Large language model (LLM) services increasingly rely on distributed inference across multiple GPU servers to sustain concurrent requests under limited compute, memory, and bandwidth resources. In such settings, a partitioned LLM can be represented as an inference chain (InFC), where the deployment decision determines both the sustainable concurrency ceiling (SCC) on the revenue side and the memory and communication overhead on the cost side. This paper studies the profit-aware inference chain deployment (InFCD) problem in heterogeneous data center networks. We show that increasing the InFC length does not monotonically improve profit: finer partitioning can relieve per-GPU resource bottlenecks and improve SCC, but may also increase deployment spread, inference path length, and internal traffic. To capture this tradeoff, we formulate profit-aware InFCD by jointly modeling static-weight vRAM occupation, per-user KV-cache occupation, user-side traffic, internal boundary traffic, and resource-coupled SCC, and prove its NP-hardness. We then propose the Maximum Sub-module Deployment Gain (MSDG) score and design an MSDG-based greedy algorithm. Theoretical analysis characterizes its online complexity and establishes a conditional positive-profit preservation property. Simulations show that MSDG improves total profit over SCC-oriented, cost-oriented, and local-profit-oriented baselines, characterize empirical optimality gaps and SLO sensitivity.

Deploying ultra-reliable and low-latency communication (URLLC) service function chains (SFCs) is imperative for applications demanding stringent latency and reliability performances. In these applications, ensuring uninterrupted service hinges on establishing fault-disjoint primary and backup service function paths (SFPs). However, existing techniques for deploying SFCs fall short in optimizing latency differentials between the primary and backup SFPs, posing risks of service disruptions in critical URLLC applications like remote surgery, smart factory, and unmanned vehicle systems. In this work, we investigate pioneering techniques to efficiently optimize the primary and backup SFP latencies while minimizing their differentials. We formally formulate the problem of ultra-reliable and low-latency SFC deployment (URLLC-SD) and show its NPhardness. We develop an innovative algorithm, the Yen-based SFP Identification in Layered Graph (YANG), which optimizes the equal-weight composite latency objectives with symmetric QoS/SLA for primary and backup SFPs at the expense of runtime complexity. Through extensive simulations, we demonstrate the YANG's superiority, surpassing state-of-the-art benchmarks. In particular, YANG achieves the highest acceptance rates under specific constraints on SFP latency and latency differentials. Furthermore, our analysis reveals interesting insights into the selection of good path candidates to optimize URLLC-SD.

The model compression techniques enable deploying compressed large language models (CMs) at network edge, facilitating convenient provision of AI-generated content (AIGC) services. To ensure timely delivery of these services, efficient placement of CMs across resource-constrained edge networks is essential. In this work, we investigate how to obtain latency-efficient CM placement across resource-constrained edge networks. With the objective of service latency optimization, we formulate the CM placement in resource-constrained network (CPRN) problem and establish its NP-hardness. We propose the Migration-agent-based Deep Reinforcement Learning (M-DRL) approach, which incorporates a specially designed migration agent tailored for such placement problems. To enhance training efficiency, we incorporate efficient heuristic placement results into the environment of M-DRL, developing our Heuristic-guided M-DRL (HM-DRL) approach. Our extensive simulation results demonstrate that HM-DRL outperforms an extended benchmark in service latency, while maintaining a low training overhead.

Cloud-based large language models (LLMs) have gained widespread adoption among human users. However, when the users shift to Internet-enabled machines, centralized LLM systems often suffer from high latency and fail to provide timely responses. Deploying LLMs over edge networks presents a promising alternative, yet maintaining an up-to-date knowledge base to address the dynamic and time-sensitive demands of diverse machine users remains a significant challenge. Consequently, there is a pressing need for fast and cost-efficient LLM deployment strategies tailored to edge environments. In this work, we address the problem of deploying mixture-of-experts (MoE) LLMs in a runtime- and cost-efficient manner. We formally define the Expert Model Deployment in Edge Networks (EMD-EN) problem, aiming to minimize deployment costs. Leveraging the inherent modularity of MoE, we propose a novel Neighbor-First Centrality (NFC) metric to facilitate the placement of model components across edge nodes and design the NFC-based Mixture of Expert layer Deployment (NFC-MoED) algorithm. Our results show that NFC-MoED substantially improves runtime efficiency and maintains near-optimal deployment costs compared to the brute-force benchmark.

To support edge intelligence, knowledge distillation (KD) is widely employed to compress large language models (LLMs) into smaller, domain-specific student models. However, due to the limited generalization capabilities of student models, they may fail to provide accurate responses across diverse domains. In such cases, the teacher model serves as a complementary component, handling queries that exceed the scope of student models. In line with the KD paradigm, this work investigates a collaborative deployment framework in which multiple student models are distributed across the network edge to serve the majority of client requests. In contrast, a centralized teacher model addresses more complex or ambiguous queries. To begin, we formally define the Student Model Placement in Edge Networks (SMP-EN) problem, aiming to minimize total access costs. We prove that SMP-EN is NP-hard, and to address this challenge, we introduce an Access Cost Measure (ACM) that quantifies the expected access costs. Building upon this measure, we propose the ACM-based Student Model Placement (ACM-SMP) algorithm to determine student model placement efficiently. Extensive simulations show that ACM-SMP significantly reduces the average expected client access cost compared to benchmarks.

With its rapid development, AIGC applications have expanded to diverse generative content, including text, images, audio, and videos. To enhance the AIGC's output quality, prompt chains are proposed to structure the generation process. Owing to the prompt's data processing nature, one compromised prompt engineering-enabled server (PES) may propagate vulnerabilities across networks, leading to unintended content generation, system risks, and potential user trust issues. To mitigate these risks, this work adopts a zero-trust (ZT) security framework to protect inter-server communications. We define the problem of prompt chain deployment in ZT-enabled networks(PCD-ZT), with the objective of minimizing total service costs, encompassing both deployment and ZT verification costs. To address this, we introduce the verification-cost-balance (VCB) factor that helps reduce ZT verifications to save the overall service cost and accordingly propose an algorithm called sub-prompt-chain brand and bound (SCBB). Extensive simulations demonstrate that our SCBB achieves overall service cost reductions of 4.67 % and 13.89 %, and cuts verification costs by 11.86 % and 30.50 %, compared to the benchmarks extended from the state-of-the-art.

With the widespread adoption of large language models (LLMs) like GPT, user experiences in various interactive applications have significantly improved. However, reports from OpenAI highlight that GPT clients are now facing high response delays and frequent interruptions, particularly during peak usage hours, due to limited computation resources. This challenge is expected to escalate as machines are interacting with GPT models at higher frequencies, with greater data volumes, and over longer lifecycles. A promising solution is to deploy LLMs across edge networks to efficiently distribute the huge resource demands. This work presents the very first efforts at exploring how to cost-effectively deploy expert models from a mixture of experts (MoE) LLM within edge networks. We introduce the expert models deployment in edge networks (EMD-EN) problem, focusing on optimizing deployment costs. To address this, we propose a novel least cost gain (LCG) measure for selecting appropriate physical nodes to host expert models and present a corresponding LCG-based expert models deployment (LCGEMD) algorithm. Extensive simulations show that our approach outperforms the benchmarks by an average of 17.31% and 36.98% in terms of deployment cost reduction.

Recent advances in large language models (LLMs) have empowered robots and drones with autonomous decision-making capabilities. Due to the stringent real-time requirements of these applications, LLM inference must be performed at the network edge. However, hosting high-precision LLMs on a single edge server is often infeasible, creating challenges in efficiently distributing LLM deployments across edge networks. This work addresses these challenges by formulating and solving the profit maximization problem for distributed LLM inference deployment. We first formally define the Profit-Centric Inference Chain Deployment (PC-InCD) problem. To solve PC-InCD, we introduce a novel Local Maximal Profit (LMP) factor that enables effective edge server selection for hosting LLM sub-modules, and we propose the LMP-based Inference Chain Deployment (LMP-InCD) algorithm. Extensive simulations demonstrate that LMP-InCD significantly outperforms benchmark methods in maximizing profit across diverse network conditions.

Mixture of experts (MoE) has shown great potential in enhancing large language models, such as DeepSeek. In MoE, expert networks collaboratively process tokens routed by the gating network, enabling advanced capabilities such as semantic understanding, computational reasoning, and code generation. To address security threats such as DDoS in these dynamic environments, it is essential to implement tailored backup measures for these experts. However, traditional backup methods often lead to inefficiencies and excessive resource consumption. To overcome these challenges, we propose a novel approach for backing up experts in MoE. We formally define and mathematically model a new problem, termed the security-aware off-site pairwise protection (SOPP) problem, and prove its NP-hardness. To solve this problem, we develop three novel techniques of latency-aware MoE construction (LMC) to reduce backup latency, partitioned backup selection (PBS) to trade off security levels and resource consumption, as well as pairwise selective identifier (PSI) to determine the appropriate backup pairwise nodes. On the basis of these techniques, we propose an efficient heuristic algorithm called the off-site distributed pairwise nodes protection (OD-PNP), providing theoretical performance guarantees. Through extensive simulations and analyses, we demonstrate that our proposed algorithm outperforms state-of-the-art methods in terms of both protection efficiency and resource consumption.

