Deliverable JRA1.1: Evaluation of current network control and management planes for multi-domain network infrastructure

This deliverable includes a compilation and evaluation of available control and management architectures and protocols applicable to a multilayer infrastructure in a multi-domain Virtual Network environment.The scope of this deliverable is mainly focused on the virtualisation of the resources within a network and at processing nodes. The virtualization of the FEDERICA infrastructure allows the provisioning of its available resources to users by means of FEDERICA slices. A slice is seen by the user as a real physical network under his/her domain, however it maps to a logical partition (a virtual instance) of the physical FEDERICA resources. A slice is built to exhibit to the highest degree all the principles applicable to a physical network (isolation, reproducibility, manageability, ...). Currently, there are no standard definitions available for network virtualization or its associated architectures. Therefore, this deliverable proposes the Virtual Network layer architecture and evaluates a set of Management- and Control Planes that can be used for the partitioning and virtualization of the FEDERICA network resources. This evaluation has been performed taking into account an initial set of FEDERICA requirements; a possible extension of the selected tools will be evaluated in future deliverables. The studies described in this deliverable define the virtual architecture of the FEDERICA infrastructure. During this activity, the need has been recognised to establish a new set of basic definitions (taxonomy) for the building blocks that compose the so-called slice, i.e. the virtual network instantiation (which is virtual with regard to the abstracted view made of the building blocks of the FEDERICA infrastructure) and its architectural plane representation. These definitions will be established as a common nomenclature for the FEDERICA project. Other important aspects when defining a new architecture are the user requirements. It is crucial that the resulting architecture fits the demands that users may have. Since this deliverable has been produced at the same time as the contact process with users, made by the project activities related to the Use Case definitions, JRA1 has proposed a set of basic Use Cases to be considered as starting point for its internal studies. When researchers want to experiment with their developments, they need not only network resources on their slices, but also a slice of the processing resources. These processing slice resources are understood as virtual machine instances that users can use to make them behave as software routers or end nodes, on which to download the software protocols or applications they have produced and want to assess in a realistic environment. Hence, this deliverable also studies the APIs of several virtual machine management software products in order to identify which best suits FEDERICA’s needs.Postprint (published version

Resource virtualisation of network routers

There is now considerable interest in applications that transport time-sensitive data across the best-effort Internet. We present a novel network router architecture, which has the potential to improve the Quality of Service guarantees provided to such flows. This router architecture makes use of virtual machine techniques, to assign an individual virtual routelet to each network flow requiring QoS guarantees. We describe a prototype of this virtual routelet architecture, and evaluate its effectiveness. Experimental results of the performance and flow partitioning of this prototype, compared with a standard software router, suggest promise in the virtual routelet architecture

On the Effect of using rCUDA to Provide CUDA Acceleration to Xen Virtual Machines

