Substrate Network Research Articles

Kalman prediction-based virtual network experimental platform for smart living

Today, the increasing investigation of smart living opens new attack surfaces to intruders. Therefore, developing an emulation platform to test new technologies for current large-scale smart living scenarios is an emerging issue. However, existing network emulation systems suffer from coarse-grained virtual link emulation, including bandwidth, delay, and packet loss rate (PLR). Furthermore, a lack of virtual link emulation accuracy results in an unpredictable emulation fidelity. In order to address this issue, this paper presents Kalman prediction-based experimental platform based on multiple virtualization technologies for smart living to test new technologies and security threats. We introduce the core deployment, link-emulation, and emulator modules in the framework and propose Delay-PLR Kalman Prediction (DPKP) algorithm, which employs Kalman prediction theory based on the packet delay and loss measurement scheme. In particular, the DPKP takes the inherent network traffic errors of the substrate network into consideration, thus improving emulation fidelity. Experiments on the emulation of a BeiDou based satellite network scenario verify the ability of our emulation system to flexibly construct a target virtual network and set the expected bandwidth for each virtual link within 5% errors. In addition, the experimental results demonstrate that the proposed DPKP-based virtual link emulation scheme outperforms the traditional method by up to 21.54% and 78.65% in terms of delay and PLR, respectively.

Integrative proteomics reveals the role of E3 ubiquitin ligase SYVN1 in hepatocellular carcinoma metastasis.

BackgroundTumor metastasis is a major factor for poor prognosis of hepatocellular carcinoma (HCC), but the relationship between ubiquitination and metastasis need to be studied more systematically. We analyzed the ubiquitinome of HCC in this study to have a more comprehensive insight into human HCC metastasis.MethodsThe protein ubiquitination levels in 15 HCC specimens with vascular invasion and 15 without vascular invasion were detected by ubiquitinome. Proteins with significantly different ubiquitination levels between HCCs with and without vascular invasion were used to predict E3 ubiquitin ligases associated with tumor metastasis. The topological network of protein substrates and corresponding E3 ubiquitin ligases was constructed to identify the key E3 ubiquitin ligase. Besides, the growth, migration and invasion ability of LM3 and HUH7 hepatoma cell lines with and without SYVN1 expression interference were measured by cell proliferation assay, subcutaneous tumor assay, umphal vein endothelium tube formation assay, transwell migration and invasion assays. Finally, the interacting proteins of SYVN1 were screened and verified by protein interaction omics, immunofluorescence, and immunoprecipitation. Ubiquitin levels of related protein substrates in LM3 and HUH7 cells were compared in negative control, SYVN1 knockdown, and SYVN1 overexpression groups.ResultsIn this study, our whole‐cell proteomic dataset and ubiquitinomic dataset contained approximately 5600 proteins and 12,000 ubiquitinated sites. We discovered increased ubiquitinated sites with shorter ubiquitin chains during the progression of HCC metastasis. In addition, proteomic and ubiquitinomic analyses revealed that high expression of E3 ubiquitin‐protein ligase SYVN1 is related with tumor metastasis. Furthermore, we found that SYVN1 interacted with heat shock protein 90 (HSP90) and impacted the ubiquitination of eukaryotic elongation factor 2 kinase (EEF2K).ConclusionsThe ubiquitination profiles of HCC with and without vascular invasion were significantly different. SYVN1 was the most important E3 ubiquitin‐protein ligase responsible for this phenomenon, and it was related with tumor metastasis and growth. Therefore, SYVN1 might be a potential therapeutic target for HCC.

Modeling Higher-Order Interactions in Complex Networks by Edge Product of Graphs

Abstract Many graph products have been applied to generate complex networks with striking properties observed in real-world systems. In this paper, we propose a simple generative model for simplicial networks by iteratively using edge corona product. We present a comprehensive analysis of the structural properties of the network model, including degree distribution, diameter, clustering coefficient, as well as distribution of clique sizes, obtaining explicit expressions for these relevant quantities, which agree with the behaviors found in diverse real networks. Moreover, we obtain exact expressions for all the eigenvalues and their associated multiplicities of the normalized Laplacian matrix, based on which we derive explicit formulas for mixing time, mean hitting time and the number of spanning trees. Thus, as previous models generated by other graph products, our model is also an exactly solvable one, whose structural properties can be analytically treated. More interestingly, the expressions for the spectra of our model are also exactly determined, which is sharp contrast to previous models whose spectra can only be given recursively at most. This advantage makes our model a good test bed and an ideal substrate network for studying dynamical processes, especially those closely related to the spectra of normalized Laplacian matrix, in order to uncover the influences of simplicial structure on these processes.