Internet-connected devices generate service function chain (SFC) requests for reliability-sensitive applications such as smart factories and intelligent healthcare. To facilitate reliable SFC provisioning, one can employ dedicated SFC protection approaches to protect the primary service function path (SFP) by constructing a fault-disjointed backup SFP. Notably, the construction processes of the fault-disjointed primary and backup SFPs are interdependent, and the direct application of existing SFC embedding approaches to construct these SFPs by separate processes may not effectively optimize overall resource consumption. In this work, for the first time, we comprehensively study how to embed and protect an SFC through collaborative processes that have provable bounds. We formally define the problem of SFC embedding and protection (SFCEP), for which we develop the novel techniques of a backup SFP identifier (BSI) and resource-aware Bellman-Ford loop (RBL) to address the challenges posed by collaborative embedding and protection. On the basis of these techniques, we propose an efficient algorithm called optimal SFC embedding and protection (Opt-SEP). When the network resources are sufficient to accommodate an incoming SFC request, we prove that Opt-SEP can minimize the overall resource cost of creating a pair of fault-disjointed primary and backup SFPs. Moreover, for cases in which the network resources are limited, our extensive simulation results show that Opt-SEP significantly outperforms the benchmarks.

In the emerging applications of edge computing (e.g., unmanned factories and meta-verse), network requests are required to be securely and reliably delivered in the form of service function chains (SFCs). To enhance security, security-aware SFs are employed in the SFC, and this type of SFC is referred to as the security-aware SFC (S-SFC). For reliability, service providers can employ a dedicated backup SFC to protect the primary one. However, no existing works addressed the SFC deployment mechanisms that jointly consider SFC reliability and security. For this, here, we investigate the problem of jointly embedding and protecting a security-aware SFC. To efficiently compose, embed, and protect an S-SFC, we propose the S-SFC asymmetric protection concept, which allows the primary and backup SFCs not necessarily to follow an identical structure as the traditional SFC dedicated protection does. Next, we formulate the problem of S-SFC composing, embedding, and protection (S-SFCEP) and prove its NP-hardness. To tackle this problem, we formulate an efficient algorithm, namely, sub-chain-based S-SFC deployment (SCB-SD). Our extensive simulation results show that the proposed SCB-SD outperforms the state-of-the-art benchmarks by an average of 13.86% and 23.19%, respectively.

To provide dedicated protection in Network Function Virtualization (NFV), a reliability-aware Service Function Chain (SFC) can be deployed using two disjoint paths: a primary path and a backup path. In the event of network failures along the primary path, the working traffic is switched to the backup path to maintain service continuity. The process of accommodating reliability-aware SFCs is commonly referred to as SFC Deployment and Protection (SFCDP), which is proven to be NP-hard. In this work, we introduce a novel Network Service Deployment (NSD) transformer that can effectively incorporate and utilize fine-grained information regarding the network re-sources and the SFC requests. We develop a deep reinforcement learning framework based on NSD transformer (DRL-NSD) to effectively optimize the process of SFCDP. We conduct extensive simulations to validate the NSD transformer and compare the proposed NSD transformer with two benchmark neural network architectures. Our experimental results demonstrate that the NSD transformer outperforms the benchmarks across a variety of network topologies and network load settings.

Frequent cyber-attacks force the service provider to employ security-aware service functions (SFs) to accommodate client network requests. Thanks to virtualization techniques' maturity, a security-aware SF can be provided by diverse vendors with various configurations, each of which needs various implementation cost and provides different security levels. When a client's network request comes, the multi-configuration SFs could compose various security-aware service function chains (S-SFCs) to flexibly satisfy the security requirement. In this paper, we investigate how to efficiently compose and embed an S-SFC to satisfy the client's security requirement. With the objective of cost optimization, we formulate the problem of security-aware service function chain deployment and prove its NP-hardness. We propose the technique of the security-cost-balance (SCB) factor to efficiently consider the capability of a physical node and the cost when the node is employed to satisfy the client's security requirement. Based on this technique, we develop an efficient algorithm called SCB-based S-SFC deployment (SCB-SD). The simulation results show that SCB-SD significantly outperforms the benchmarks directly extended from the state-of-the-art.

In network function virtualization (NFV), service providers can accommodate services from clients by deploying software-based functions, called service functions (SFs), on commodity servers. The deployed SFs are connected to SF forwarders for efficient management in these commodity servers. Many efforts have been put into protecting SFs from failures. However, little attention has been paid to protecting SF forwarders from failures. In this paper, we investigate how to efficiently allocate backup computing resources when facing SF and SF forwarder failures. To protect the deployed SFs with the minimum backup computing resources, we define a new SF forwarder off-site shared protection problem and approve its NP-hardness. An efficient heuristic algorithm is proposed and proved to be logarithmic approximate. Extensive simulation results show that the proposed algorithm significantly outperforms the approaches directly extended from the existing work.

Emerging machine learning techniques enable Internet-connected devices to generate service function chain (SFC) requests for reliability-sensitive applications. To facilitate reliable SFC provisioning, prior works have proposed the SFC dedicated protection approach for fog and cloud networks, which generally have abundant resources and connectivity. However, with limited resources and connectivity, it might be inefficient to directly employ these approaches at the network edge. In this work, we study how to efficiently embed and provide dedicated protection when accommodating SFCs at the network edge. We formally define the problem of SFC embedding with dedicated protection (SFCE-DP) to minimize the bandwidth resources usage at the network edge. Based on the proposed technique of augmenting path in a layered graph, we construct an efficient algorithm, called augmenting-path-based SFC embedding with dedicated protection (AP-SDP), to optimize SFCE-DP. When the network resources are limited, our results show that AP-SDP significantly outperforms the benchmark that is directly extended from the state-of-the-art.

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of${k}$-heterogeneous-faults-tolerance and propose an augmented protection graph, called${k}$-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called${k}$-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques:${k}$-connected network slicing (KC-NS) and${k}$-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when${k}$= 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

The ultra-reliable and low latency communication (URLLC) service in 5G/6G will be delivered through a sequence of software-based network functions, also known as a service function chain (SFC). To satisfy the ultra-reliable requirement of the URLLC service, fault-tolerance in URLLC SFC processes is required. However, achieving deterministic fault-tolerance in URLLC SFC delivery is challenging as physical/virtual network failures and hardware/software failures have to be jointly consid-ered. In this work, we first introduce an augmented SF protection graph, called k-connected service function slicing (KC-SFS), which can facilitate the SF protection against multiple concurrent physical/virtual node and physical link failures. Based on the KC-SFS, we define a new problem called deterministic fault-tolerant service function slicing (DFT-SFC) and formulate it with a mathematical model. To solve DFT-SFC, we propose an efficient heuristic algorithm, called service function slice embedding (SFSE), which employs the k-connected network slicing technique (k-NST). Via thorough mathematical analysis, we prove that k-NST achieves 2-approximation. Meanwhile, our extensive experimental results show that the proposed SFSE guarantees deterministic fault-tolerance and outperforms the schemes directly extended from the stste -of - the art.

Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.

In network function virtualization (NFV), the customer may request a set of virtual network functions (VNFs) that the customer traffic will go through. To accommodate such requests, the service providers have to embed the requested VNFs onto the substrate network (SN) to form an actual traffic forwarding path called service function path (SFP). In the elastic optical network (EON), how to protect the running network services against VNF failures becomes an attractive research focus. Most existing work concentrates on the protection or restoration of the physical node or fiber link failures in the SN. Few research attention has been paid to the failure of the virtual machines running a VNF. In this paper, we study how to protect VNFs when a VNF failure occurs in an EON. We define a new VNF failure protection cover (VFPC) problem and mathematically formulate VFPC. We propose a protection cover list based VNF protection (PCL-VP) algorithm against any single VNF failure while satisfying the latency requirement. Extensive simulations and analysis show the effectiveness of the proposed algorithm.

With online social networks (OSNs), people are exposed to tons of fake information or misleading posts. Celebrities sometimes intentionally create misleading posts in OSNs to guide people for commercial or marketing purposes. The intentional phrases from such posts can affect the online rating and even lead to a frenzy shopping. That’s part of the reasons that the top social media influencers are targeted by merchants to help promote products. The Federal Trade Commission (FTC) requires that all sponsored posts must be clearly disclosed. However, many influencers do not follow the FTC rules. As a result, people may be misled by the undisclosed sponsorship. In this study, for the first time, we explore the credibility of posts on Instagram and analyze if an influencer complies with the FTC requirements. We build an effective Undisclosed Sponsored Post Detection (USPD) framework based on an ensemble of machine learning classifiers. The USPD framework consists of three main processes: (i) feature extraction, (ii) model construction and (iii) credibility and integrity analysis. Our analysis and experiments demonstrate that the proposed framework can achieve a high accuracy of 83% for undisclosed sponsored post detection. The proposed framework also takes advantages of the text, user and image features in OSN posts to effectively analyze how much an influencer can be trusted.

