LLM agents increasingly rely on tool calling, and the Model Context Protocol (MCP) standardizes it between agents and tool providers, reducing integration cost and driving rapid growth in tool scale. Yet a standardized interface does not make tool access work at production scale: legacy services are not MCP-callable, fast protocol evolution creates compatibility cost, large tool sets exhaust the context window, and stateful sessions complicate load balancing. We solve these with a shared control point, a centralized MCP Gateway System that makes MCP operational at cloud scale. The gateway breaks the direct-connect data plane and consolidates legacy API integration, protocol bridging, access control, and session-aware routing, while scaling out elastically at low per-call overhead. It scales agent tool access to thousands of cloud operations.

While commodity NIC-based back-end AI networks offer ultra-high intra-cluster bandwidth for distributed training, their limited programmability and on-chip resources hinder the implementation of advanced VPC features such as fine-grained isolation and stateful security policies. Furthermore, access to resources within the VPC needs to be routed through the front-end DPU, which is shared by the scale-up domain. The mismatch between the front-end DPU’s bandwidth and the back-end requirements causes GPU underutilization when intensive VPC communication is required for content recommendation, AIGC, and federated learning workloads. We propose an architecture that decouples complex policy enforcement from high-speed packet forwarding to support VPC semantics on back-end NICs and enable front-end/back-end integration. Evaluations show near-full GPU utilization in our analytical model and 71 μ s P999 extra latency of the first packet, suggesting that commodity hardware can support both high-throughput AI training and flexible VPC features.

Many IoT systems require deadline-constrained workflow scheduling, where missed deadlines can have serious consequences. Scheduling such IoT workflows in Fog–Cloud environments is challenging due to resource heterogeneity and the variability in workflow patterns and deadlines. Existing approaches, including heuristic and meta-heuristic algorithms, often fail to reliably satisfy deadline constraints while simultaneously minimizing the cost associated with the computational resources used for executing workflows. This paper introduces the Internet of Things Genetic Algorithm with Selective Repair under Combined Criteria (IoTGA-SRC 2 ) to effectively tackle these challenges. IoTGA-SRC 2 introduces a novel selection mechanism that prioritizes solutions based on deadline violations and execution costs. It also features an innovative repair method, which can systematically detect infeasible solutions, perform a root cause analysis to identify the key factors causing deadline violations, and reallocate critical tasks using a multi-criteria method. By properly managing delays caused by execution time, communication time, and waiting time, IoTGA-SRC 2 can consistently satisfy deadline constraints across a wide range of problem configurations. Extensive experiments demonstrate that IoTGA-SRC 2 consistently outperforms multiple state-of-the-art methods in reducing execution costs while adhering to stringent deadline constraints, making it a valuable choice for various real-world applications in heterogeneous IoT–Fog–Cloud computing environments.

Low Earth Orbit (LEO) satellite constellations are becoming a viable means for Internet access. However, their topology changes as satellites move towards or away from orbital intersection points, leading to constant link down or up. This may cause routing table entry updates and thus path changes between satellites. A path change during transmission may lead to out-of-order packet delivery and invalidate the current TCP congestion window. While some path changes are inevitable, some are avoidable. Dijkstra's algorithm is a popular choice among the routing protocols proposed for LEO satellite networks. We observe that many next-hop route updates by Dijkstra's algorithm are avoidable. Motivated by this, we propose StableR-oute, which stabilizes routing paths from different perspectives. StableRoute Local (SR_L) leverages equal-cost shortest paths and stays with the current one if it is still valid. StableRoute K-Short (SR_K) allows a path longer than the shortest path. StableRoute Global (SR_G) leverages the predictable satellite trajectories and topology variations, and thus works out a next-hop route selection sequence that minimizes the number of route updates over a time period. The evaluation shows that SR_L, SR_K and SR_G outperform Dijkstra's algorithm, substantially reducing the number of route updates in changing topologies.

Ensuring secure, dynamic, and fine-grained access control across independently managed domains is a major challenge in modern multi-domain environments. An access control mechanism designed for multi-domain systems needs to account for the unique characteristics of such systems, like a distributed environment, a dynamic nature, and cross-domain collaboration. This paper proposes an intelligent framework for mining Attribute-Based Access Control (ABAC) policies using a supervised learning technique for multi-domain systems. In the proposed architecture, each domain employs its own Policy Decision Engine (PDE) to evaluate access requests originating either from within the same domain or from a remote domain. For each domain, the ABAC policy is derived by training a supervised machine learning model using the access logs of that domain. Our proposed method enables the different domains to retain their autonomy by allowing them to make access decisions based on their own independent policies. We evaluate the performance of our approach on two access control datasets in terms of accuracy, precision, recall, and F1 score. Moreover, we compare our method with an existing clustering-based policy mining technique. Our proposed method significantly outperforms the existing approach in terms of the accuracy of access decision-making. This implies that our proposed framework exhibits strong generalization across domains, supporting scalable and decentralized ABAC policy learning, thereby enabling secure and interoperable access control in complex, multi-domain environments.

