Towards Bandwidth Guarantee for Virtual Clusters Under Demand Uncertainty in Multi-Tenant Clouds

In the cloud, multiple tenants share the resource of datacenters and their applications compete with each other for scarce network bandwidth. Current studies have shown that the lack of bandwidth guarantee causes unpredictable network performance, leading to poor application performance. To address this issue, several virtual network abstractions have been proposed which allow the tenants to reserve virtual clusters with specified bandwidth between the Virtual Machines (VMs) in the datacenters. However, all these existing proposals require the tenants to deterministically characterize the bandwidth demands in the abstractions, which can be difficult and result in inefficient bandwidth reservation due to the demand uncertainty. In this paper, we explore a virtual cluster abstraction with stochastic bandwidth characterization to address the bandwidth demand uncertainty. We propose Stochastic Virtual Cluster (SVC), which models the bandwidth demand between VMs in a probabilistic way. Based on SVC, we develop a stochastic framework for virtual cluster allocation, in which the admitted virtual cluster's bandwidth demands are satisfied with a high probability. Efficient VM allocation algorithms are proposed to implement the framework while reducing the possibility of link congestion through minimizing the maximum bandwidth occupancy of a virtual cluster on physical links. Using simulations, we show that SVC achieves the trade-off between the job concurrency and the average job running time, and demonstrate its effectiveness for accommodating cloud application workloads with highly volatile bandwidth demands and its improvement to work-conserving bandwidth enforcement.

[1]  Qiong Wang,et al.  Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management , 2005, TNET.

[2]  Albert G. Greenberg,et al.  Sharing the Data Center Network , 2011, NSDI.

[3]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[4]  Ming Zhang,et al.  Understanding data center traffic characteristics , 2010, CCRV.

[5]  Lei Yu,et al.  Bandwidth Guarantee under Demand Uncertainty in Multi-tenant Clouds , 2014, 2014 IEEE 34th International Conference on Distributed Computing Systems.

[6]  Albert G. Greenberg,et al.  Reining in the Outliers in Map-Reduce Clusters using Mantri , 2010, OSDI.

[7]  Ion Stoica,et al.  FairCloud: sharing the network in cloud computing , 2011, SIGCOMM '12.

[8]  Hitesh Ballani,et al.  Towards predictable datacenter networks , 2011, SIGCOMM 2011.

[9]  Dan Li,et al.  Towards bandwidth guarantee in multi-tenancy cloud computing networks , 2012, 2012 20th IEEE International Conference on Network Protocols (ICNP).

[10]  Sujata Banerjee,et al.  Application-driven bandwidth guarantees in datacenters , 2015, SIGCOMM.

[11]  George Varghese,et al.  Netshare and stochastic netshare: predictable bandwidth allocation for data centers , 2012, CCRV.

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

[13]  Lei Yu,et al.  Dynamic scaling of virtual clusters with bandwidth guarantee in cloud datacenters , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[14]  Sujata Banerjee,et al.  ElasticSwitch: practical work-conserving bandwidth guarantees for cloud computing , 2013, SIGCOMM.

[15]  Helen J. Wang,et al.  SecondNet: a data center network virtualization architecture with bandwidth guarantees , 2010, CoNEXT.

[16]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[17]  Bing Yu,et al.  Time-Varying Network Tomography: Router Link Data , 2000 .

[18]  Jorge-Arnulfo Quiané-Ruiz,et al.  Runtime measurements in the cloud , 2010, Proc. VLDB Endow..

[19]  Christophe Diot,et al.  Traffic matrix estimation: existing techniques and new directions , 2002, SIGCOMM 2002.

[20]  Ning Ding,et al.  The only constant is change: incorporating time-varying network reservations in data centers , 2012, SIGCOMM.

[21]  Samuel Kotz,et al.  Exact Distribution of the Max/Min of Two Gaussian Random Variables , 2008, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

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

[23]  Hai Jin,et al.  Fair Network Bandwidth Allocation in IaaS Datacenters via a Cooperative Game Approach , 2016, IEEE/ACM Transactions on Networking.

[24]  Dorgival O. Guedes,et al.  Gatekeeper: Supporting Bandwidth Guarantees for Multi-tenant Datacenter Networks , 2011, WIOV.