The ultra-fast speed and massive capacity in 5G networks push huge amounts of data to networks. With network function virtualization, these data will go through multiple service functions (SFs) and big data processing/analysis. As a result, the processing delay from such SFs and data processing/analysis can significantly impact the delivery of latency-sensitive services. To reduce the processing delay, network function parallelism techniques are introduced to allow multiple SFs running parallelly for the same request. In this work, we study how to apply network function parallelism into SF chaining and embedding to optimize the latency. When physical nodes have unlimited computing resource, we propose the mixed integer programming based parallelism-aware SFC optimization (MIP-PS) algorithm. Our analysis proves the proposed MIP-PS is integer-approximation. When physical nodes have limited computing resource, we propose the latency factor based parallelism-aware SFC optimization (LF-PS) algorithm. Our extensive simulations demonstrate that our proposed schemes outperform the approaches extended directly from the existing work.

Network function virtualization (NFV) is introduced to effectively deliver end-to-end network services for the emerging Internet of Things (IoT), multiaccess edge computing, and 5G communication techniques. In NFV, the network service request can be accommodated in the form of a service function chain (SFC). The SFC will have to reserve abundant resources, such as link bandwidth, service functions, and computation in the physical network to meet the demands of customers. Minimizing the cost from the resource reservation in NFV remains challenging, even though a few works in the literature proposed cost-optimization methodologies with assumptions to guarantee their correctness. In this article, we comprehensively investigate how to minimize the cost when delivering network services as SFCs with provable bounds and fewer assumptions. We formally define the problem of minimum cost service function chaining and embedding (MC-SFCE) and propose an algorithm, namely, cost factor-based SFCE optimization with shortcut (COFO-SC), for MC-SFCE. Novel mathematical analysis is provided to demonstrate the correctness of our approaches and related bounds. Our extensive simulations and analysis also show that the proposed COFO-SC outperforms the schemes directly extended from the existing work.

In network function virtualization (NFV), the client’s service requests will go through multiple service functions (SFs). The instances of the required SFs will be hosted on the geographically-distributed physical nodes in physical networks (PNs). The failure of any physical nodes or virtual nodes (i.e., SF instances) will impact the delivery of services and the client’s experience. It is essential for service providers to take the node failure and network reliability into account. Different from physical node failures that will affect the PN’s topology and connectivity, virtual node failures impact the services delivery of certain clients. As a result, applying traditional backup schemes that are designed for physical node failures may not efficiently provide protection for virtual node failures. In this work, we define and mathematically formulate a new latency-bounded off-site virtual node protection (LOVNP) problem in NFV. After proving the NP-hardness of the LOVNP problem, we introduce a novel shared protection technique called pairwise node protection to effectively facilitate the protection of node failures in NFV. Then, we propose an efficient heuristic algorithm called protection centrality based pairwise node protection (PC-PNP) to optimize the LOVNP problem and prove that PC-PNP has a logarithm-approximation boundary. Our extensive simulations and analysis show that the proposed algorithm significantly outperforms the algorithms that are extended from the existing work.

In Network Function Virtualization (NFV), to satisfy the Service Functions (SFs) requested by a customer, service providers will composite a Service Function Chain (SFC) and embed it onto the shared Substrate Network (SN). For many latency-sensitive and computing-intensive applications, the customer forwards data to the cloud/server and the cloud/server sends the results/models back, which may require different SFs to handle the forward and backward traffic. The SFC that requires different SFs in the forward and backward directions is referred to as hybrid SFC (h-SFC). In this paper, we, for the first time, comprehensively study how to optimize the latency in Hybrid SFC composition and Embedding (HSFCE). When each substrate node provides only one unique SF, we prove the NP-hardness of HSFCE and propose the first 2-approximation algorithm to jointly optimize the processes of h-SFC construction and embedding, which is called Eulerian Circuit based Hybrid SFP optimization (EC-HSFP). When a substrate node provides various SFs, we extend EC-HSFP and propose the efficient Betweenness Centrality based Hybrid SFP optimization (BC-HSFP) algorithm. Our extensive simulations and analysis show that EC-HSFP can hold the 2-approximation, while BC-HSFP outperforms the algorithms directly extended from the state-of-art techniques by an average of 20%.

The emerging multiaccess edge computing (MEC) architecture brings the needed computing resource to the network edge. Many 5G and Internet of Things (IoT) applications are latency sensitive and computation intensive in MEC systems. To flexibly provide and manage the network service requests in MEC systems, network function virtualization (NFV) can be employed to create a chain of service functions (SFs), namely, SF chain (SFC). Through SFC, the customer forwards user data to the edge server/cloud, and the edge server/cloud may return the processed results/models to the customer. When the forward and backward traffic is carrying different content, different SFs may be required for the forward and backward traffic, which requires a hybrid SFC (h-SFC). In this article, we study how to minimize the latency cost when embedding an h-SFC in MEC systems. We define a new problem called minimum latency hybrid SFC embedding (ML-HSFCE) and propose an algorithm, namely, optimal hybrid SFC embedding (Opt-HSFCE) to optimally embed a given h-SFC in MEC systems. Our extensive simulations and analysis show that the proposed Opt-HSFCE needs much less runtime compared with the brutal force algorithm and significantly outperforms the schemes that are directly extended from the existing techniques.

With the technology of Network Function Virtualization (NFV), a multicast service (e.g., real-time multimedia streaming or event monitoring) may be accommodated with a Service Function Chain (SFC). The SFC consists of an ordered set of network functions running on generic physical hardware to provide services from source node to each destination node of the multicast service. In this paper, we define the problem of Multicast-aware Service Function Tree Embedding (M-SFTE), which allows the multicast flows to traverse through network service functions before reaching destination nodes. The M-SFTE maps user's SFC-based multicast requests onto a shared substrate network while considering the constraints of network functionality, computing demand of each virtual network function node, and the bandwidth demand of the request. We propose a novel algorithm, called Minimum Cost Multicast Service Function Tree (MC-MSFT) to jointly optimize the process of constructing SFC-based multicast tree and allocating requested resource to embed the tree onto the physical network. The experimental results show that the MC-MSFT algorithm outperforms the traditional greedy-based algorithms as much as by 35% in terms of the total bandwidth consumption.

Network Function Virtualization (NFV) takes advantages of the emerging technologies in virtualization and automation to offer new ways in design, deployment, and management of networking services. In NFV, the proprietary hardware-based network functions are replaced by the software-based modules named as Virtual Network Functions (VNFs) or Service Functions (SFs). A network service request from the customer can be formed by multiple SFs. To satisfy a network service request, the service provider has to chain the SFs in the request into a Service Function Chain (SFC) and embed the constructed SFC onto the shared substrate network. In this paper, we comprehensively study how to composite and embed an SFC onto a shared substrate network with unique service function. We formulate this problem with the Integer Linear Programming (ILP) technique. We also propose an efficient heuristic algorithm with 2-approximation boundary, namely, Spanning Closed Walk based SFC Embedding (SCW-SFCE). Our extensive simulations and analysis show that the proposed approach can achieve near-optimal performance in a small network and outperform the Nearest Neighbour (NN) algorithm.

Measuring the similarity between nodes is challenging in social networks. The SimRank and CoSimRank are techniques widely used to calculate the similarity of two nodes in a social graph. They can be applied to many applications such as recommending friends and detecting communities in social networks. Both SimRank and CoSimRank are based on random walk and only consider first-order transition probabilities, in which the next node to visit in random walk solely depends on the current node, like a Markov chain. However, in many real-world situations, simply considering the current node may not be enough. Previously visited node may provide extra information for measuring similarities. In this paper, we propose a novel similarity measure technique by investigating CoSimRank to take advantage of the second-order information in a random walk process. Our extensive analysis and experiments show that the proposed second-order CoSimRank significantly outperforms the existing techniques.

Network Function Virtualization (NFV) technology decouples network functions from proprietary hardware equipments. As a result, Internet Service Providers (ISPs) implement software-based network functions on generic highvolume substrate network devices. In NFV, a Service Function Chain (SFC) is defined as an ordered set of abstract network functions running on specific substrate nodes (e.g., servers). A challenging issue in NFV management and orchestration is how to optimize the Dependence-aware SFC Embedding in substrate Optical networks (D_SFCE_O). In this paper, we propose a novel algorithm, namely, Dependence-aware SFC embedding with Least-Used consecutive subcarriers (D_SFC_LU), which jointly optimizes SFC design, SFC mapping and spectrum allocation in optical networks. To minimize resource consumption, D_SFC_LU takes advantages of the proposed techniques: Impact Factor based Node Selection (IFNS), Chain Node Mapping (CNM) and Chain-Fit (CF) spectrum allocation. Our simulation and analysis demonstrate that D_SFC_LU can efficiently embed a network requests while minimizing the required substrate resource in optical networks.

