadech Thanakornworakij,et al.  An Economic Model for Maximizing Profit of a Cloud Service Provider , 2012, 2012 Seventh International Conference on Availability, Reliability and Security.

[63]  Gianmario Motta,et al.  SLA-aware broker for Public Cloud , 2017, 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS).

[65]  Marc St-Hilaire,et al.  Economic and Energy Considerations for Resource Augmentation in Mobile Cloud Computing , 2018, IEEE Transactions on Cloud Computing.

[66]  Sasko Ristov,et al.  Scaling the performance and cost while scaling the load and resources in the cloud , 2013, 2013 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[67]  Ryan Shea,et al.  Energy Efficiency of Cloud Virtual Machines: From Traffic Pattern and CPU Affinity Perspectives , 2017, IEEE Systems Journal.

[68]  Jun Chen,et al.  A Live Migration Algorithm for Virtual Machine in a Cloud Computing Environment , 2015, 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom).

[69]  S.K. Sonkar,et al.  A review on resource allocation and VM scheduling techniques and a model for efficient resource management in cloud computing environment , 2016, 2016 International Conference on ICT in Business Industry & Government (ICTBIG).

[70]  Wouter Joosen,et al.  Extending sensor networks into the Cloud using Amazon Web Services , 2010, 2010 IEEE International Conference on Networked Embedded Systems for Enterprise Applications.

[71]  Ronald M. Summers,et al.  Machine learning and radiology , 2012, Medical Image Anal..

[72]  Salvatore Venticinque,et al.  An SLA-based Broker for Cloud Infrastructures , 2013, Journal of Grid Computing.

[73]  Kenli Li,et al.  Optimal Multiserver Configuration for Profit Maximization in Cloud Computing , 2013, IEEE Transactions on Parallel and Distributed Systems.

[74]  M. F. Luce,et al.  Organizational Behavior and Human Decision Processes When Time Is Money: Decision Behavior under Opportunity-cost Time Pressure , 2022 .

[75]  M. Kunze,et al.  Cloud Federation , 2011 .

[76]  Jie Xu,et al.  Neural Network-Based Overallocation for Improved Energy-Efficiency in Real-Time Cloud Environments , 2012, 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing.

[77]  Sudip Misra,et al.  Cost-Effective Mapping between Wireless Body Area Networks and Cloud Service Providers Based on Multi-Stage Bargaining , 2017, IEEE Transactions on Mobile Computing.

[78]  David Abramson,et al.  A Computational Economy for Grid Computing and its Implementation in the Nimrod-G Resource Brok , 2001, Future Gener. Comput. Syst..

[79]  Raouf Boutaba,et al.  Emulating an infrastructure with EASE , 2016, 2016 12th International Conference on Network and Service Management (CNSM).

[80]  Paul J. Kühn,et al.  Modeling and Analysis of Virtualized Multi-Service Cloud Data Centers with Automatic Server Consolidation and Prescribed Service Level Agreements , 2016, 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops).

[81]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[82]  Elizabeth Chang,et al.  Conceptual SLA framework for cloud computing , 2010, 4th IEEE International Conference on Digital Ecosystems and Technologies.