A Framework for eBPF-Based Network Functions in an Era of Microservices

By moving network functionality from dedicated hardware to software running on end-hosts, Network Functions Virtualization (NFV) pledges the benefits of cloud computing to packet processing. While most of the NFV frameworks today rely on kernel-bypass approaches, no attention has been given to kernel packet processing, which has always proved hard to evolve and to program. In this article, we present Polycube, a software framework whose main goal is to bring the power of NFV to in-kernel packet processing applications, enabling a level of flexibility and customization that was unthinkable before. Polycube enables the creation of arbitrary and complex network function chains, where each function can include an efficient in-kernel data plane and a flexible user-space control plane with strong characteristics of isolation, persistence, and composability. Polycube network functions, called Cubes, can be dynamically generated and injected into the kernel networking stack, without requiring custom kernels or specific kernel modules, simplifying the debugging and introspection, which are two fundamental properties in recent cloud environments. We validate the framework by showing significant improvements over existing applications, and we prove the generality of the Polycube programming model through the implementation of complex use cases such as a network provider for Kubernetes

The Progressive mapping system architecture for global resources management

There are a myriad of computing resources in the World, most of which are underused. Efficiently using some of these devices raises several interesting opportunities. For instance, devices are spread everywhere, so locality can be deeply exploited. Furthermore, combining the computational capacity of all, or some, of them may conform a virtual cluster more powerful than any current data center. And, finally, by using resources already there avoids the need for a specific investment on specific infrastructures, as well as significantly reduces the operational costs. In this paper we propose a research architecture for global resources management which aims at exploiting the availability of this vast amount of heterogeneous resources at the edge of the network. We have designed the Progressive Mapping System (PMS) as a system that keeps track of the available resources and, upon an application launching, finds the best devices configuration for optimal application execution, considering both, the application performance requirements and the resources availability and performance features, while guaranteeing the Quality of Service (QoS). The PMS implements a graph of devices where nodes describe their performance features and edges describe their geographic distance as well as communication capabilities. Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. One of the novelties of the PMS system is that it considers the geographic positioning of the devices and the application locality requirements to deeper exploiting locality issues. The PMS system is yet in a preliminary implementation stage. In this paper we describe the system overview, the architectural design, and discuss some research challenges which are critical for an effective PMS implementation.Peer ReviewedPostprint (published version

The Progressive mapping system architecture for global resources management

There are a myriad of computing resources in the World, most of which are underused. Efficiently using some of these devices raises several interesting opportunities. For instance, devices are spread everywhere, so locality can be deeply exploited. Furthermore, combining the computational capacity of all, or some, of them may conform a virtual cluster more powerful than any current data center. And, finally, by using resources already there avoids the need for a specific investment on specific infrastructures, as well as significantly reduces the operational costs. In this paper we propose a research architecture for global resources management which aims at exploiting the availability of this vast amount of heterogeneous resources at the edge of the network. We have designed the Progressive Mapping System (PMS) as a system that keeps track of the available resources and, upon an application launching, finds the best devices configuration for optimal application execution, considering both, the application performance requirements and the resources availability and performance features, while guaranteeing the Quality of Service (QoS). The PMS implements a graph of devices where nodes describe their performance features and edges describe their geographic distance as well as communication capabilities. Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. One of the novelties of the PMS system is that it considers the geographic positioning of the devices and the application locality requirements to deeper exploiting locality issues. The PMS system is yet in a preliminary implementation stage. In this paper we describe the system overview, the architectural design, and discuss some research challenges which are critical for an effective PMS implementation.Peer Reviewe

The Progressive mapping system architecture for global resources management

There are a myriad of computing resources in the World, most of which are underused. Efficiently using some of these devices raises several interesting opportunities. For instance, devices are spread everywhere, so locality can be deeply exploited. Furthermore, combining the computational capacity of all, or some, of them may conform a virtual cluster more powerful than any current data center. And, finally, by using resources already there avoids the need for a specific investment on specific infrastructures, as well as significantly reduces the operational costs. In this paper we propose a research architecture for global resources management which aims at exploiting the availability of this vast amount of heterogeneous resources at the edge of the network. We have designed the Progressive Mapping System (PMS) as a system that keeps track of the available resources and, upon an application launching, finds the best devices configuration for optimal application execution, considering both, the application performance requirements and the resources availability and performance features, while guaranteeing the Quality of Service (QoS). The PMS implements a graph of devices where nodes describe their performance features and edges describe their geographic distance as well as communication capabilities. Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. One of the novelties of the PMS system is that it considers the geographic positioning of the devices and the application locality requirements to deeper exploiting locality issues. The PMS system is yet in a preliminary implementation stage. In this paper we describe the system overview, the architectural design, and discuss some research challenges which are critical for an effective PMS implementation.Peer Reviewe

Image Compression Network Structure Based on Multiscale Region of Interest Attention Network

In this study, we proposed a region of interest (ROI) compression algorithm under the deep learning self-encoder framework to improve the reconstruction performance of the image and reduce the distortion of the ROI. First, we adopted a remote sensing image cloud detection algorithm for detecting important targets in images, that is, separating the remote sensing background from important regions in remote sensing images and then determining the target regions because most traditional ROI-based image compression algorithms utilize the manual labeling of the ROI to achieve region separation in images. We designed a multiscale ROI self-coding network from coarse to fine with a hierarchical super priority layer to synthesize images to reduce the spatial redundancy more effectively, thus greatly improving the distortion rate performance of image compression. By using a spatial attention mechanism for the ROI in the image compression network, we achieved better compression performance