In Network Function Virtualization (NFV), Service Function Chain (SFC) is composed of Virtual Network Function (VNF) nodes that are chained via VNF links. SFCs can be specified as unidirectional or bidirectional. A unidirectional SFC (u-SFC) demands the traffic being forwarded via the VNFs in one direction, while a bidirectional SFC (b-SFC) requires bidirectional traffic flows. In this paper, for the first time, we investigate the problem of how to efficiently deploy a hybrid SFC (h-SFC), whereas some VNF nodes are required to process bidirectional traffic while others only handle unidirectional traffic. We define a new problem called hybrid SFC Deployment (h-SFCD). When each substrate node provides one unique VNF, we prove the NP-hardness of the h-SFCD problem and propose an approximate algorithm, namely, 2-approximation Hybrid Service function chain Deployment in Unique function networks (2-HSD-U). Our experimental results show that the proposed 2-HSD-U algorithm significantly outperforms the heuristic algorithm based on the traditional Nearest-Neighbor technique.

Network virtualization in optical networks enables the decoupling of network services from the underlying hardware infrastructure to allow multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The challenge of mapping VORs onto the shared SON lies on how to efficiently allocate physical resource for the VORs, which is referred to as Virtual Optical Network Embedding (VONE). Many recent research focus on the NP-Hard VONE optimization problem. In this paper, for the first time, we explore how to efficiently map a given VOR for a multicast service onto a shared SON while considering the fanout (splitting/forwarding) limitation of the physical optical switches. We propose a novel algorithm, namely, Centrality-based Degree Bounded Shortest Path Tree (C-DB-SPT) to minimize the resource usage while satisfying the degree limitation in the shared SON. The experimental results show that the C-DB-SPT algorithm outperforms the traditional greedy-based algorithms as much as by 35% in terms of the total bandwidth consumption.

BACKGROUND: The Epi-Info software suite, built and maintained by the Centers for Disease Control and Prevention (CDC), is widely used by epidemiologists and public health researchers to collect and analyze public health data, especially in the event of outbreaks such as Ebola and Zika. As it exists today, Epi-Info Desktop runs only on the Windows platform, and the larger Epi-Info Suite of products consists of separate codebases for several different devices and use-cases. Software portability has become increasingly important over the past few years as it offers a number of obvious benefits. These include reduced development time, reduced cost, and simplified system architecture. Thus, there is a blatant need for continued research. Specifically, it is critical to fully understand any underlying negative performance issues which arise from platform-agnostic systems. Such understanding should allow for improved design, and thus result in substantial mitigation of reduced performance. In this paper, we present a viable cross-platform architecture for Epi-Info which solves many of these problems. RESULTS: We have successfully generated executables for Linux, Mac, and Windows from a single code-base, and we have shown that performance need not be completely sacrificed when building a cross-platform application. This has been accomplished by using Electron as a wrapper for an AngularJS app, a Python analytics module, and a local, browser-based NoSQL database. CONCLUSIONS: Promising results warrant future research. Specifically, the design allows for cross-platform form-design, data-collection, offline/online modes, scalable storage, automatic local-to-remote data sync, and fast analytics which rival more traditional approaches.

With network virtualization over Elastic Optical Networks (EONs), network services are decoupled from the underlying hardware infrastructure to enable multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The embedding process of VORs onto the shared SON while satisfying the computing resource and spectrum allocation constraints is referred to Virtual Optical Network Embedding (VONE), which is an NP-Hard problem. In this paper, for the first time, we investigate how to efficiently map a given VOR in the form of virtual optical multicast tree onto an SON. We propose a novel algorithm that is called Impact Factor based Virtual Optical Multicast Tree Embedding (IF-VOMTE) to minimize the resource usage and avoid redundant multicast transmission in the shared SON. The experimental results show that our algorithm outperforms the schemes based on traditional techniques such as Greedy Node Mapping (GNM-SP) and First-Fit Node Mapping (FFNM-SP) in terms of the total cost of bandwidth consumption and the reduction of redundant multicast transmission.

The emerging Network Function Virtualization (NFV) technology decouples network functions from the proprietary hardware, which allows the Internet Service Providers (ISPs) to implement network functions as software running on top of a physical (or substrate) node. With NFV, a Service Function Chain (SFC) is defined as an ordered set of network function instances running on specific substrate network nodes to provide services for client users. In this paper, we define the problem of Dependence- Aware Service Function Chain (D_SFC) design and mapping. We study how to efficiently accommodate user's D_SFC requests in the substrate network while considering the constraints of function dependence, computing demand of virtual nodes and bandwidth demand of the D_SFC. We propose a novel heuristic algorithm, called D_SFC design and resource allocation with Adaptive Mapping (D_SFC_AM), which jointly optimizes the processes of designing a D_SFC and allocating resources requested by the chain. D_SFC_AM employs the proposed techniques of dependence sorting and independent grouping that effectively take into account the node dependence and the resource status of the substrate network. Our experimental results show that the proposed algorithm significantly outperforms the scheme based on the traditional topological sorting in which the process of designing a D_SFC and allocating resources requested by the chain is done sequentially.

Network virtualization enables the decoupling of network services from the underlying hardware infrastructure to allow the same Substrate/physical Network (SN) shared by multiple Virtual Network (VN) requests. The process of mapping virtual nodes and links onto a shared SN while satisfying the computing and bandwidth constraints is referred to Virtual Network Embedding (VNE) as an NP-hard problem. In this paper, for the first time, we explore how to efficiently map a given Virtual Multicast Tree (VMT) request onto a substrate network. We propose a novel algorithm, namely, Virtual Multicast Tree Embedding based on dynamic Impact Factor (VMTE-IF) to minimize the required resource and redundant multicast transmission in the substrate network. The experimental results show that our algorithm outperforms the traditional greedy-based algorithms over 50% in terms of the cost of bandwidth consumption.

In this paper, we propose to build a comprehensive cyberphysical system (CPS) with virtual reality (VR) and intelligent sensors for motion recognition and training. We use both wearable wireless sensors (such as electrocardiogram, motion sensors) and nonintrusive wireless sensors (such as gait sensors) to monitor the motion training status. We first provide our CPS architecture. Then we focus on motion training from three perspectives: 1) VR-first we introduce how we can use motion capture camera to trace the motions; 2) gait recognition-we have invented low-cost small wireless pyroelectric sensor, which can recognize different gaits through Bayesian pattern learning. It can automatically measure gait training effects; and 3) gesture recognition-to quickly tell what motions the subject is doing, we propose a low-cost, low-complexity motion recognition system with 3-axis accelerometers. We will provide hardware and software design. Our experimental results validate the efficiency and accuracy of our CPS design.

In network virtualization, the network services are decoupled from the underlying hardware infrastructure such that multiple virtual network requests can be mapped onto the same physical substrate network. The process of mapping virtual networks onto the substrate network with minimum resources while satisfying the constraints such as computing capacity, bandwidth and memory is referred to as virtual network embedding. In this paper, we investigate how to efficiently map a given virtual network with multicast services. We propose a closeness-centrality based multicast-aware virtual network embedding (CC-MVNE) algorithm to minimize the needed resources for the virtual nodes/links mapping and multicast transmission. Our extensive simulation and analysis show that the proposed approach outperforms the traditional greedy algorithm as much as by 40% in terms of transmission bandwidth consumption.

Network Function Virtualization (NFV) explores the virtualization technologies to offer Network-as-a- Service (NaaS) through connected virtual network functions. The network operations that were previously performed by specialized hardware are consolidated as software-based virtual network functions (VNFs). These VNFs can be implemented in the telecom clouds with high volume servers, switches and storage. With the NFV orchestration, a service function graph (SFG) can be built to provide network services. In this paper, we study how to efficiently construct the SFG from a set of VNF requests and map the SFG onto the substrate network while considering the priority dependence between the VNFs. We define the problem of service function graph design and mapping (SFG_PD) and propose an SFG_PD mapping with dependent directional acyclic graph (SFG_DAG) algorithm. The proposed SFG_DAG algorithm can jointly construct the VNFs graph and map VNFs onto the substrate network while minimizing the bandwidth consumption in the substrate network. Our simulation and analysis show that accommodating the VNFs based on the requested bandwidth yields the best performance in terms of the total bandwidth consumption.