Abstract P-3: The Structure of Self-Assembled Surfactant Micellar Networks by in situ Cryo-Electron Tomography

Background: Surfactant molecules can form various self-assembled structures in aqueous solutions, including spherical and cylindrical micelles, lamellae, vesicles, etc. Elongated cylindrical (wormlike) micelles can entangle and form a dense network. The study of the un-perturbed native structure of wormlike micelles in such networks presents a great challenge, since the micelles are formed due to weak non-covalent interactions and may easily break when external conditions are changed. In this work in situ cryo-electron tomography (cryo-ET) was applied to reveal the relaxed structure of such entangled systems. Methods: To prepare samples for the cryo-ET study 1 µl of the aqueous surfactant-containing solution was applied to the glow discharged grid, blotted with filter paper for 10 sec, drained for 60 sec to allow for the relaxation of the system and plunge-frozen with Vitrobot Mark IV. The vitrified sample was transferred to Versa 3D cryo-focused ion beam / scanning electron microscope (cryo-FIB/SEM) to prepare thin (100-150 nm) sections of the sample. Cryo-ET study was conducted using Titan Krios. IMOD and Avizo software packages were used for data processing. Results: In this work, wormlike micelles formed by a mixture of an anionic and a cationic surfactant were investigated at the excess of the anionic surfactant. Cryo-ET study of the obtained lamellae demonstrated the formation of two different phases, consisting of straight rods oriented along the grid substrate (phase 1) and isotropic network formed by wormlike micelles (phase 2) above it. The topology of the second phase corresponded to the branched saturated network or entangled network depending on cation/anion ratio of the sample. However, the analysis of the thin samples obtained without cryo-FIB demonstrated only the presence of the metastable phase (phase 1), which could lead to false conclusions regarding the morphology of the micelles. Conclusion: Here we discuss the influence of different sample preparation approaches on the sample structure and demonstrate that the native un-perturbed conformation of charged cylindrical surfactant micelles in the dense network is that of a slightly bent rod or a wormlike chain with high persistence length.

Node ranking strategy in virtual network embedding: An overview

Network virtualization (NV) is a highprofile way to solve the ossification problem of the nowadays Internet, and be able to support the diversified network naturally. In NV, Virtual Network Embedding (VNE) problem has been widely considered as a crucial issue, which is aimed to embed Virtual Networks (VNs) onto the shared substrate networks (SNs) efficiently. Recently, some VNE approaches have developed Node Ranking strategies to drive and enhance the embedding efficiency. Node Ranking Strategy rank/sort the nodes according to the attributes of the node, including both residual local attributes (CPU, Bandwidth, storage, Etc.) and the global topology attributes (Number of neighborhood Nodes, Delay to other nodes, Etc.). This paper presents an overview of Node Ranking Strategies in Virtual Network Embedding, and possible directions of VNE Node Ranking Strategy.

A novel evaluation function for higher acceptance rates and more profitable metaheuristic-based online virtual network embedding

Virtual network embedding (VNE) is a challenging combinatorial optimization problem that has been widely addressed through metaheuristics. However, one key element for the successful application of metaheuristics in the VNE problem is the evaluation function, which should be pivotal to making fine distinctions among all the potential embeddings to avoid producing zones with solutions of the same fitness value (i.e., plateaus zones). The existence of plateaus zones in the search space is a critical aspect that would jeopardize the detection of promising search directions in metaheuristic-based VNE. In this paper, we demonstrate that the classical VNE evaluation function based on the embedding cost has poor discrimination capabilities, namely it fails in establishing preferences among the potential embedding solutions. To cope with this important issue, we propose the Alternative Function Based on Degree (AFBD), which takes into account the degree of the nodes of the substrate network to drive the search process. Our results demonstrate that VNE based on the AFBD objective function outperforms the state-of-the-art evaluation functions in terms of the acceptance rate and revenue-to-cost metric. Using AFBD as a reference, we report performance decreases in VNE algorithms with the state-of-the-art evaluation functions ranging from 1% to 7% (relative) for the acceptance rate and decreases close to 12% for the revenue-to-cost metric, which means that the AFBD-based VNE algorithm accepts more virtual networks at lower embedding cost. Formal analysis demonstrates that it is possible to enhance the performance of metaheuristic-based VNE through the use of the AFBD objective function, even with substrate network topologies where the performance of metaheuristic based-VNE decreases drastically.

