A hierarchical control framework of load balancing and resource allocation of cloud computing services

Abstract Service providers must guarantee Quality of Service (QoS) requirements of the co-hosted applications in a data center and simultaneously achieve optimal utilization of their infrastructure under varying workload. This paper presents a hierarchical control framework that aims at compromising antagonistic objectives inside a data center. The local control level tackles simultaneously the problems of resource allocation and admission control of virtual machines while the upper level addresses together the load balancing of the incoming requests and placement of virtual machines into a cluster of physical servers. Numerical results show that the cooperation of the two control layers guarantees the satisfaction of the system’s constraints and the user’s requirements towards the fluctuations of incoming requests.

[1]  Spyros G. Denazis,et al.  On load balancing and resource allocation in cloud services , 2014, 22nd Mediterranean Conference on Control and Automation.

[2]  Ulas C. Kozat,et al.  Dynamic resource allocation and power management in virtualized data centers , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[3]  Rajkumar Buyya,et al.  Maximum revenue-oriented resource allocation in cloud , 2016, Int. J. Grid Util. Comput..

[4]  Enzo Baccarelli,et al.  Energy-saving adaptive computing and traffic engineering for real-time-service data centers , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[5]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

[6]  Ítalo S. Cunha,et al.  Joint admission control and resource allocation in virtualized servers , 2010, J. Parallel Distributed Comput..

[7]  Enzo Baccarelli,et al.  Q*: Energy and delay-efficient dynamic queue management in TCP/IP virtualized data centers , 2017, Comput. Commun..

[8]  Spyros G. Denazis,et al.  Adaptive admission control of distributed cloud services , 2010, 2010 International Conference on Network and Service Management.

[9]  Cheng-Zhong Xu,et al.  vPnP: Automated coordination of power and performance in virtualized datacenters , 2010, 2010 IEEE 18th International Workshop on Quality of Service (IWQoS).

[10]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[11]  Lui Sha,et al.  Queueing-Model-Based Adaptive Control of Multi-Tiered Web Applications , 2008, IEEE Transactions on Network and Service Management.

[12]  Barbara Panicucci,et al.  Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments , 2012, IEEE Transactions on Services Computing.

[13]  Cheng-Zhong Xu,et al.  URL: A unified reinforcement learning approach for autonomic cloud management , 2012, J. Parallel Distributed Comput..

[14]  Alexander Clemm,et al.  Integrated and autonomic cloud resource scaling , 2012, 2012 IEEE Network Operations and Management Symposium.

[15]  Xiaobo Zhou,et al.  Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee , 2013, TAAS.

[16]  Qian Wang,et al.  Modeling and Control Design for Performance Management of Web Servers Via an LPV Approach , 2007, IEEE Transactions on Control Systems Technology.

[17]  Yefu Wang,et al.  Coordinating Power Control and Performance Management for Virtualized Server Clusters , 2011, IEEE Transactions on Parallel and Distributed Systems.

[18]  Xiaobo Zhou,et al.  PERFUME: Power and performance guarantee with fuzzy MIMO control in virtualized servers , 2011, 2011 IEEE Nineteenth IEEE International Workshop on Quality of Service.

[19]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[20]  Spyros G. Denazis,et al.  ACRA: A unified admission control and resource allocation framework for virtualized environments , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

[21]  John H. Lilly,et al.  Fuzzy Control and Identification , 2010 .

[22]  Athanasios Christakidis,et al.  A control‐theoretic approach towards joint admission control and resource allocation of cloud computing services , 2015, Int. J. Netw. Manag..