Social networks such as Twitter and Facebook have become important sources for users to acquire information. In those social networks, users obtain information from the posts/reposts of their social connections. To acquire information efficiently, users are motivated to connect to users who offer attractive and timely information. In this paper, we study how to effectively optimize social connections to optimize the efficiency of information Acquisition. We define this as the problem of Social Connection Optimization for efficient Information Acquisition (SCOIA). We present our analysis on the information accuracy and timeliness to measure the efficiency of information acquisition. Based on the analysis, a novel User Set Selection (USS) algorithm is then proposed to efficiently solve the SCOIA problem. Our simulations based on the crawled Twitter dataset show that the proposed algorithm can efficiently identify user connections, leading to high information acquisition accuracy, low spam rate and low information acquisition latency.

Network Function Virtualization (NFV) is promising to lower the network operator's capital expenditure and operational expenditure by replacing proprietary hardware-based network equipment with software-based virtual network functions that can be consolidated into telecom clouds. In particular, NFV provides an efficient way to deploy network services using service function chains that consist of a set of virtual network functions interconnected by virtual links. A practical and yet theoretically challenging issue related to NFV Management and Orchestration is how to jointly optimize the topology design and mapping of multiple service function chains, which is called the JTDM problem. In this paper, we develop an Integer Linear Programming (ILP) model to formulate the JTDM problem with the objective of minimizing the bandwidth consumption in the physical substrate. We propose a novel heuristic algorithm, namely Closed-loop with Critical Mapping Feedback (CCMF), to efficiently address this problem. Through comprehensive simulations, we demonstrate that the CCMF algorithm is efficient in terms of the bandwidth consumption in various scenarios, and can achieve a bandwidth consumption that is close to the minimum obtained by ILP.

Network Function Virtualization (NFV) provides an effective way to reduce the network provider's cost by allowing multiple Virtual Networks (VNs) to share the underlying physical infrastructure. In the NFV environment, especially when supporting multicast service over the VNs, reliability is a critical requirement in the process of VN mapping since the failure of one virtual node can cause the malfunction of all the subsequent nodes that receive multicasting data from it. In this paper, for the first time, we study how to efficiently map VNs for reliable multicast services, while taking into consideration the max-min fairness of the reliability among distinct VNs. We propose a Mixed Integer Linear Programming (MILP) model to determine the upper bound on the max-min fairness reliability. In addition, an efficient heuristic, namely Uniform Reliability Mutation based Genetic (URMG) algorithm, is developed to address reliable multicast VN mapping with a low computational complexity. By encoding multicast tree construction and link mapping into path selection, taking into consideration the max-min reliability fairness goal, and the networking reliability factors during mutation, URMG can globally optimize the reliability and its fairness of all the multicast VN requests. Through extensive simulations, we demonstrate that URMG achieves close to the optimal reliability fairness with a much lower time complexity than the MILP and yields a significant performance improvement in terms of reliability fairness, bandwidth consumption and transmission delay comparing with other heuristic solutions.

Network Function Virtualization (NFV) allows multiple Virtual Networks (VNs) to share the underlying physical infrastructure via VN mapping, thus improving the utilization of physical resources. In this paper, for the first time, we study the multicast service-oriented VN mapping that can support big data applications over Elastic Optical Networks (EONs). Since the problem of minimizing the spectrum consumption in multicast service-oriented VN mapping is NP-hard, we propose an efficient heuristic algorithm, called Integrated Genetic and Simulated Annealing (IGSA) algorithm to address the problem with low computational complexity. By encoding node mapping, multicast tree construction, link mapping and spectrum requirements in the same gene and auto-adjusted evolution, and utilizing simulated annealing to find the fittest multicast requests mapping order, IGSA can perform joint optimization for all the multicast requests in a global way. Through extensive simulations, we demonstrate that IGSA outperforms the other heuristic solutions in terms of spectrum consumption, blocking probability and normalized throughput, while achieving close to minimum spectrum consumption with a much lower time complexity than MILP.

The authorized content is the content which can only be generated by the content provider (CP). The content copies delivered to a user may bring some reward to the CP if the content is adopted by the user. The overall reward obtained by the CP depends on how much the user is interested in the content and how the user may help disseminate the content copies. Hence, to maximize the reward obtained, the content provider are motivated to disseminate the authorized content to the most interested users. In this paper, we study how to effectively disseminate the authorized content in interest-centric opportunistic social networks (IOSNs) such that the reward is maximized. We first derive the Social Connection Pattern (SCP) to statistically describe the interest distribution of the users contacted or connected. The SCP is used to predict the interests of possible contactors and connectors. We then propose our Social Connection Pattern based Dissemination algorithm (SCPD) to calculate the best number of content copies to disseminate when two users meet. Our dataset based simulation shows that our SCPD algorithm is effective and efficient to disseminate the authorized content in IOSNs

In this paper, scheduling and control of appliance's energy consumption in a household or a small office building is studied. The household appliances include Heating, Ventilating and Air Conditioning (HVAC), and deferrable loads such as Electric Vehicle (EV) and washer/dryer. The appliances in the office building include HVAC, non-deferrable and non-interruptible loads such as computers/servers as well as deferrable and interruptible loads such as batch printing/photocopying. Given the temperature and price forecasts, a Linear Programming (LP) model for the system is derived to maximize the user comfort. The paper also presents a heuristic solution, namely Comfort Prioritizing Greedy (CPG), which employs iterative greedy algorithm to prioritize user comfort and enables the scheduling even in case of blocking. We compare our proposed greedy algorithm with the bin-packing algorithm and show that the proposed greedy algorithm can effectively be used to schedule the load in Home Energy Management (HEM) system as well as in small offices without much overhead.

No abstract available.

Recent innovations in Network Virtualization and Elastic Optical Networks (EONs) enable flexible deployment of optical networks as a service. However, one open challenge is how to embed Virtual Optical Network (VON) requests onto the physical substrate network to maximize the sharing of physical resources, which is the so called Virtual Network Embedding (VNE) problem. EONs are prone to the fragmentation of spectral resources during the process of routing and spectrum allocation. The fragmentation of spectral resources in the substrate fiber links may lead to the blocking of incoming virtual network requests. This degrades the utilization of the physical resources of the Infrastructure Providers and also, decreases the revenue of the Service Providers. In this paper, we propose a novel virtual network embedding algorithm called Alignment and Consecutiveness-aware Virtual Network Embedding (ACT-VNE), which takes into account the spectrum alignment and relative loss in spectrum consecutiveness when mapping virtual nodes/links onto the physical substrate nodes/links. We also propose a min-max reconfiguration scheme called Relative Consecutiveness Loss-aware and Misalignment-aware Virtual Network Reconfiguration (RCLM-VNR) that minimizes relative consecutiveness loss and maximizes alignment with adjacent links when reconfiguring the virtual network. Simulation results show that ACT-VNE and RCLM-VNR yield a lower blocking probability and a higher link utilization ratio, which leads to better utilization of the physical resources and increased revenue.

In network virtualization, a traditional ISP collapses into two independent tiers, where the infrastructure provider (InP) manages the physical (or substrate) networks and the service provider (SP) operates the service (or virtual) networks. In this work, we investigate the virtual network embedding (VNE) problem, which bridges above two tiers by mapping the virtual network request to the substrate networks. Existing VNE approaches are either optimal Integer Linear Programming formulations that suffer from extensive computational time, or relaxation/heuristics (mostly decompose the VNE problem into link mapping (LM) and node assignment (NA) sub-problems) that are unable to provide an optimal solution or near-optimal solution with guaranteed quality. In this paper, we attempt to fill this gap with a new VNE solution, which relies on an iterative process enabling feedbacks between the NA, and LM sub-problems (obtained based on the Primal-dual analysis of the VNE problem). With our approach, one can reach either an optimal solution or a near optimal solution with a per-instance guarantee on its closeness to the optimal solution.

Social networks are widely used for information dissemination. In this work, for the first time, we investigate the Semi-controlled Authorized Information Dissemination (SAID) problem in content-based social networks. Within SAID, one challenge is how the authorized content providers effectively disseminate limited authorized content copies to proper interested users. We model this problem as a new Maximum Weighted Connected subgraph with node Quota (MWCQ) problem. To solve the MWCQ problem, we then propose a Dynamic Programming based SAID (DP-SAID) algorithm for the MWCQ problem. Our study shows that DP-SAID can achieve an approximation factor of O(R/2R-2) when comparing to the optimal solution.

A scheme for preserving privacy in MobilityFirst (MF) clean-slate future Internet architecture is proposed in this paper. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered approach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users.

In this paper, attacks/faults detection and isolation in the Smart Grid system are studied. First, a mathematical model of the power grid system is derived. Then, detection and isolation of attacks/faults using Generalized Observer Scheme (GOS) implementing Kalman Filter are discussed. The attack assessment is performed using χ2-detector. Even though, GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. In order to isolate simultaneous attacks on multiple sensors, an Iterative Observer Scheme (IOS) is proposed. The proposed (IOS) scheme divides the set of sensors providing measurements in the power grid system into subsets and performs the tests on the subsets iteratively, eventually isolating the region/sensors under attack. Simulation results show the effectiveness of the proposed scheme in detecting and isolating attacks or faults in the power grid system.

