Provenance in additive manufacturing (AM) supply chains requires tamper-evident records without severely degrading ledger performance. We present a lightweight hybrid data model that anchors only an average of 270 B of metadata comprising event type, stakeholder MSP ID, timestamp, and a SHA-256 digest on Hyperledger Fabric, while persisting the complete artefact in a MinIO S3-compatible object store addressed through a long-lived presigned URL. A Docker-Compose test-bed (Fabric v3.1.0, four organizations, two peers for each organization, one Raft orderer) demonstrates the scalability limits of on-chain payloads and the gains of decoupling data and provenance. Under an intense write workload, the lightweight scheme sustains a median end-to-end latency of 47 ms and approximately 471 transactions per second (TPS), while peer CPU remains below 35 % and memory remains stable. Embedding the raw artefact instead increases median latency by over an order of magnitude and reduces throughput to below 10 TPS at a 1 MB payload, with larger transactions unable to commit due to Fabric’s 2 MB block limit. By reducing the AM lifecycle’s on-chain footprint from 7.23 MB to 1.6 kB ( > 4,000×), the study shows that separating bulk artefacts from their cryptographic proofs is a prerequisite for maintaining low latency, high throughput, and manageable ledger growth in cross-organisational provenance systems.

This work presents a blockchain-based communication architecture for multi-agent smart manufacturing systems, enhanced with federated learning to improve security, data privacy, and scalability. Comparative evaluation with traditional centralized systems across various configurations reveals that our approach achieves up to 12 % higher model accuracy and 15 % greater security, particularly in large-scale deployments, with scalability scores surpassing those of centralized systems by 18 % as agent numbers increase. Although initial latency and energy consumption are higher, these factors improve significantly as the system scales, indicating suitability for complex manufacturing networks. This research underscores the potential of combining federated learning and blockchain to enhance resilience, security, and efficiency in Industry 4.0 smart manufacturing systems.

The softwarization of networks is increasingly spreading, and one of the main paradigms is SDN (Software-Defined Networking), which allows overcoming the limitations mainly arising from the integration of the control plane and the forwarding plane within the network devices. It extracts the control plane to place it within a new logically centralized component: the SDN controller. Since this is a monolithic architecture that limits reliability and scalability, distributed solutions based on microservices have been proposed in the literature. In parallel, Agents are fully intelligent, atomic, and autonomous decision-making units that can be flexibly recomposed to create a completely autonomous network system. They also have the ability to replicate single or multiple decision-making processes that collaborate with each other. The development of future networks such as 5G, including 6G, is pushing towards the concept of network management automation and integration of intelligence, making agents an excellent means to meet this trend. This paper first introduces intelligence in the form of agents to a distributed SDN controller based on microservices, by implementing two new functionalities: topology _learning and shortest path, Then, it leverages a microservices-based SDN solution based on Ryu SDN framework, named MSN, to run agents in a Docker Container environment. Multiple measurements were performed locally in a single machine. Results show the topology learning performances compared with several network topologies. Moreover, the shortest patti agent experimental evaluations show the knowledge size depends on the network topology and the performances of different algorithms.

The emergence of 6G connectivity heralds a transformative era in wireless communication, emphasizing the necessity of network-wide intelligence for fully automated operations. At the heart of this paradigm shift lies the crucial need for an efficient algorithm capable of accurately predicting network traffic dynamics. This paper presents a comprehensive comparative study between two pivotal neural network architectures, LSTM (Long Short-Term Memory), and SP-LSTM (Speed-Optimized LSTM), within the context of network traffic prediction for the burgeoning 6G landscape. While LSTM stands as a widely acknowledged recurrent neural network, our innovative SP-LSTM is meticulously engineered for rapid and efficient decision-making. Through rigorous evaluation in a controlled environment, both models are examined for their efficacy in forecasting network traffic patterns. LSTM's proficiency in capturing long-term dependencies in sequential data is juxtaposed against SP-LSTM's emphasis on delivering swift and precise predictions. Our comparative analysis elucidates the distinctive strengths of these algorithms, assessing LSTM's effectiveness under varying conditions and scrutinizing SP-LSTM's proficiency in providing rapid, accurate forecasts. Additionally, we provide a GitHub link for accessing the project. This study offers vital insights into the relative merits and limitations of LSTM and SP-LSTM in network traffic prediction, essential for the advancement of intelligent 6G networking. These insights inform the selection of the optimal algorithm for realizing a seamless, intelligent 6G future with unparalleled capabilities.