Cloud providers use SmartNIC-accelerated virtual switches (vSwitches) to offer rich network functions (NFs) for tenant VMs. Constrained by limited SmartNIC resources, it is a challenge to provide sufficient network performance for high-demand VMs. Meanwhile, we observed a significant number of idle vSwitches in the data center, which led us to consider leveraging them to build a remote resource pool for high-demand virtual NICs (vNICs). In this work, we propose Nezha, a distributed vSwitch load sharing system. Nezha reuses the existing idle SmartNICs to handle the excess load from the local SmartNIC without adding new devices. Nezha offloads stateless rule/flow tables to the remote, while keeping states locally. This eliminates the need for state synchronization, facilitating load sharing and failover. The deployment cost of Nezha is only a small fraction of that required to deploy new devices. Data collected from production show that our CPS capability bottleneck has shifted from the vSwitch to the VM kernel stack, with #concurrent flows and #vNICs increased by up to 50.4x and 40x, respectively.

Effectively scheduling the execution of deadline-constrained IoT workflows in Fog-Cloud computing is an im-portant practical problem. The execution of IoT workflows must be carefully controlled, especially in applications like IoT health, where missed deadlines can be life-threatening. Existing approaches, including heuristic and meta-heuristic algorithms, have limited capabilities of handling deadline constraints while ensuring efficient execution of workflows. In this paper, we develop a Genetic Algorithm (GA) with a novel repair method to properly allocate workflow tasks to IoT devices and Fog/Cloud servers in order to meet deadlines while minimizing the resource costs. To effectively handle deadline constraints, our repair method first identifies infeasible solutions evolved by GA that can be potentially repaired. It then carries out root cause analysis to determine the primary reasons for deadline violation, and finally reallocate critical tasks to resolve violated deadlines. In this way, the risks of violating deadline constraints can be substantially reduced. On a wide range of problem instances commonly studied in literature, our experiments show that the new algorithm can clearly outperform multiple recently developed methods since it enjoys better chance of finding solutions that satisfy deadline constraints and can notably reduce the resource cost simultaneously.

Driven by technological advances and new business models, we have seen a renewed interest in LEO satellite constellations. The deployment of large-scale LEO satellite networks is becoming a reality. The network topology changes periodically, as satellites orbit the Earth. This imposes a great challenge to network monitoring. Meanwhile, as a new network monitoring method, In-band Network Telemetry (INT) can provide per-hop granular telemetry metadata, needed to tackle the mobile nature of LEO satellite constellations. Given this, we apply INT to LEO satellite networks for real-time fine-grained monitoring. We propose a path planning solution to identify the paths for network-wide telemetry and the paths for disseminating the telemetry data to the ground facilities. By taking advantage of the predictable satellite trajectories and topology variations, the path planning solution is designed to achieve network-wide coverage and minimize telemetry overhead. We take the LEO48 constellation as an example to visually show the detailed paths of the monitoring scheme. We conduct experiments on different sizes of networks to evaluate the original path planning algorithm and the improved balanced algorithm in this paper, demonstrating the timeliness and balance of the telemetry solution.

Migrating enterprise IT services to the cloud is becoming a trend. However, configuring virtual networks in the cloud is a costly and error-prone task. In this paper, we model the problem of VPC intent compilation and prove that the problem is NP-hard. We design a heuristic method to automatically translate the intents into VPC configurations. To facilitate the heuristic, we propose an algorithm for finding small cuts (AFSC) based on the Louvain algorithm, which is used to separate subnets across VPCs. The generated configurations find an equilibrium between maximizing the network performance and minimizing the resource consumption. Experimental results show that our heuristic method reaches 100% correctness, compiles large intent sets from real-world networks with hundreds of subnets and thousands of intents in under a minute, and improves the effectiveness by ∼2.8× in terms of resource consumption relative to network performance.