Symmetry breaking meets multisite modification.

Multisite modification is a basic way of conferring functionality to proteins and a key component of post-translational modification networks. Additional interest in multisite modification stems from its capability of acting as complex information processors. In this paper, we connect two seemingly disparate themes: symmetry and multisite modification. We examine different classes of random modification networks of substrates involving separate or common enzymes. We demonstrate that under different instances of symmetry of the modification network (invoked explicitly or implicitly and discussed in the literature), the biochemistry of multisite modification can lead to the symmetry being broken. This is shown computationally and consolidated analytically, revealing parameter regions where this can (and in fact does) happen, and characteristics of the symmetry-broken state. We discuss the relevance of these results in situations where exact symmetry is not present. Overall, through our study we show how symmetry breaking (i) can confer new capabilities to protein networks, including concentration robustness of different combinations of species (in conjunction with multiple steady states); (ii) could have been the basis for ordering of multisite modification, which is widely observed in cells; (iii) can significantly impact information processing in multisite modification and in cell signalling networks/pathways where multisite modification is present; and (iv) can be a fruitful new angle for engineering in synthetic biology and chemistry. All in all, the emerging conceptual synthesis provides a new vantage point for the elucidation and the engineering of molecular systems at the junction of chemical and biological systems.

On the Separate Extraction of Self-Heating and Substrate Effects in FD-SOI MOSFET

This paper proposes an original approach to separately characterize self-heating and substrate effects in Fully-Depleted Silicon-on-Insulator (FD-SOI) devices. As both dynamic self-heating and drain to source coupling through the back-gate and substrate of an FD-SOI MOSFET induce a frequency transition in the Y-parameters in a common frequency range, it is crucial to properly separate them for further modeling. The proposed novel method is based on the extraction of the back-gate and substrate networks from the S-parameters measured at the zero-temperature coefficient bias. It enables the accurate and unambiguous extraction of thermal impedance for different biases, thus providing the extraction of the device thermal resistance and capacitance for different power levels from S-parameters measurements.

An orbitofrontal cortex to midbrain projection modulates hypersensitivity after peripheral nerve injury.

Neuropathic pain is a debilitating condition that is often refractory to treatment. The network of neural substrates for pain transmission and control within the brain is complex and remains poorly understood. Through a combination of neuronal tracing, optogenetics, chemogenetics, electrophysiological recordings, and behavioral assessment, we demonstrate that activation of layer 5 pyramidal neurons in the ventrolateral orbitofrontal cortex (vlOFC) attenuates mechanical and thermal hypersensitivity and cold allodynia in mice with neuropathic pain induced by spared nerve injury (SNI). These vlOFC output neurons project to the posterior ventrolateral periaqueductal gray (vlPAG) region and receive inputs from the ventromedial thalamus (VM). Specific optogenetic and chemogenetic activation of the vlOFC-vlPAG and the VM-vlOFC circuits inhibits hypersensitivity associated with neuropathy. Thus, we reveal a modulatory role of the vlOFC and its projections to the vlPAG circuit in the processing of hypersensitive nociception.

An Elliptical CPW Fed UWB Slot Antenna

A new CPW fed elliptical antenna for ultra-wideband (UWB) is proposed. The novelty of this antenna are very small size and uniplanar structure, which results in easy and low cost fabrication. This antenna has elliptical slot configuration which is fed by the CPW. This elliptical slot UWB antenna is enclosed in the extended surface of ground for decreasing the chip surface area. This antenna is designed with the optimal design parameters for the UWB spectrum. The designed antenna is printed on FR4 substrate and Vector Network Analyser is used for measuring return loss. The results of measurement results are similar to simulation results. The complete chip volume of antenna is 18.3 × 23 × 1.6 mm3.