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

Future networks such as 6G are projected to incorporate in-network intelligence toward achieving a zero-touch network. In this regard, several approaches proposed for the organizational architecture of future networks. Similar to microservice-based service design, a multi-agent-based network automation architecture was proposed as a competing paradigm for service-oriented architecture. The proposed architecture outlines the design guideline for intelligent network systems using agents as atomic and autonomous service units that can be used as building blocks. As a continuation of this approach, we design a Quality of Service (QoS) agent to control and manage stringent services such as remote surgery. QoS agent is intelligent that can capture and respond proactively to network traffic and workload distribution, showing hourly and seasonal patterns. QoS agents can be used as a building block along with traffic classification and traffic prediction agents for an intelligent networking system. For evaluation, a campus network is designed using a NetSim environment considering a three-tier network architecture. The QoS agent dynamically finds the best path for a particular service depending on the requirements. The agent communicates with the traffic prediction agent and traffic classifier agent to collect information about the network and services. Moreover, the QoS agent also observes the network states. Using these values along with existing network topology knowledge, it ranks the available paths to proactively allocate the best path for a service. Evaluation results suggest that with the appropriate accuracy of traffic prediction, the proposed approach can autonomously adapt in allocating a path for a given service.

Periodic Automatic Replenishment (PAR) is an inventory management policy that assists the healthcare sector in keeping the right amount of stock on hand to avoid excess stock and the potential for products to expire. Traditionally, hospitals rely on the experience and firsthand knowledge of stock management technicians to keep their store supplies with enough equipment. However, manual management based on “gut feeling“ and/or nursing feedback may lead to missing products or incorrect stock orders. Extracting accurate data is often too complex or time-consuming, resulting in a lack of critical reporting data to manage product inventories across the organization properly. However, adopting forecasting techniques and incorporating them into traditional PAR management policies can provide efficient solutions for controlling hospital warehouse inventory at the lowest cost. We have proposed a deep learning-based framework to monitor inventories to reduce costs and variation, create efficiencies, and improve the quality of patient care in hospitals. Furthermore, the proposed forecasting framework's performance is assessed using a real-world scenario within a hospital. The findings indicate that the system is capable of accurately tracking the usage of medical equipment, which results in a significant reduction in the unavailability of assets.

Quantum computing advances threaten the security of today's public key infrastructure, and have led to the pending standardization of alternative, quantum-resistant key encapsulation and digital signature cryptography schemes. Unfortunately, authentication algorithms based on the new post-quantum (PQ) cryptography create significant performance bottlenecks for TLS due to larger certificate chains which introduce additional packets and round-trips. The TLS handshake slowdown will be unacceptable to many applications, and detrimental to the broader adoption of quantum safe cryptography standards. In this paper, we propose a novel framework for Intermediate Certificate Authority (ICA) certificate suppression in TLS that reduces the authentication message size and prevents excessive round-trip delays. Our approach utilizes an approximate membership query (AMQ) data structure (probabilistic filter) to advertise known ICA certs to remote TLS endpoints so that unnecessary ICA certificates are omitted from the TLS handshake exchange. We showcase the extend of the PQ authentication overhead challenge in TLS, and evaluate the feasibility of AMQ filters for ICA suppression in terms of space and computational overhead. Finally, we experimentally evaluate the potential gains form our approach and showcase a 70% reduction in exchanged ICA cert data that translates to 15--50 MB of savings in PQ TLS and for certain Web-based application scenarios.

Owing to the resource-constrained feature of Internet of Things (IoT) devices, offloading tasks from IoT devices to the nearby mobile edge computing (MEC) servers can not only save the energy of IoT devices but also reduce the response time of executing the tasks. However, offloading a task to the nearest MEC server may not be the optimal solution due to the limited computing resources of the MEC server. Thus, jointly optimizing the offloading decision and resource management is critical, but yet to be explored. Here, offloading decision refers to where to offload a task and resource management implies how much computing resource in an MEC server is allocated to a task. By considering the waiting time of a task in the communication and computing queues (which are ignored by most of the existing works) as well as tasks priorities, we propose the Deep reinforcement lEarning based offloading deCision and rEsource managemeNT (DECENT) algorithm, which leverages the advantage actor critic method to optimize the offloading decision and computing resource allocation for each arriving task in real-time such that the cumulative weighted response time can be minimized. The performance of DECENT is demonstrated via different experiments.

