Network testing is critical for evaluating the performance, reliability, and security of modern computer networks. A key challenge is creating an accurate, cost-effective, and high-performance network emulation environment. Network Impairment Emulators (NIEs) emulate real-world network conditions such as bandwidth constraints, latency, and packet loss, but existing CPU- and FPGA-based solutions suffer from limited performance, high costs, and poor flexibility. This paper proposes Software-Defined Network Impairment Emulation (SDNIE), a novel framework that leverages programmable switches for scalable, cost-efficient network impairment emulation. SDNIE introduces three key techniques: (1) intent-driven network impairment configuration, automating impairment modeling; (2) serial-parallel combined execution, optimizing performance; and (3) CPU-Tofino collaborative deployment, offloading complex computations. Experimental results show that SDNIE matches commercial emulators in performance while significantly reducing costs. This work demonstrates the potential of programmable switches in network testing, offering a scalable, cost-effective, and high-performance alternative for next-generation network impairment emulation.

Previous studies have revealed that Bitcoin’s anonymity is not absolute. Coin mixing protocols have been proposed to address these anonymity issues by breaking or obfuscating the relationship between transaction inputs and outputs, thereby ensuring privacy. Due to their ability to enhance anonymity, these protocols are often associated with illegal money laundering activities, and some mixing services have faced sanctions. This paper examines coin mixing protocols by analyzing the techniques they employ to obscure input-output relationships. Furthermore, it explorers the potential threats to which mixing protocols may be exposed and provides a comprehensive analysis of their vulnerabilities.

Live volumetric video streaming enables immersive user experiences but poses significant challenges due to the high bandwidth requirement that 3D representations entail. Recent research has focused on reducing volumetric video bandwidth, but it struggles to effectively address temporal redundancy under real-time constraints, limiting its applicability for live streaming scenarios. To address these challenges, we present DeltaStream, a novel live volumetric video streaming system that efficiently encodes 3D point clouds by leveraging 2D information. By utilizing 2D RGB and depth frames, DeltaStream efficiently infers inter-frame changes to reduce the streaming bandwidth of volumetric video. Furthermore, DeltaStream introduces an adaptive block-based approach that can reduce the client-side decoding load. Through extensive evaluations, our results demonstrate that DeltaStream reduces bandwidth by up to 71% with 1.63× faster decoding speed while maintaining visual quality compared to state-of-the-art systems.

This paper presents a novel approach to automating the deployment of Virtual Network Functions (VNFs) in an OpenStack environment using Large Language Models (LLMs). Building on the concept of Intent-Driven Networking (IDN), which allows network administrators to manage complex networks via natural language commands, we explore the feasibility of using LLMs to automate VNF deployment tasks. A dataset of Method of Procedure (MOP) documents was created and utilized to prompt LLMs to generate Python code for deploying and configuring VNFs. Our LLM-based AI agent framework tests the generated code within an OpenStack environment, comparing the performance of various LLMs. Our findings highlight both the potential and current challenges of using LLMs in network automation, suggesting pathways for future research, including advanced prompt engineering and real-time error correction.

Bitcoin proved the potential of blockchain technology through decentralized, transparent, and immutable transactions. However, there are still challenges for fast and stable transitions. Optimizing block propagation times within the network is one of them. Prolonged propagation times can restrict efficiency, scalability, and security. This paper presents a novel approach to reducing block propagation time through leveraging reinforce-ment learning (RL) for the node’s neighbor selection strategies. We implemented a Deep Q-Network (DQN) model in minimizing block receive times at each node, thereby impacting overall block propagation time. We used a model that defines node states based on latencies of outbound connections, which present the node’s region. By evaluating this model through simulations using SimBlock, a robust Bitcoin network simulator, we observed a significant reduction in block propagation time—approximately 30% for smaller networks and 20% for larger ones. Our analysis extended to node connections generated by our model and comparative evaluation against existing methodologies.

Unlike the traditional stock markets, the 24/7 nature of the cryptocurrency market poses unique challenges and opportunities, particularly in asset trading and management. These dynamic market conditions have accelerated the development of sophisticated trading strategies, increasingly leveraging the power of Artificial Intelligence (AI). Among these, AI-driven trading bots have become a prominent tool, offering enhanced decision-making capabilities over conventional methods. This paper proposes the application of the Advantage Actor-Critic (A2C) model, a reinforcement learning technique ideally suited for the unpredictable nature of the cryptocurrency market. Our research aims to optimize asset allocation within a diverse portfolio, including both high-volatility cryptocurrencies and the more stable US Dollar. The proposed A2C model strategically leverages current and predicted price data of cryptocurrencies with current asset allocation to make new asset allocation decisions. Our experiments demonstrate the A2C model’s efficacy in managing asset allocations under varying market conditions. We particularly focus on how the model responds to alterations in the loss penalty factor within its reward function, which enables a shift between aggressive and conservative investment strategies. The model effectively balances risk and return, showing promising potential in achieving stable asset growth in rising markets while mitigating losses during market downturns.

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.

In modern network structures that become more complex and emphasize flexibility, the demand for the automation of network management and Intent Driven Network (IDN) continues to increase. In response, technology utilizing virtualization and control plane separation has developed, and research on network automation based on this is being actively conducted. However, limited studies focus on building an automated network in environments comprised of traditional switches that lack support for these advanced functionalities. Consequently, this study presents a technology proposal that creates the CLI command for existing commercial switches by incorporating user requests conveyed through natural language. For this purpose, we applied Large Language Model (LLM) for generating and Network Digital Twin for verification environment.

Networks are becoming more complex, and there’s a growing focus on making them more flexible. To meet this need, Network Functions Virtualization (NFV) has been introduced. Accordingly, efficient scheduling for Virtual Network Functions (VNFs) and Service Function Chains (SFCs) is highlighted. Many research about SFC scheduling focus on deploying new SFC. However, during off-peak times, like early morning hours, it’s essential to reorganize existing SFCs to cut down on power usage. This study propose reorganization method that not only reduce power consumption, but also maintain or improve service performance (especially, we try to reduce service latency). To achieve this, we integrated VM consolidation, a strategy from Cloud Data Centers, with SFC scheduling. We also utilized a Deep Reinforcement Learning technique called Rainbow, and Self-Attention Mechanism. Consequently, our approach resulted in improved performance compared to other rule-based methods.

Network orchestration is pivotal in automating device, equipment, and service management within the network system. Currently, Network Function Virtualization (NFV) offers immense potential, but the challenge still lies in designing a capable orchestrator for dynamic networks. To address this challenge, we propose an AI-based NFV Orchestration Framework that leverages self-learning capabilities to detect dynamic network changes and make optimal decisions. This framework covers a range of essential functionalities, including NFV Orchestration, VNF Deployment, Service Function Chaining (SFC), Auto-Scaling, Migration, Anomaly Detection, Power Management, and Attack & Intrusion Detection. These functions collectively form a comprehensive ML-driven orchestration framework that offers adaptability, intelligence, and efficiency across the entire NFV environment. Our proposed structure aims for zero-touch automation, contributing to the efficient management of dynamic NFV network environments, and making it a compelling solution for the future of networking.

As the user count for video-related services continues to grow, ensuring high-quality service (QoS) for them will become even more crucial in the future. Many studies have been conducted to enhance the quality of on-demand video streaming using adaptive bitrate (ABR) algorithms and artificial intelligence (AI). This study addresses a more complex challenge than that of on-demand video streaming: enhancing service quality in multi-party, full-duplex communication scenarios, such as video conferences. We propose a deep reinforcement learning (DRL)-based video bitrate allocation framework for a media server in the video conferencing system. Our framework aims to increase overall QoS by applying an appropriate bitrate for each connection in a video conferencing call, considering the network conditions for users. We train the DRL model to maximize the aggregate QoS of users in a meeting by constructing a feedback loop between a media server and a DRL server. Our experimental results demonstrate that our framework can adaptively control the video bitrate according to changes in network conditions. As a result, it achieves higher video bitrates in the user application (approximately, 5% under stable network conditions and 35% over the highly dynamic network conditions) compared to the existing rule-based bandwidth allocation.

In this study, we focused on anomaly detection of network switches using log analysis. In previous research, we designed a log parser to find common patterns within logs, which replaced logs with ’pattern’ and classified similar patterns into the same ’event’. In this study, we utilize the log parser and calculate abnormality scores for each log pattern. In log analysis, analyzing the occurrence patterns is more important than the meaning of individual logs. With this perspective, we design the following three items to analyze the tendency of occurrence of each log pattern to better suit network switch log analysis. 1) Log-TF-IDF: the computed rarity of each log pattern, 2) Log-Prob: the probability of occurrence of each event predicted by machine learning, 3) Log-Freq: the numerical value for the degree of repetition of each log event. We calculate the ’Abnormal Score’ by considering all three metrics. By calculating Abnormal Scores from both normal and abnormal log data, we demonstrate the effectiveness of our approach. We implemented a simple anomaly detection system utilizing our proposed Abnormal Scores and achieved an F1 score of 88.2% in detecting anomalies within the log data collected from L2 and L3 switches.

Many studies are applying reinforcement learning to real-world problems. However, this is a difficult problem, and its application in the real world requires solving many challenges. Therefore, in order to solve this problem well, it is necessary to understand the process of data collection in the real world and the data collected through this process. Video conferencing is also an example of the application of reinforcement learning in the real world, so it is necessary to understand the video conferencing system used and the data collected through it. To this end, this paper presents a process to collect network status information data from a video conferencing system and introduces data and model analysis methods for effective reinforcement learning environment settings and problem definition. In addition, among various problems in video conferencing, the video quality selection problem is set as the target problem, and we trained the model to perform the introduced feature importance analysis. We also included the performance evaluation results and correlation with data analysis results.

Network Function Virtualization (NFV) enables the execution of Virtual Network Functions (VNFs) on standard commodity servers. This brings flexibility, allowing for the rapid deployment of various network services while reducing costs. However, NFV configurations are becoming increasingly complex, necessitating experts for the setup. Intent-based network configuration has emerged as a solution to simplify NFV configuration and management. Nonetheless, it presents challenges, such as translating high-level natural language intents into low-level network configurations. In this work, we propose NFV-Intent - a system that leverages in-context learning in Large Language Models to perform the intent translation task. In-context learning enables NFV-Intent to work without retraining the Large Language Models, which is a difficult and expensive task. NFV-Intent uses a JSON template as the desired output, allowing Large Language Models to learn with a small number of examples and enabling easy verification of the configuration. Our evaluation showed that the intent can be translated into JSON configuration with high accuracy. To demonstrate the feasibility of NFV-Intent, we implemented and integrated it into the NI-testbed, our previously developed system for AI-based NFV life-cycle management.

This study introduces Virtual Machine (VM) Consolidation using a Transformer-based Deep Reinforcement Learning (DRL) method, to address the complexity and inefficiency in operating Software Defined Networks-enabled Network Function Virtualization (SDN-enabled NFV). The distribution of Virtual Network Functions (VNFs) as VMs across servers often leads to energy loss due to irregular deployment. The proposed approach enhances energy efficiency while maintaining the performance of Service Function Chains (SFCs). By refining the VM consolidation process and leveraging a more sophisticated DRL method, this approach promises a more efficient solution to VM consolidation in SDN-enabled NFV environments.

In this study, we propose a three-step anomaly detection system for network switches. The proposed system consists of the following steps: 1) Log parsing, where log messages from switches are analyzed to identify patterns and events, 2) Analysis of the identified event flow to distinguish normal and abnormal event sequences, and 3) Prediction of the next log message, with detection of anomalies if the predicted log message differs from the normal log messages. For event classification, a log parser is proposed by modifying existing algorithms, and experimental results confirm that similar log patterns are correctly classified into the same event. To learn normal event sequences, both FSM and LSTM models are trained. Lastly, we proposed a BERT-LSTM model to predict the next log message and detect unexpected log messages. The proposed system is validated using data collected from a constructed testbed and achieves a high-performance level with an F1 score of 83.72%. Notably, our system achieved a recall of 94.74%. Our system has an advantage in that if misclassified cases occur, network administrators can retrain each model to improve precision during system operation.

With the rise of NFTs, which serve as proof of ownership for assets, security tokens that enable transactions without intermediaries have become a popular topic. Tokenization eliminates the need for centralized markets and allows for fast trades. In addition to tokenization, there are attempts to increase asset liquidity by fractionalizing them, a concept known as fractional ownership. Despite the existence of some platforms that use tokenization and fractional ownership, there is still limited research on fractional NFTs, and institutional support is lacking. In this paper, we propose possible implementations of fractional NFTs and evaluate their gas costs, which are crucial for providing fractional NFT-related services. As most NFTs are minted based on the Ethereum blockchain, we implement fractional NFTs using ERC standards. Our evaluation shows that ERC-721 or ERC-1155 NFTs fractionalized into ERC-20 FTs have the lowest long-term gas costs.