Wireless sensor networks (WSNs) have witnessed rapid advancement in medical applications from real-time telemonitoring and computer-assisted rehabilitation to emergency response systems. In this paper, we present the state-of-the-art research from the ubiquity perspective, and discuss the insights as well as vision of future directions in WSN-based healthcare systems. First, we propose a novel tiered architecture that can be generally applied to WSN-based healthcare systems. Then, we analyze the IEEE 802 series standards in the access layer on their capabilities in setting up WSNs for healthcare. We also explore some of the up-to-date work in the application layer, mostly on the smartphone platforms. Furthermore, in order to develop and integrate effective ubiquitous sensing for healthcare (USH), we highlight four important design goals (i.e., proactiveness, transparency, awareness, and trustworthiness) that should be taken into account in future systems.

Video sharing is popular and attractive for mobile users. However, the limited resources such as the connection, power and bandwidth, coupled with the social characteristics in mobile social networks pose unique challenges for effective video sharing. Users personal attributes and preferences on the communication pattern or the videos themselves may impede or promote the disperse process of the videos. It is important to take both the network and social behavior factors into consideration to comprehensively analyze the video sharing process. In this work, for the first time, we investigate the factors influencing users decision and model them using the Bayesian technique. The social confidence, interest matching, and resources status are incorporated in the proposed Bayesian-based model to analyze the interactions and influence among social network users. Novel streaming schemes are proposed to provide reliable streaming video transmissions. Our analysis results show that the proposed model is able to help user to establish the most reliable and effective routing paths to obtain the videos.

Spectrum-sLICed Elastic optical path (SLICE) networks enable elastic and flexible allocation of spectral resources. SLICE networks distribute data on a number of sub-carriers overlapped in frequency domain to provide efficient sub-wavelength and super-wavelength traffic accommodation. In SLICE networks, a routing and spectrum allocation algorithm assigns a spectrum path to any demand with just enough contiguous sub-carriers while following the sub-carrier consecutiveness and spectrum-continuity constraints. In this paper, we propose novel sub-carrier allocation algorithms that employ the proposed Interference Graph technique to assign sub-carriers to a spectrum path based on the historic traffic profile. These algorithms try to achieve minimal disruptions to the live connections while minimizing the blocking probability. Simulation results show that the proposed schemes can effectively accommodate the dynamic traffic while minimizing the network reconfiguration cost in SLICE networks, with or without the traffic prediction.

Network virtualization decouples the traditional Internet Service Provider (ISP) into the infrastructure provider (InP) and the service provider (SP). Disruptive technologies hence can be easily employed by the SP and transparently mapped to the physical network managed by the InP after resolving the network embedding problem. In this work, we investigate the survivable network embedding (SNE) problem. We transform the SNE problem into a multi-commodity network flow problem, and present an Integer Linear Programming (ILP) model to achieve joint optimal resources allocation for both the working and backup demands. For large-scale problems, we propose an efficient algorithm based on the tabu-search meta-heuristic, which is shown to be close to the optimal results from the ILP.

In this paper, we study the virtual network embedding (VNE) problem in the network virtualization context, which aims at mapping the virtual network requests of the service providers (SPs) to the substrate networks managed by the infrastructure providers (InPs). Given the NP-Completeness of the VNE problem, prior approaches primarily rely on solving/relaxing the link-based Integer Linear Programming (ILP) formulations, which lead to either extensive computational time, or non-optimal solutions. In this paper, for the first time, we present a path-based model for the VNE problem, namely P-VNE. By analyzing the dual formulation of the P-VNE model, we propose a column generation process, with which an optimal solution to the VNE problem can be found efficiently (when embedded into a branch-and-bound framework).

Traffic grooming can effectively utilize the transmission capacity of WDM networks by properly multiplexing low-speed traffic flows onto high-capacity wavelength channels. In order to maximize the wavelength resource and cut down network costs associated with, e.g. OEO conversion for time-varying yet predictable traffic, we propose a novel predictive and incremental (PI) traffic grooming scheme, named PI-grooming. A conventional traffic grooming approach for fluctuated traffic is to run an algorithm that (re)assigns the traffic flows to as a few wavelengths as possible based only on the current traffic demands of these flows. This however will lead to a lot of OEO traffic. The proposed PI-grooming considers the existing flow assignment, the current traffic demands, and the expected traffic demands in the near future. We show that, compared with the conventional approach, PI-grooming can effectively minimize the amount of OEO traffic while still using a very small number of wavelengths.

Batch scheduling has been extensively studied in the context of Job-machine scheduling, where a group of time-constrained tasks are scheduled to run over a limited number of servers, with the goal of maximizing the overall revenue from successfully scheduled tasks. Depending on whether buffers are employed, batch scheduling can be in the form of a pure-loss system or system with buffers. In optical burst/packet switching networks, bursts/packets (i.e., tasks) that simultaneously arrive or locate within the same time window can be treated as a batch, and scheduled over a limited number of available wavelengths (i.e., servers). Due to the unavailability of Random Access Memory in optical networks, buffering is generally achieved with Fiber Delay Lines, which can only offer discrete and predefined duration of buffering. The discrete feature of FDLs hence leads to a new Job-machine scheduling with Discrete-time Buffers (JDB) problem, which can be shown to be NP-Complete. In this work, we study how to optimally co-schedule FDLs and wavelengths in the batch scheduling process over two types of FDL architectures, namely feed-forward and feedback FDLs. We mathematically model the JDB problem over both feed-forward and feedback FDLs, and propose a heuristic algorithm to enable fast on-line scheduling. The performance of proposed schemes over feed-forward and feedback FDLs are simulated and compared.

Abstract For extremely sensitive applications, it may be advantageous for users to transmit certain types of data covertly over the network. This provides an additional layer of security to that provided by the different layers of the protocol stack. In this paper we present a covert side channel that uses the 802.11 MAC rate switching protocol. The covert channel provides a general method to hide communications within currently deployed 802.11 LANs. The technique uses a one‐time password (OTP) algorithm to ensure high‐entropy randomness of the covert messages. We investigate how the covert side channel affects network throughput under various rate‐switching conditions with UDP‐based and TCP‐based application traffic. We also investigate the covertness of the covert side channel using standardized entropy. The theoretical analysis shows that the maximum covert channel bandwidth is 60 bps. The simulation results show that the impact on network throughput is minimal and increases slightly as the covert channel bandwidth increases. We further show that the channel has 100% accuracy with minimal impact on rate switching entropy for scenarios where rate switching normally occurs. Finally, we present two applications for the covert channel: covert authentication and covert WiFi botnets. Copyright © 2010 John Wiley & Sons, Ltd.

Recently, the OFDM-based Spectrum-sliced Elastic Optical Path (SLICE) network attracts significant interests due to its elastic band-width allocation. The OFDM technology, on one hand, enables both sub-wavelength and super-wavelength traffic accommodation by allocating appropriate number of sub-carriers. On the other hand, it can provide high signal quality by overcoming various impairments. In SLICE networks, one fundamental problem is to establish spectrum paths by allocating sub-carriers along the corresponding route to accommodate traffic demands. This is referred to as the routing and spectrum allocation (RSA) problem. The optimal RSA problem is NP-Hard and different from the traditional routing and wavelength assignment (RWA) problem in WDM networks. In this work, we formulate the RSA problem using the Integer Linear Programming (ILP) formulations to achieve different optimization objectives. We then analyze the lower/upper bounds for the sub-carrier number in a SLICE network. We also propose two efficient heuristic algorithms to minimize the required sub-carrier number in a large SLICE network when the ILP model becomes intractable. The results show that the proposed algorithms can match the analysis and approximate the optimal solutions from the ILP model.

To address the challenge of the node-size (i.e., number of ports in the switching fabric) increase faced by the wavelength routed networks (WRNs), waveband switching (WBS) is introduced to group multiple wavelengths together as a band or fiber and switch them using a single port whenever possible. In WBS networks with dynamic traffic, one important goal is to minimize the traffic blocking probability, which can be caused by two factors: shortage of wavelengths and limited number of switching ports. In this work, for the first time, we study wavelength retuning in WBS networks, which was used in WRNs to reduce the traffic blocking caused by the wavelength shortage. We analyze the unique features of wavelength retuning in WBS networks as well as the various cases of port consumption due to wavelength retuning. Two strategies, namely, Inter-band retuning and Intra-band retuning are introduced to reduce the blocking caused by both the wavelength continuity constraint and the port shortage. We also propose a new wavelength retuning scheme, namely port-aware wavelength retuning (PAWR), which seeks a compromise between these two strategies and takes the advantage of both. Our simulations show that the proposed schemes can effectively reduce the blocking probability in WBS networks by as much as 20%.