Detecting link failures is critical to ensuring the operation of data center networks (DCNs). However, some gray link failures may go undetected by switches, leading to silent packet drops. In this paper, we propose FTA-detector, a gray link failure detection and localization approach leveraging Fault Tree Analysis (FTA), a technique previously applied in the field of reliability engineering. On the data plane, we collect fine-grained hop-by-hop information through In-band Network Telemetry (INT), detect the bidirectional connectivity of end-to-end paths through a novel aging mechanism, and implement fast reroute in response to gray link failures. On the control plane, we introduce a faulty link localization algorithm based on FTA to recommend the most likely faulty links. Specifically, we use Top K and progressive failure repair to discover and repair link faults as early as possible during failure inference, significantly reducing the overall computation complexity of sequential root cause analysis. For large-scale network topology, we propose a divide and conquer optimization scheme for scalability. To verify the efficiency of our system, we build a virtual network test platform with P4 switch software and Redis database. The test results show that FTA-detector can troubleshoot multi-point failures in DCNs in a very short time with high accuracy.

Software-defined networking (SDN) allows flexible and centralized control in cloud data centers. An elastic set of distributed SDN controllers is often required to provide sufficient yet cost-effective processing capacity. However, this introduces a new challenge: Request Dispatching among the controllers by SDN switches. It is essential to design a dispatching policy for each switch to guide the request distribution. Existing policies are designed under certain assumptions, including a single centralized agent, global network knowledge, and a fixed number of controllers, which often cannot be satisfied in practice. This article proposes MADRina, Multiagent Deep Reinforcement Learning for request dispatching, to design policies with high dispatching adaptability and performance. First, we design a multiagent system to address the limitation of using a centralized agent with global network knowledge. Second, we propose a Deep Neural Network-based adaptive policy to enable request dispatching over an elastic set of controllers. Third, we develop a new algorithm to train the adaptive policies in a multiagent context. We prototype MADRina and build a simulation tool to evaluate its performance using real-world network data and topology. The results show that MADRina can significantly reduce response time by up to 30% compared to existing approaches.

In-band Network Telemetry (INT) enables hop-by-hop device-internal state exposure for maintaining and troubleshooting data center networks. To achievenetwork-widetelemetry coverage, orchestration on top of the INT primitive is required. A straightforward solution would flood the network with INT probe packets for maximum measurement coverage, which leads to a huge bandwidth overhead. A refined solution leverages the SDN controller to collect the network topology information and carry out centralized probing path planning, which, however, is inefficient in reacting to topology changes. To tackle the above problems, we proposeINT-label, a lightweight In-band Network-Wide Telemetry architecture via the distributed labeling approach. INT-label periodically labels the sampled packets with device-internal states. It is cost-effective with a minor bandwidth overhead and able to seamlessly adapt to topology changes. In order to reduce the number of labeled packets, we introduce a times-based probabilistic labeling algorithm, which allows fewer packets to carry more INT information than the interval-based algorithm. In addition, to counteract the degradation of telemetry resolution due to loss of labeled packets, we design a feedback mechanism which can adaptively change the instant labeling frequency. We provide theoretical proof that INT-label can achieve network-wide telemetry. We analyze the impact of transmission delay on coverage rate and labeling times distribution under the INT-label architecture. Evaluation on software P4 switches suggests that INT-label can achieve 99.72% measurement coverage under the labeling frequency of 20 times per second. With the adaptive labeling enabled, even if 60% of the packets are lost, the coverage can still reach 92%.

We present SAZyzz, a leader-based Byzantine Fault Tolerant consensus protocol for partially synchronous networks. SAZyzz exhibits a better performance/scalability compared to the state-of-the-art leader-based BFT consensus protocols. It is built on top of AZyzzyva and has adopted a tree-based communication model which enables it to enhance the scalability of AZyzzyva. Additionally, SAZyzz reduces the communication complexity toO(logN) in two paths of the protocol. However, the tree-based topology has been argued that has a shortcoming when used in designing BFT consensus protocols. This refers to the strong assumption that all the internal nodes of the tree are honest, which leads to a trade-off between tolerating Byzantine faults and better performance and scalability. This paper shows that, with the current technological infrastructures available for industrial systems, such as Trusted Execution Environment (TEE) and Public Key Infrastructure (PKI), this assumption is realistic. SAZyzz comprises of fast-path and backup-path, each of which has two modes:simple modeandscalable mode. To demonstrate the efficiency and feasibility of SAZyzz's adoption for blockchain systems, we designed and implemented the ZyConChain blockchain system based on SAZyzz. The evaluation results show that SAZyzz can significantly improve the performance/scalability of blockchain systems.

