The emerging multiaccess edge computing (MEC) architecture brings the needed computing resource to the network edge. Many 5G and Internet of Things (IoT) applications are latency sensitive and computation intensive in MEC systems. To flexibly provide and manage the network service requests in MEC systems, network function virtualization (NFV) can be employed to create a chain of service functions (SFs), namely, SF chain (SFC). Through SFC, the customer forwards user data to the edge server/cloud, and the edge server/cloud may return the processed results/models to the customer. When the forward and backward traffic is carrying different content, different SFs may be required for the forward and backward traffic, which requires a hybrid SFC (h-SFC). In this article, we study how to minimize the latency cost when embedding an h-SFC in MEC systems. We define a new problem called minimum latency hybrid SFC embedding (ML-HSFCE) and propose an algorithm, namely, optimal hybrid SFC embedding (Opt-HSFCE) to optimally embed a given h-SFC in MEC systems. Our extensive simulations and analysis show that the proposed Opt-HSFCE needs much less runtime compared with the brutal force algorithm and significantly outperforms the schemes that are directly extended from the existing techniques.
<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>
Network function virtualization (NFV) provides an effective way to decouple network functions from the proprietary hardware, allowing the network providers to implement network functions as virtual machines running on standard servers. In the NFV environment, an NFV service request is provisioned in the form of a service function chain (SFC). The SFC defines the exact sequence of actions or virtual network functions (VNFs) that the data stream from the service request is subjected to. These actions or VNFs need to be mapped onto specific physical networks to provide network services for end users. In this paper, we investigate the problem of dependence-aware service function chain (D_SFC) design and mapping. We study how to efficiently map users' service requests over the physical network while taking into consideration the computing resource demand, function dependence of the VNFs, and the bandwidth demand for the service request. We propose an efficient algorithm, namely, Dependence-Aware SFC Embedding With Group Mapping (D_SFC_GM), which integrates the proposed techniques of dependence sorting, independent grouping, adaptive mapping, and tetragon remapping to jointly design and map users' service requests. The proposed D_SFC_GM algorithm takes advantage of VNF's dependence relationships and the available resources in the physical network to efficiently design the chain and reserve the computing/bandwidth in the physical network. The extensive performance analysis in both IP and physical networks shows that the proposed D_SFC_GM significantly outperforms the traditional approach based on topological sorting and sequential embedding.
AbstractPurpose Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking hidden information, or use black-box deep learning models, which limits their clinical acceptance.This study introduces a novel deep learning-assisted paradigm for creating interpretable, multi-view risk scores to stratify prognostic risk in hepatocellular carcinoma (HCC) patients. Methods 510 HCC patients were enrolled in an internal dataset (SYSUCC) as training and validation cohorts to develop the Hybrid Deep Score (HDS): The Attention Activator (ATAT) was designed to heuristically identify tissues associated with high prognostic risk, and a multi-view risk scoring system based on ATAT established HDS from microscopic to macroscopic levels. The HDS was also validated on an external testing cohort (TCGA-LIHC) with 341 HCC patients. We assessed the prognostic significance using Cox regression and the concordance index (c-index). Results The ATAT first heuristically identified regions where necrosis, lymphocytes, and tumor tissues converge, particularly focusing on their junctions in high-risk patients. From this, this study developed three independent risk factors: microscopic morphological, co-localization, and deep global indicators, ultimately predicting HDS for each patient. The HDS outperformed existing clinical prognostic staging systems, showing higher hazard ratios (HR 3.24, 95% CI 1.91-5.43 in SYSUCC; HR 2.34, 95% CI 1.58-3.47 in TCGA-LIHC) and c-index (0.751 in SYSUCC; 0.729 in TCGA-LIHC) for Disease-Free Survival (DFS). Conclusion This novel paradigm, from identifying high-risk tissues to constructing prognostic risk scores, offers fresh insights into HCC research. It more precisely stratifies HCC patients into high- and low-risk groups for DFS and Overall Survival (OS) compared to existing clinical risk staging systems.