6G is aiming at fully incorporating in-network intelligence towards automated network management. In this regard, a multi-agent-based network automation architecture as a service design is proposed. The architecture introduces in-network intelligence, designing intelligent agents as the fundamental unit which is used as a building block in autonomous network system design. This work focuses on the dynamic agent placement problems in edge/cloud data centers. Agents are softwarized and intelligent versions of network functions that are traditionally implemented in hardware such as firewalls, packet gateways, etc. This paper, based on a combination of proactive and reactive solutions, considered decision accuracy in developing an intelligent decision algorithm that can be used in the prediction agent design. The proactive decision is based on a deep learning prediction algorithm using time-series workload forecasting. However, in case of unforeseen events that are missing from the historical dataset, the proactive decisions could be less reliable. Therefore, network-state feedback should be considered to determine the current network conditions using the change in instant arrival rate as a reactive decision. Then combining it with the proactive decision should be able to capture the unpredictable traffic spikes. The result is used to determine the number and type of agents to instantiate at a given time in the edge/cloud data centers. Using a public dataset in our algorithms, we predicted the workload request for a few days. The result shows improved decision accuracy over the existing solutions using the appropriate amount of dataset, machine learning models, and rate estimation.

Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients in IoT devices train their local models over the local data sets. The derived local models are uploaded to an FL server to generate a global model, broadcasted to the clients in the next global iteration for further training. Owing to the heterogeneous feature of the clients, client selection is critical to determine the overall training time. Traditionally, the objective of client selection is to select the maximum number of clients who can derive and upload their local models before the deadline in each global iteration. However, selecting more clients increases the energy consumption of the clients. Moreover, selecting the maximum number of clients is unnecessary as having fewer clients in early global iterations and more clients in later global iterations have been proved to achieve higher model accuracy. Hence, this article proposes to dynamically adjust and optimize the tradeoff between maximizing the number of selected clients and minimizing the total energy consumption of the clients by selecting suitable clients and allocating appropriate resources in terms of CPU frequency and transmission power. We formulate the joint client selection and resource management problem and design the energy and latency-aware resource management and client selection (ELASTIC) algorithm to efficiently solve the problem. Extensive simulations are conducted to demonstrate the performance of ELASTIC.

This article presents a novel cloud-based charging management system for electric vehicles (EVs). Two levels of cloud computing, i.e., local and remote clouds, are employed to meet the different latency requirements of the heterogeneous EVs while exploiting the lower-cost computing in remote clouds. Specifically, we consider time-sensitive EVs at highway exit charging stations and EVs with relaxed timing constraints at parking lot charging stations. We propose algorithms for the interplay among EVs, charging stations, system operator, and clouds. Considering the contention-based random access for EVs to a 4G Long-Term Evolution network, and the quality of service metrics (average waiting time and blocking probability), the model is composed of: queuing-based cloud server planning, capacity planning in charging stations, delay analysis, and profit maximization. We propose and analyze aprice-incentive methodthat shifts heavy load from peak to off-peak hours, acapacity expansion methodthat accommodates the peak demand by purchasing additional electricity, and a hybrid method of price incentives and capacity expansion that balances the immediate charging needs of customers with the alleviation of the peak power grid load through price-incentive based demand control. Numerical results demonstrate the effectiveness of the proposed methods and elucidate the tradeoffs between the methods.

Network softwarization, containerization, and cloudification in a distributed and centralized environment are the current tread in 5G, Beyond 5G, and 6G. In that sense, significant activities are going on in the research community to softwarize the network functions deploying them in a cloud-native environment. Cloud-native architecture is an approach for network function and service to be built specifically to deployed in the cloud. In this paper, we emulated Long LTE/LTE-A/5G in lightweight containers. We have evaluated the feasibility of using very lightweight environments such as k3s. We show the possibility of emulating a simple 4G/5G scenario without requiring a full Kubernetes infrastructure. The final results are based on available open projects, used as inspiration and as a starting point to modify deployment techniques and configurations. We have also explored scalability, orchestration, automation, and reliability of the deployments. Finally, we measured and tested the performance. The performance evaluation shows the potential application of the open platform-based emulations and deployment in 5G and beyond.