In multi-tenant clouds, off-the-shelf x86 boxes are widely deployed as middleboxes. With the rapid growth of cloud traffic and the migration to NFV deployment in recent years, CPU overloading at middleboxes becomes more of an issue. From our data centers, we observed that the CPU overloading was caused by heavy hitters. To address this issue, we propose MIMIC, a cloud-scale flow backpressure system, implemented onto our existing SmartNIC with FPGA acceleration. MIMIC rate-limits the selected heavy hitters through a new per-flow backpressure protocol and a new heavy-hitter detection system, to protect the other tenants. The detection system is based on hierarchical memory design, leveraging on-chip SRAM and off-chip DRAM, which can handle highly concurrent cloud traffic without the losses of flow information. We extend the design by adding a pre-filtering procedure for rapid detection. To avoid CPU being flooded by FPGA through frequent heavy-hitter reporting, due to their performance disparity, the CPU queries the FPGA on demand. The backpressure protocol is non-invasive to protect tenant privacy and allows controllable rate-limiting through the novel use of ECN and meter tables. The SmartNIC acts as a man in the middle to facilitate heavy-hitter detection and per-flow backpressuring. In a production setting, we observe that MIMIC can react quickly and bring down CPU load to the normal level within 10ms without packet losses.

Network programming languages (NPLs) empower operators to program network data planes (NDPs) with unprecedented efficiency. Currently, various NPLs and NDPs coexist and no one can prevail over others in the short future. Such diversity is raising many problems including: (1) programs written with different NPLs can hardly interoperate in the same network, (2) most NPLs are bound to specific NDPs, hindering their independent evolution, and (3) compilation techniques cannot be readily reused, resulting in much wasteful work. These problems are mostly owing to the lack of modularity in the compilers, where the missing part is an intermediate representation (IR) for NPLs. To this end, we proposeNetwork Transaction Automaton (NTA), a highly-expressive and language-independent IR, and show it can express semantics of 7 mainstream NPLs. Then, we designCODER, a modular compiler based on NTA, which currently supports 2 NPLs and 3 NDPs. Experiments with real and synthetic programs show CODER can correctly compile those programs for real networks within moderate time.

Network programming languages (NPLs) empower operators to program network data planes (NDPs) with unprecedented efficiency. Currently, various NPLs and NDPs coexist and no one can prevail over others in the short future. Such diversity is raising many problems including: (1) programs written with different languages can hardly interoperate in the same network, and (2) most NPLs are bound to specific NDPs, hindering their independent evolution. These problems are mostly owing to the lack of modularity in the compilers, where the missing part is an intermediate representation (IR) for NPLs. To this end, we propose Network Transaction Automaton (NTA), a highly-expressive and language-independent representation as the IR. We show that NTA can express semantics of 6 mainstream NPLs, and can be composed efficiently without any semantics loss.

Network programming languages (NPLs) empower operators to program network data planes (NDPs) with unprecedented efficiency. Currently, various NPLs and NDPs coexist and no one can prevail over others in the short future. Such diversity is raising many problems including: (1) programs written with different NPLs can hardly interoperate in the same network, (2) most NPLs are bound to specific NDPs, hindering their independent evolution, and (3) compilation techniques cannot be readily reused, resulting in much wasteful work. These problems are mostly owing to the lack of modularity in the compilers, where the missing part is an intermediate representation (IR) for NPLs. To this end, we propose Network Transaction Automaton (NTA), a highly-expressive and language-independent IR, and show it can express semantics of 7 mainstream NPLs. Then, we design CODER, a modular compiler based on NTA, which currently supports 2 NPLs and 3 NDPs. Experiments with real and synthetic network programs show CODER is efficient and scalable.

The evaluation of the web‐browsing quality of experience (QoE) is difficult to complete through traditional methods (e.g. deducing formulas or setting thresholds) due to the diversity of websites and their contents. To evaluate web‐browsing QoE through a general way, the authors propose a web QoE evaluation architecture based on machine learning, consisting of two parts: traffic classification sub‐system and QoE prediction sub‐system. When evaluating user experience, traffic classification sub‐system first classifies the packets generated by visiting a website into a flowthrough some fields in the packet header, to model each website separately. The traffic classification accuracy of packets over six websites reaches 96.63%. Then, in the network layer, the traffic metric cumulative traffic volume is generated from the size and arrival time of packets. When a user visits a web page, their regression model predicts the above‐the‐fold time (ATF) and thus QoE. The output of the regression model is an exact ATF value that is mapped to user experience. In addition, reversing input variables further improves the model, which is evaluated on two popular websites. The QoE prediction results of the improved method for 5400 visits are obtained within 0.0975 s, reaching 0.9 .