Bitcoin transactions include unspent transaction outputs (UTXOs) as their inputs and generate one or more newly owned UTXOs at specified addresses. Each U TXO can only be used as an input in a transaction once, and using it in two or more different transactions is referred to as a double-spending attack. Ultimately, due to the characteristics of the Bitcoin protocol, double-spending is impossible. However, problems may arise when a transaction is considered final even though i ts finality has not been fully guaranteed in order to achieve fast payment. In this paper, we propose an approach to detecting Bitcoin double-spending attacks using a graph neural network (GNN). This model predicts whether all nodes in the network contain a given payment transaction in their own memory pool (mempool) using information only obtained from some observer nodes in the network. Our experiment shows that the proposed model can detect double-spending with an accuracy of at least 0.95 when more than about 1% of the entire nodes in the network are observer nodes.

Managing identity is a crucial issue, and with the advent of digital identity, Self-Sovereign Identity (SSI) has been highlighted. SSI allows the owner to have sovereignty over their identity information. Currently, blockchain technology is widely used to implement SSI management system. Issuing a verifiable credential (VC) based on blockchain has two advantages: it ensures the VC has not been tampered with and makes the VC trustworthy by sharing the same ledger among untrusted parties. This paper presents a SSI management system which was designed and implemented for police or firefighters who still manage identity information centrally. Furthermore, given the inherent risks associated with these occupations, it is imperative to have a secure healthcare system to meet their specific needs. We propose a system that links these healthcare systems, allowing owners of identity information to monitor how their identity information has been used. The usage history of the system is stored in a permissioned blockchain, specifically Hyperledger Fabric.

Non-Fungible Tokens (NFT) is a unique digital token based on blockchain technology, which is used as a means to prove ownership of assets in various areas. With the huge popularity of NFT, the launch of new NFTs attracted many people to mint NFTs. However, because Ethereum has low processing speed and then cannot accommodate the explosive demands, it causes Crypto Gas War, which increases the overall transaction fee and wastes unnecessary gas. In this work, we propose a Raffle-based N FT launch to solve t he C rypto Gas War. We demonstrated that our proposed NFT launch solves this problem but other existing solutions do not solve the problem. Moreover, our NFT smart contract is gas-efficient. Our proposed method reduces the gas usage by 15.5% or more compared to general method, and the efficiency is improved a ccording to increasing the total number of NFTs.

As blockchain protocols exhibit diverse characteristics and performances, it is crucial to decide whether to develop a custom blockchain protocol, select an existing platform, or identify a promising protocol before initiating a blockchain-based service or starting a new business. To assist the general public and business operators in assessing the desirability of each blockchain protocol, various services provide evaluation and ranking services for blockchain projects. However, most of these services rely on qualitative evaluation based on reports provided by the blockchain development team. Therefore, we propose a quantitative evaluation method for blockchain protocols that compares and analyzes the technological status, future development direction, and technological differences of Layer 1 and Layer 2 protocols. Our approach incorporates indicators that are distinct from other services and indicators specific to layer 2 solutions, facilitating a more comprehensive analysis of blockchain protocols. This allows for objective evaluation of protocol performance and mutual comparison between protocols.

Many studies have applied machine learning to bitrate control to increase Quality of Experience (QoE) of video streaming services in highly dynamic networks. However, their solutions mainly focused on HTTP adaptive streaming with one-to-one connections. This paper studies video conferencing applications where multi-party, full-duplex communication happens among participants. In particular, we propose Muno, a Deep Reinforcement Learning (DRL)-based bandwidth prediction framework for multi-party video conferencing systems. Muno learns and predicts an appropriate bitrate for each connection in a multi-party conferencing call. We trained Muno to maximize the QoE of individual connections by constructing a feedback loop between a media server and DRL servers. Our experimental results show that Muno achieves a higher video streaming rate and lower delay compared to state-of-the-art rulebased algorithms.

Nowadays, Machine Learning (ML) approaches gain a lot of intention for automating and managing Software-Defined Networking, and Network Function Virtualization (SDNNFV) enabled networks. These networks are highly dynamic and flexible due to centralized control and scaleable Virtual Network Functions (VNFs). But the automatic management of the VNF lifecycle in a data center is still challenging; it includes several tasks such as VNF resource usage prediction, placement, consolidation, autoscaling, live migration, etc. So, accurately predicting VNF resource usage can be used for the several tasks mentioned above for performing VNF lifecycle management. It can also help Mobile Network Operators (MNOs) to ensure QoS by reducing Service-Level-Agreement (SLAs) violations. This article introduces an efficient mechanism that uses Adaptive Ensemble Learning to predict resource usage of virtual network functions. This mechanism has three modules: Machine-Learning Predictors (MLPs), Predictor Selector (PS), and Predictor Combiner (PC). The MLPs module contains several ML models for performing prediction. The PS module has a pretrained Random Forest model that is used to choose the best predictors from the MLPs. The PC module combines the selected predictors using an ensemble learning mechanism to generate the final prediction. In tests on three datasets, our method achieved a high${R}^{2}=0.96$for predicting CPU utilization and$R^{2}=0.97$for predicting memory utilization.

Several years after NFTs first appeared, people began to use NFT to prove ownership of assets in diverse domains. Then the values of NFTs rapidly increased and accordingly the interests in NFTs have grown. Because Ethereum has the most active transactions and has a lot of users, many projects use Ethereum to deploy their NFT smart contracts. However, Ethereum has a chronic disadvantage of low scalability. During the NFT drop period for famous and popular NFTs, users are crowding the event, resulting in a large number of transactions to get NFTs. The low scalability leads to a fierce competition called the Crypto Gas War. In this work, we choose three famous NFT drop events to collect on-chain data of Ethereum over the drop period in order to analyze the impact of the Crypto Gas War on the Ethereum network in detail. In addition, we analyze the collected data to uncover critical and hidden problems, and present insights to solve them.

While many studies have been conducted to apply reinforcement learning (RL) to real world problems beyond games such as Atari, video conferencing is also one of real world applications. In video conferencing, reinforcement learning is used to control the bitrate to improve the user's quality of experience (QoE). However, real world problems such as video conferencing have different characteristics compared to electronic games. Usually the rewards in real world problems are not clear or abstract, and this makes it difficult to design the RL model and training process of the model to maximize the cumulative reward. Therefore, in this paper, we present the method for stabilizing the training of the models that apply reinforcement learning to video conferencing. In addition, we established a simulation environment that can train deep RL models in 1-to-1 video conferencing. An evaluation is performed to analyze the difference between the baseline model and the models generated using the stabilization method in the simulation environment.

Softwarization of the internet network is promising for network service providers (NSPs) to satisfy various types of user requests while dynamically operating the networking system. However, it is challenging to provide optimal service quality as the complexity of the network increases. In particular, deployment of the VNF instances is a critical issue in providing better QoS while maintaining resources optimally. For the management of the VNF deployment task, dynamic programming algorithms are only feasible in small networks or rely on heuristics. In this paper, we propose a VNF deployment method based on reinforcement learning (RL) that can effectively satisfy the QoS with minimized resource consumption in the network. Our approach makes adjustment decisions, which are scale-in/keep/out for target nodes and target VNF types given ILP-based deployments. We formulate this VNF deployment task as RL by setting the reward as QoS and resources. Moreover, we propose a model architecture for our RL agent's policy, based on Graph Neural Network. In the experiment, our approach optimizes VNF deployment with improved QoS while keeping the similar or slightly less amount of resources, compared to ILP-based deployment.

In this study, we present a failure prediction study of VMs and VNFs in an NFV environment. For the proof of concept, we designed a machine learning model to predict the failure with log analysis and observed the cases where the failure-related logs do not exist in the failed VM, but in the server, or in other VMs operating on the same server. Therefore, in this paper, we propose a model which analyzes the logs of all the related VMs and the server and predicts the possibility that any of the VMs operating on the server will fail. To reduce the huge size of the logs collected from the server and VMs, we propose a pre-processing and tagging method that can improve the performance of our model. In addition, we designed a machine learning model using CNN with BERT, which has performed SOTA in various fields of NLP, to receive logs as input and calculate failure probabilities for the next 30 minutes. To validate the proposed model, we collected failure-related logs and normal logs from an OpenStack testbed, and the experimental result shows that the proposed model can predict the failure of VMs operating in the server with an F1 score of 0.74.

In accordance with the guidelines of the Financial Action Task Force (FATF), Virtual Asset Service Providers (VASPs) should comply with a ‘travel rule’, which requires them to exchange originator’s and beneficiary’s personal information when transferring virtual assets. In this paper, we propose a novel blockchain-based travel rule compliance system that supports fully-decentralized data exchange. The proposed system uses a permissioned blockchain, and thereby eliminates the possibility of leakage of personal information to third parties or even to travel rule service providers, and ensures that travel rule data can be managed securely.

Blockchain has the potential to enhance supply chain management systems by providing stronger assurance in transparency and traceability of traded commodities. However, blockchain does not overcome the inherent issues of data trust in IoT enabled supply chains. Recent proposals attempt to tackle these issues by incorporating generic trust and reputation management methods, which do not entirely address the complex challenges of supply chain operations and suffers from significant drawbacks. In this paper, we propose DeTRM, a decentralised trust and reputation management solution for supply chains, which considers complex supply chain operations, such as splitting or merging of product lots, to provide a coherent trust management solution. We resolve data trust by correlating empirical data from adjacent sensor nodes, using which the authenticity of data can be assessed. We design a consortium blockchain, where smart contracts play a significant role in quantifying trustworthiness as a numerical score from different perspectives. A proof-of-concept implementation in Hyperledger Fabric shows that DeTRM is feasible and only incurs relatively small overheads compared to the baseline.

Since Bitcoin was launched, the blockchain technology and the cryptocurrencies have been in the spotlight. Ethereum, the second-generation blockchain introduced smart contracts, and many DApps have emerged due to them. Those DApps showed the feasibility of blockchain in various industries. However, even though the growth of blockchain technology, still many DApps are based on Ethereum, and supper its performance issue. Since the performance evaluation in Ethereum mainnet is almost impossible, and there are no formalized performance evaluation frameworks, it is hard to perform appropriate performance evaluation of Ethereum. Detail performance evaluations on Ethereum networks are essential for developing and operating DApps. In this paper, we use Hyperledger Caliper, an automated performance evaluation framework to evaluate an Ethereum private network, and the Ropsten testnet to overcome above problems. We evaluate the performance with a specific smart contract and analyze the results. Our evaluation results show that the Ethereum private network performs better than the Ropsten testnet, and the Ropsten testnet is unstable for performance evaluation. In addition, our results show that the performance of the transactions can differ following their content.

With the advent of e-commerce and electronic payment systems, the use of paper currency is decreasing. Therefore, it is sufficiently predictable that most paper currencies will disappear and digital currencies will become the mainstream. This phenomenon is further accelerated by advances in blockchain technology and COVID-19. This is why the Central Bank Digital Currency (CBDC) has recently begun to attract attention. Currently, there are studies on CBDC with a blockchain-based distributed ledger. In this paper, we propose Cosmos blockchain based CBDC (Cos-CBDC) that enables communication between blockchains using Inter-Blockchain Communication (IBC) protocol to ensure interoperability. We not only analyze the requirements of Cos-CBDC but also design and implement it using Cosmos-SDK. Furthermore, we propose a Group Key Management system in Cos-CBDC. It can give different user privileges, and privacy-preserving is possible in the key generation process.

In this study, we propose a machine learning model that predicts failures by analyzing logs before failures occur in virtual machines (VMs) used in network function virtualization (NFV) environments. The proposed model utilizes convolutional neural network (CNN) and includes pre-processing and pre-failure tagging techniques. We collected log data from VMs built on OpenStack to validate the proposed model. We classified failures based on early fault messages and built a CNN model to predict VM failures. The experimental results showed that the proposed model can predict failures before 5 minutes with the F1 score of 0.67. The proposed model will be used for VM proactive live migration to avoid service degradation and interruptions caused by failures.

With the advancement of artificial intelligence(AI) technology, AI is being used in various industries such as factory automation and autonomous driving. Video conferencing systems have also added functions that use AI to overcome the limitations of existing algorithms, for example, super resolution and virtual background functions using image segmentation. However, web-based video conferencing limits the application of these features due to a limited web browser environment. In this paper, we introduce several approaches to apply deep learning in a web browser environment to provide the features that use deep learning models, and introduce image segmentation models used for virtual background functions in each method and evaluate their performance. Finally, we discuss areas that need to be considered to apply deep learning models to web-based video conferencing.

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption, and to provide uninterrupted service during the maintenance of hardware and software updates on servers. It is critical to use VM live migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. Especially in NFV (Network Function Virtualization) environment, timely use of VNF (Virtual Network Function) live migration can maintain system availability and reduce operator's loss due to service failure. In this paper, we propose a proactive live migration method for vEPC (Virtual Evolved Packet Core) based on failure prediction. A machine learning model learns periodic monitoring data of resource usage and logs from servers and VMs/VNFs to predict future vEPC paging failure probability. We implemented the proposed method in OpenStack-based NFV environment to evaluate the real service performance gains for open source vEPC implementations.

