32 research outputs found
A Kernel-space POF virtual switch
Protocol Oblivious Forwarding (POF) aims at providing a standard southbound interface for sustainable Software Defined Networking (SDN) evolvement. It overcomes the limitations of popular Open Flow protocols (an existing widely-adopted southbound interface), through the enhancement of SDN forwarding plane. This paper pioneers the design and implementation of a Kernel-space POF Virtual Switch (K_POFVS) on Linux platform. K_POFVS can improve the packet processing speed, through fast packet forwarding and the capability of adding/deleting/modifying protocol fields in kernel space. In addition, it is able to enhance flow table matching speed, by separating the mask table (consisting of flow entry masks used to figure out the matching field) and the flow table under a caching mechanism. Furthermore, K_POFVS can achieve efficient communication between the kernel space and the user space, via extending the Netlink communication between them. Experimental results show that K_POFVS can provide much better performance than existing user-space POF virtual switches, in terms of packet forwarding delay, packet processing delay and packet transmission rateThis work is partially supported by the National Program on Key Basic Research Project of China (973
Program) under Grant No. 2012CB315803, the Strategic Priority Research Program of the Chinese Academy of
Sciences under grant No. XDA06010306, the National Natural Science Foundation of China under Grant No.
61303241, and the University of Exeter’s Innovation Platform – Link Fund under Award No. LF207
Automatic virtual network embedding: A deep reinforcement learning approach with graph convolutional networks
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Virtual network embedding arranges virtual network services onto substrate network components. The performance of embedding algorithms determines the effectiveness and
efficiency of a virtualized network, making it a critical part of the
network virtualization technology. To achieve better performance,
the algorithm needs to automatically detect the network status
which is complicated and changes in a time-varying manner,
and to dynamically provide solutions that can best fit the current
network status. However, most existing algorithms fail to provide
automatic embedding solutions in an acceptable running time.
In this paper, we combine deep reinforcement learning with
a novel neural network structure based on graph convolutional networks, and propose a new and efficient algorithm for
automatic virtual network embedding. In addition, a parallel
reinforcement learning framework is used in training along
with a newly-designed multi-objective reward function, which
has proven beneficial to the proposed algorithm for automatic
embedding of virtual networks. Extensive simulation results
under different scenarios show that our algorithm achieves best
performance on most metrics compared with the existing stateof-the-art solutions, with upto 39.6% and 70.6% improvement
on acceptance ratio and average revenue, respectively. Moreover,
the results also demonstrate that the proposed solution possesses
good robustness
Cerebrospinal fluid oligoclonal bands in Chinese patients with multiple sclerosis: the prevalence and its association with clinical features
BackgroundCerebrospinal fluid oligoclonal band (CSF-OCB) is an established biomarker in diagnosing multiple sclerosis (MS), however, there are no nationwide data on CSF-OCB prevalence and its diagnostic performance in Chinese MS patients, especially in the virtue of common standard operation procedure (SOP).MethodsWith a consensus SOP and the same isoelectric focusing system, we conducted a nationwide multi-center study on OCB status in consecutively, and recruited 483 MS patients and 880 non-MS patients, including neuro-inflammatory diseases (NID, n = 595) and non-inflammatory neurological diseases (NIND, n=285). Using a standardized case report form (CRF) to collect the clinical, radiological, immunological, and CSF data, we explored the association of CSF-OCB positivity with patient characters and the diagnostic performance of CSF-OCB in Chinese MS patients. Prospective source data collection, and retrospective data acquisition and statistical data analysis were used.Findings369 (76.4%) MS patients were OCB-positive, while 109 NID patients (18.3%) and 6 NIND patients (2.1%) were OCB-positive, respectively. Time from symptom onset to diagnosis was significantly shorter in OCB-positive than that in OCB-negative MS patients (13.2 vs 23.7 months, P=0.020). The prevalence of CSF-OCB in Chinese MS patients was significantly higher in high-latitude regions (41°-50°N)(P=0.016), and at high altitudes (>1000m)(P=0.025). The diagnostic performance of CSF-OCB differentiating MS from non-MS patients yielded a sensitivity of 76%, a specificity of 87%.InterpretationThe nationwide prevalence of CSF-OCB was 76.4% in Chinese MS patients, and demonstrated a good diagnostic performance in differentiating MS from other CNS diseases. The CSF-OCB prevalence showed a correlation with high latitude and altitude in Chinese MS patients
A new discrete gaussian sampler over orthogonal lattices
Discrete Gaussian is a cornerstone of many lattice-based cryptographic constructions. Aiming at the orthogonal lattice of a vector, we propose a discrete Gaussian rejection sampling algorithm, by modifying the dynamic programming process for subset sum problems. Within O(nq2) time, our algorithm generates a distribution statistically indistinguishable from discrete Gaussian at width s>ω(log n). Moreover, we apply our sampling algorithm to general high-dimensional dense lattices, and orthogonal lattices of matrices \matA\in\Z_q^{O(1)\times n}. Compared with previous polynomial-time discrete Gaussian samplers, our algorithm does not rely on the short basis