One major research challenge for Software-Defined Networking is to properly deploy and efficiently utilize multiple controllers to improve resource utilization and maintain high network performance. While addressing this Controller Placement Problem (CPP), many existing studies overlooked the importance and influence of the Controller Scheduling Problem (CSP) with the central focus on proper distribution of requests from all switches among all controllers. In this paper, we define a new Controller Placement and Scheduling Problem (CPSP), emphasizing on the necessity and importance of tackling both CPP and CSP simultaneously in a coherent framework. To solve CPSP, we must seek a combination of solutions to both problems. Particularly, CSP is addressed based on a given solution to CPP and a Gradient-Descent-based (GD-based) scheduling algorithm is developed to optimize the probabilistic distribution of requests among all controllers. Built on the GD-based approach for controller scheduling, a Clustering-based Genetic Algorithm with Cooperative Clusters (CGA-CC) is further proposed to address CPP. In comparison to the majority of heuristic methods developed in the past, CGA-CC has two unique strengths. Specifically, it partitions a large network to substantially reduce the search space of the Genetic Algorithm (GA), resulting in fast identification of high-quality CPP solutions. Moreover, a greedy load re-distribution mechanism is developed to handle unexpected demand variations by dynamically forwarding bursting requests to neighboring sub-networks. Extensive simulations showed that our algorithms can significantly outperform several existing algorithms, including a recently proposed approach called Multi-controller Selection and Placement Algorithm (MSPA), in terms of both response time and controller utilization.

Extracting fields from layer 7 protocols such as HTTP, known as L7 parsing, is the key to many critical network applications. However, existing L7 parsing techniques center around protocol specifications, thereby incurring large human efforts in specifying data format and high computational/memory costs that poorly scale with the explosive number of L7 protocols. To this end, this paper introduces a new framework namedcontent-based L7 parsing, where the content instead of the format becomes the first class citizen. Under this framework, users only need to label what content they are interested in, and the parser learns an extraction model from the users’ labeling behaviors. Since the parser is specification-independent, both the human effort and computational/memory costs can be dramatically reduced. To realize content-based L7 parsing, we propose REPLAY which builds on recurrent neural network (RNN) and addresses a series of technical challenges like large labeling overhead and slow parsing speed. We prototype REPLAY on GPUs, and show it can achieve a precision of 98% and a recall of 97%, with a throughput as high as 12Gbps for diverse extraction tasks.

Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a scheduling function (SF) to properly dispatch requests from each switch to suitable controllers becomes critical. However, existing literature tends to design the SF targeted at specific network settings. In this paper, a reinforcement-learning-based (RL) approach is proposed with the aim to automatically learn a general, effective, and efficient SF. In particular, a new dispatching system is introduced in which the SF is represented as a neural network that determines the priority of each controller. Based on the priorities, a controller is selected using our proposed probability selection scheme to balance the tradeoff between exploration and exploitation during learning. In order to train a general SF, we first formulate the scheduling function design problem as an RL problem. Then a new training approach is developed based on a state-of-the-art deep RL algorithm. Our simulation results show that our RL approach can rapidly design (or learn) SFs with optimal performance. Apart from that, the trained SF can generalize well and outperforms commonly used scheduling heuristics under various network settings.

The prohibitive cost of deploying a sophisticated DNS-based CDN makes anycast-based CDN an attractive alternative for new or small CDN operators. In anycast-based CDNs, user requests are naturally routed to the “closest” server determined by Internet routing. For the operators, however, this comes at a cost—loss of control—how the traffic is routed is entirely at the mercy of BGP routing. The “closest” server may be overloaded, or simply not the best choice. This “loss of control” undermines thescalabilityof anycast-based CDN architectures. To have control over how traffic is routed, existing work either requires adding a large amount of complexity to the system (high Capex/Opex) or is unable to achieve precise and fine-grained control. This paper proposes T-SAC, a scalable anycast-based CDN architecture that capitalizes on the programmability and flexibility of SDN/NFV, enabling fine-grained traffic redirection among CDN servers. T-SAC achieves precise control by leveraging a load-based redirection algorithm and a single 1-bit no-redirect flag. We implement T-SAC in the real system and evaluate its performance from various aspects using DASH and web applications. The results show that T-SAC is capable of redirecting the right amount of traffic at the right time to the right servers, making the system highly scalable.

Distributed controller architectures have been proposed for Software-Defined Networking (SDN) to ensure scalability and reliability. One major drawback of the existing architectures is the uneven load distribution among controllers stemming from the static binding between controllers and switches. To address this issue, several existing studies introduce dynamic binding by adopting some switch migration mechanisms that re-associate switches from overloaded controllers to underutilized controllers. However, the migration process adds a considerable amount of complexity to the system and may incur significant network latency. In this paper, we propose BLAC, a novel BindingLess Architecture for distributed Controllers (BLAC), in which load balance is achieved with the help of the proposed scheduling layer, which intercepts flow requests from switches and dispatches them to different controllers as determined by selected scheduling algorithms. The process is proceeded transparently with no extra modification required for off-the-shelf SDN switches. Besides, the scheduling layer can flexibly support various scheduling algorithms and causes neither disruption of service nor significant network delay. We build a prototype that can work with various distributed controller systems and conduct experiments to demonstrate its efficacy. The results show that our design outperforms the static-binding controller system in terms of both system throughput and response time without the complexity of the dynamic-binding controller system.