Network Function Virtualization (NFV) and service orchestration has simplified the Service Function Chain management (SFC) tasks, while the edge cloud infrastructure has reduced the latency. Due to these existing technological advantages, there is an urgent need for a dynamic and flexible service chain placement model that performs resource allocation of substrate network in a delay-sensitive and resource-efficient manner. We propose an off-policy Deep Reinforcement Learning algorithm EdgeDQN for efficient SFC placement in the edge cloud environment. The problem of edge resource scarcity is handled by designing a network model that allows worst-case resource renting from neighbors and data centers. This network model is integrated with EdgeDQN using several constraints. This paper aims to find the optimal placement by minimizing the underlying resource utilization and SFC end-to-end delay for multiple SFCs at the same time. To achieve that, an intuitive reward model is proposed. We compare the proposed EdgeDQN algorithm with DQN, Q-learning, and EdgeQL algorithms in terms of performance parameters such as cumulative reward, cumulative standard deviation, latency, and learning convergence time for 420 different test cases. Extensive test results on a simulated and physical (OpenStack) testbed demonstrate the effectiveness of the proposed EdgeDQN algorithm.

Video conferencing systems have been developed for a long time. However, due to COVID-19, people came to realize the importance of such systems that their widespread usage increased drastically. With the growing number of users of video conferencing systems, it is essential to be able to manage the system better. Even though Network Function Virtualization (NFV) has helped in handling the dynamic nature of video conferencing systems by providing the ability to deploy and/or remove virtual instances, being able to manage the virtual instances efficiently and optimally is a necessity. One of the main problems to be solved to achieve this goal is the auto-scaling problem. In this problem, an auto-scaling agent has to decide whether the number of a virtual instance type has to be increased, decreased, or maintained. In this paper, we designed and experimented a new auto-scaling algorithm by using Reinforcement Learning (RL), specifically the Deep Q-Network (DQN) algorithm. The experiment was done in both emulated and simulated environments. We then evaluated our proposed approach by comparing it with a threshold-based auto-scaler, the baseline algorithm. The results showed that the DQN-based auto-scaler performed better than the baseline algorithm, using fewer virtual instances with a better quality of service overall, as well as being more robust to shifting data distribution. Overall, the applications of DQN on auto-scaling algorithms for cost-effective and high-performance video conferencing systems are promising.

Data center networks are designed with multi-rooted topologies to provide the large bisection bandwidth. These topologies provide high path diversity and need a load balancing algorithm to utilize bisection bandwidth efficiently. To distributes traffic efficiently, congestion-aware and fine-grained load balancing algorithms are suggested. However, these algorithms need to set parameters depending on the network states. This paper presents a reinforcement weight-cost multipath (RWCMP) load-balancing method on a data center network to balance the traffic. It uses reinforcement learning to learn the optimal traffic split ratio of egress ports for each node and determines the routes of multiple flows simultaneously. We compared the network utilization of our RWCMP load balancing algorithm with that of the LetFlow algorithm. In a fat tree topology, the RWCMP load balancing algorithm outperforms the traditional load balancing algorithm with 24-36% lower network utilization in three different traffic patterns. In the same topology with a link failure, the RWCMP load balancing algorithm outperforms the traditional load balancing algorithm with 12-27% lower network utilization. These results demonstrate that RWCMP learns traffic split ratio depending on underlying traffic patterns and topology of the data center.

Services in today’s network produce a heavy amount of traffic, and all this traffic might have to pass through a Service Function Chain (SFC). The service provider must place these SFCs in the appropriate locations, and scale them as the request to the service increases. Appropriate placement and scaling of these SFCs will ensure Service Level Agreement (SLA) in terms of throughput, end-to-end delay, or successful serving of the requests. SFC placement and scaling are correlated tasks, however need to be modeled separately. For placing SFCs we require to consider server resources such as memory, CPU, bandwidth, and server locations. On the other hand for scaling, we need to consider resource utilization of VNFs as extra matrixes. Scaling in and out would require an additional cost and time to deploy new VNFs, hence it is important to predict VNF resource requirements in advance and prepare the resources for incoming traffic needs. In this paper we predicted an incoming resource demand using Recurrent Neural Network (RNN) based Gated Recurrent Unit (GRU) model. We predict the resource demands 2 min in advance and deploy the VNFs using the EdgeQ-Leaning model. We call this integrated algorithm as GRU-EdgeQL. To validate our approach we compared it with threshold and random algorithms. We implemented our algorithm in the OpenStack testbed. Our validation reveals that GRU based prediction and EdgeQ-Learning based placement does not only meet the Service Level Agreement (SLA) requirement by reducing the overall latency and SLA violation but also reduces the number of scaling operations thereby reducing the overall scaling cost.

Network softwarization paradigms such as SDN and NFV provide network operators with advantages in terms of scalability, cost and resource efficiency, as well as flexibility. However, in order to fully reap these benefits and cope with new challenges regarding the heterogeneity of user demands and an ever-growing service landscape, management and operation of such networks requires a high degree of automation that ensures fast and proactive decision making. With the recent success of machine learning (ML) across numerous domains, a shift from traditional rule-based policies towards ML-based approaches in the context of network management is taking place. Although many individual contributions cover use cases such as predicting various network characteristics or optimizing the configuration of components, a fully integrated architecture for achievingNetwork Intelligenceis still missing. Hence, in this work, we propose such an architecture that combines the capabilities of softwarized networks with ML-based management. The contribution of this article is threefold: first, we present the proposed architecture alongside its components. Second, we implement a proof-of-concept version of all components in our OpenStack-based testbed. Finally, we demonstrate in a case study regarding VNF resource prediction how the proposed architecture can be used to generate realistic data sets to train and evaluate ML-based models for this task.

By softwarizing the legacy network functions, Network Function Virtualization (NFV) allows rapid development and deployment of network services as well as simplicity and flexibility in network operations and management. Monitoring the performance characteristics of Virtual Network Functions (VNFs), particularly packet processing time, is important to ensure that VNFs are operating correctly with desired performance. This is especially crucial for low-latency network services. In this paper, we present Packet Processing Time Monitoring (PPTMon), a real-time, fine-grained, and end-to-end solution for VNF packet processing time monitoring. PPTMon can provide per-hop monitoring for a single VNF as well as end-to-end monitoring for multiple VNFs in a service function chain. PPTMon allows monitoring in both sampling and continuous fashions. Continuously monitoring every packet may greatly degrade the performance of the VNFs and generate a huge amount of monitoring data. PPTMon’s event-filtering algorithm effectively filters out non-important data and reduces the performance overhead. PPTMon processes packets in-stack by embedding timestamp information directly into the packets, thus further reducing the effect on the VNF performance. PPTMon is implemented on top of extended Berkeley Packet Filter (eBPF) – a Linux framework that allows high-speed packet processing. Our experiment results shows that PPTMon can monitor VNF packet processing time with high accuracy and low impact on performance.

Bitcoin users are guaranteed to be anonymous, increasing the number of cryptocurrency trading related to crimes and fraudulent activities. While most studies about detecting illegal transactions try to distinguish trading patterns and classify them from legitimate ones, classification performance is poor since the class distributions of transaction data are highly imbalanced. In general, the Synthetic Minority Over-sampling TEchnique (SMOTE) is used to deal with class-imbalanced data, but SMOTE has a problem that it does not fully represent the diversity of the data. In this paper, we introduce another oversampling technique using Generative Adversarial Networks (GAN) to generate artificial training data for classification model. In order to verify similarity between artificial data and the actual one, oversampled dataset is evaluated with a classification model using XGBoost algorithm. We show classification performance is improved on average with synthetic data generated by both SMOTE and well-designed GAN model.

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) help reduce OPEX and CAPEX as well as increase network flexibility and agility. But at the same time, operators have to cope with the increased complexity of managing virtual networks and machines, which are more dynamic and heterogeneous than before. Since this complexity is paired with strict time requirements for making management decisions, traditional mechanisms that rely on, e.g., Integer Linear Programming (ILP) models are no longer feasible. Machine learning has emerged as a possible solution to address network management problems to get near-optimal solutions in a short time. In this paper, we propose a Graph Neural Network (GNN) based algorithm to manage VNFs. The proposed model solves the complex VNF management problem in a short time and gets near-optimal solutions.

Supply chain is an entire network of producing and delivering a specific product to a final consumer. Stainless steel is a specific product being delivered on a supply chain. It is not easy to manage and monitor the entire supply chain because a supply chain has high complexity, including various organizations and activities. Due to this difficulty, several issues occur in the process of supplying stainless steel, such as forgery and alteration. Blockchain is a decentralized and distributed ledger technology that specializes in transparency and immutability. Many companies try to introduce this blockchain technology into supply chain management to conveniently monitor their supply chain. Accordingly, the blockchain technology can make steel companies be able to investigate and protect high-quality products from counterfeited low quality products. This paper proposes a design of a blockchain-based stainless steel tracking system to thoroughly track the entire process involved in supplies from stainless steel mills to the final customers. This proposed design is based on the hyperledger fabric which is one of the most popular private blockchain platforms.

With the advent of the 5G network era, it is required to flexibly build and manage networks to meet rapidly changing service requirements. Software-defined networking (SDN) and network function virtualization (NFV) are key technologies that enable flexible network management by transforming networks into software-based networks. Besides, NFV has the advantage of virtualizing network functions, operating those on commercial (COTS) servers, and managing the network functions dynamically. However, the numerous virtual networks and resources created by NFV can cause problems complicating network management. To solve the problems, research on managing complex NFV environments using artificial intelligence (AI) has recently attracted attention. In particular, service function chaining (SFC) is one of the essential NFV technologies, and it is required to create an efficient SFC path in dynamic networks. In this paper, we propose a method of finding optimal SFC path considering the resource utilization of virtual network function (VNF) and VNF placement by using Q-learning, one of the reinforcement learning (RL) algorithms.

Data center network traffic fluctuates over time while applications in the data center require high throughput and low latency. To satisfy these requirements, many load balancing algorithms have been proposed. But existing load balancing algorithms show difficulties in processing short flows and have limited scalability. This paper proposes a load balancing algorithm using probe packets. Probe packet collects detailed network information such as link utilization, hop latency at packet units periodically. The algorithm uses link utilization, hop latency, and queue occupancy to calculate the degree of congestion. The algorithm uses the network status information for load balancing by designating best hops for each hop. Metrics that measure congestion was collected and tested through probe packets in the virtual programmable network environment, and the result shows 24.18% higher performance compared with ECMP.

Reinforcement learning (RL) has been used in various path finding applications including games, robotics and autonomous systems. Deploying Service Function Chain (SFC) with optimal path and resource utilization in edge computing environment is an important and challenging problem to solve in Software Defined Network (SDN) paradigm. In this paper we used RL based Q-Learning algorithm to find an optimal SFC deployment path in edge computing environment with limited computing and storage resources. To achieve this, our deployment scenario uses a hierarchical network structure with local, neighbor and datacenter servers. Our Q-Learning algorithm uses an intuitive reward function which does not only depend on the optimal path but also considers edge computing resource utilization and SFC length. We defined regret and empirical standard deviation as evaluation parameters. We evaluated our results by making 1200 test cases with varying SFC-length, edge resources and Virtual Network Function's (VNF) resource demand. The computation time of our algorithm varies between 0.03~0.6 seconds depending on the SFC length and resource requirement.

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment in consideration of the cost and Quality of Service (QoS) is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point when the deployment decision is applied because it takes processing time to apply the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain optimal VNF deployment and Service Function Chaining (SFC) result from an Integer Linear Programming (ILP) solution. We use the simulation data to train a machine learning model which predicts the optimal VNF deployment at a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution for the 5-minute future time point.

By enabling the deployment of softwarelized network functions on commodity servers, Network Function Virtualization (NFV) brings many benefits such as rapid development and deployment, simplicity and flexibility in network operations and management. Monitoring the performance characteristics of Virtual Network Functions (VNFs), such as packet processing time, is crucial to achieving maximum benefit from NFV. In this paper, we present Packer Processing Time Monitoring (PPTMon) - a solution for real-time and lightweight VNF packet processing time monitoring. PPTMon embeds timestamp information directly into the packets. PPTMon is implemented using extended Berkeley Packet Filter (eBPF) - a new Linux framework that allows high-speed packet processing. Our experiments showed that PPTMon can monitor VNFs with high accuracy and low performance overhead.

Since the concept of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has been proposed, telcos and service providers have leveraged these concepts to provide their services more efficiently. However, as the virtual network in the data centers becomes more complex, a variety of new network management problems arise. To deal with these management problems, it is necessary to monitor and analyze resource usage and traffic load of Virtual Network Functions (VNFs) operating on the virtual network. Recently, there have been many attempts to develop technologies that enable network management without human intervention. In this paper, we specify our anomaly detection problem with scenarios involving SLA violations to satisfy the practical needs of network management. Also, we set the real-world NFV environment to generate anomalous data corresponding to each scenario and extend our approach to implementing the system for root-cause localization which identifies the exact VNF instance causing the SLA-related anomalies. We use the datasets collected from the VNFs' service function chain scenarios implemented on OpenStack environment, and compare the accuracy of the anomaly detection models generated by various machine learning algorithms. Our experimental results show the best model has F1-measure over 95% for anomaly detection and 93% for root-cause localization.