Cytosolic protein quality control machinery: Interactions of Hsp70 with a network of co-chaperones and substrates.

The chaperone heat shock protein 70 (Hsp70) and its network of co-chaperones serve as a central hub of cellular protein quality control mechanisms. Domain organization in Hsp70 dictates ATPase activity, ATP dependent allosteric regulation, client/substrate binding and release, and interactions with co-chaperones. The protein quality control activities of Hsp70 are classified as foldase, holdase, and disaggregase activities. Co-chaperones directly assisting protein refolding included J domain proteins and nucleotide exchange factors. However, co-chaperones can also be grouped and explored based on which domain of Hsp70 they interact. Here we discuss how the network of cytosolic co-chaperones for Hsp70 contributes to the functions of Hsp70 while closely looking at their structural features. Comparison of domain organization and the structures of co-chaperones enables greater understanding of the interactions, mechanisms of action, and roles played in protein quality control.

A Load-Balanced Re-Embedding Scheme for Wireless Network Virtualization

In wireless network virtualization processes, load imbalance in substrate nodes and substrate links may significantly reduce the number of virtual network requests served by a substrate network. In addition, load imbalance also impairs resource utilization and increases embedding cost of the virtual networks. To tackle these issues, this work proposes a re-embedding scheme for virtual node (RSVN). Based on the load condition of a substrate network, RSVN first determines whether to start re-embedding procedure or not, and then selects proper re-embedded virtual nodes according to their re-embedding factors. Next, RSVN re-embeds the selected virtual nodes to balance the loads of a wireless substrate network. The extensive simulation results showed that RSVN improves the acceptance ratio of virtual network requests, increases the resource utilization of substrate networks, and reduces the average embedding cost of the entire virtual networks.

Energy-Efficient Deep Reinforced Traffic Grooming in Elastic Optical Networks for Cloud–Fog Computing

Cloud-fog computing emerges to satisfy the low latency and high computation requirements of Internet of Things (IoT) services. Elastic optical networks (EONs) are excellent substrate communication networks between fog datacenters and cloud datacenters. However, the uneven traffic of massive cloud-fog services incurs many spectrum fragments, leading to high extra energy consumption. To solve this problem, we propose an energy-efficient deep reinforced traffic grooming (EDTG) algorithm based on deep reinforcement learning. Unlike existing manually network features extracting methods, we convert the traditional network modal and the service routing path into colored network images to represent their states and extract the features automatically by MobilenetV3 according to these images. With the extracted features, we implement an advantage actor-critic (A2C) algorithm, whose actor module and critic module share an artificial neural network (ANN) to get optimal grooming actions. Additionally, after repeated attempts and experiments, we set up an objective reward and punishment mechanism to evaluate the grooming actions. We conduct extensive simulations for performance evaluation, and the results have shown that EDTG can significantly reduce energy consumption compared with two well-performed traffic grooming algorithms.

Asynchronous Time-Sensitive Networking for 5G Backhauling

Fifth Generation (5G) phase 2 rollouts are around the corner to make mobile ultra-reliable and low-latency services a reality. However, to realize that scenario, besides the new 5G built-in Ultra-Reliable Low-Latency Communication (URLLC) capabilities, it is required to provide a substrate network with deterministic Qual-ity-of-Service support for interconnecting the different 5G network functions and services. Time-Sensitive Networking (TSN) appears as an appealing network technology to meet the 5G connectivity needs in many scenarios involving critical services and their coexistence with Mobile Broadband traffic. In this article, we delve into the adoption of asynchronous TSN for 5G backhauling and some of the relevant related aspects. We start motivating TSN and introducing its mainstays. Then, we provide a comprehensive overview of the architecture and operation of the Asynchronous Traffic Shaper (ATS), the building block of asynchronous TSN. Next, a management framework based on ETSI Zero-touch network and Service Management (ZSM) and Abstraction and Control of Traffic Engineered Networks (ACTN) reference models is presented for enabling the TSN transport network slicing and its interworking with Fifth Generation (5G) for backhauling. Then we cover the flow allocation problem in asynchronous TSNs and the importance of Machine Learning techniques for assisting it. Last, we present a simulation-based proof-of-concept (PoC) to assess the capacity of ATS-based forwarding planes for accommodating 5G data flows.