An autonomic network management system (ANMS) is expected to play a significant role in fifth and sixth-generation (5G and 6G) networks. It enables the network to manage itself with minimum or no human intervention. Recently, an ANMS architecture called multi-agent-based network automation of the network management system (MANA-NMS) architecture was presented. The article discussed a multi-agent service decomposition architecture, defining atomic network-functions (ANFs). These ANFs are proposed to be intelligent and autonomous agents. The agents are designed as independent atomic decision elements incorporating machine learning (ML) as an internal cognitive component. The atomic units are used as a building block for an ANMS. In line with this approach, this article proposes a network traffic classifier agent (NTCA) as a part of the network traffic management system. We first design and implement a NTCA using an ML algorithm as a cognitive component of the agent. To compare, we used K-Nearest Neighbors (K-NN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes in the agent design. We perform an evaluation using classification accuracy, training latency, and classification latency. Finally, we tested the performance of the NTCA by implementing it in the MANA-NMS conceptual framework. The results show that the Decision Tree NTCA has the highest mean classification accuracy, the least mean training latency, and the lowest mean classification latency.

Internet of Things (IoT) network is dominating both the research and industry. There are numerous emerging IoT connectivity Technologies such as Sigfox, LoRa, NB-IoT, LTEM. However, these IoT connectivity technologies have different protocols and packet/message formatting. Thus, IoT devices are usually not able to interact with one another, causing interoper-ability challenges. This is creating the so-called network island or silos. Interoperability between different IoT networks needs to be achieved to fully exploit IoT potential. This is required at each level of the network. Different solutions have been proposed to tackle the interoperability problem at different levels reducing the difficulty in defining a solution breaching the vertical silos barrier. In this article, we focus on addressing network-level interoperability. We provide a network format translator in a virtualized environment as a flexible and lightweight deployment. As a proof of concept, a testbed is developed implementing the proposed translator using NS3. Using the testbed, we can communicate with different IoT technologies sending packets between each device in each type of IoT network. For example, sending a LoRaWAN packet to Wi-Fi and 6LoWPAN and visa-versa. Finally, we have measured the latency introduced by the translator.

The introduction of the Internet of Things (IoT) and massive machine-type communications has implied an increase in network size and complexity. In particular, there is already a huge number of IoT devices in the market in various sectors, such as smart agriculture, smart city, smart home, smart transportation, etc. The IoT interconnectivity technologies are also increasing. Therefore, these are increasingly overwhelming the efforts of network administrators as they try to design, reconfigure and manage such networks. Relying on humans to manage such complex and dynamic networks is becoming unsustainable. Network automation promises to reduce the cost of administration and maintenance of network infrastructure, by offering networks the capability to manage themselves. Network automation is the ability of the network to manage itself. Various standardization organizations are taking the initiative in introducing network automation, such as European Telecommunication Standardization Institute (ETSI). ETSI is leading the standardization activities for network automation. It has provided different versions of reference architecture called generic autonomic network architecture (GANA), which describes a four-level abstraction for network-management decision elements (DEs), protocol level, function level, node level, and network level. In this article, we review and survey the existing works before and after the introduction of software-defined networking (SDN) and network-function-virtualization (NFV). We relate the main trending paradigms being followed, such as SDN, NFV, machine learning (ML), microservices, multiagent system (MAS), containerization, and cloudification, as a pivotal enabler of full network automation. We also discuss the autonomic architectures proposed in the literature. Finally, we presented possible future research directions and challenges that need to be tackled to progress in achieving full network automation.

Distribution and transmission protection systems are considered vital parts of modern smart grid ecosystems due to their ability to isolate faulted segments and preserve the operation of critical loads. Current protection schemes increasingly utilize cognitive methods to proactively modify their actions according to extreme power system changes. However, the effectiveness and robustness of these information-driven solutions rely entirely on the integrity, authenticity, and confidentiality of the data and control signals exchanged on the underlying relay communication networks. In this paper, we outline a scalable adaptive protection platform for distribution systems, and introduce a novel blockchain-based distributed network architecture to enhance data exchange security among the smart grid protection relays. The proposed mechanism utilizes a tiered blockchain architecture to counter the current technology limitations providing low latency with better scalability. The decentralized nature removes singular points of failure or contamination, enabling direct secure communication between smart grid relays. We also present a security analysis that demonstrates how the proposed framework prohibits any alterations on the blockchain ledger providing integrity and authenticity of the exchanged data (e.g., realtime measurements/relay settings). Finally, the performance of the proposed approach is evaluated through simulation on a blockchain benchmarking framework with the results demonstrating a promising solution for secure smart grid protection system communication.