Within Content Delivery Networks (CDNs), users are typically geographically load-balanced across multiple servers in order to provide better performance and locality - users are assigned to the content servers close to them. One approach to user-server assignment is the use of IP anycast, where all the content servers use the same IP address. A user request is then routed to the server closest to the user, determined by the routing protocols such as BGP. However, there are problems associated with this anycast-based approach. IP anycast is generally incapable of updating or redirecting users to a different (better) server without breaking already established TCP sessions. Moreover, the CDN operators do not have much control on where to redirect the users - it is completely controlled by Internet routing. In this paper we present our Man-In-the-Middle Anycast (MIMA) architecture, a novel anycast-based design that leverages Network Functions Virtualization (NFV) and Software Defined Networking (SDN) techniques to enable flexible and efficient user-server assignment. We demonstrate that the MIMA architecture is capable of performing flexible user-server assignment and offloading during times of high demand, such as flash crowd events that are becoming more common in a media-focused Internet. These capabilities offered by MIMA provide CDN operators a higher degree of flexibility in network management and content provisioning by enabling flexible user-server assignment.

Network management is one of the biggest challenges in operating the Internet network today. This is mainly due to its complexity and inflexibility, which result from the tight coupling of the control plane and data plane. Efforts to address this challenge have led to the introduction of Software Defined-Networking (SDN), an emerging architecture in which a logical single controller controls the behaviour of the whole network. This paper presents a solution that exploits the separation to simplify many management tasks. Our work is based on RouteFlow, a framework that enables IP routing and virtualization in OpenFlow networks. We extend the framework to allow live migration of virtual topologies to new physical infrastructure in order to simplify management tasks such as planned maintenance. Our evaluation in emulated environments shows that the proposed system is capable of performing live migration operation with little impacts on network traffic.

Current Content Delivery Networks (CDNs) primarily utilize the Domain Name System (DNS) to direct users towards optimal content replica servers. Typically, these CDNs will leverage specialized DNS servers to perform load balancing, by providing different users with different IP endpoints for a particular hostname. However, there typically exists some form of DNS cache between the user and the authoritative DNS for a particular web service (Examples include web browsers, operating systems, and home routers). While these DNS caches typically improve user experience and network performance by reducing the number of redundant DNS requests, there exist scenarios where the presence of a DNS cache can actually harm network performance and user experience. For example: if a server or datacentre were to suddenly become unavailable, end users may become `stuck' trying to access the old IP address contained in their DNS caches, even though the authoritative DNS server may have updated it's records accordingly. In order to address this issue, we propose a system that enables network operators to transparently redirect users towards optimal servers during times of network congestion or high server load. By leveraging SDN and NFV, we are able to implement this system over a variety of network infrastructures, and to various levels of scale. Our initial tests indicate that our methodology has the capability to vastly improve user experience by several orders of magnitude during times of high network load, over a variety of network conditions. Our system is application-layer agnostic, and is compatible with various networking protocols.

Content replication and placement is an effective technique to improve data availability and enhancing network performance. This area received attention in Content Delivery Networks (CDNs), but is less explored by the research community in Wireless Mesh Networks (WMNs). A number of replica placement algorithms were specifically designed for CDNs, but they do not consider the special features of wireless networks such as insufficient bandwidth, low server capacity, contention to access the wireless medium, etc. In this paper, we propose a fully-fledged object replication and placement scheme for WMNs. In our model, each mesh router acts as a replica server with limited storage capacity. The challenge is to minimize the demand-weighted access cost by replicating popular objects and placing them at replica servers (mesh routers) as close as possible to the requesting mesh clients, while minimizing the number of replicas subject to the storage capacity constraint. Our scheme is lightweight, distributed, scalable and adaptable. Using simulation tests, we demonstrate the scalability and performance gain of our scheme over its counterparts with respect to access latency, network load and server load.

