Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

<p>In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called <i>k</i>-connected service function slices layered graph (KC-SLG), protecting against <i>k</i>-1 fiber link failures and <i>k</i>-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: <i>k</i>-connected network slicing (KC-NS) and <i>k</i>-connected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is <i>2</i>-approximate. For KC-FS, we demonstrate that <i>k</i> = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency.</p>

Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

<p>In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called <i>k</i>-connected service function slices layered graph (KC-SLG), protecting against <i>k</i>-1 fiber link failures and <i>k</i>-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: <i>k</i>-connected network slicing (KC-NS) and <i>k</i>-connected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is <i>2</i>-approximate. For KC-FS, we demonstrate that <i>k</i> = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency.</p>

Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas

Classifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classification framework is addressed in this study. The hierarchical framework includes a bottom layer that defines the features and classifies point clouds at the point level as well as a top layer that defines the features and classifies the point cloud at the object level. A novel adaptive local modification method is employed to model the interactions between these two layers. The iterative graph cuts algorithm runs around the bottom and top layers to optimize the classification. In this way, the addressed framework benefits from the integration of point features and object features to improve the classification. The experiments demonstrate that the proposed method is capable of producing classification results with high accuracy and efficiency.

An Efficient Hierarchical Layered Graph Approach for Multi-Region Segmentation

We proposed a novel efficient seed-based method for the multiple region segmentation of images based on graphs, named Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). It uses a tree of the relations between the image objects, represented by a node. Each tree node may contain different individual high-level priors and defines a weighted digraph, named as layer. The layer graphs are then integrated into a hierarchical graph, considering the hierarchical relations of inclusion and exclusion. A single energy optimization is performed in the hierarchical layered weighted digraph leading to globally optimal results satisfying all the high-level priors. The experimental evaluations of HLOIFT and its extensions, on medical, natural and synthetic images, indicate promising results comparable to the state-of-the-art methods, but with lower computational complexity. Compared to hierarchical segmentation by the min-cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios, and has a better running time.

Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach

Background Determining the association between tumor sample and the gene is demanding because it requires a high cost for conducting genetic experiments. Thus, the discovered association between tumor sample and gene further requires clinical verification and validation. This entire mechanism is time-consuming and expensive. Due to this issue, predicting the association between tumor samples and genes remain a challenge in biomedicine. Results Here we present, a computational model based on a heat diffusion algorithm which can predict the association between tumor samples and genes. We proposed a 2-layered graph. In the first layer, we constructed a graph of tumor samples and genes where these two types of nodes are connected by “hasGene” relationship. In the second layer, the gene nodes are connected by “interaction” relationship. We applied the heat diffusion algorithms in nine different variants of genetic interaction networks extracted from STRING and BioGRID database. The heat diffusion algorithm predicted the links between tumor samples and genes with mean AUC-ROC score of 0.84. This score is obtained by using weighted genetic interactions of fusion or co-occurrence channels from the STRING database. For the unweighted genetic interaction from the BioGRID database, the algorithms predict the links with an AUC-ROC score of 0.74. Conclusions We demonstrate that the gene-gene interaction scores could improve the predictive power of the heat diffusion model to predict the links between tumor samples and genes. We showed the efficient runtime of the heat diffusion algorithm in various genetic interaction network. We statistically validated our prediction quality of the links between tumor samples and genes.