a reading-list for my research
1.Han B, Gopalakrishnan V, Ji L, et al. Network function virtualization: Challenges and opportunities for innovations[J]. IEEE Communications Magazine, 2015, 53(2): 90-97.(done)
1.Go Y, Jamshed M A, Moon Y G, et al. APUNet: Revitalizing GPU as Packet Processing Accelerator[C]//NSDI. 2017: 83-96.
2.Palkar S, Lan C, Han S, et al. E2: a framework for NFV applications[C]//Proceedings of the 25th Symposium on Operating Systems Principles. ACM, 2015: 121-136.
3.Panda A, Han S, Jang K, et al. NetBricks: Taking the V out of NFV[C]//OSDI. 2016: 203-216.
4.Martins J, Ahmed M, Raiciu C, et al. ClickOS and the art of network function virtualization[C]//Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation. USENIX Association, 2014: 459-473.(done)
5.Hwang J, Ramakrishnan K K, Wood T. NetVM: high performance and flexible networking using virtualization on commodity platforms[J]. IEEE Transactions on Network and Service Management, 2015, 12(1): 34-47.(done)
6.Bremler-Barr A, Harchol Y, Hay D. OpenBox: a software-defined framework for developing, deploying, and managing network functions[C]//Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference. ACM, 2016: 511-524.(done)
7.Gember-Jacobson A, Viswanathan R, Prakash C, et al. OpenNF: Enabling innovation in network function control[C]//ACM SIGCOMM Computer Communication Review. ACM, 2014, 44(4): 163-174.(done)
8.Li B, Tan K, Luo L L, et al. Clicknp: Highly flexible and high-performance network processing with reconfigurable hardware[C]//Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference. ACM, 2016: 1-14.
9.Sun C, Bi J, Zheng Z, et al. SLA-NFV: an SLA-aware High Performance Framework for Network Function Virtualization[C]//Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference. ACM, 2016: 581-582.
10.HyperNF: Building a High Performance, High Utilization and Fair NFV Platform Kenichi Yasukata, Felipe Huici (NEC Laboratories Europe); Vincenzo Maffione, Giuseppe Lettieri (University of Pisa); Michio Honda (NEC Laboratories Eur) (10-10)
11.AidOps: A Data-Driven Provisioning of Virtual Network Functions Diego Lugones, Jordi Arjona Aroca, Yue Jin, Alessandra Sala, Volker Hilt (Nokia Bell Labs)
12.Heterogeneous virtualized network function framework for the data center 2017 fpl ----(not reading)
13.NFP: Enabling Network Function Parallelism in NFV. Chen Sun, Jun Bi, Zhilong Zheng, and Heng Yu (Tsinghua University) and Hongxin Hu sigcomm (done)
14.NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains SIGCOMM2017 (done)
Fiber-based architecture for NFV cloud databases - industrial Mohamed Ziauddin
Pronto: A Software-Defined Networking based System for Performance Management of Analytical based System for Performance Management of Analytical Queries on Distributed Data Store
A Software-Defined Networking based Approach for Performance Management of Analytical Quries on Distributed Data Stores.
1.Benchmarking NFV Software Dataplanes:Zhixiong Niu, Hong Xu, Yongqiang Tian, Libin Liu, Peng Wang, Zhenhua Li(done)
2.Bonafiglia R, Cerrato I, Ciaccia F, et al. Assessing the performance of virtualization technologies for nfv: a preliminary benchmarking[C]//Software Defined Networks (EWSDN), 2015 Fourth European Workshop on. IEEE, 2015: 67-72.
3.Capability Models for Manycore Menmory Systems:A Case-Study with Xeon Phi KNL.
1.Blancoa B, Taboadaa I, Fajardoa J O, et al. A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities[J].
1.Vasiliadis G, Koromilas L, Polychronakis M, et al. GASPP: A GPU-Accelerated Stateful Packet Processing Framework[C]//USENIX Annual Technical Conference. 2014: 321-332.
2.Yi X, Duan J, Wu C. GPUNFV: a GPU-Accelerated NFV System[C]//Proceedings of the First Asia-Pacific Workshop on Networking. ACM, 2017: 85-91.(done)
3.Han S, Jang K, Park K S, et al. PacketShader: a GPU-accelerated software router[C]//ACM SIGCOMM Computer Communication Review. ACM, 2010, 40(4): 195-206.
1.Xu Z, Liu F, Wang T, et al. Demystifying the energy efficiency of Network Function Virtualization[C]//Quality of Service (IWQoS), 2016 IEEE/ACM 24th International Symposium on. IEEE, 2016: 1-10.
1.Liu H, Jin H, Liao X, et al. Live migration of virtual machine based on full system trace and replay[C]//Proceedings of the 18th ACM international symposium on High performance distributed computing. ACM, 2009: 101-110.
2.Liu H, Jin H, Xu C Z, et al. Performance and energy modeling for live migration of virtual machines[J]. Cluster computing, 2013, 16(2): 249-264.
learning to read chest x-rays:recurrent neural cascade model for automated image annotation (done)
Depp Convolutional neural networks for computer-aided detection:CNN architectures,Dataset Characteristics and Transfer Learning
1.Generative Deep Neural Networks for Dialogue:A Short Review 2.DIALOG CONTEXT LANGUAGE MODELING WITH RECURRENT NEURAL NETWORKS 3.Applying Deep Learning To Answer Selection- A Study And An Open Task (2015) 4.sirius 5.chat detrction in an intelligent assistant:combining task-oriented and non-task-oriented spoken dialogue systems 6.from msra:sequential matching network: a new archecture for multi-turn response selection in retrieval-based chatbots.