In network function virtualization (NFV), the customer's service requests are delivered by going through a set of deployed service functions (SFs). To accommodate customer's requests, service providers have to install the required SFs onto a physical network (PN) and route the traffic through the installed SFs to form a traffic forwarding path called the service function path (SFP). Any failures of physical nodes or SF instances can significantly impact the service delivery. In the literature, existing work has focused on how to protect the PN from physical node failures. Few research attention has been paid to efficiently protecting the network while jointly taking physical and virtual node failures into consideration. In this work, we investigate a novel minimum cost hybrid node protection (MC-HOP) problem, which is proved to be an NP-hard problem. We propose an efficient algorithm called minimum cost pairwise node assistance (NOTION) to optimize the MC-HOP problem with latency requirements. Extensive simulations and analysis show that the proposed scheme significantly outperforms the algorithms that are directly extended from the existing work.
In network function virtualization (NFV), the client's service requests will go through multiple service functions (SFs). The instances of the required SFs will be hosted on the geographically-distributed physical nodes in physical networks (PNs). The failure of any physical nodes or virtual nodes (i.e., SF instances) will impact the delivery of services and the client's experience. It is essential for service providers to take the node failure and network reliability into account. Different from physical node failures that will affect the PN's topology and connectivity, virtual node failures impact the services delivery of certain clients. As a result, applying traditional backup schemes that are designed for physical node failures may not efficiently provide protection for virtual node failures. In this work, we define and mathematically formulate a new latency-bounded off-site virtual node protection (LOVNP) problem in NFV. After proving the NP-hardness of the LOVNP problem, we introduce a novel shared protection technique called pairwise node protection to effectively facilitate the protection of node failures in NFV. Then, we propose an efficient heuristic algorithm called protection centrality based pairwise node protection (PC-PNP) to optimize the LOVNP problem and prove that PC-PNP has a logarithm-approximation boundary. Our extensive simulations and analysis show that the proposed algorithm significantly outperforms the algorithms that are extended from the existing work.
Network virtualization enables the decoupling of network services from the underlying hardware infrastructure to allow the same Substrate/physical Network (SN) shared by multiple Virtual Network (VN) requests. The process of mapping virtual nodes and links onto a shared SN while satisfying the computing and bandwidth constraints is referred to Virtual Network Embedding (VNE) as an NP-hard problem. In this paper, for the first time, we explore how to efficiently map a given Virtual Multicast Tree (VMT) request onto a substrate network. We propose a novel algorithm, namely, Virtual Multicast Tree Embedding based on dynamic Impact Factor (VMTE-IF) to minimize the required resource and redundant multicast transmission in the substrate network. The experimental results show that our algorithm outperforms the traditional greedy-based algorithms over 50% in terms of the cost of bandwidth consumption.
Network virtualization in optical networks enables the decoupling of network services from the underlying hardware infrastructure to allow multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The challenge of mapping VORs onto the shared SON lies on how to efficiently allocate physical resource for the VORs, which is referred to as Virtual Optical Network Embedding (VONE). Many recent research focus on the NP-Hard VONE optimization problem. In this paper, for the first time, we explore how to efficiently map a given VOR for a multicast service onto a shared SON while considering the fanout (splitting/forwarding) limitation of the physical optical switches. We propose a novel algorithm, namely, Centrality-based Degree Bounded Shortest Path Tree (C-DB-SPT) to minimize the resource usage while satisfying the degree limitation in the shared SON. The experimental results show that the C-DB-SPT algorithm outperforms the traditional greedy-based algorithms as much as by 35% in terms of the total bandwidth consumption.
In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of ${k}$ -heterogeneous-faults-tolerance and propose an augmented protection graph, called ${k}$ -connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called ${k}$ -heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques: ${k}$ -connected network slicing (KC-NS) and ${k}$ -connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when ${k}$ = 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(144 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(216 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(288 450 300)'/%3e%3c/svg%3e)