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) help reduce OPEX and CAPEX as well as increase network flexibility and agility. But at the same time, operators have to cope with the increased complexity of managing virtual networks and machines, which are more dynamic and heterogeneous than before. Since this complexity is paired with strict time requirements for making management decisions, traditional mechanisms that rely on, e.g., Integer Linear Programming (ILP) models are no longer feasible. Machine learning has emerged as a possible solution to address network management problems to get near-optimal solutions in a short time. In this paper, we propose a Graph Neural Network (GNN) based algorithm to manage Virtual Network Functions (VNFs). The proposed model solves the complex VNF management prob-lem in a short time and gets near-optimal solutions.

Network function virtualization (NFV) is a key technology of the 5G network era. NFV decouples a network function from proprietary hardware so that the network function can operate on commercial off-the-shelf (COTS) servers as a form of virtual network functions (VNFs). Owing to the advantage of NFV, network functions can be applied dynamically to the networks. However, NFV complicates network management because this technology creates numerous virtual resources that should be managed. To solve the problem of complicated network management, studies on applying artificial intelligence (AI) to the NFV-enabled networks, i.e., VNF life cycle management, have attracted attention. In particular, autoscaling, which is one of the essential functions of VNF life cycle management, adds or removes VNF instances to meet service requirements. It is a challenging task to determine the optimal number of VNF instances in dynamic networks, satisfying service requirements. In this paper, we propose a novel auto-scaling method using reinforcement learning (RL) for scale-in/out of multi-tier VNF instances, i.e., service function chaining (SFC) in NFV environments. The proposed approach defines RL's states using a status of SFC composed of multi-tier VNF instances and uses service level objectives (SLO) to make a reward model. We validate the proposed approach in an OpenStack environment, and it shows that our proposed auto-scaling method provides the optimal number of VNF instances in each tier while minimizing SLO violation.

We proposed a best nexthop load-balancing algorithm that helps avoid the congestion path by using global network information, then compared the best nexthop algorithm with the ECMP algorithm. The best nexthop algorithm collects the network information in real time by using In-band network telemetry. The collected network information is hop latency, queue depth, and link utilization. Each switch stores the network information and calculates the degree of congestion as the sum of metric values for each path. When the traffic arrives, each switch divides the traffic into flowlet units and forwards each flowlet to the least-congested path. We compared the throughput of the best nexthop algorithm to that of the ECMP algorithm in three scenarios. In the first scenario, we send 10 flows sequentially and measured the throughput per the number of flows. The best nexthop algorithm shows stable throughput, but the ECMP algorithm shows significant descent throughput after the number of flows exists three. In the second scenario, we send traffic from two different sources to two destinations. The best nexthop algorithm showed 27% higher throughput than the ECMP algorithm in this scenario. In the third scenario, we send a large burst of traffic from a single source to a single destination. The best nexthop algorithm showed 81% higher throughput than the ECMP algorithm in this scenario. These results show that the best nexthop algorithm performs better than the ECMP algorithm in congested status.

Biggest challenge in deploying Service Function Chain (SFC) in the Edge Computing environment is the lack of resources at the edge. Hence while finding the optimum path for SFC deployment, the resource constraint environment should be observed and incorporated well in deployment scenarios. In this paper, we developed an environment aware adaptive Q-Learning algorithm to find an optimal SFC deployment path in edge computing environment. The available servers are divided into hierarchical network structure with local, neighbor, and datacenter servers to model an edge computing environment. The resource dynamics in the environment is modeled as a state transition probability. We compared the new algorithm with our base case algorithm that solely depends on Q-Learning and doesn't incorporate the state transition probabilities. An intuitive reward function is designed to give maximum reward to complex deployment with minimum delays. We integrated our algorithm with physical testbeds using OpenStack and open source REST APIs. We evaluated SFC deployment on physical testbed using 42 different scenarios by measuring RTT.

Network Function Virtualization (NFV) is the key to enable rapid development and deployment of network services as well as simplicity and flexibility in network operations and management. To achieve the maximum benefit of NFV, monitoring the performance characteristics of Virtual Network Functions (VNFs) is crucial. Packet processing time is one of the most important performance metrics when it comes to VNF monitoring. In this paper, we present Packet Processing Time Monitoring (PPTMon) - a real-time, end-to-end solution for VNF packet processing time monitoring. PPTMon can provide per-hop monitoring for a single VNF as well as end-to-end monitoring for multiple VNFs in service function chains. PPTMon works by embedding timestamp information directly into the packets. PPTMon is implemented on top of extended Berkeley Packet Filter (eBPF) - a new Linux framework that allows high-speed packet processing. Our experiment results showed that PPTMon can monitor VNF packet processing time with high accuracy and negligible performance impact.

By decoupling network functions from dedicated, proprietary hardware network devices, Network Function Virtualization (NFV) allows building Virtual Network Functions (VNFs) that can run on standard, commodity servers to reduce cost and gain flexibility in network deployment, operation, and management. However, building VNFs with high-throughput and low-latency is a big challenge. In this paper, we propose eVNF - a hybrid fast-slow path architecture to build and accelerate VNFs with eXpress Data Path (XDP), which is a Linux kernel framework that enables high performance and programmable network processing. The programmability of XDP is limited to ensure kernel safety, thus causing difficulties when using XDP to accelerate VNFs. eVNF solves this problem by taking a hybrid approach: leave the simple but critical tasks inside the kernel with XDP, and let complex tasks be processed outside XDP, e.g., in user-space. With the hybrid architecture, eVNF allows building fast and flexible VNFs. We applied eVNF to build four prototype VNFs: Flow Monitoring (eFM), Firewall (eFW), Deep Packet Inspection (eDPI), and Load Balancer (eLB). These VNFs are evaluated individually and in service function chains (SFCs) using OpenStack. Our experiments showed that eVNF can significantly improve service throughput as well as reduce latency and CPU usage. eVNF-based VNFs also can scale out with the number of CPU cores and can combine with Open vSwitch - Data Plane Development Kit (OvS-DPDK) for better performance.

Since the concept of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has been proposed, service providers leverage the concepts to provide their services efficiently. Mobile CORD (M-CORD) is a platform to provide 5G network services using containerized VNFs in Multi-access Edge Clouds (MECs) at the central offices of telcos. However, current M-CORD does not include network and resource monitoring functions. In this paper, we present the design and implementation of a monitoring system to help efficient management and operation of the M-CORD platform configured in multi-site. Through this monitoring system, we can monitor the computing resource usages and data traffic load of each VNF container in M-CORD. We conducted experiments on our multi-cluster testbed to evaluate our monitoring system in terms of CPU and memory usage. As a result, our proposed monitoring system has a slight increase in CPU and memory usage, but it was able to perform M-CORD monitoring functions well.

Service Function Chaining (SFC) can be a basic deployment unit that composes a chaining order of required network functions to provide a network service. With the proliferation of Software-Defined Networking (SDN) and cloud computing, such virtualized network functions can be dynamically deployed in different sites, depending on SFC requests. While offering the advantages of flexibility and efficiency, this also leaves management complexity and room for optimization where Machine Learning (ML) can be applicable to solve the problems based on monitoring data. In this paper, we treat SFC as a problem of finding a best source-to-destination routing path from multiple candidates with different link costs and a required traversal order of network functions. There are many mathematical approaches that ensure best optimum but not scalable to the problem size, whereas our approach hides underlying considerations by applying ML technique on measured SFC data to quickly find suboptimal routing paths on a new SFC request, based on their predicted network performance such as the number of successful requests or end-to-end delay. So, we developed a measurement system that records the performance and path costs of SFCs in emulated networks with different per link costs and chain lengths. Then, we evaluate four different ML models for the approach described above.

In addition to providing network operators with benefits in terms of flexibility and cost efficiency, softwarization paradigms like SDN and NFV are key enablers for the concept of Service Function Chaining (SFC). The corresponding networks need to support a wide range of services and applications with highly dynamic temporal profiles and heterogeneous demands. Hence, efficient management and operation of such networks requires a high degree of automation that is paired with fast and proactive decisions in order to cope with these phenomena. In particular, determining the optimal number of VNF instances that is required for accommodating current and upcoming demands is a crucial task that also affects subsequent management decisions. To enable fast and proactive decisions in this context, we propose a machine learning-based approach that uses recent monitoring data to predict whether to adapt the current number of VNF instances of a given type. Furthermore, we present a work flow for generating labeled training data that reflects temporal dynamics and heterogeneous demands of real world networks. In addition to demonstrating the feasibility of the approach in a case study, we provide guidelines regarding the choice of monitoring data that should be collected for reliable prediction as well as the amount of data that is required to train such a predictor.

One challenge of Network Function Virtualization (NFV) is to provide high throughput and low-latency services. In this paper, we propose eVNF - a hybrid architecture to build and accelerate Virtual Network Functions (VNFs) with eXpress Data Path (XDP). XDP is a framework in Linux kernel that enables high-performance and programmable network processing. However, the programmability of XDP is limited to ensure kernel safety, thus causing difficulties in applying XDP to NFV. eVNF solves this problem by taking a hybrid approach: leave the simple but critical tasks inside the kernel with XDP, and let complex tasks be processed outside XDP (e.g., in user-space). eVNF allows building VNFs with both speed and flexibility. We used eVNF architecture to build three VNFs: Firewall (eFW), Deep Packet Inspection (eDPI), and Load Balancer (eLB); then tested them in service function chains. Our experiments showed that eVNF can significantly improve service throughput as well as reduce latency and CPU usage.

In addition to providing network operators with benefits in terms of flexibility and cost efficiency, softwarization paradigms like SDN and NFV are key enablers for the concept of Service Function Chaining (SFC). The corresponding networks need to support a wide range of services and applications with highly heterogeneous requirements that change dynamically during the network's lifetime. Hence, efficient management and operation of such networks requires a high degree of automation that is paired with fast and proactive decisions in order to cope with these phenomena. In particular, determining the optimal number of VNF instances that is required for accommodating current and upcoming demands is a crucial task that also affects subsequent management decisions. To enable fast and proactive decisions in this context, we propose a machine learning-based approach that uses recent monitoring data to predict whether to adapt the current number of VNF instances of a given type. Furthermore, we present a methodology for generating labeled training data that reflects temporal dynamics and heterogeneous demands of real world networks. We demonstrate the feasibility of the approach using two different network topologies that represent WAN and mobile edge computing use cases, respectively. Additionally, we investigate how well the models generalize among networks and provide guidelines regarding the prediction horizon, i.e., how far ahead predictions can be performed in a reliable manner.

Network Function Virtualization (NFV) decouples network functions from dedicated, proprietary hardware into software Virtual Network Functions (VNFs) that can run on standard, commodity servers. One challenge of NFV is to provide high-throughput and low-latency network services. In this paper, we propose eVNF - a hybrid architecture to build and accelerate VNFs with eXpress Data Path (XDP). XDP is a Linux kernel framework that enables high-performance and programmable network processing. However, the programmability of XDP is limited to ensure kernel safety, thus causing difficulties in applying XDP to NFV. eVNF solves this problem by taking a hybrid approach: leave the simple but critical tasks inside the kernel with XDP, and let complex tasks be processed outside XDP, e.g., in user-space. With the hybrid architecture, eVNF allows building fast and flexible VNFs. We used eVNF to build three prototype VNFs: Firewall (eFW), Deep Packet Inspection (eDPI), and Load Balancer (eLB). We evaluated these VNFs in two service function chains using OpenStack. Our experiments showed that eVNF can significantly improve service throughput as well as reduce latency and CPU usage.

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) greatly facilitate network service management. Specifically, these new network paradigms help manage the network environment dynamically and cost-efficiently. Virtual Network Function (VNF) and Service Function Chaining (SFC) are important aspects of the NFV environment. In terms of NFV management, resource demand of VNFs can be predicted at a future time to handle Quality of Service (QoS) and resource allocation problems efficiently. Hence, researchers study and build a management system where machine-learning-based predictions of VNF information are used to handle auto-scaling, deployment and migration of VNFs. In addition, in recent studies, these systems have involved SFC to obtain useful information, not just a lone VNF. However, not many of studies explain clearly how chaining dependency among VNFs in a SFC can be used to predict future resource demand of a VNF. In this paper, we introduce VNF resource prediction machine learning model that maximizes the benefits of using SFC. Then, we compare several machine learning models and analyze how SFC data can help predict resource usage patterns of VNFs. We also show benefits of Attention model to improve prediction accuracy and convergence time through experiments.