The advances in quantum computing present a threat to public key primitives due to their ability to solve hard cryptographic problems in polynomial time. To address this threat to critical Internet security protocols like the Transfer Layer Security (TLS), and Secure Shell (SSH), the National Institute of Standards and Technology (NIST) is currently working on the new generation of quantum-resistant key encapsulation and authentication schemes. In this paper, we evaluate protocol handshake performance when both post-quantum key exchange and authentication are integrated into TLS and SSH. Our experiments consider realistic network conditions and reveal that the introduced handshake latency ranges between 1-300% for TLS and 0.5-50% for SSH depending on the post-quantum algorithms used. In addition, we examine how the initial TCP window size affects post-quantum TLS and SSH performance, and show that even a small size increase can reduce the observed post-quantum slowdown by 50%. Finally, we discuss alternatives that can encourage the early adoption of post-quantum cryptography with minimum protocol performance degradation.

A large scale of electric vehicles (EVs) and the operation of smart grid requires the support of a reliable and robust communication infrastructure. Cloud computing has gained popularity in smart grid for reducing computational and communication complexity. Based on cloud computing services, this paper considers the issues of high charging demand in fast charging stations (FCSs) during peak hours and communication among a large-scale of EVs, a network of FCSs, and system operator (SO). More specifically, we propose a novel cloud-based hierarchical charging management model of EVs, whereby two levels of cloud computing infrastructures are considered to meet different latency requirements of customers in highway exits and parking lots. Considering the quality of service (QoS) metrics (average waiting time in the queue, and blocking probability), the model is composed of: server planning in the cloud, capacity planning in FCSs, and profit maximization. Meanwhile, a price incentive mechanism is applied to shift the heavy load from peak hours to off-peak hours. Numerical results demonstrate the effectiveness of the proposed method, which can guarantee QoS and system profit, thereby more customers can satisfy their charging demand.

Multipurpose devices have emerged as part of modern Internet of Things (IoT) ecosystems. Such nodes are able of interchanging their operation between different sensing modes providing a large mixture of information towards various IoT applications. In this paper, a novel framework is introduced to govern and properly define the dynamic operation of a multipurpose device network deployment. Initially, the problem of socio- physical energy-efficient device sensing mode selection is confronted. Each multipurpose device acts as a learning automaton and through a machine learning mechanism selects the most appropriate operation mode, in terms of maximizing the revenue/cost relation of the provider. In addition, towards improving the communication efficiency, a coalition formation mechanism among the nodes is proposed, which considers: (a) nodes' spatial proximity reflecting physical conditions such as channel quality, (b) energy availability, and (c) operation mode correlation between multipurpose devices expressing social metrics. Given the mode selection and coalition formation among nodes, a distributed utility-based power control mechanism is proposed to determine each device's optimal transmission power in a Non- Orthogonal Multiple Access (NOMA) wireless network environment in order to fulfill its Quality of Service (QoS) prerequisites. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority is demonstrated.

The accelerated evolution of Internet of Things (IoT) architectures and their incorporation in vehicles, buildings, or cities provide the ideal environment for the development and optimization of smart services. Under this light, positioning services that harvest location fingerprinting based on received signal strength indications (RSSIs) are widely popular due to the massive data generation that IoT settings provide. However, the labor-intensive and repetitive task of the radio map construction through offline RSSI fingerprint collection prevents such services from becoming standard equipment for future smart facilities. In this paper, we present a location-aware infrastructure that combines a broad sensing layer, edge computing, and centralized cloud federation support. Our setting gives rise to a sensing mechanism that enables in-facility crowdsourcing able to aid fingerprinting localization services. To that end, instead of extensive offline measurements, we use the facility occupants to gather unlabeled RSSI samples. To support the localization functionality, we develop a probabilistic cell-based model that is constructed by an unsupervised learning algorithm. Our black-box approach maintains the positioning accuracy regardless of changes in the underlying hardware or indoor environment. To evaluate our approach, we have deployed a multistorey facility testbed and performed an extensive real-subject trial to gather the unlabeled fingerprint dataset. The proposed unsupervised method yields average location classification accuracy of 0.8 that can rise up to 0.9 when a semi-supervised approach is considered. We also provide insights into the performance of the proposed infrastructure regarding mobility tracking, and under varying deployment scenarios.