[EN] Nowadays, many data centers use virtual machines (VMs) in order to achieve a more efficient use of hardware resources. The use of VMs provides a reduction in equipment and maintenance expenses as well as a lower electricity consumption. Nevertheless, current virtualization solutions, such as Xen, do not easily provide graphics processing units (GPUs) to applications running in the virtualized domain with the flexibility usually required in data centers (i.e., managing virtual GPU instances and concurrently sharing them among several VMs). Therefore, the execution of GPU-accelerated applications within VMs is hindered by this lack of flexibility. In this regard, remote GPU virtualization solutions may address this concern. In this paper we analyze the use of the remote GPU virtualization mechanism to accelerate scientific applications running inside Xen VMs. We conduct our study with six different applications, namely CUDA-MEME, CUDASW++, GPU-BLAST, LAMMPS, a triangle count application, referred to as TRICO, and a synthetic benchmark used to emulate different application behaviors. Our experiments show that the use of remote GPU virtualization is a feasible approach to address the current concerns of sharing GPUs among several VMs, featuring a very low overhead if an InfiniBand fabric is already present in the cluster.This work was funded by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Prades, J.; Reaño González, C.; Silla Jiménez, F. (2019). On the Effect of using rCUDA to Provide CUDA Acceleration to Xen Virtual Machines. Cluster Computing. 22(1):185-204. https://doi.org/10.1007/s10586-018-2845-0185204221Kernel-Based Virtual Machine, KVM. http://www.linux-kvm.org (2015). Accessed 19 Oct 2015Xen Project. http://www.xenproject.org/ (2015). Accessed 19 Oct 2015VMware Virtualization. http://www.vmware.com/ (2015). Accessed 19 Oct 2015Oracle VM VirtualBox. http://www.virtualbox.org/ (2015). Accessed 19 Oct 2015Semnanian, A., Pham, J., Englert, B., Wu, X.: Virtualization technology and its impact on computer hardware architecture. In: Proceedings of the Information Technology: New Generations, ITNG, pp. 719–724 (2011)Felter, W., Ferreira, A., Rajamony, R., Rubio, J.: An updated performance comparison of virtual machines and linux containers. In: IBM Research Report (2014)Zhang, J., Lu, X., Arnold, M., Panda, D.: MVAPICH2 over OpenStack with SR-IOV: an efficient approach to build HPC Clouds. In: Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid, pp. 71–80 (2015)Wu, H., Diamos, G., Sheard, T., Aref, M., Baxter, S., Garland, M., Yalamanchili, S.: Red Fox: an execution environment for relational query processing on GPUs. In: Proceedings of the International Symposium on Code Generation and Optimization, CGO (2014)Playne, D.P., Hawick, K.A.: Data parallel three-dimensional Cahn-Hilliard field equation simulation on GPUs with CUDA. In: Proceedings of the Parallel and Distributed Processing Techniques and Applications, PDPTA, pp. 104–110 (2009)Yamazaki, I., Dong, T., Solcà, R., Tomov, S., Dongarra, J., Schulthess, T.: Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems. Concurr. Comput.: Pract. Exp. 26(16), 2652–2666 (2014)Luo, D.Y.: Canny edge detection on NVIDIA CUDA. In: Proceedings of the Computer Vision and Pattern Recognition Workshops, CVPR Workshops, pp. 1–8 (2008)Surkov, V.: Parallel option pricing with Fourier space time-stepping method on graphics processing units. Parallel Comput. 36(7), 372–380 (2010)Agarwal, P.K., Hampton, S., Poznanovic, J., Ramanthan, A., Alam, S.R., Crozier, P.S.: Performance modeling of microsecond scale biological molecular dynamics simulations on heterogeneous architectures. Concurr. Comput.: Pract. Exp. 25(10), 1356–1375 (2013)Luo, G.H., Huang, S.K., Chang, Y.S., Yuan, S.M.: A parallel bees algorithm implementation on GPU. J. Syst. Arch. 60(3), 271–279 (2014)NVIDIA GRID Technology. http://www.nvidia.com/object/grid-technology.html (2015). Accessed 19 Oct 2015Song, J., et al: KVMGT: a full GPU virtualization solution. In: KVM Forum (2014)AMD Multiuser GPU, Hardware-Based Virtualized Solution. http://www.amd.com/Documents/Multiuser-GPU-Datasheet.pdf (2015). Accessed 19 Oct 2015V-GPU: GPU Virtualization. https://github.com/zillians/platform_manifest_vgpu (2015). Accessed 19 Oct 2015Oikawa, M., Kawai, A., Nomura, K., Yasuoka, K., Yoshikawa, K., Narumi, T.: DS-CUDA: a middleware to use many GPUs in the cloud environment. In: Proceedings of the SC Companion: High Performance Computing, Networking Storage and Analysis, SCC, pp. 1207–1214 (2012)Reaño, C., Silla, F., Shainer, G., Schultz, S.: Local and remote GPUs perform similar with EDR 100G InfiniBand. In: Proceedings of the Industrial Track of the 16th International Middleware Conference, ACM, Middleware Industry ’15, pp. 4:1–4:7 (2015)Reaño, C., Silla, F., Duato, J.: Enhancing the rCUDA remote GPU virtualization framework: from a prototype to a production solution. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE Press, CCGrid ’17, pp. 695–698 (2017)Shi, L., Chen, H., Sun, J.: vCUDA: GPU accelerated high performance computing in virtual machines. In: Proceedings of the IEEE Parallel and Distributed Processing Symposium, IPDPS, pp. 1–11 (2009)Liang, T.Y., Chang, Y.W.: GridCuda: A grid-enabled CUDA programming toolkit. In: Proceedings of the IEEE Advanced Information Networking and Applications Workshops, WAINA, pp. 141–146 (2011)Giunta, G., Montella, R., Agrillo, G., Coviello, G.: A GPGPU transparent virtualization component for high performance computing clouds. In: Proceedings of the Euro-Par Parallel Processing, Euro-Par, pp. 379–391 (2010)Gupta, V., Gavrilovska, A., Schwan, K., Kharche, H., Tolia, N., Talwar, V., Ranganathan, P. GViM: GPU-accelerated virtual machines. In: Proceedings of the ACM Workshop on System-level Virtualization for High Performance Computing, HPCVirt, pp. 17–24 (2009)Merritt, A.M., Gupta, V., Verma, A., Gavrilovska, A., Schwan, K.: Shadowfax: scaling in heterogeneous cluster systems via GPGPU assemblies. In: Proceedings of the International Workshop on Virtualization Technologies in Distributed Computing, VTDC, pp. 3–10 (2011)Shadowfax II—Scalable Implementation of GPGPU Assemblies. http://keeneland.gatech.edu/software/keeneland/kidron (2015). Accessed 19 Oct 2015Walters, J.P., Younge, A.J., Kang, D.I., Yao, K.T., Kang, M., Crago, S.P., Fox, G.C.: GPU-passthrough performance: a comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL applications. In: Proceedings of the IEEE International Conference on Cloud Computing, CLOUD (2014)Yang, C.T., Wang, H.Y., Ou, W.S., Liu, Y.T., Hsu, C.H.: On implementation of GPU virtualization using PCI pass-through. In: Proceedings of the IEEE Cloud Computing Technology and Science, CloudCom, pp. 711–716 (2012)Jo, H., Jeong, J., Lee, M., Choi, D.H.: Exploiting GPUs in virtual machine for BioCloud. BioMed Res. Int. 2013, 11 (2013). https://doi.org/10.1155/2013/939460NVIDIA: CUDA C Programming Guide 7.5. http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf (2015a). Accessed 19 Oct 2015NVIDIA: CUDA Runtime API Reference Manual 7.5. http://docs.nvidia.com/cuda/pdf/CUDA_Runtime_API.pdf (2015b). Accessed 19 Oct 2015NVIDIA: The NVIDIA GPU Computing SDK Version 5.5 (2013)iperf3: A TCP, UDP, and SCTP Network Bandwidth Measurement Tool. https://github.com/esnet/iperf (2015). Accessed 19 Oct 2015Reaño, C., Silla, F.: Reducing the performance gap of remote GPU virtualization with InfiniBand Connect-IB. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 920–925 (2016)Mellanox: Connect-IB Single and Dual QSFP+ Port PCI Express Gen3 x16 Adapter Card User Manual. http://www.mellanox.com/related-docs/user_manuals/Connect-IB_Single_and_Dual_QSFP+_Port_PCI_Express_Gen3_%20x16_Adapter_Card_User_Manual.pdf (2014a). Accessed 19 Oct 2015Mellanox: ConnectX-3 VPI Single and Dual QSFP+ Port Adapter Card User Manual 1.7. http://www.mellanox.com/related-docs/user_manuals/ConnectX-3_VPI_Single_and_Dual_QSFP_Port_Adapter_Card_User_Manual.pdf (2013). Accessed 19 Oct 2015Pérez, F., Reaño, C., Silla, F.: Providing CUDA acceleration to KVM virtual machines in InfiniBand clusters with rCUDA. In: 16th International Conference Distributed Applications and Interoperable Systems (DAIS), pp. 82–95. Springer International Publishing (2016)Mellanox: Mellanox OFED for Linux User Manual. http://www.mellanox.com/related-docs/prod_software/Mellanox_OFED_Linux_User_Manual_v2.3-1.0.1.pdf (2014b). Accessed 19 Oct 2015Reaño, C., Mayo, R., Quintana-Ortí, E., Silla, F., Duato, J., Peña, A.: Influence of InfiniBand FDR on the performance of remote GPU virtualization. In: Proceedings of the IEEE International Conference on Cluster Computing, CLUSTER, pp. 1–8 (2013)Laboratories, S.N.: LAMMPS Molecular Dynamics Simulator. http://lammps.sandia.gov/ (2013). Accessed 19 Oct 2015Liu, Y., Schmidt, B., Liu, W., Maskell, D.L.: CUDA-MEME: accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units. Pattern Recognit. Lett. 31(14), 2170–2177 (2010)Liu, Y., Wirawan, A., Schmidt, B.: CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions. BMC Bioinformat. 14(1), 1–10 (2013)Vouzis, P.D., Sahinidis, N.V.: GPU-BLAST: using graphics processors to accelerate protein sequence alignment. Bioinformatics 27(2), 182–188 (2011)NVIDIA: NVIDIA Popular GPU-Accelerated Applications Catalog. http://www.nvidia.com/content/gpu-applications/PDF/GPU-apps-catalog-mar2015.pdf (2015c). Accessed 19 Oct 2015Liu, Y. CUDA-MEME. https://sites.google.com/site/yongchaosoftware/mcuda-meme (2014). Accessed 19 Oct 2015Polak, A.: Counting triangles in large graphs on GPU. In: IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 740–746 (2016)Prades, J., Silla, F.: Turning GPUs into floating devices over the cluster: the Beauty of GPU Migration. In: Proceedings of the 6th Workshop on Heterogeneous and Unconventional Cluster Architectures and Applications (HUCAA) (2017

Continuous and Concurrent Network Connection for Hardware Virtualization

This project addresses the network connectivity in virtualization for cloud computing. Each Virtual Machine will be able to access the network concurrently and obtains continuous internet connectivity without any disruption. This project proposes a new method of resource sharing which is the Network Interface Card (NIC) among the Virtual Machines with each of them having the full access to it with near-native bandwidth. With this, could computing can perform resource allocation more effectively. This will be essential to migrate the each Operating System (Virtual Machine) that resides on one physical machine to another without disrupting its internet or network connection