Human Sensory Cortex Contributes to the Long-Term Storage of Aversive Conditioning.

Growing animal data evince a critical role of the sensory cortex in the long-term storage of aversive conditioning, following acquisition and consolidation in the amygdala. Whether and how this function is conserved in the human sensory cortex is nonetheless unclear. We interrogated this question in a human aversive conditioning study using multidimensional assessments of conditioning and long-term (15 d) retention. Conditioned stimuli (CSs; Gabor patches) were calibrated to differentially activate the parvocellular (P) and magnocellular (M) visual pathways, further elucidating cortical versus subcortical mechanisms. Full-blown conditioning and long-term retention emerged for M-biased CS (vs limited effects for P-biased CS), especially among anxious individuals, in all four dimensions assessed: threat appraisal (threat ratings), physiological arousal (skin conductance response), perceptual learning [discrimination sensitivity (d') and response speed], and cortical plasticity [visual evoked potentials (VEPs) and cortical current density]. Interestingly, while behavioral, physiological, and VEP effects were comparable at immediate and delayed assessments, the cortical substrates evolved markedly over time, transferring from high-order cortices [inferotemporal/fusiform cortex and orbitofrontal cortex (OFC)] immediately to the primary and secondary visual cortex after the delay. In sum, the contrast between P- and M-biased conditioning confirms privileged conditioning acquisition via the subcortical pathway while the immediate cortical plasticity lends credence to the triadic amygdala-OFC-fusiform network thought to underlie threat processing. Importantly, long-term retention of conditioning in the basic sensory cortices supports the conserved role of the human sensory cortex in the long-term storage of aversive conditioning.SIGNIFICANCE STATEMENT A growing network of neural substrates has been identified in threat learning and memory. The sensory cortex plays a key role in long-term threat memory in animals, but such a function in humans remains unclear. To explore this problem, we conducted multidimensional assessments of immediate and delayed (15 d) effects of human aversive conditioning. Behavioral, physiological, and scalp electrophysiological data demonstrated conditioning effects and long-term retention. High-density EEG intracranial source analysis further revealed the cortical underpinnings, implicating high-order cortices immediately and primary and secondary visual cortices after the long delay. Therefore, while high-order cortices support aversive conditioning acquisition (i.e., threat learning), the human sensory cortex (akin to the animal homolog) underpins long-term storage of conditioning (i.e., long-term threat memory).

Phosphoproteomics Reveals the AMPK Substrate Network in Response to DNA Damage and Histone Acetylation

AMP-activated protein kinase (AMPK) is a conserved energy sensor that plays roles in diverse biological processes via phosphorylating various substrates. Emerging studies have demonstrated the regulatory roles of AMPK in DNA repair, but the underlying mechanisms remain to be fully understood. Herein, using mass spectrometry-based proteomic technologies, we systematically investigate the regulatory network of AMPK in DNA damage response (DDR). Our system-wide phosphoproteome study uncovers a variety of newly-identified potential substrates involved in diverse biological processes, whereas our system-wide histone modification analysis reveals a link between AMPK and histone acetylation. Together with these findings, we discover that AMPK promotes apoptosis by phosphorylating apoptosis-stimulating of p53 protein 2 (ASPP2) in an irradiation (IR)-dependent manner and regulates histone acetylation by phosphorylating histone deacetylase 9 (HDAC9) in an IR-independent manner. Besides, we reveal that disrupting the histone acetylation by the bromodomain BRD4 inhibitor JQ-1 enhances the sensitivity of AMPK-deficient cells to IR. Therefore, our study has provided a resource to investigate the interplay between phosphorylation and histone acetylation underlying the regulatory network of AMPK, which could be beneficial to understand the exact role of AMPK in DDR.