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are paradigms that help administrators to manage dynamic networks. While SDN allows centralized network control, NFV provides flexible and scalable Virtual Network Functions (VNFs). These paradigms are also enablers for concepts such as Service Function Chaining (SFC) where chains are composed of several VNFs to provide a specific service. However, in order to maximize the benefits from the above-mentioned flexibility, new research questions need to be addressed, e.g., regarding effective management processes for dynamic networks. We proposed a novel learning model based on the flexibility of softwarization and abundant volume of monitoring data in NFV environments to predict VNF resource demands using SFC data. Our model is based on Context and Aspect Embedded Attentive Target Dependent Long Short Term Memory (CAT-LSTM) that consists of Target-Dependent LSTM (TD-LSTM), context embedding, aspect embedding, and attention. We developed this model to obtain high accuracy for the prediction of VNF resources such as the CPU. Our model uses two labeling systems: the qualitative resource state and the quantitative resource usage, both of which are used to evaluate its performance. This assists the administrator in understanding the network conditions, improves prediction performance, and provides practically useful information. Our learning model for predicting VNF resource demands can be utilized to solve essential SFC problems such as auto-scaling and optimal placement, which in turn prevent service interruption and provide high reliability.

As the number of connected devices in the network is growing rapidly, network management is becoming more complex. Closed-loop network management can be a solution to address the problem, and self-driving network concept is one of the most promising solution. To realize the self-driving network, Knowledge-Defined Networking (KDN), network telemetry and Software-Defined Networking (SDN) are essential parts. As a first step toward realizing self-driving network, in this paper, we propose an architecture for self-driving network and suggest its use cases. We also present network monitoring system implementation on SDN controller using INT, and discuss its limitations.

Currently, server (host) virtualization technology that brings effective use of server resources to a data center is promising as cloud services are being prevalent with increasing traffic volumes and requirements for higher service quality. Proposed network TAP, named vTAP (Virtual Test Access Port), overcomes the problem that existing hardware TAP devices cannot be utilized for virtual network links to monitor traffic among virtual machines (VMs) at a packet level. vTAP can be implemented by a virtual switch that gives network connectivity to VMs by switching packets over the virtual network links. The port mirroring feature of a virtual switch can be a naive solution to provide packet level monitoring among VMs. However, using the feature in an environment that needs to treat large volume of network traffic with low delay such as NFV (Network Function Virtualization) incurs performance degradation in packet switching capability of the switch and error-prone manual configurations. This paper provides design and implementation approaches to vTAP using Open vSwitch with DPDK (Data Plane Development Kit) and an OpenFlow SDN (Software-Defined Networking) controller to overcome the problems. DPDK can accelerate overall packet processing operations needed in vTAP, and OpenFlow controller can provide a centralized and flexible way to apply and manage TAP policies in an SDN network. This paper also provides performance comparisons of the proposed vTAP and the naive method, port mirroring.

In order to manage data center networks, distributed SDN controllers such as ONOS controller have been researched and used. In this paper, we propose simple live distributed controller migration scheme and design the orchestrator for this scheme. This scheme basically transfers an distributed SDN controller from the overloaded physical machine to the under-loaded physical machine. According to experimental results, a data center network is broken without our scheme when computing load increases. On the other hands, our proposed scheme reduces an average delay as well as avoids a breakdown of the data center network.

Today's network is rapidly growing in terms of the number of connected devices and becomes more complex, making the complexity of network management increasing. Self-driving network concept is arising to solve those problems and a good candidate for the closed-loop network management solution. Knowledge-defined Networking (KDN), network telemetry and Software-defined Networking (SDN) are fundamental building blocks to realize self-driving network. Using the self-driving network, OPEX can be reduced and the performance of the network can be grown. In this paper, we propose an architecture for collecting network telemetry and combining KDN to work with SDN and P4 INT. We also suggest several use-cases for the self-driving network architecture.

Software-defined Networking (SDN) is a network paradigm to resolve the challenges of traditional networks. SDN can utilize its concept of control and data plane separation to improve Quality of Service (QoS) of certain network traffic efficiently. Because traffic engineering in SDN is a promising way to satisfy the requirement well, we propose an application-aware traffic engineering system that cooperates with Deep Packet Inspection (DPI). In the system, port number and DPI-based traffic classification are used to identify application or service flows. The system improves QoS of the identified flows by distributing them to multiple queues with different priorities in each switch port. A network admin can define a mapping table between an identified flow and its QoS priority (queue) in the system. To demonstrate the feasibility of the system, we designed and implemented the system by constructing an SDN controller application and data plane entities. The results of the experiment shows increased throughput and reduced packet delay for identified application traffic.

Owing to the development of information technology, many people can access various services through the Internet. In addition, with the emergence of the Web 2.0 concept of opening, participating, and sharing, public institutions and companies have made it possible to use their data and services in a variety of ways. In this environment, service providers have developed new services by linking open data and services to meet the rapidly changing requirements of Internet users. A new service developed through the interworking of data and services is called a mashup service. A mashup service has the advantage of providing useful functions required by users by combining the existing data and services. However, to develop a mashup service, it is necessary to process the data collected from other sources and link the services, which greatly increases the developers burden. In this study, we propose an open application programming interface (openAPI)-based message router for mashup service development to overcome this problem. The message router supports the development of the mashup service by providing useful functions required by the developer in cooperation with various platforms according to the request messages transmitted through openAPI.

A common streaming service is the one typically provided by a Client-Server model, where tasks are partitioned between providers of the service, called servers, and the service requesters, called clients; thus, the quality of the content depends on the network condition existing between the server and the client. However, this streaming service model is changing as streamers, such as private Internet broadcasters, are replacing the role of content providers and allowing content to be sent to the end user in real time. Because streamers transmit the content in real time, efficient models are needed on the streamer, the server and the client side for users to receive reliable content. In this paper, we measure and analyze the quality of the content according to the streamers network condition on the streamer side. On the server side, we add flexibility to the existing content delivery network structure and verify our model by assessing metrics such as throughput, delay and jitter. On the client side, we propose a grid-based P2P model that allows users to receive content reliably.

In the modern era of software-defined networking (SDN), network monitoring is becoming more important in providing information for SDN controllers to control networks. However, current monitoring methods, based primarily on sampling and polling, have performance and granularity limitations. Inband Network Telemetry (INT) is a new method that can perform end-to-end monitoring directly in the data plane, enabling real-time and fine-grained network monitoring. In this paper, we present an INT architecture for the UDP and discuss its design and implementation in Open Network Operating System (ONOS) controller. To the best of our knowledge, this is the first INT monitoring system in ONOS.

This paper provides a temporal cellular and WiFi networks analysis from a nationwide crowdsourcing measurement study. Our dataset consists of 2.98M user-initiated quality tests on 3G/LTE/WiFi involving 157K mobile devices from Nov. 2012 to July 2016 (187 weeks) in South Korea. Our analysis explains changes in QoS from the user perspective, not Mobile Network Operators (MNO). We revealed that WiFi shows twice higher compounded quarterly growth rate for download throughput against LTE. Yet, LTE and WiFi show almost no difference in absolute download throughput value as of mid 2016. Second, LTE delivers relatively low latency, less-varying loss rate, and higher throughput in overall. Finally, the result shows that the evolution for the high-end LTE services has been faster than user adoption, where the majority of the LTE users stays below 75 Mbps of throughput.

Network virtualization is a technique that abstracts the underlying physical infrastructures into multiple isolated networks. Currently, network virtualization based on Software-Defined Networking (SDN) has attracted interests from industry and academia to utilize limited network resources by using benefits of SDN. SDN has useful features such as programmability, flexibility, and agility. In order to virtualize networks in SDN, a network hypervisor intercepts and modifies OpenFlow messages so that it provisions multiple virtual networks, virtual Software-Defined Networks (vSDNs). However, existing SDN-based network hypervisors do not provide an easy-to-use method to connect a created vSDN with external networks. It limits the usefulness of vSDNs. To resolve this problem, we propose a virtual gateway for external connectivity in vSDN. The proposed virtual gateway is implemented using ONOS virtualization subsystem. The virtual gateway is able to provide external connectivity and other useful network functions such as firewall, traffic shaping, and load-balancing. To demonstrate the feasibility of virtual gateway, we evaluate round trip time and deployment time to show a connectivity and overhead of the virtual gateway deployment.

Network Function Virtualization (NFV) is one of the important aspects of modern network architecture. NFV decouples Network Functions (NFs) from hardware, therefore produces Virtual Network Functions (VNFs) that can run on standard, commodity servers, which in turn mostly run Linux kernel. In this paper, we propose a general architecture for building hybrid kernel-user space VNFs which leverages extended Berkeley Packet Filter (eBPF). eBPF is a framework in Linux kernel that enables network programmability inside kernel for optimal performance. However, the programmability of eBPF is limited due to safety and security of the kernel. Our proposed architecture applies hybrid approach: leave the simple work inside the kernel with eBPF and let complex work be processed in the user space. This architecture allows building complex VNFs to have both speed and flexibility. To demonstrate, we use the proposed architecture to build two VNFs: Dynamic Load Balancer and Deep Packet Inspection with Dynamic Sniffing. The evaluation results show that both VNFs significantly outperform the widely used solutions.

Software-Defined Networking (SDN) is one of the emerging network technologies that aims to operate and manage networks in more flexible and efficient manner. Among various features from SDN, Network Virtualization (NV) is one of the most promising network technologies that provides the ability to provision multiple virtual networks on top of underlying physical networks in a way to improve network utilization and provide flexibility. Since virtual networks are dependent on underlying physical network, failures in the physical network in turn can affect virtual networks. To provide a transparent failover solution for virtual networks, a Network Hypervisor (NH)-based approach gains more and more popularity, as this approach does not expose too much details of the physical network to tenant controllers. NH-based approach can be further categorized into two folds - restoration and protection. Restoration has a drawback that failover time increases proportionally with the number of switches along the path, while protection has a weakness that it cannot deal with dynamic changes of network states. To address the drawbacks of these approaches, we propose a dynamic failover method by properly combining the two approaches that aims to preserve the dynamicity while reducing the failover time. To show the feasibility, we have designed and implemented the proposed failover method by enhancing existing open source network hypervisor, OpenVirteX (OVX), and evaluated its performance in an emulated network environment. Our outcomes show the improvement of performance on delay compared to the exiting method.

Software-Defined Networking (SDN) is an emerging networking paradigm aims to improve network management flexibility and efficiency. OpenFlow is the popular SDN de-facto standard, which has been prevalently adopted by both academia and industry for research and development purpose. OpenFlow provides rich programmable interface to network administrator to ease traffic monitoring and control. Because OpenFlow supports L4 network stack, it is feasible to provide application level traffic control by specifying TCP/UDP port number in flow rules. The major deficiency of the port-based traffic control is that it only provides the ability to control traffic from applications which have well-known TCP/UDP port numbers. In the case of port number change or dynamic (ephemeral) port allocation to an application, it is difficult to accurately control the application traffic. To be a solution, we propose an application-aware traffic management method by integrating Deep Packet Inspection (DPI) function with SDN controller. To show the feasibility, we design and implement Firewall and Bandwidth Manager applications based on the proposed management method. The applications perform on top of ONOS [1] controller, and FTP rate control example is shown to prove the feasibility of the proposed flow management method.

With the advancement of the Internet technologies, cloud services and various open data, there have been many active attempts to develop mashup services. To develop a mashup service, many data sources and service platforms should be interlinked. Therefore, efficient ways of interconnecting various service platforms and managing data are essential to provide easy service development environment. However, current mashup service development environments do not provide any of them, which forces each developer to fully understand every platforms, interfaces and data format to develop a mahsup service. So, it gives a big burden to mashup service developers and also hinders growth of mashup service industry. In this paper, we propose ICBMS Smart Mediator which connects IoT, cloud, big data, mobile, and security platforms and helps developers to easily utilize them with less overhead and provides easy access to data. We have designed and implemented the proposed ICBMS Smart Mediator and created a new mahsup service with the ICBMS Smart Mediator.

Currently, the Software Defined Networking (SDN) paradigm has attracted significant interests from industry and academia as a future network architecture. SDN brings many benefits to network operations and management including programmability, agility, elasticity, and flexibility. With SDN and OpenFlow, one of the promising SDN protocols, software defined Network Virtualization (NV) techniques can be designed and implemented via flow table segmentation to provision independent virtual networks (VNs). In this paper, we propose an intent based virtual network management platform based on software defined NV. The objective of the proposed NV platform is to automate the management and configuration of virtual networks based on high level tenant requirement specifications, called intents. The design and implementation of the platform is based on ONOS, an open-source SDN controller, and OpenVirteX, a network hypervisor. The platform is designed to provide multiple VNs over the same physical infrastructure to multiple tenants.

To preserve consistent throughput, smartphones are equipped with a network switch feature (handover in heterogeneous networks). Frequent switching is often blamed to be a QoE downgrader in populated areas. In this paper, we measured auto switch occurrences between Wi-Fi and mobile data networks. We deployed an Android monitoring application for 89 participants and collected network status information up to 10 days long. We observed that auto switch occurred on average 2.53 times per hour and RTT decreased as the smartphone preferred to stay in Wi-Fi. Also, 68% of all users connected to Wi-Fi longer than the mobile data networks.

