Interference aware prediction mechanism for auto scaling in cloud

Abstract Advancements in cloud computing has transformed it into the most promising computing paradigm for business organizations. In a dynamic cloud environment, Virtual Machine (VM) migration is a critical step in resource management, especially in large scale datacenters. Most of the VM migration and load balancing policies are based on power consumption and response time, with little attention on the interferences caused due to VM migrations. Such interferences degrade the overall performance of the system, and consequently violates the service level agreement (SLA) between the customer and the provider. This paper introduces an interference aware prediction mechanism for VM migration, with auto scaling. The automatic scaling policies help to handle sudden load changes with precise prediction and minimum VM migration. The experimental results and comparative analysis show that the proposed model is capable of predicting the interference more accurately. It also provides an optimum threshold range for VM migration.

[1]  Hai Jin,et al.  Falloc: Fair network bandwidth allocation in IaaS datacenters via a bargaining game approach , 2013, 2013 21st IEEE International Conference on Network Protocols (ICNP).

[2]  Daniel Grzonka,et al.  Security Supportive Energy Aware Scheduling And Scaling For Cloud Environments , 2017, ECMS.

[3]  Huankai Chen,et al.  User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing , 2013, 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH).

[4]  Matei Ripeanu,et al.  VMFlock: virtual machine co-migration for the cloud , 2011, HPDC '11.

[5]  Dieter Kranzlmüller,et al.  Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows , 2017, Future Gener. Comput. Syst..

[6]  Binh Minh Nguyen,et al.  A Proactive Cloud Scaling Model Based on Fuzzy Time Series and SLA Awareness , 2017, ICCS.

[7]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[8]  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).

[9]  Arpan Roy,et al.  Keep it moving: Proactive workload management for reducing SLA violations in large scale SaaS clouds , 2013, 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE).

[10]  Christopher D. Wickens,et al.  Multiple resources and performance prediction , 2002 .

[11]  S. Khaddaj,et al.  A QoS Based Load Balancing Framework for Large Scale Elastic Distributed Systems , 2011, 2011 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

[12]  Philip Samuel,et al.  Virtual Machine Placement for Improved Quality in IaaS Cloud , 2014, 2014 Fourth International Conference on Advances in Computing and Communications.

[13]  Xiaofang Li,et al.  An Improved Max-Min Task-Scheduling Algorithm for Elastic Cloud , 2014, 2014 International Symposium on Computer, Consumer and Control.

[14]  Qian Zhu,et al.  Power-Aware Consolidation of Scientific Workflows in Virtualized Environments , 2010, 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis.

[15]  Nan Liu,et al.  Multi-resource Allocation Scheduling in Dynamic Environments , 2012 .

[16]  Monica S. Lam,et al.  Optimizing the migration of virtual computers , 2002, OPSR.

[17]  Bo Li,et al.  iAware: Making Live Migration of Virtual Machines Interference-Aware in the Cloud , 2014, IEEE Transactions on Computers.

[18]  Tao Lu,et al.  Successor: Proactive cache warm-up of destination hosts in virtual machine migration contexts , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[19]  Hyeonsang Eom,et al.  Enabling Consolidation and Scaling Down to Provide Power Management for Cloud Computing , 2011, HotCloud.

[20]  Chita R. Das,et al.  Migration, Assignment, and Scheduling of Jobs in Virtualized Environment , 2011, HotCloud.

[21]  S. N. Sivanandam,et al.  Pareto based hybrid Meta heuristic ABC - ACO approach for task scheduling in computational grids , 2014, Int. J. Hybrid Intell. Syst..

[22]  Yefu Wang,et al.  Performance-controlled server consolidation for virtualized data centers with multi-tier applications , 2014, Sustain. Comput. Informatics Syst..

[23]  Hiroshi Yamada,et al.  Towards unobtrusive VM live migration for cloud computing platforms , 2012, APSys.

[24]  Mohammad Masdari,et al.  An overview of virtual machine placement schemes in cloud computing , 2016, J. Netw. Comput. Appl..