In OFDM-based optical networks, multiple subcarriers can be allocated to accommodate various size of traffic demands. By using the multi-carrier modulation technique, subcarriers for the same node-pair can be overlapping in the spectrum domain. Compared to the traditional wavelength routed networks (WRNs), the OFDM-based Spectrum-sliced Elastic Optical Path (SLICE) network has higher spectrum efficiency due to its finer granularity and frequency-resource saving. In this work, for the first time, we comprehensively study the routing and spectrum allocation (RSA) problem in the SLICE network. After proving the NP-hardness of the static RSA problem, we formulate the RSA problem using the Integer Linear Programming (ILP) formulations to optimally minimize the maximum number of sub-carriers required on any fiber of a SLICE network. We then analyze the lower/upper bounds for the sub-carrier number in a network with general or specific topology. We also propose two efficient algorithms, namely, balanced load spectrum allocation (BLSA) algorithm and shortest path with maximum spectrum reuse (SPSR) algorithm to minimize the required sub-carrier number in a SLICE network. The results show that the proposed algorithms can match the analysis and approximate the optimal solutions using the ILP model.

To handle the challenge of increasing node size in wavelength routing networks (WRNs), waveband switching (WBS) is introduced to group multiple wavelengths together as a band or fiber and switch them using a single port whenever possible. Literature studies with the off-line traffic confirm that WBS can effectively reduce the port count and cost as well as control complexity. In the cases with online traffic, both the port reduction and call blocking probability should be considered due to the unknown traffic pattern and limited resources. In this work, we first analyze a reconfigurable switching architecture and the blocking probability in WBS networks. Based on the analysis, we then propose a novel dynamic graph-based waveband assignment algorithm in conjunction with adaptive routing. The proposed algorithm employs the ant optimization techniques to reduce ports and blocking probability in the network with online traffic in a distributed manner. Our simulation results show that our graph-based waveband assignment algorithm combined with the adaptive routing scheme can achieve the best performance when compared to other schemes. Our study also shows that even with limited resources, WBS can achieve port saving and an allowable blocking probability.

Optical Burst Switching (OBS) is considered to be a promising paradigm for bearing IP traffic in Wavelength Division Multiplexing (WDM) optical networks. In OBS networks, a key challenge is to reduce the data loss rate with efficient scheduling algorithms. In this work, we propose novel algorithms for batch scheduling in OBS networks with different optimization criteria. The algorithms effectively consider the strong correlations among the multiple bursts, and employ the proposed interval graphs and min-cost circular flow techniques to achieve optimized network performance in terms of data loss rate in the network. Simulation results show that our algorithms achieve a loss rate which is as much as 20% less than one of the best previously known algorithms, LAUC-VF, and suffer only a minor increase (about 1-hop link propagation) in the data latency.

Waveband switching (WBS) has recently attracted attention from a wide range of industry and academia groups for its practical and hierarchial concepts in handling massive lightpaths with reduced control complexity and nodal size. Previous studies demonstrate that non-uniform waveband switching, in which wavebands contain various number of wavelengths, can provide more flexibility for wavelength grouping thus achieving more port saving. In this work, for the first time, we model the non-uniform waveband switching problem in a mesh topology with static off-line traffic, by using Integer Liner Programming (ILP) formulations. As an optimal approach, the proposed ILP model takes routing, wavelength grouping, traffic demands and non-uniform band settings into consideration. In the cases that the optimal ILP becomes intractable, we develop an efficient heuristic called simulated non-uniform waveband assignment (SNWS). Our simulations and analysis show that the proposed heuristic SNWS can achieve smaller port count than the existing near-optimal uniform waveband switching scheme.

The spectrum-agile cognitive radio has been developed to significantly increase spectrum utilization and relieve the spectrum exhaustion problem, by enabling secondary users to dynamically access the licensed spectrum bands. As such cognitive radio will be a key feature of future wireless technologies. In this paper, we propose an architecture for cognitive radio network (CRN). Our architecture uses one radio per node and does not need a common control channel, and is highly adaptable and resilient to maintain connectivity between neighboring nodes. In particular, we present a channel selection algorithm that not only spreads nodes into different channels to reduce cochannel interference, but also enables a node to easily compute the channel of a neighbor without the need to negotiate with the neighbor, which is highly desirable for CRN, as a channel may become inaccessible abruptly due to that the licensed user suddenly starts using it. Simulation results show that our CRN model and channel selection algorithm are highly adaptable and resilient to dynamic channels, and can achieve a close performance to the scheme that uses an extra radio and a static control channel to exchange channel information.

In the wavelength routing networks (WRNs), one of the major challenges is the dramatic increase of node size and complexity. As an alternative solution, waveband switching (WBS) is introduced to group multiple wavelengths together as a band or fiber and switch them using a single port whenever possible. The WBS efficiency is defined as the ratio between the ports required under the WBS networks and the WRNs. In this paper, we illustrate that the WBS efficiency can be influenced by factors such as bypass-traffic, node-degree, and the overlapping between routing paths. We then propose a new Hierarchical Waveband Assignment (HWA) scheme while taking these factors into consideration to efficiently group the traffic in multi-granular optical networks. Our simulation shows that HWA provides a significant improvement over an existing WBS algorithm which offers near-optimal performance in terms of port number.

A new scheduling algorithm called serialised batch scheduling (SBS) for optical burst switching (OBS) networks is proposed. SBS aggregates and serialises bursts along a shared path into one composite burst, which is switched as one unit. There are two major processes in SBS, namely, batching and serialising. While the batching process chooses a set of bursts to form the composite burst, the serialising process determines how to organise the OBS bursts within the composite burst and generates a corresponding control packet for this burst. Several SBS batching and serialising schemes are introduced and analysed here. The study by the authors shows that the guard band and burst overlap can be reduced in the SBS and, thus, the packet loss rate and the number of switch reconfigurations can be significantly reduced. In addition, it is indicated that the proposed SBS algorithm can be coupled with other OBS scheduling algorithms and reduce the requirements for a high-speed optical switch in OBS networks.

The Distributed and Fair Access (DFA) protocol is proposed for multihop wireless networks. The proposed protocol eliminates several problems existed in the original binary countdown (BCD) algorithm, such as lack of fairness, data collision and the inefficiency of channel usage, by introducing hidden station elimination and second chance channel contention that are suitable for multihop networks. Further in this paper, numerical analysis of modeling the behavior of DFA in multihop networks are presented. With low computational complexity, the proposed model estimates the transmission probability and the channel throughput. In our analysis, the data transmission influenced by the remote stations is carefully monitored and analyzed. Our extensive simulation results have verified the proposed model and demonstrated the superior performance of DFA comparing with other existing MAC protocols including the IEEE 802.11 and SYN-MAC. Equipped with many attractive features such as high efficiency, fairness, simplicity and robustness, DFA can be served as a promising alternative MAC protocol for the distributed wireless networks.

Passive RFID tags transmit their ID information to a tag reader upon energization by the reader. These transmitted signals, or IDs, may collide if multiple tags transmit their ID simultaneously, requiring repeated energizations by the reader to read all tag IDs successfully. The number of energizations increases with the number of ID bits in the tag and the number of tags to be read. An increase in energizations results in a greater incurred latency to read the tags, and additionally, more transmissions. This is undesirable in an industry environment especially, and with this comes higher expended energy by the reading system, leading to higher system costs. In this paper, a new proposed scheme is evaluated, the shortcut bisected countdown scheme (SBCS), in terms of the number of energizations by a reader. Three different techniques are progressively combined into three schemes in order to cut down on the number of energizations. The final scheme, SBCS, which is a combination of the three techniques, stabilizes on the number of energizations irrespective of the tag ID length and the number of tags to be read, resulting in a distinct advantage for bulk reading. The three schemes are memoryless meaning that the tags are unable to remember if they were read or not; hence the performance of each of the schemes is compared with the Query Tree protocol since it is also memoryless.

In this paper, a new scheduling algorithm, Serialized Batch Scheduling (SBS) for Optical Burst Switching (OBS) networks is proposed. SBS aggregates and serializes bursts along shared path into one composite burst which is switched as a single unit. There are two important processes in SBS namely, batching and serializing. While the batching process chooses proper set of bursts to form the composite burst, the serializing process determines how to organize the OBS bursts within the composite burst and generates a corresponding composite control packet. Several SBS batching and serializing schemes are introduced and analyzed. Our study shows that the guard band and bursts overlap can be reduced in the proposed SBS, and therefore, the packet loss rate and the number of switch reconfigurations can be significantly reduced.

An enhanced synchronized medium access protocol, named ES-MAC, for wireless ad hoc networks is proposed in this paper. ES-MAC employs a binary-countdown scheme to resolve contentions between wireless stations. Multiple contention periods and hidden station elimination periods are adopted to increase the throughput and channel utilization of the system. Our simulation and analysis show that ES-MAC can achieve a promising performance in terms of throughput, fairness and channel utilization. With channel utilization as high as 96%, ES-MAC can also be employed as an alternative MAC protocol in wireless access network.