Distributed Virtual Network Embedding System With Historical Archives and Set-Based Particle Swarm Optimization

Virtual network embedding (VNE) is an important problem in network virtualization for the flexible sharing of network resources. While most existing studies focus on centralized embedding for VNE, distributed embedding is considered more scalable and suitable for large-scale scenarios, but how virtual resources can be mapped to substrate resources effectively and efficiently remains a challenging issue. In this paper, we devise a distributed VNE system with historical archives (HAs) and metaheuristic approaches. First, we introduce metaheuristic approaches to each delegation of the distributed embedding system as the optimizer for VNE. Compared to the heuristic-based greedy algorithms used in existing distributed embedding approaches, which are prone to be trapped in local optima, metaheuristic approaches can provide better embedding performance for these distributed delegations. Second, an archive-based strategy is also introduced in the distributed embedding system to assist the metaheuristic algorithms. The archives are used to record the up-to-date information of frequently repeated tasks. By utilizing such archives as historical memory, metaheuristic algorithms can further improve embedding performance for frequently repeated tasks. Following this idea, we incorporate the set-based particle swarm optimization (PSO) as the optimizer and propose the distributed VNE system with HAs and set-based PSO (HA-VNE-PSO) system to solve the VNE problem in a distributed way. HA-VNE-PSO is empirically validated in scenarios of different scales. The experimental results verify that HA-VNE-PSO can scale well with respect to substrate networks, and the HA strategy is indeed effective in different scenarios.

Virtual network embedding: ensuring correctness and optimality by construction using model transformation and integer linear programming techniques

Virtualization technology allows service providers to operate data centers in a cost-effective and scalable manner. The data center network (substrate network) and the applications executed in the data center (virtual networks) are often modeled as graphs. The nodes of the graphs represent (physical or virtual) servers and switches, and the edges represent communication links. Nodes and links are annotated with the provided and required resources (e.g., memory and bandwidth). The NP-hard virtual network embedding (VNE) problem deals with the embedding of a set of virtual networks to the substrate network such that (i) all (resource) constraints of the substrate network are fulfilled, and (ii) an objective is optimized (e.g., minimizing the communication costs). The existing two-step highly customizable model-driven virtual network embedding (MdVNE) approach combines model transformation (MT) and integer linear programming (ILP) techniques to solve the VNE problem based on a declarative specification. MdVNE generates element mapping candidates from an MT specification and identifies an optimal and correct embeddings using an ILP solver. In the past, developers created the MT and ILP specifications manually and needed to ensure carefully that both are compatible and respect the problem description. In this article, we present a novel construction methodology for synthesizing the MT and ILP specification from a given declarative model-based VNE problem description. This problem description consists of a metamodel for substrate and virtual networks, additional OCL constraints, and an objective function that assigns costs to a given model. This methodology ensures that the derived embeddings are correct w.r.t. the metamodel and the OCL constraints, and optimal w.r.t. the optimization goal. The novel model-based VNE specification is applicable to different network domains, environments, and constraints. Thus, the construction methodology allows to automate most of the steps to realize a correct and optimal VNE algorithm compared to a hand-crafted VNE implementation. Furthermore, the simulative evaluation confirms that using MT techniques reduces the time for solving the VNE problem considerably in comparison with a purely ILP-based approach.

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements.

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

Virtual Network Embedding (VNE), which provides methods to assign multiple Virtual Networks (VN) to a single physical Substrate Network (SN), is an important task in network virtualization. The main problem in VNE is the efficiency of assigning customers’ virtual network requests to the substrate network. This problem is known to be a Non-deterministic Polynomial-time hard (NP-hard) and heuristic solutions have been developed to solve this kind of problem. The current trend toward Software-Defined Networking (SDN) has allowed new possibilities in virtual network embedding. In this work, we propose a distributed virtual network embedding for SDNs called DVSDNE using multi-agent systems. This framework could be used to run a centralized VNE algorithm in a distributed manner to scale these algorithms with respect to network size. DVSDNE uses agents to spread the load across the substrate network. Our simulation results show the effectiveness of the proposed algorithm. Results show that DVSDNE improves execution time of embedding algorithms in large scale substrate networks, while embedding results such as acceptance ratio, revenue to cost ratio, average latency to controller, and maximum latency to controller remain comparable.