LTE-Advanced (LTE-A) theoretically can provide better network performance than 4G LTE. Mobile carriers around the globe are eager to deploy LTE-A to attract more subscribers. However, is it really making difference to the user experience compared to the existing LTE service? To investigate this question, we collected the network performance log from 111 user smartphones on 3G, LTE, and LTE-A. For in-depth analysis, we also asked for privacy information, such as data plan, subscribed network, monthly payment details, and etc. By analyzing the collected log, we observed that (i) LTE-A was faster than LTE only for download bandwidth by 14.1%, yet the users pay on average 13.6% more for LTE-A; (ii) the mVoIP traffic was blocked by all carriers when the users exceed their mVoIP quota; (iii) the subscribed data plan showed no discrimination in network performance.

Mobile network operators are deploying IPv6 to their mobile networks in recent years, to support ever increasing mobile and IoT devices. They also adopt IPv4-IPv6 transition techniques to provide connectivity from their IPv6 networks to IPv4 networks. Since IPv6 deployment to mobile network is still in an early stage, the performance assessment on IPv6 based mobile network is needed, regarding both IPv6 and IPv4. In this paper, we focus on assessing the performance of IPv6 based mobile networks. We first analyze the detailed TCP connection procedures in accordance with the different IP versions that the end hosts use. Based on the analysis result, we suggest improvement points to speed up the connection establishment. We also compare the performance difference between IPv6 and IPv4 by measuring the connection establishment time to 40 popular dual-stack websites. We witnessed that IPv4 can be connected faster than IPv6 in most cases because of the immaturity of the IPv6 infrastructure.

Software-Defined Network (SDN) is an emerging network paradigm which enables flexible network management by separating control plane from data plane. Since SDN adopts a centralized management scheme, with large scale SDN network, it brings intensive overhead to control plane. One way of limiting this overhead is to either distribute the overhead to multiple controllers, or offload the overhead to switches. However, both of the approaches require modification on SDN de-facto standard, therefore, it is not viable for practical use. As an alternative solution, we propose a new control plane management method without changing the underlying SDN protocol. The key idea of the proposed method is to maximize the switch resource utilization by maintaining as much information as possible inside the switches, so that the switches may less frequently query the controller for new information. However, it is non-trivial problem to control the switch resource utilization under the capacity, due to the absence of a detailed correlation model between resource utilization and the affecting parameters. To resolve this issue, our management method adopts a lightweight feedback loop based control scheme - Proportional-Integral-Derivative (PID) to adaptively tune the affecting parameters to minimize the control plane overhead, while avoiding the switch's resource exhaustion. We design and implement the proposed method as an SDN application and evaluate its performance in an emulated network.

Public cloud storage has recently surged in popularity. However, cloud storage providers (CSPs) today offer fairly rigid services, which cannot be customized to meet individual users' needs. We propose a distributed, client-defined architecture that integrates multiple autonomous CSPs into one unified cloud and allows individual clients to specify their desired performance levels and share files. We design, implement, and deploy CYRUS (Client-defined privacY-protected Reliable cloUd Service), a practical system that realizes this architecture. CYRUS ensures user privacy and reliability by scattering files into smaller pieces across multiple CSPs, so that no one CSP can read users' data. We develop an algorithm that sets reliability and privacy parameters according to user needs and selects CSPs from which to download user data so as to minimize latency. To accommodate multiple autonomous clients, we allow clients to upload simultaneous file updates and detect conflicts after the fact from the client. We finally evaluate the performance of a CYRUS prototype that connects to four popular commercial CSPs in both lab testbeds and user trials, and discuss CYRUS's implications for the cloud storage market.

The number of data centers has been increased for various reasons such as cloud computing, big-data analysis, multimedia service, etc. With public interests on data center, many novel technologies for data center networks have been proposed and deployed to support data center operations more efficiently and effectively. However, the construction of data center network incurs significant costs. Moreover, various technologies interplay each other to achieve multiple objectives, and it makes difficult to validate and/or verify characteristics of data center network. In addition, it difficult to perform experiments with a number of hosts and switches. Therefore, it is necessary to observe the characteristics of target data center network before building it. A common approach to evaluate data center is to run simulations that should be similar with real-world data center environment. However, generating traffic with the characteristics of data center networks is not matured yet. People still employ a traffic generator based on the characteristics of Internet traffic. We design a traffic generator that shows more accurate characteristics of data center network traffic. Various traffic characteristics exploited explored by several studies are considered. The proposed method generates flow-level network traffic matrix based on Poisson Shot-Noise model. We implemented the traffic generator using Python programming language to create traffic matrix. To evaluate the proposed method, we compare the results with real data center network traffic. Our results show that the generated traffic owns similar characteristics with the real network traffic in terms of flow size, duration, and the mean and variance of total traffic rate.

Voice-over-LTE (VoLTE) is a VoIP-based multimedia service which is provided using All-IP based LTE networks. VoLTE service was first commercialized by Korean telcos in 2012, and now more and more telcos are trying to adopt this technology. With the increased VoLTE service popularity, it is inevitable to have large VoLTE traffic volume (possibly degrading the service quality) and the potential attacks (possibly degrading the service reliability and availability) in the near future. Therefore, in order to avoid such potential issues, we need to perform thorough analysis on VoLTE traffic. As a first step, we propose a VoLTE traffic classification method and its distributed architecture. As the proposed classification method relies on Deep Packet Inspection (DPI) technique, it severely suffers from the large processing time and scalability issues. To overcome these issues, we further propose a distributed architecture for VoLTE traffic classification by adopting a high throughput computing framework — HTCondor. We performed a set of experiments using real-world traces captured from a commercial LTE core network, and have shown that with the proposed architecture, we can achieve up to 23.869 Gbps classification throughput which was almost 35 times faster than the system without distributed processing.

With the increased popularity of mobile data applications, Wi-Fi power consumption on smartphones is now a significant portion of mobile energy expenditure. In their efforts to develop more energy efficient applications, the applications developers use energy estimation tools as a benchmark. Although hardware based power meter has high estimation accuracy, it is cumbersome to operate as it relies on physically attaching wires to the battery, and moreover the price of hardware based power meter is expensive. Therefore software based power estimation tools such as PowerTutor are popular. In our prior research using PowerTutor as power meter, we discovered that PowerTutor has large Wi-Fi power estimation error (over 1000%) on post 2012 phones. In this work, we propose a new FSM-based Wi-Fi power model based on IEEE 802.11 communication patterns and Wi-Fi hardware configuration with significantly increased power estimation accuracy. We designed and implemented our power model as an estimation tool called PowerGuide. Our experiments with PowerGuide in field operations showed that PowerGuide can achieve an average estimation accuracy of 86% compared to hardware power meters even with moderate polling period.

Cloud computing is a popular computing paradigm which provides the virtual resource as a form of VM to customers in an on demand manner. Existing provisioning solutions were only limited to provision computing resource (e.g., CPU, RAM and etc.), therefore, it was difficult to provide more advanced cloud services. Virtual Data Center (VDC) embedding, which is known as mapping the VDC resources to their physical counterparts, was recently introduced to provide more advanced cloud services. However, existing VDC embedding solutions were mostly focus on consolidating VMs in single physical data center. Therefore, in this work, 1) we expand the consolidated targets from VMs to network fabrics (e.g., paths and switches); 2) we also consider the VDC embedding problem in multiple physical data centers. In former point, a Traffic Engineering (TE) technique is required to realize the network fabrics consolidation. While, in latter point, a multi-data center VM live migration technique is required to realize the VM migration across the different data centers. SDN, which is a new network paradigm, can fulfill the requirements raised in above two points by providing flow-level virtualization and network address virtualization. With the help of SDN, we propose a Sdn Assisted Vdc Embedding solution - SAVE by consolidating network fabrics along with physical hosts. SAVE includes two VDC embedding algorithms with a TE heuristic, and a holistic system architecture is provided to realize the proposed algorithms. Our experiment results show the feasibility of the SAVE system architecture, and the effectiveness of the proposed algorithms.

As the current Internet architecture is suffering from scalability issues, the network research community has proposed alternative designs for the Internet architecture. Among those solutions, the Locator/Identifier Separation Protocol (LISP) has been considered as the most promising solution due to its incrementally deployable feature. Despite of various advantages provided by LISP, many ISPs are still conservative to adopt LISP into their production network due to the fact that the standard LISP does not fully satisfy ISP's requirements on LISP-enabled services. In this paper, we propose LISP controller, a centralized LISP management system. By using the LISP controller, we evaluate ISP's three representative LISP use cases: traffic engineering, VM live migration and vertical handover. The results show that the proposed LISP controller allows an ISP to control and manage its LISP-enabled services while satisfying ISP's requirements.

Today's Data Center Networks (DCNs) contain tens of thousands of hosts with significant bandwidth requirements as the needs for cloud computing, multimedia contents, and big data analysis are increasing. However, the existing DCN technologies accompany the following two problems. First, power consumptions of a DCN is constant regardless of the utilization of network resources. Second, due to a static routing scheme, a few links in DCNs are experiencing congestions while other majority links are being underutilized. To overcome these limitations of the current DCNs, we propose a Software Defined Networking (SDN)-based Traffic Engineering (TE), which consists of optimal topology composition and traffic load balancing. We can reduce the power consumptions of the DCN by turning off links and switches that are not included in the optimal subset topology. To diminish network congestions, the traffic load balancing distributes ever-changing traffic demands over the found optimal subset topology. Simulation results revealed that the proposed SDN-based TE approach can reduce power consumptions of a DCN about 41% and Maximum Link Utilization (MLU) about 60% on average in comparison with a static routing scheme.

With the emergence of the Long Term Evolution (LTE) technology, LTE based VoIP services are gaining more and more popularity in these days. Voice over LTE (VoLTE) is a VoIP based multimedia service on All-IP based LTE network, which was initially introduced and commercialized by Korea telecommunication companies in 2012. As more and more subscribers use VoLTE service, the VoLTE traffic volume increases, and this may in turn lower down the VoLTE quality; therefore to preserve QoS of VoLTE service, we need to periodically monitor VoLTE traffic among LTE traffic, and perform appropriate actions to LTE network. To be the first step of realizing this goal, in this paper, we propose a VoLTE traffic classification method and its distributed processing architecture to achieve high throughput. For the classification, we analyze SIP/SDP packets for VoLTE service and generate VoLTE tailored SIP packet signature using SIP User-Agent header field. Since the proposed classification method is relying on Deep Packet Inspection (DPI) method, the classification accuracy can be guaranteed, yet it suffers from the poor processing performance. Therefore, we further propose a distributed and parallel traffic processing architecture with the help of HTCondor.

In this paper we propose a data center traffic engineering method based on multi-objective optimization. We define a problem of traffic engineering in data center networks as a multi-objective optimization problem with two contrary objectives: load balancing and energy saving. Traffic engineering models for load balancing and energy saving are represented as a linear programming equation. Our simulation results show that the proposed method enables data center operators to change the relative importance between load balancing and energy saving. We also show the traffic engineering results with the predefined upper bound of maximum link utilization and energy cost in response to service requirements and energy OPEX. The contributions of proposed traffic engineering method are (a) to minimize both maximum link utilization and energy cost simultaneously; (b) to minimize maximum link utilization with upper bound of energy cost; and (c) to minimize energy cost with upper bound of maximum link utilization.

Wi-Fi is a popular wireless communication technology for smart devices such as smartphones, however, Wi-Fi related energy consumption in smartphones contributes to a significant portion of its energy expenditure. Hence it is important to carefully analyze and profile Wi-Fi energy expenditure in order to improve mobile applications' energy efficiency. Although hardware based power meters provide very accurate power measurement result, it is infeasible to expect mobile application developers to rely on power meters. As alternative solutions, software based power estimation tools are popular. Most of the existing power estimation solutions to date do not provide accurate estimation result, as we disclose in this paper by analyzing the popular PowerTutor. Therefore, we propose a new Wi-Fi power model by taking into account important IEEE 802.11 communication patterns as well as Wi-Fi hardware settings in a way that increases the accuracy of power estimation. We design and implement the proposed power model as a smartphone application and deploy it into a real device for validation. The evaluation results show that our solution can achieve an estimation accuracy up to 86% compared to hardware power meters, and is much more accurate than PowerTutor.

The number of data centers deployed by governments, enterprises, and universities has been increased affected by the development of cloud computing technologies to reduce CAPAX and OPEX. Many architectures or topologies for data center networks have been proposed to address the diverse purposes and requirements. However, the construction of data centers incurs significant costs. Moreover, there are many technologies that can affect the structure of the data center. Before building a data center, it must be confirmed that it possesses the characteristics necessary to satisfy requirements. Efficient ways to find and confirm network characteristics include simulation and tests using a traffic generation method. Our proposed method is designed to generate network traffic that address many characteristics of data center networks explored by several studies. The proposed method generates network traffic utilizing flow-level traffic matrix, not directly generates packets. We used Python programming language to create traffic matrix and iPerf to generate network packets. To evaluate it, we compared the generation results to real network traffic collected from a data center network. The result shows that the generated traffic is similar with the real network traffic.