Proximity-based Services (PBS) require high detection accuracy, energy efficiency, wide reception range, low cost and availability. However, most existing technologies cannot satisfy all these requirements. Apple's Bluetooth Low Energy (BLE), named iBeacon, has emerged as a leading candidate in this domain and has become an almost industry standard for PBS. However, it has several limitations. It suffers from poor proximity detection accuracy due to its reliance on Received Signal Strength Indicator (RSSI). To improve proximity detection accuracy of iBeacons, we present two algorithms that address the inherent flaws in iBeacon's current proximity detection approach. Our first algorithm, Server-side Running Average (SRA), uses the path-loss model-based estimated distance for proximity classification. Our second algorithm, Server-side Kalman Filter (SKF), uses a Kalman filter in conjunction with SRA. Our experimental results show that SRA and SKF perform better than the current moving average approach utilized by iBeacons. SRA results in about a 29% improvement while SKF results in about a 32% improvement over the current approach in proximity detection accuracy.

The paper proposes a new idea, called "Interactive Energy", for the dynamic pricing and dispatching of the charging services of Electric Vehicles (EVs) in a micro-grid. In the proposed approach, the customers can choose their recharge time through an interactive application able to indicate different recharge times and related prices in function of the real time conditions of the micro-grid. The available power at each charging station is conditioned to the grid conditions and the prices change as a function of the requested energy, power and local renewable energy produced in the micro-grid. In the paper, a statistical model is proposed and applied for analyzing the impact that the proposed system can have on the power grid. The proposed system can gain ground with the ongoing battery improvements: the recharging times for EVs could become comparable to the present fuel stop times at the gas station through the continuous improvement of the battery technologies.

To facilitate the adoption of electric vehicles (EVs) and their plug-in hybrid (PHEVs) counterparts and to avoid straining the capacity of the power grid there is a strong need for developing a network of fast charging facilities and coordinate their service. Incorporation of EVs in the vehicle fleet would decrease green house gas emissions and overall dependency on fossil fuels. A key issue in charging EVs is that the corresponding time is fairly large, which can lead to very long delays. Hence, for the network of charging stations to provide good quality of service to customers, we first propose an admission control mechanism based on pricing for a single charging station. Subsequently, we develop a decentralized routing scheme of EV drivers, employing a game theoretic model. The latter entices drivers through price incentives to require charging from less busy stations, thus leading to a more efficient utilization of power across the network, while it enhances profit for the charging facilities operator. Of note, the proposed scheme does not require advanced monitoring tools for power usage and pricing calculations. The drivers receive and send back the necessary information through the a communications infrastructure and the routing is initiated only when the network has exceeded a critical threshold. The numerical results illustrate the discussed benefits of the proposed scheme.

In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging and complex problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked (and hence not served) by the closest charging station to their location. Overall, the paper addresses key issues related to the efficient operation, both from the perspective of the power grid and the drivers satisfaction, of a network of charging stations.

Due to replacement infeasibility, methods to extend the life-time of sensors have been an issue in Wireless Sensor Networks (WSNs) and these should consider network connectivity simultaneously. Controlling the sleep/awake of sensors is one simple way to reduce their energy consumption. However, this causes a network connectivity degradation by varying network connection topology. For this reason, we propose a simple and autonomous sensor sleep/awake method to achieve their balance. The size of clusters can be a metric to measure network connectivity in that a path exists among any cluster node. From percolation theory, we observe that a cluster size suffers a sharp transition based on edge connection patterns. This allows us to design a sensor sleep/awake algorithm which has an immense simplicity, but still requires global topology information. In many cases, sensors are not aware of the global topology. Further, managing the information becomes challenging under physical topology changes such as sensor add/drop. We show that the global knowledge requirement can be resolved by using a consensus algorithm. Through several graph tests, we show that our method achieves a network balancing between connectivity and life-time with preserving its simplicity. Also, the balancing is autonomous even under physical topology variations.