Software Defined Networking (SDN) is an active area for network research, with many organizations exploring the opportunities provided by the decoupling of network control from packet forwarding. However, questions remain over the operation of such systems in production. In order to build operational confidence, we built Cardigan -a distributed router using OpenFlow-and deployed it at a public Internet exchange. Cardigan applies a routing as a service abstraction to a RouteFlow controlled IP network in an effort to reduce operational complexity. The implementation and deployment efforts provide insights into the challenges involved with using these technologies, and suggests the viability of mixed device environments despite the limitations of early OpenFlow implementations.

Recently, Wireless Mesh Networks (WMNs) have attracted much of interest from both academia and industry, due to their potential to provide an alternative broadband wireless Internet connectivity. However, due to different reasons such as multi-hop forwarding and the dynamic wireless link characteristics, the performance of current WMNs is rather low when clients are soliciting Web contents. Due to the evolution of advanced mobile computing devices; it is anticipated that the demand for bandwidth-onerous popular content (especially multimedia content) in WMNs will dramatically increase in the coming future. Content replication is a popular approach for outsourcing content on behalf of the origin content provider. This area has been well explored in the context of the wired Internet, but has received comparatively less attention from the research community when it comes to WMNs. There are a number of replica placement algorithms that are specifically designed for the Internet. But they do not consider the special features of wireless networks such as insufficient bandwidth, low server capacity, contention to access the wireless medium, etc. In this paper, we propose a distributed heuristic solution for the replica placement problem in WMNs. This problem is known to be NP-complete. Our heuristic considers the local popularity of an object replica. The local popularity can be defined as the relative demand for an object within a partition of the network compared to the whole network. The heuristic collects popularity information periodically to compute the number of replicas for future period requests. Using extensive simulation tests, we demonstrate the performance gain of our heuristic compared to other relevant ones.

In this paper, we propose a distributed heuristic solution for the replica placement problem in Wireless Mesh Networks (WMNs). This problem is known to be NP-complete. Our heuristic considers the local popularity of an object replica. The local popularity can be defined as the relative demand for an object within a partition of the network compared to the whole network. The heuristic collects popularity information periodically to compute the number of replicas for future period requests. We perform simulation experiments to investigate the performance of our heuristic.

Rate adaptation varies the transmission rate of a wireless sender to match the wireless channel conditions, in order to achieve the best possible performance. It is a key component of IEEE 802.11 wireless networks. Minstrel is a popular rate adaptation algorithm due to its efficiency and availability in commonly used wireless drivers. However, despite its popularity, little work has been done on evaluating the performance of Minstrel or comparing it to the performance of fixed rates. In this paper, we conduct an experimental study that compares the performance of Minstrel against fixed rates in an IEEE 802.11g testbed. The experiment results show that whilst Minstrel performs reasonably well in static wireless channel conditions, in some cases the algorithm has difficulty selecting the optimal data rate in the presence of dynamic channel conditions. In addition, Minstrel performs well when the channel condition improves from bad quality to good quality. However, Minstrel has trouble selecting the optimal rate when the channel condition deteriorates from good quality to bad quality.

Wireless mesh networks (WMNs) were proposed to provide low-cost, easy deployment and robust access to the Internet. One of the objectives in WMNs is the improvement of client throughput, which can be achieved using content replication. Many replication schemes are specifically designed for the Internet. However, they do not account for the different characteristics of wireless networks, such as insufficient and fluctuating bandwidth, packet loss, contention to access the wireless medium, etc. In this paper, we study the problem of object replication and placement in WMNs, where mesh nodes act as replica servers in a P2P model that improves Quality of Experience by replicating content as close as possible to the requesting mesh clients. Furthermore, we aim to optimize the number of replicas per object to better utilize the storage capacity per node. In WMNs, the wireless link-quality is paramount in the placement decision and the measurement of the object access cost. Therefore, we propose a link-quality aware, distributed and scalable scheme for object replication. The proposed scheme exploits the long-term link-quality routing metrics to augment the replica placement decision and the instantaneous link-quality metrics for replica server selection. The simulation results show that our proposed scheme has better performance compared to other replication schemes.

Content replication has gained much popularity in recent years both in the wired and wireless infrastructures. A key challenge faced by Wireless Mesh Networks (WMNs) is to determine the number and locations of content replicas (e.g. video clip) such that the mesh clients access cost is minimized. Furthermore, the dynamic nature of the wireless environment favors a distributed and adaptive solution to this problem. In this paper, we present an efficient, lightweight and scalable object replication and placement scheme for WMNs. Since the placement problem is NP-Complete, the scheme decomposes the problem into smaller sub-problems to facilitate the distributed approach in a P2P fashion. Moreover, it exploits the long-term link-quality routing metrics to augment the replica placement decision and the instantaneous link-quality metrics for replica server selection. The effectiveness of our scheme is evaluated through extensive simulation studies.