In this paper a new approach to medium access in a wireless ad hoc network based on neighbor knowledge and their activity is presented. Nodes take turns to transmit based on their neighbors and their transmissions, which reduces the collision probability and avoids the latency due to backoff (and exponential backoff) experienced in random access protocols. The scheme uses features from 802.11 DCF MAC and adopts a loose scheduling approach which is distributed. The performance of this scheme is compared with 802.11 DCF MAC as this is the more popular random access MAC for wireless ad hoc networks.

Our research aims to address the challenging trustworthiness issues in wireless sensor and actuator networks (WSANs). As trustworthiness requires data to be transmitted among actuators and sensors with desired 'reliability' and 'security', we propose a low-complexity transmission reliability scheme that is based on local wireless path repair and hop-to-hop retransmission. Since WSANs have specific network constraints and data transmission requirements compared to general ad hoc networks and other wireless/wired networks, the security issues need to be tackled accordingly. We propose to seamlessly integrate WASN security with a promising routing architecture that is scalable and energy-efficient. In this paper, we also develop two-level re-keying/re-routing schemes that can not only adapt to a dynamic network topology but also securely update keys for each data transmission session.

With the huge capacity, wavelength division multiplexing (WDM) optical networks are predominantly used as the transport infrastructure to carry inter-domain traffic for client networks. Traffic grooming refers to the aggregation of low-speed client traffic flows onto high-capacity optical connections, to achieve cost-effective traffic transport. In this paper, we propose a route segment technique to efficiently compute the client traffic blocking probability in dynamic traffic grooming, where client traffic randomly arrives/departs. In our experiments, the blocking probabilities computed by this technique closely match the results obtained by simulations.

Traffic grooming in wavelength division multiplexing (WDM) optical networks controls how to consolidate client calls with sub-wavelength data rates onto lightpaths. It can be classified into static or dynamic traffic grooming depending on whether the client traffic is static or dynamic. The principal problem in traffic grooming is to construct a logical topology to route client traffic over a given physical topology. For dynamic traffic grooming, the logical topology may be dynamically configured, or designed a priori given the stationary traffic demands between client nodes (and reconfigured on relatively large time scale, e.g., on the order of hours, to adapt to traffic demands changes). Both approaches have their pros and cons. This paper studies the latter case and develops a heuristic algorithm to design logical topology, constrained by client traffic blocking probability requirement and the maximum by-pass traffic amount allowed at each client node. We have compared logical topologies designed the heuristic and an ILP model. The heuristic performance is impressive.

Waveband switching (WBS) wherein wavelengths are grouped into bands and switched as a single entity can reduce cost and complexity of switching nodes by minimizing the port count. In this paper, we study the effect of wavelength conversion on the performance of WBS networks with reconfigurable multi-granular optical cross-connects (MG-OXC) to satisfy online traffic. Since wavelength conversion is still expensive and can potentially increase the number of used ports in WBS networks, efficient usage of wavelength converters is of practical interest. We propose a novel heuristic algorithm, called waveband assignment with path-graph (WAPG), which takes efficient wavebanding and efficient usage of wavelength converters into consideration when satisfying new lightpath requests. We apply the WAPG algorithm in WBS networks with full, intra-band, or limited number of wavelength converters, and compare with the FirstFit and RandomFit algorithms. Our results indicate that the proposed algorithm performs significantly better in terms of the blocking probability as well as the number of used wavelength converters.

Waveband switching (WBS) in conjunction with multigranular optical cross-connect (MG-OXC) architectures can reduce the cost and complexity of switching nodes. In this paper, we study two MG-OXC architectures: the single-layer and the multilayer MG-OXCs, and compare their performances with both off-line (static) and on-line (dynamic) traffic. In the off-line case, a near-optimal integer linear programming models (called off-ILP models) for each of the MG-OXC architectures aims to reduce the size of the MG-OXC, and compares them with the balanced path routing with heavy-traffic first waveband assignment (BPHT) heuristic developed for the multilayer MG-OXCs. The two architectures are then compared in terms of the number of wavelength a fixed number of wavelengths on each link. We also propose a novel efficient heuristic algorithm, called maximum overlap ratio (MOR) to satisfy new requests and compare it with the on-ILP, first-fit, and random-fit algorithms. We compare the two architectures in terms of the blocking probability, weighted (request) acceptance ratio, which serves as an indication of hops (WH) and MG-OXC ports required to satisfy a given set of traffic demands. In the on-line case, we develop an on-line ILP model called on-ILP, which aims to minimize the number of used ports for each of the MG-OXC architectures, given the revenue generated by satisfying the requests. Our results indicate that using WBS with either single-layer or multilayer MG-OXCs can reduce the number of ports (hence the size and cost) of the switching nodes compared to using ordinary OXCs (without waveband switching). In particular, in the off-line case, using single-layer MG-OXCs provides a greater reduction in size than multilayer MG-OXCs, while in the online case, using the multilayer MG-OXC is better.

Wavelength band switching (WBS) has only recently attracted attention from the optical networking industry for its practical importance in reducing the control complexity and cost of optical cross-connects (OXCs). However, WBS-related problems of theoretical interest have not been addressed thoroughly by the research community, and many issues are still wide open. In particular, WBS is different from wavelength routing, and thus techniques developed for wavelength-routed networks (including e.g., those for traffic grooming) cannot be directly applied to effectively address WBS-related problems. In this paper, we first propose a new multigranular OXC (MG-OXC) architecture for WBS, which is more flexible than any existing WBS node architectures. We also adopt the most powerful waveband assignment strategy, and develop an efficient heuristic algorithm called Balanced Path routing with Heavy-Traffic first (BPHT). To verify its near-optimality, we also develop an integer linear programming (ILP) model. Both the ILP and the BPHT algorithms can handle the case with multiple fibers per link and hence are more general than our previous single-fiber solutions X. Cao et al. (2002). We conduct a comprehensive evaluation of the benefits of WBS through detailed analysis and simulations. We show that the proposed heuristic BPHT can perform much better than a heuristic which applies the optimal routing and wavelength assignment (RWA) method. We also show that WBS using BPHT is even more beneficial in multifiber networks than in single-fiber networks in terms of reducing the port count. Our analytical and simulation results also provide valuable insights into the effect of wavelength band granularity, as well as the trade-offs between the wavelength-hop and the port count required in WBS networks.

Waveband switching (WBS) has attracted attention from the optical networking industry for its practical importance in reducing port count, the associated control complexity, and cost of optical cross-connects (OXCs). However, WBS-related problems of theoretical interest have not been addressed thoroughly by the research community and many issues are still wide open. In particular, WBS is different from wavelength routing and, thus, techniques developed for wavelength-routed networks (including for example, those for traffic grooming) cannot be directly applied to effectively address WBS-related problems. In this paper, we first develop an integer linear programming (ILP) model, which for a given set of lightpath requests, determines the routes and assigns wavelengths to the lightpaths so as to minimize the number of ports needed. Since the optimal WBS problem of minimizing the port count in WBS networks contains an instance of routing and wavelength assignment (RWA), which is NP-complete, we adopt a powerful waveband assignment strategy and develop an efficient heuristic algorithm called balanced path routing with heavy-traffic first waveband assignment (BPHT). Both the ILP and the heuristic algorithm can handle the case with multiple fibers per link. We conduct a comprehensive evaluation of the benefits of WBS through detailed analysis and simulations. For small networks, our results indicate that the performance of the BPHT heuristic is close to that achievable by using the ILP model and, hence verifying its near-optimality. We show that for larger networks, BPHT can perform better than its variation called balanced traffic routing with maximum-hop first waveband assignment and much better than another heuristic based on optimal (but waveband oblivious) RWA that minimizes wavelength resources. We also show that WBS using BPHT is even more beneficial in multifiber networks than in single-fiber networks in terms of reducing the port count. Our analytical and simulation results provide valuable insights into the effect of wavelength band granularity, as well as the tradeoffs between the wavelength-hop and the port count required in WBS networks.

Optical burst switching (OBS) is a promising paradigm for the next-generation Internet infrastructure. We study the performance of TCP traffic in OBS networks and in particular, the effect of assembly algorithms on TCP traffic. We describe three assembly algorithms in this paper and compare them using the same TCP traffic input. The results show that the performance of the proposed adaptive-assembly-period (AAP) algorithm is better than that of the min-burstlength-max-assembly-period (MBMAP) algorithm and the fixed-assembly-period (FAP) algorithm in terms of goodput and data loss rate. The results also indicate that burst assembly mechanisms affect the behavior of TCP in that the assembled TCP traffic becomes smoother in the short term, and more suitable for transmission in optical networks.