Proxy caches or Redundancy Elimination (RE) systems have been used to remove redundant bytes in WAN links. However, they come with some inherited deficiencies. Proxy caches provide less savings than RE systems, and RE systems have limitations related to speed, memory and storage overhead. In this paper we advocate the use of a hybrid approach, in which each type of cache acts as a module in a system with shared memory and storage space. A static scheduler precedes the cache modules and determines what types of traffic should be forwarded to which module. We also propose several optimizations for each of the modules, such that the storage and memory overhead are minimized. We evaluate the proposed system by performing a trace driven emulation. Our results indicate that a hybrid system is able to provide better savings than a proxy cache, or a standalone RE system. The hybrid system requires less memory, less disk space and provides a speed-up ratio equal to three compared to an RE system.

In this paper, we conduct feasibility studies on the average delay space for Cloud computing, and we propose a heuristic method to control the vector of average delays, subject to predefined delay constraints. Our work is strongly motivated by the fact that delay control plays a critical role to improve Service Level Agreements (SLA) between users and Cloud service providers, which is necessary for empowering online business. Specifically, our main contributions are two-fold: First, the feasible regions of various routing algorithms for the system's dispatcher are investigated in depth. Second, a simple heuristic algorithm is designed, to move the average delay point along the feasible direction until achieving the delay constraints. Average delay is dependent on multiple factors such as job size, inter-arrival time, flow rate, and the dispatching rules of the system. Therefore, we vary their distribution, parameters and routing rules to examine how the feasible regions move or change. After establishing the feasible delay space, then by moving along the feasible directions, we show that a simple heuristic algorithm can achieve the delay constraints for a two queue system.

A massive amount of energy consumption currently stems from the transportation sector. Therefore, improvements in power usage by commuting vehicles are being studied and becoming an increasingly popular research topic. In particular, there is a growing need to model the envisioned smart infrastructure, including charging stations, some of which might include energy storage devices and swappable, pre-charged batteries. For such new stations, power management is indeed crucial for operation costs, driver convenience, and overall smart grid efficiency. Information technology, communications and vehicle intelligence need to play a crucial role in this process. In this paper, we describe a quantitative model and propose a guiding and control system for the charging of PHEVs in a future smart infrastructure. Specifically, we describe an algorithm that can be used for the joint guidance and power control of smarter electric vehicles in the smart grid. We envision it as part of a larger Smart Guide for the Smart Grid (SGSG) system. Its function is to guide PHEV drivers, directing them to the appropriate charging station, while attempting to achieve an optimization goal at the same time. Our algorithm aims at a joint guiding and power control, in order to heuristically maximize the weighted sum of the average of throughput and energy cost consumption from multiple vehicle charging stations, while satisfying a cost constraint at each station, as well as system stability.

The proliferation of multiplay services is creating design dilemmas for service providers, related to where certain key networking functionality should be placed. For example, service providers need to know whether to distribute more network intelligence closer to the subscriber or cluster it in a central location. In view of this, we quantify the cost differences among service provider architectures, identified based on the functionality distribution (centralized vs. distributed, clustered vs. unclustered and single vs. multi edge). For this purpose, we formulate a modular mixed-integer programming model based on a set of close-to-real-case scenarios. Given the complexity of such problems, we propose methodologies that can reduce the number of locations. Our results indicate that distributing the IP intelligence and the video replication is preferable. Moreover, deploying edge systems with faster backplane has little benefit in the aggregation network, and providers should rather invest in faster interfaces.

In this paper, we propose the use of network decomposition under an optimal resource allocation framework. We develop a methodology where recursive formulas can be utilized for calculating the desired end-to-end performance bounds (i.e., backlog bound violation probability) of flows traversing tandem, acyclic queueing networks. We use those performance metrics in an optimization framework that allocates resources to network services with specific quality-of-service requirements. Finally, we evaluate our framework and compare its performance against a system utilizing deterministic bounds obtained from network calculus.

Heterogeneous computing platforms such as Grid and Cloud computing are becoming prevalent and available online. As a result, resource management in these platforms is fundamentally critical to their global performance. Under the assumption of jobs comprised of subtasks forming DAG jobs, we focus on how to increase utilization and achieve near-optimal throughput performance on heterogeneous platforms. Our analysis and proposed algorithm are analytically derived and establish that, by aggregating multiple jobs using good scheduling, a near-optimal throughput can be achieved. Consequently, its limit is asymptotically converging to a certain value and can be written in the form of the service time of subtasks. Furthermore, our analysis shows how to explicitly compute the optimal throughput of computing systems, an important task for such a complex scheduling problem. In addition, we derive a simple super-job scheduling and show that its performance in term of throughput is better than the well-known Heterogeneous Earliest-Finish-Time (HEFT) algorithm.