With the emergence of significant amounts of mobile and cloud services, the scale of Data Center Networks (DCNs) has been growing rapidly. A DCN has different network requirements compared to a traditional Internet Protocol (IP) network, and the existing Ethernet/IP style protocols constrain the DCNs scalability and its manageability. In this paper, we present a scalable failover method for large scale DCNs. Because most of the current DCNs are managed in a logically centralized manner with a specialized topology and growth model, we adopt Fat-Tree [1] as the reference DCN topology and design our failover method using an OpenFlow-based approach. Further, to provide scalability, we design our failover algorithm in a local optimal manner, with which only three switches must be modified for handling a single fault, regardless of the size of the target network. We evaluate our failover method in terms of failover time by varying the network size and load balancing capability during failover. The experiment results show that our method scales well, even for a large-scale DCN with more than ten thousands hosts.

Data communication is a significant component of smart device energy consumption today, and is likely to increase with the rising connectivity demands of today's mobile applications. Wi-Fi is a major supporting technology and as such, efficient energy management solutions are much needed. In this paper, we detail the Wi-Fi energy consumption characteristics and propose two energy saving schemes: packing and alignment, implementable at the application layer. Our investigations also reveal that these schemes are highly reliant on the network metric - available bandwidth, which is not readily obtainable on smartphones and existing wired solutions are ill-suited due to the dynamics of the wireless environment, as well as the energy constraint of mobile devices. Therefore, we propose a new energy efficient bandwidth measurement tool called BreezChirp. As validation, we implemented both BreezChirp and the Wi-Fi management schemes on modern Andriod smartphones and evaluated their performance through field experiments.

Recently, the number of Internet applications available for use on both desktop computers and smartphones has rapidly increased. The Internet traffic generated by these applications has increased significantly as well. Network operators are required to be aware of the status of managed networks in terms of application usage. However, the application traffic observed in a network is dependent on users and the geographical location of the network. As a result, selecting applications that are expected to appear frequently in the network is required as the initial step in the generation of ground-truth traffic and extraction of classifiers. In this paper, we propose a traffic identification methodology for the initial step of the classifier generation procedure. The proposed approach is based on remote subnet grouping, which refers to the collection of the same (or similar) application traffic. We also validate the proposed methodology in terms of completeness, feasibility, and accuracy by using the traffic in a campus network.

In managing multimedia services, it is important to understand how network performance affects user experience. The model presented in this paper aims to estimate user perception of video quality based on defect events, which are automatically classified by machine learning techniques. The underlying principle of our model is that human experience is event-based and there is a strong correlation between defective events and user MOS. Through experiments, we show that our model can detect different types of defect events with good accuracy even under small data set, and we find that indeed different defect event types affect user experience with different sensitivity.

This paper proposes a proactive security system to forecast Distributed Denial of Service (DDoS) attacks. A reactive system focused on detection after network attacks occur has difficulties responding rapidly to massive distributed attacks, such as DDoS. By forecasting the attack, we can take active countermeasures such as strengthening the power of the security devices and it would also enable us to plan a recovery procedure and countermeasures beforehand, providing a more rapid response. In this paper, we discuss previous studies related to intrusion forecasting, define the concept of intrusion forecasting and propose the Internet Intrusion Forecasting System Architecture. To obtain intrusion factors for DDoS attack forecasts, Honeynet was deployed and we analyze Hflow data gathered from Honeynet.

Vast majority of the services running on the smart phone today are networked in that significant amount of communication is required. Smart phone energy expenditure due to Wi-Fi communications constitutes significant portion of the battery discharge. In this paper, we first investigate the key factors influencing Wi-Fi energy consumption, and propose three energy management schemes: 1) Dynamic control of Wi-Fi on/off interface; 2) improve communication efficiency via application packing; and 3) elongation of Wi-Fi Power Save Mode (PSM) via application alignment under mixed application workload. We also design and test our solution as a device-side application utilizing general system process scheduling and network firewall techniques. As the result, our solution is easy to deploy, applicable to most mobile devices, and we explicitly tackle the challenging case of download management. Through extensive experimentation and solution prototyping, we show the effectiveness of device side Wi-Fi energy management and the importance of considering application characteristics.

Large and various amounts of context data related to a user's environment are available from different domains including mobile devices, smarthomes, wearable sensors, and social networking services. These context domains are interconnected and the context data from them can be shared thanks to mobile, pervasive, convergent, and ubiquitous technologies. We can provide user-centric context-aware services by aggregating and associating the diverse types of context data distributed over multiple domains around a user. However, state-of-the-art research efforts have been devoted to managing context only in a single domain. In this paper, for user-centric context management, we propose 1) a flexible and extensible technology-neutral information model that can represent generic concepts of context; 2) a hierarchical context management architecture that can understand and manage complex interactions among multiple contextual entities.

We propose a novel approach to detect anomalous network traffic by analyzing communication patterns in time series. The method is based on graph theory concepts such as degree distribution and maximum degree, and we introduce the new concept of dK-2 distance [1]. In our approach, we use traffic dispersion graphs (TDGs) to extract communication structure [2]. By analyzing differences of TDG graphs in time series we are able to detect anomalous events such as botnet command and control communications, which cannot be identified by using volume-based approaches or flows/packets counters. We evaluate our approach with the 1999 DARPA intrusion detection data set and the network trace from POSTECH on July 2009.

This paper proposes an automated classifier generation system for application-level mobile traffic identification. The proposed system comprises traffic classifier generation system architecture and mobile traffic measurement agents (mTMAs) that are installed on mobile devices to monitor their application-level traffic. To generate classifiers, our adaptive algorithm selects the lowest cost classifiers from among the available classifier candidates, while assuring acceptable identification accuracy. To verify the efficacy of the proposed identification system, we implemented and deployed it on a campus network. The results of mobile application traffic identification obtained using the system in this deployment are also presented in this paper.

Recently, several mobile services are changing to cloud-based mobile services with richer communications and higher flexibility. We present a new mobile cloud infrastructure that combines mobile devices and cloud services. This new infrastructure provides virtual mobile instances through cloud computing. To commercialize new services with this infrastructure, service providers should be aware of security issues. In this paper, we first define new mobile cloud services through mobile cloud infrastructure and discuss possible security threats through the use of several service scenarios. Then, we propose a methodology and architecture for detecting abnormal behavior through the monitoring of both host and network data. To validate our methodology, we injected malicious programs into our mobile cloud test bed and used a machine learning algorithm to detect the abnormal behavior that arose from these programs.

This paper presents an efficient fault management approach based on cognitive control loops in order to support autonomic network management for the Future Internet. The cognitive control loops determines urgency of network alarms, processes urgent alarms more quickly, and then infers root causes of the problems based on learning and reasoning. We show that we reduce a number of alarms by correlation and detect alarm priorities using an ontology model based on the policy.

Multimedia services have become a dominant part of the network and content provider's service portfolio. A formal modeling methodology for video quality assessment not only affords the providers a clear cause-effect management view of their services, but also helps to guide management and planning operations. We examine the relation between network performance and perceived video quality through VIDAR, a comprehensive VIDeo quality Analyzer in Real-time. VIDAR links network performance to objective frame quality (eSSIM), and modify eSSIM values by subjective filters. Through experiments, we show that VIDAR helps providers to better understand and assess the impact of the network performance on perceived video quality.

IP prefix hijacking is a well-known security threat that corrupts Internet routing tables and has some common characteristics such as MOAS conflicts and invalid routes in BGP messages. We propose a simple but effective IP prefix hijacking detection method which is based on reachability monitoring. Network reachability means a characteristic that a packet must reach the destination network although the network path is changed due to routing instability. However, when IP prefix hijacking occurs, the traffic sent to victim network does not reach the intended destination but is delivered to attacker network. By identifying the characteristics of the destination network such as network fingerprints, we can know whether the traffic reach the correct destination. In this paper, we present the method of collecting network fingerprints for verifying destination reachability and also propose an IP prefix hijacking detection method using the collected fingerprints. The IP prefix hijacking detection method based on network reachability is effective and useful, which uses a simple active probing and denotes a present network condition.

Recently, mobile traffic has increased tremendously due to the deployment of smart devices such as smartphones and smart tablets. These devices use various types of access networks such as 3G, WiFi, and mobile WiMAX. Network service providers also provide these access networks with various types of plans. There is a growing need to manage these smart devices and mobile networks. However, research on mobile network management has focused on the performance of the network itself. Few research has focused on applying the usage patterns of smartphone users to mobile network management. In this paper, we present an analysis of smartphone usage patterns. We define the five possible states of a smartphone based on such a phone's basic operations. We collected real usage log data from real smartphone users over a two month period. We show that all users have their own usage pattern. We present a case study in order to show how to apply usage pattern information to power management of smartphones. We also discuss how to apply such information to mobile device management and network management.

Machine to Machine (M2M) technology has the potential to increase the revenue, decrease the costs and improve the customer services of an organization. We have analyzed the management requirements of M2M systems, which are based on existing M2M network use cases and services. The most important characteristics including sleeping devices, low power lossy area networks, heterogeneous networks, device intelligence, mobility, two way communication, network dynamics, time sensitivity of data and data volume of M2M systems have been comprehensively investigated and reflected in management requirements discussed in this paper. The main management functionalities are fault, configuration, mobility, QoS and security management.

Wireless Body Area Networks (WBANs) are mainly characterized by deployment of biomedical sensors around human body which transmit vital signs measurements about healthy status to the coordinator. Depending on the relevance between symptoms and diseases, it may not be necessary for every sensor to transmit its measurements for diagnoses. This paper shows how the relevance can be exploited on the Medium Access Control (MAC) layer by utilizing the mutual information. A theoretical framework is developed for sensor scheduling under an operation cost constraint. It is shown that the compact subset of sensors can be found to provide necessary information for timely and correct diagnoses. Based on the theoretical framework, an algorithm combining sensor selection and information gain is then designed. Simulation results show that the algorithm achieves high performance in terms of energy, latency and collision rate.

Peer-to-peer (P2P) systems have many important advantages. However, most existing P2P systems are limited to providing resource searches based on simple keyword matching, and do not provide any semantic information about the content of the objects stored and the relationships between those objects. This paper proposes the design of a hierarchical semantic overlay network that can be used for content-based full-text search, and is part of our work to use semantics in network management. Our semantic overlay network is based on creating a semantic cluster of objects that is associated with each node in the P2P DHT to provide semantic search. We validate some research questions in our approach by conducting simulations.

Since the IEEE 802.16 first standard was proposed in 2004 to provide broadband wireless service, the standard has not only been widely studied, but also broadly commercialized. The current IEEE 802.16-2009 standard document specifies five Quality of Service classes. As is typical with most standards, IEEE 802.16 does not require the use of a specific scheduler. In this paper, we first evaluate the performance of four popular schedulers. By analyzing the results, we highlight that no single scheduler type performs the best in all traffic situations; however, we shown that there exist the most favorable scheduler type in each situation. Based on this rationale, our idea is to propose an adaptive scheduling schema where the scheduler is dynamically chosen based on the current traffic context, such as the number of flows of each Quality of Service class. We investigate this approach and evaluate its performance against existing static schemas. The results show that our approach presents some interesting performances in terms of throughput, delay, and packet loss ratio regarding state of art approaches.

IPTV is emerging as a new service that can be very lucrative for telecommunication service providers. In addition, work is progressing to make IPTV an entertainment platform that can replace traditional TV for customers. Guaranteeing service quality is one of the most important factors to make IPTV services successful. For this, an IPTV service provider and a customer make a contract that uses specific quality indicators in a Service Level Agreement (SLA). To efficiently manage and guarantee SLAs, a method is required that can manage all hierarchical performance indicators from raw device level performance indicators to key quality indicators. In this paper, we analyze various IPTV performance indicators from various standard organizations and suggest an IPTV performance indicator hierarchy by extending the DEN-ng information model. Then, we propose an architecture that uses an ontology and Semantic Web Rule Language to manage SLAs, and more specifically, to detect SLA violations. The proposed architecture is implemented and tested using a simple scenario. The test results showed a possibility that the architecture can be used for detecting SLA violations.

In this paper, we focus on the problem of constructing an information gain model for stroke prevention in Ubiquitous Healthcare (U-Health) Wireless Body Area Networks (WBANs). We have constructed an information-based probabilistic relation model among the key indicators and sequenced their data gathering priority and precedence in the WBAN. Then, we constructed a cost function over the energy expenditure involved in their data gathering, and expressed the relationship between utility gain and energy loss as a constrained optimization problem. We also designed an algorithm to carry out the proposed approach. Through simulation study, we demonstrated the validity of some aspects of the approach.

In this paper, we present demand-side energy management under real-time demand-response pricing as a task scheduling problem which is NP-hard. Using minmax as the objective, we show that the schedule produced by our minMax scheduling algorithm has a number of salient advantages: significant peak-shaving, cost reduction, and risk-aversion for the consumers. We prove that our algorithm finds near-optimal solutions and our simulation study show that the actual performance is better than the worst-case bound. The algorithm is simple to implement and efficient at the scale of large enterprises.