Content replication and placement is an area that has been well explored in the scope of Content Delivery Networks (CDNs), but has received relatively less attention from the research community when it comes to Wireless Mesh Networks (WMNs). There are a number of Replica Placement Algorithms (RPAs) that are specifically designed for CDNs. But they do not consider the special features of wireless networks. In this paper, we propose a new heuristic called MP-DNA (Multiple Partitions per Delegate Node Assignment). We study the problem of optimal content replication and placement in WMNs. In our model, each mesh router acts as a replica server with limited storage capacity. The challenge is to replicate content as close as possible to the requesting clients and thus reduce the access latency per object, while minimizing the number of replicas. We formulate this problem in terms of combinatorial optimization and propose a novel, distributed, scalable heuristic for content replication. Using simulation tests, we demonstrate that MP-DNA is scalable, performing well with respect to the number of replica servers and the number of objects. Furthermore, MP-DNA can achieve a performance close to the Greedy-Global heuristic, with a significant reduction in running time, mean throughput and storage space required.

Wireless Mesh Networks (WMNs) extend Internet access in areas where the wired infrastructure is not available. A problem that arises is the congestion around gateways, delayed access latency and low throughput. Therefore, object replication and placement is essential for multi-hop wireless networks. Many replication schemes are proposed for the Internet, but they are designed for CDNs that have both high bandwidth and high server capacity, which makes them unsuitable for the wireless environment. Object replication has received comparatively less attention from the research community when it comes to WMNs. In this paper, we propose an object replication and placement scheme for WMNs. In our scheme, each mesh router acts as a replica server in a peer-to-peer fashion. Our scheme exploits graph partitioning to build a hierarchy from fine-grained to coarse-grained partitions. The challenge is to replicate content as close as possible to the requesting clients and thus reduce the access latency per object, while minimizing the number of replicas. Using simulation tests, we demonstrate that our scheme is scalable, performing well with respect to the number of replica servers and the number of objects. The proposed scheme can give improved performance in terms of convergence time, throughput, hop count and hit ratio.

IEEE 802.11 wireless systems usually contain rate control algorithms which are designed to adapt the transmission rate between several available rates in response to varying channel conditions. The efficiency of the rate control algorithm in selecting optimal data rates for the channel conditions directly impacts on the throughput of the wireless system. Minstrel is a rate control algorithm that has good performance compared with other algorithms, and is widely implemented in popular wireless drivers such as MadWiFi, Ath5k and Ath9k. However, surprisingly, there is very little literature studying the performance of Minstrel. In this paper, we present an experimental study using a commercial wireless access point which evaluates the performance of Minstrel against fixed rates in a real-world IEEE 802.11g testbed. This experimental study takes a number of factors into account, such as uplink and downlink channels, transmission power, channel dynamics, Access Point (AP) location, and datagram size. The results show that while Minstrel performs well in many cases (particularly with “good” or “stable” channel conditions), the algorithm has difficulty achieving optimal throughput performance with “poor” or highly “dynamic” channel conditions. Our findings provide useful information on the design of rate control algorithms.

It is well known that the standard TCP has become a performance bottleneck in the networks with large bandwidth-delay products. The situation gets worse if high-speed wireless links are part of the networks, due to the frequent random losses over the wireless links. This is because the standard TCP increments its congestion window too slowly in the absence of packet losses and decrements it too drastically in response to packet losses. A natural solution is to make TCP more aggressive. This approach has been exercised in the recent TCP development such as HSTCP and MulTCP which have the capability of using a single logical connection to emulate the behaviour of a set of multiple standard TCP connections. In the meantime, TCP Parallelisation uses a set of parallel TCP connections to transfer data for an application process. Then, a question arises - can the single-connection based approach achieve the similar performance as TCP Parallelisation in the environments where random packet losses prevail. Our analysis shows that TCP Parallelisation has the better performance and is more efficient for performance improvement.

With the emergence of high bandwidth-delay product and heterogeneous wired / wireless networks, the standard TCP appears to be too conservative to offer reasonable performance. The use of parallel TCP connections has been suggested in such environments. We have demonstrated that, with the same level of aggressiveness, this approach can outperform the single-connection based approach. However, this approach also introduces new problems: the complexity of managing multiple connections and its performance sensitive to the number of the connections in use. This paper presents a few methods to overcome these problems. In particular, we propose a novel method using a delay-based Congestion Avoidance (CA) algorithm with multiple connections. This method is not sensitive to the number of the connections in use. We also suggest using a smaller number of connections to emulate the behaviours of a number of standard TCP connections, in order to reduce the complexity of connection management.