The Evaluation, Management and Outcome about an Experience with Sharp Force Abdominal Injury

Background and Objective: Sharp force injuries (SFI), inflicted by cutting or stabbing, result in variable outcome depending upon the nature and site of the injury. This study evaluated the cases of SFIs and their outcome with reference to the time of presentation, demographic data, wounded organs, and surgical procedure performed.Methods: This retrospective study analyzed the clinical data of 20 patients who presented with sharp force injury (knife stabbing and penetrating abdominal trauma) and were admitted between April 2015 and November 2016. The management and outcome of patients were recorded.Results: All patients in this study were male and aged between 21 and 30 years. Knife stabbing was the only mechanism of injury in all cases. Colon (50%) was the commonest organ injured followed by intestine (40%) and liver (30%). Mortality rate was 10%. There were two cases with negative laparotomy (10%). Wound sepsis (10%) was the commonest complication.Conclusions: SFI involving abdominal area are managed either conservatively or with primary repair and laparotomy to save internal organs. Early presentation and prompt management leads to reduced chances of complications and mortality.</p

Highly efficient cobalt-doped carbon nitride polymers for solvent-free selective oxidation of cyclohexane

Selective oxidation of saturated hydrocarbons with molecular oxygen has been of great interest in catalysis, and the development of highly efficient catalysts for this process is a crucial challenge. A new kind of heterogeneous catalyst, cobalt-doped carbon nitride polymer (g-C3N4), was harnessed for the selective oxidation of cyclohexane. X-ray diffraction, Fourier transform infrared spectra and high resolution transmission electron microscope revealed that Co species were highly dispersed in g-C3N4 matrix and the characteristic structure of polymeric g-C3N4 can be retained after Co-doping, although Co-doping caused the incomplete polymerization to some extent. Ultravioletâvisible, Raman and X-ray photoelectron spectroscopy further proved the successful Co doping in g-C3N4 matrix as the form of Co(II)î¸N bonds. For the selective oxidation of cyclohexane, Co-doping can markedly promote the catalytic performance of g-C3N4 catalyst due to the synergistic effect of Co species and g-C3N4 hybrid. Furthermore, the content of Co largely affected the activity of Co-doped g-C3N4 catalysts, among which the catalyst with 9.0 wt% Co content exhibited the highest yield (9.0%) of cyclohexanone and cyclohexanol, as well as a high stability. Meanwhile, the reaction mechanism over Co-doped g-C3N4 catalysts was elaborated. Keywords: Selective oxidation of cyclohexane, Oxygen oxidant, Carbon nitride, Co-dopin

Spectral Correlation and Spatial High–Low Frequency Information of Hyperspectral Image Super-Resolution Network

Hyperspectral images (HSIs) generally contain tens or even hundreds of spectral segments within a specific frequency range. Due to the limitations and cost of imaging sensors, HSIs often trade spatial resolution for finer band resolution. To compensate for the loss of spatial resolution and maintain a balance between space and spectrum, existing algorithms were used to obtain excellent results. However, these algorithms could not fully mine the coupling relationship between the spectral domain and spatial domain of HSIs. In this study, we presented a spectral correlation and spatial high–low frequency information of a hyperspectral image super-resolution network (SCSFINet) based on the spectrum-guided attention for analyzing the information already obtained from HSIs. The core of our algorithms was the spectral and spatial feature extraction module (SSFM), consisting of two key elements: (a) spectrum-guided attention fusion (SGAF) using SGSA/SGCA and CFJSF to extract spectral–spatial and spectral–channel joint feature attention, and (b) high- and low-frequency separated multi-level feature fusion (FSMFF) for fusing the multi-level information. In the final stage of upsampling, we proposed the channel grouping and fusion (CGF) module, which can group feature channels and extract and merge features within and between groups to further refine the features and provide finer feature details for sub-pixel convolution. The test on the three general hyperspectral datasets, compared to the existing hyperspectral super-resolution algorithms, suggested the advantage of our method

Catalytic Performance of MgO-Supported Co Catalyst for the Liquid Phase Oxidation of Cyclohexane with Molecular Oxygen

A highly-efficient and stable MgO-supported Co (Co/MgO) catalyst was developed for the oxidation of cyclohexane with oxygen. The effects of the Co loading and support on the catalytic activity of the supported Co3O4 catalyst were investigated. The results show that the Co supported on MgO presented excellent activity and stability. When the Co/MgO catalyst with the Co content of 0.2 wt% (0.2%Co/MgO) was used, 12.5% cyclohexane conversion and 74.7% selectivity to cyclohexanone and cyclohexanol (KA oil) were achieved under the reaction conditions of 0.5 MPa O2 and 140 °C for 4 h. After being repeatedly used 10 times, its catalytic activity was hardly changed. Further research showed that the high catalytic performance of the 0.2%Co/MgO catalyst is attributed to its high oxygen-absorbing ability and the high ratio between the amount of weak and medium base sites with the help of the synergistic interaction between Co and MgO

A Service-Agnostic Software Framework for Fast and Efficient In-Kernel Network Services

This paper presents Polycube, an open-source software framework based on eBPF, that enables the creation of arbitrary and complex network function chains. Each function can include an efficient in-kernel data plane and a flexible userspace control plane with strong characteristics of isolation, persistence (e.g., across server reboots) and composability. In addition, a generic model for the control and management plane of each network function simplifies the manageability and accelerates the development of new network services. We validate the framework by creating different network services and benchmarking their performance in a complex scenario, namely a network provider for Kubernetes. Results show that Polycube programs are about 20x shorter than equivalent programs implemented with vanilla eBP