Application level traffic classification is one of the major issues in network monitoring and traffic engineering. In our previous study, we proposed a new traffic classification method that utilizes a flow similarity function based on Cosine Similarity. This paper compares the classification accuracy of three similarity metrics, Jaccard Similarity, Cosine Similarity, and Gaussian Radius Based Function, to select appropriate similarity metrics for application traffic classification. This paper also defines a new two-stage traffic classification algorithm that can guarantee high classification accuracy even under an asymmetric routing environment, with reasonable complexity.

The current Internet, while successful in many aspects, has a set of associated architectural and business problems. This article discusses problems of the Internet and presents challenges of the future Internet, with a particular focus on manageability. This paper advocates that the data, control, and management planes should be separated; this provides more granular path control selection for traffic differentiation, load balancing, and other issues. We perform experiments based on an exemplar monitoring scenario, comparing the benefits of path control resulting from plane separation to current network monitoring methods.

Network management has suffered from increases in business, system, and operational complexity. This has been exacerbated by the heterogeneity in management data as well as the high quality requirements of multimedia services. Autonomic networking manages this growing complexity by adding intelligence inside network nodes and network management applications. While most autonomic applications simply use a control loop to monitor and configure entities, our work is aimed at building a self-governing network that is able to fulfill the requirements of current and future services. This means that management applications need a detailed and dynamic view of the contextual status of the network nodes as a whole in order to adapt their behaviour to changing context. In this paper, we propose an algorithm to semi-automatically generate filter rules based on existing information in a network management information model. These filter rules are used to determine the set of contextual data that needs to be exchanged with other nodes. The algorithm exploits the reasoning capabilities of ontologies and relies on the introduction of additional semantic relationships to achieve a fine-grained context exchange model. Large scale evaluations were conducted to characterise the performance of this ontological approach.

With the increase of the aging society worldwide, hospitals, medical practitioners and health insurers are now increasingly seeking ways to reduce the cost of healthcare while maitaining its quality. One solution subject to attention from gouvernement and healthcare providers is the U-Health Smart Home that aims to provide non-intrusive and non-invasive monitoring and assistance to the elderly directly in their own home. At POSTECH, the U-Health Smart Home project is focused on building a smart home along with an autonomic system to monitor the home as well as the inhabitants to provide intelligent support and assistance in any situation at anytime. This paper presents our initial results from this project. The first contribution is a general framework for the U-Health smart home and the second one is an initial semantic model that can be used in the autonomic system to provide autonomic support to the elderly.

Industrial process control IP networks support communications between process control applications and devices. Communication faults in any stage of these control networks can cause delays or even shutdown of the entire manufacturing process. The current process of detecting and diagnosing communication faults is mostly manual, cumbersome, and inefficient. Detecting early symptoms of potential problems is very important but automated solutions do not yet exist. Our research goal is to automate the process of detecting and diagnosing the communication faults as well as to prevent problems by detecting early symptoms of potential problems. To achieve our goal, we have first investigated real-world fault cases and summarized control network failures. We have also defined network metrics and their alarm conditions to detect early symptoms for communication failures between process control servers and devices. In particular, we leverage data mining techniques to train the system to learn the rules of network faults in control networks and our testing results show that these rules are very effective. In our earlier work, we presented a design of a process control network monitoring and fault diagnosis system. In this paper, we focus on how the fault detection part of this system can be improved using data mining techniques.

Traditionally, Internet applications have been identified by using predefined well-known ports with questionable accuracy. An alternative approach, application-layer signature mapping, involves the exhaustive search of reliable signatures but with more promising accuracy. With a prior protocol knowledge, the signature generation can guarantee a high accuracy. As more applications use proprietary protocols, it becomes increasingly difficult to obtain an accurate signature while avoiding time-consuming and manual signature generation process. This paper proposes an automated approach for generating application-level signature, the LASER algorithm, that does not need to be preceded by an analysis of application protocols. We show that our approach is as accurate and efficient as the approach that uses preceding application protocol analysis.

To reduce frequent changes and upgrades of management systems, we need a guideline of OSS's architecture and development methods of the OSSs. TMF has proposed NGOSS technology-neutral architecture (TNA) which describes major concepts and architectural details of the NGOSS architecture in a technologically neutral manner. The NGOSS TNA can be mapped onto appropriate technology-specific architectures (TSAs) using specific technologies such as XML, Java and CORBA Web services, which is a distributed and services-oriented computing technology, can be applied to NGOSS TSA. In this paper, we examine the architectural requirements of TNA, and provide a design of Web services- based TSA in accordance with the TNA requirements.

Traditional telecommunications service providers are undergoing a transition to a shared infrastructure in which multiple services will be delivered by peer and server computers interconnected by IP networks. IP transport networks that can transfer packets according to differentiated levels of QoS, availability and price are a key element to generating revenue through a rich offering of services. Automated service and network management are essential to creating and maintaining a flexible and agile service delivery infrastructure that also has much lower operations expense than existing systems. In this paper we focus on the SLA-based IP packet transport service on a core network infrastructure and we argue that the above requirements can be met by a self-management system based on autonomic computing and virtual network concepts. We present a control and management system based on this approach.

Today, we are witnessing more distributed systems on enterprise networks and on the Internet. In general, a distributed system is composed of many subsystems. It is difficult to effectively manage the configuration information of distributed systems because they may be deployed with different software components and run on heterogeneous computing platforms. In addition, the configuration information of a subsystem has complex relations with the information of other subsystems, so it is difficult to provide automatic reconfiguration of related subsystems. To overcome the difficulties, we propose a management information model that considers the relations among subsystems and the Simple Object Access Protocol (SOAP) as a communication method. This paper presents the design and implementation of X-CONF (XML-based configuration management system) for a distributed system. For validation, we have developed the X-CONF for NG-MON, which is a distributed and real-time Internet traffic monitoring and analysis system.

One recent trend in network security attacks is an increasing number of indirect attacks which influence network traffic negatively, instead of directly entering a system and damaging it. In future, damages from this type of attack are expected to become more serious. In addition, the bandwidth consumption by these attacks influences the entire network performance. This paper presents an abnormal network traffic detecting method and a system prototype. By aggregating packets that belong to the identical flow, we can reduce processing overhead in the system. We suggest a detecting algorithm using changes in traffic patterns that appear during attacks. This algorithm can detect even mutant attacks that use a new port number or changed payload, while signature-based systems are not capable of detecting these types of attacks. Furthermore, the proposed algorithm can identify attacks that cannot be detected by examining only single packet information.

Embedded Web servers are widely used today for IP-based element management. We present a new management architecture that combines this technology with XML, DOM, and XPath to unify element management and network management. XML is used for both management information modeling and manager-agent communication. By taking advantage of modern Web technologies, the proposed architecture provides a method to develop management applications efficiently and to manage network devices effectively. We also explain how legacy SNMP agents are integrated into our proposed architecture.

To cope with the explosive increase in the number of requests to Internet server systems, one popular solution is a load-balancing technique that uses a dispatcher in the front-end of a cluster farm. A cluster group is viewed as a single system image with very high performance and also gives a good scalability. But a failure in any single host in a cluster group can cause an overall system failure. The high availability in a cluster group is desperately needed for stable and fault-tolerant service to clients. So it is necessary to develop a cluster management system that integrates all these cluster functions and user-friendly management functions. We present the design and implementation of a load cluster management system (LCMS) based on SNMP and Web technology. Our LCMS implementation has been deployed on a commercial ultra dense server like an EnterFLEX. First we examine the requirements of LCMS to provide efficient and stable management operations and high availability. Our LCMS follows the client-server management paradigm of SNMP, and consists of three managers having different roles, which distribute management functionality to all hosts in a cluster group. By using SNMP we can reduce the network bandwidth required in management operations. This system also provides automatic cluster configuration and current status monitoring of each host in a cluster group through a Java and Web technologies.

An embedded Web server (EWS) is a Web server that runs on an embedded system with limited computing resources and serves embedded Web documents to a Web browser. By embedding a Web server into a network device, it is possible for an EWS to provide a powerful Web-based management user interface constructed using HTML, graphics and other features common to Web browsers. When applied to embedded systems, Web technologies offer graphical user interfaces which are user-friendly, inexpensive, cross-platform, and network-ready. This paper explores the topic of an efficient and lightweight embedded Web server for Web-based network element management. We present the architecture of an embedded Web server that can provide a simple but powerful API. We also present the design and implementation of POS-EWS, which is an embedded Web server that we have developed for Web-based network element management. As well, we present the results of POS-EWS's performance evaluation and EWS optimization methods in a commercial Internet router.

In this paper we introduce interface mechanisms for use between embedded management applications and embedded Web servers, and provide a guideline for choosing an efficient interface mechanism. Also we provide effective integration mechanisms for each interface mechanism for the sake of cost-effective development of Web-based network element management.

Electronic mail service is one of the most essential and well-known Internet/Intranet application services. Users expect their electronic mail service to be reliable and efficient, while administrators demand reliable and efficient tools to satisfy users' expectations. The mail server management system is a good solution for such requirements from different user groups. We have designed and implemented an SNMP manager and SNMP agent system that can manage Internet/Intranet mail server systems. The system has been integrated with WWW technology such as Java and common gateway interface (CGI). The Java-based GUI system enables human users to manage mail server systems from anywhere with friendly, easy-to-use Web browser interfaces. The system architecture is also general enough so that it can be easily extended to manage any other Internet/Intranet application services.

Local number portability (LNP) is an intelligent network (IN) service, which provides end users the ability to change local telephone service providers without changing their telephone numbers. LNP is a key service for increasing competition in the local telephone marketplace. To implement LNP, a number portability administration center (NPAC) is needed to manage the LNP databases. Service providers also must implement the carrier-level system and update their existing IN components to provide LNP. In this paper, we present our work on applying the TMN and CORBA technology to the service management of the intelligent network, particularly the LNP. We propose a TMN-based LNP system architecture and present a design and implementation of a NPAC service management system using CORBA.

This paper proposes a TMN-based integrated network management system architecture using WWW and CORBA technologies. WWW technology enables human users to be free from complicated system interfaces and CORBA technology enables developers to create and add distributed management components easily. Our work focuses on developing a TMN-based manager system using CORBA and the Web, and attempts to achieve integration of SNMP/CMIP-based managed systems through gateways. Our management system architecture provides generalized TMN management interfaces on the protocol-oriented gateway systems. Using the generalized interfaces, the TMN management functionality can be easily added to provide complicated TMN management services to users via WWW browsers.

In this paper, we present a framework for managing ATM customer networks using WWW and CORBA technologies. The WWW technology may provide network management operators with platform independence, location independence, friendly and/or consistent management behavior as well as secure management operations. The main problems in a Web-based network management system may include limited management capabilities of HTTP operations and lack of supporting mechanisms for trap or event notifications. These problems are, in this paper, solved by the integration of Java and CORBA technologies. We extend the current Web technology which is being used for transferring various information on Internet to those which can be used to transfer management requests and replies as well as event notifications. A Web browser is used as a universal user interface for monitoring and controlling the activities of the ATM customer networks. We have developed an integrated customer network management system which can manage SNMP-enabled or CMIP-enabled network devices and provide ATM CNM services. In particular, we applied the CORBA technology to accommodate several management protocols such as SNMP, CMIP and other management protocols. Through the CORBA IDL-to-Java mapping, complex management operations as well as event notifications are supported without modification of the current WWW systems.

As high-speed, broadband networks replace slower, narrowband networks, multimedia applications are more widely used today than ever before. In this paper, we propose an object-oriented modeling of services in a distributed multimedia system that can support a wide variety of distributed multimedia applications. The distributed multimedia services include naming service, session service, multimedia storage/retrieval service, system and applications management service as well as multimedia communication service. Using the services, one can easily develop, operate and manage multimedia applications. Particularly, our system incorporates management service to multimedia services and applications so that they can be easily monitored and controlled during the operation.

In a telecommunication management network (TMN), the interworking of manager and agent needs to exchange and process management information which is defined as the shared management knowledge (SMK) in the ITU-T Recommendation M.3010. The SMK includes information on the protocol knowledge, management functions, managed object classes and their instances and authorized capabilities. We examine in detail the design issues in developing an SMK system for supporting management systems. We present a design of a CORBA-based SMK system including the procedures of obtaining the SMK information from the management information base (MIB) and of the SMK context negotiations. Finally, our effort on the prototype implementation of an SMK system using ORBeline and OSIMIS is presented.

The X.500 Directory Service provides a powerful mechanism for storing and retrieving information about objects in a distributed computing environment. This requires the functional components of the Directory Service to have knowledge of the structure and representation, or schema, of the information held within the directory. The management of the Directory Schema is a subject requiring further research and development. We identify the three major technical elements required for properly managing the Directory Schema. We then focus on one of these elements; propagation of the schema between functional components of the Directory Service. Three subproblems from within schema propagation are presented, along with several alternative solutions.>