On the Performance, Availability and Traffic Control Analysis of Virtualized Servers in Cloud Computing Environment

Traffic management is a complex task due to high arrival rate of different type and size of packets from many resources in cloud computing environment. Enhancing the quality of service of virtualized servers by traffic control mechanism play important role in such environment. The aim of this paper is to analyze the traffic management of the virtualized servers with the dynamic resource utilization when the system is fault tolerant in cloud computing. Thus, an analytical model is considered to get quality of service measurements considering availability of the virtualized servers in this paper. The traffic control management is proposed and considered together with the availability issues in order to obtain more realistic quality of service measures. The resulting analytical model solved by using an exact spectral expansion solution approach to get performance measurements. The evaluation on system performance done by analyzing the mean queue length, blocking probability and mean response time of the proposed system. The analytical results obtained show that the proposed model can improve the quality of service in cloud computing environment.

[1]  Yuan Yu,et al.  Dryad: distributed data-parallel programs from sequential building blocks , 2007, EuroSys '07.

[2]  Ashutosh Sabharwal,et al.  An Axiomatic Theory of Fairness in Network Resource Allocation , 2009, 2010 Proceedings IEEE INFOCOM.

[3]  Laurent Massoulié,et al.  A queueing analysis of max-min fairness, proportional fairness and balanced fairness , 2006, Queueing Syst. Theory Appl..

[4]  Mark Handley,et al.  Re-architecting datacenter networks and stacks for low latency and high performance , 2017, SIGCOMM.

[5]  Yu Cao,et al.  Explicit multipath congestion control for data center networks , 2013, CoNEXT.

[6]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[7]  T. N. Vijaykumar,et al.  Deadline-aware datacenter tcp (D2TCP) , 2012, SIGCOMM '12.

[8]  David Mazières,et al.  EyeQ: Practical Network Performance Isolation for the Multi-tenant Cloud , 2012, HotCloud.

[9]  Ankit Singla,et al.  Jellyfish: Networking Data Centers Randomly , 2011, NSDI.

[10]  Albert G. Greenberg,et al.  Data center TCP (DCTCP) , 2010, SIGCOMM '10.

[11]  Amin Vahdat,et al.  A scalable, commodity data center network architecture , 2008, SIGCOMM '08.

[12]  Prashant Dahiwale,et al.  An efficient dynamic resource allocation strategy for VM environment in cloud , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[13]  Amin Vahdat,et al.  Practical TDMA for datacenter ethernet , 2012, EuroSys '12.

[14]  Jung Ho Ahn,et al.  HyperX: topology, routing, and packaging of efficient large-scale networks , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[15]  Cauligi S. Raghavendra,et al.  Datacenter Traffic Control: Understanding Techniques and Tradeoffs , 2017, IEEE Communications Surveys & Tutorials.

[16]  Albert G. Greenberg,et al.  VL2: a scalable and flexible data center network , 2009, SIGCOMM '09.

[17]  Paramvir Bahl,et al.  Flyways To De-Congest Data Center Networks , 2009, HotNets.

[18]  Li Chen,et al.  PIAS: Practical Information-Agnostic Flow Scheduling for Data Center Networks , 2014, HotNets.

[19]  Yonal Kirsal,et al.  Exploring Analytical Models for Performability Evaluation of Virtualized Servers using Dynamic Resource , 2019, Int. J. Comput. Commun. Control.

[20]  Mohammad Alizadeh,et al.  On the Data Path Performance of Leaf-Spine Datacenter Fabrics , 2013, 2013 IEEE 21st Annual Symposium on High-Performance Interconnects.

[21]  Inhyuk Kim,et al.  NHVM: Design and Implementation of Linux Server Virtual Machine Using Hybrid Virtualization Technology , 2010, 2010 International Conference on Computational Science and Its Applications.

[22]  Lei Shi,et al.  Dcell: a scalable and fault-tolerant network structure for data centers , 2008, SIGCOMM '08.

[23]  Mark Handley,et al.  Equation-based congestion control for unicast applications , 2000, SIGCOMM.

[24]  Eui-Nam Huh,et al.  A Framework of Smart Internet of Things based Cloud Computing , 2014 .

[25]  Yonal Kirsal,et al.  Analytical modelling and optimization analysis of large-scale communication systems and networks with repairmen policy , 2018, Computing.

[26]  Robert N. M. Watson,et al.  Queues Don't Matter When You Can JUMP Them! , 2015, NSDI.

[27]  Albert G. Greenberg,et al.  The nature of data center traffic: measurements & analysis , 2009, IMC '09.

[28]  D. Zats,et al.  DeTail: reducing the flow completion time tail in datacenter networks , 2012, CCRV.

[29]  Nick McKeown,et al.  Why flow-completion time is the right metric for congestion control , 2006, CCRV.

[30]  Srikanth Kandula,et al.  Speeding up distributed request-response workflows , 2013, SIGCOMM.

[31]  Ali Munir,et al.  Minimizing flow completion times in data centers , 2013, 2013 Proceedings IEEE INFOCOM.

[32]  Dan Li,et al.  TAPS: Software Defined Task-Level Deadline-Aware Preemptive Flow Scheduling in Data Centers , 2015, 2015 44th International Conference on Parallel Processing.

[33]  Michael Dinitz,et al.  Xpander: Towards Optimal-Performance Datacenters , 2016, CoNEXT.

[34]  Narendra M. Patel,et al.  Resource Management in Cloud Computing: Classification and Taxonomy , 2017, ArXiv.

[35]  Christian E. Hopps,et al.  Analysis of an Equal-Cost Multi-Path Algorithm , 2000, RFC.