An Adaptive Resource Scheduling Mechanism Based on User Behavior Feedback in Cloud Computing

Scheduling mechanism in cloud computing environment concerns more about the needs of users due to the commercial purpose of cloud computing. In response to this user-oriented feature, an Adaptive Resource Scheduling Mechanism based on User Behavior Feedback(ARSM-UBF) is developed to schedule cloud resources from the perspective of users. In ARSM-UBF, information collected from scheduling interaction is adopted as the reflection of user behavior and by introducing the relevance feedback mechanism, user behavior information is integrated into the resource scheduling and continuous feedback is proceeded on the following scheduling process to make scheduling results closer to the user's subjective perception. Furthermore, ARSM-UBF gives attention to the efficiency of cloud system. Simulation results confirm that this user behavior feedback based scheduling mechanism is much more efficient since it can not only provide guaranteed services for users with diverse requirements, but also improve the resource utilization rate from the system's perspectives.

[1]  Xuejie Zhang,et al.  A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

[2]  Hector Garcia-Molina,et al.  Semantic Overlay Networks for P2P Systems , 2004, AP2PC.

[3]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[4]  Lakshmi Sobhana Kalli,et al.  Market-Oriented Cloud Computing : Vision , Hype , and Reality for Delivering IT Services as Computing , 2013 .

[5]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[6]  Kang Chen,et al.  Cloud Computing: System Instances and Current Research: Cloud Computing: System Instances and Current Research , 2010 .

[7]  Peter Mell,et al.  "The NIST Definition of Cloud Computing," Version 15 , 2009 .

[8]  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 .

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

[10]  Jian Xie,et al.  Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[11]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[12]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[13]  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.

[14]  Gao Zhan A Grid Resource Retrieval Based on User Behavior Feedback , 2010 .

[15]  Xuejie Zhang,et al.  Realization of open cloud computing federation based on mobile agent , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[16]  Zheng Wei,et al.  Cloud Computing:System Instances and Current Research , 2009 .

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

[18]  Jianhua Gu,et al.  A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment , 2012, J. Comput..