With the advent of cloud computing, organizations tend to buy services from data centers of major cloud vendors. In contrast, community cloud computing as described in give the alternative way to reduce the costs or even obtain free resources, by sharing among communities. Because the utilization of computing resources in an organization is not constantly 100%, other members of the community can exploit these excessive resources. In this paper, we design an algorithm for admission control and resource allocation, in order to deal with unreliably excessive computing resources. Furthermore, we introduce a social price in order to manage the allocation more efficiently both in term of social relations and of revenue.

Triple-play services and P2P IPTV have not only led to an increasing demand for bandwidth in broadband access networks, but also to the need for new service delivery architectures. The choice of an appropriate architectural approach and sizing model for the aggregation network is studied in this paper through cost optimization models, which encompass aspects of non-stop delivery, service flexibility, policy management and cost allocation. We propose two independent quantitative programming models that identify the cost of each architecture and the corresponding effect of each of the hardware constraints and traffic flows. We show that due to the next generation applications, the ISPs will need to re-engineer the broadband access infrastructure to accommodate intelligent aggregation and optimize for QoS-sensitive services.

A significant portion of the HTTP multimedia traffic on the Internet comes from sites like Youtube which serve short videos. Caching of Youtube-like multimedia content, when possible, can reduce traffic on the backbone while providing faster access. The performance of such a caching system will depend on identifying the videos which should be cached and the appropriate duration. In this paper, we look at both of these questions from a social network perspective. We propose that the decision to cache a video should be based on the combined popularity of the individual as well as related videos rather than simply based on individual popularity of a video. We identify timescales at which the inter-relationships between the videos can change through a longitudinal data set. Using the concepts of centrality of nodes, we rank the set of videos in the data set according to their perceived importance. In doing so, we compare three centrality techniques - degree, closeness and betweenness. We evaluate how these centralities affect the performance of a cache. We show that ``Closeness" centrality always performs at least as well as the other two in all cases. Finally, we show that a distributed cache mechanism employing the centrality method to rank videos can reduce the load on the network significantly for even moderate content cache sizes.

In this paper, we propose a novel autonomic service delivery platform for service-oriented network environments. The platform enables a self-optimizing infrastructure that balances the goals of maximizing the business value derived from processing service requests and the optimal utilization of IT resources. We believe that our proposal is the first of its kind to integrate several well-established theoretical and practical techniques from networking, microeconomics, and service-oriented computing to form a fully distributed service delivery platform. The principal component of the platform is a utility-based cooperative service routing protocol that disseminates congestion-based prices among intermediaries to enable the dynamic routing of service requests from consumers to providers. We provide the motivation for such a platform and formally present our proposed architecture. We discuss the underlying analytical framework for the service routing protocol, as well as key methodologies which together provide a robust framework for our service delivery platform that is applicable to the next-generation of middleware and telecommunications architectures. We discuss issues regarding the fairness of service rate allocations, as well as the use of nonconcave utility functions in the service routing protocol. We also provide numerical results that demonstrate the ability of the platform to provide optimal routing of service requests.

Social connectivity networks and the associated socio-technical context are an important determinant of a user's resource requirements. Despite the recent attention on wireless network design, little quantitative work has been done in order to combine purely technical design methods with explicit dependencies on the social or business connectivity and utility concerns. In this paper, we quantify the effect of the socio-technical context through the notion of effective distance, and then use this distance as a parameter in the user utility function. We explore the premise that efficient and targeted resource allocation can be achieved better if the network is aware of the effective distance and the expected quality at the receiver. We demonstrate this for the case of a 802.11e compliant WLAN, where users in the socio-technical context choose to communicate through VoIP. We determine a utility function for VoIP calls in the presence of background HTTP traffic in the WLAN. We then form an objective function which will achieve better resource optimization, with knowledge regarding the effective distance and expected quality requirements. Our results show that a network which is aware of the social context can almost double the utility of the network to the users in case of VoIP traffic.

For voice/video applications that are based on Peer-to-Peer (P2P) models, ensuring the end-to-end Quality of Service (QoS) is crucial, especially if users are paying fees. In this paper we propose an End-to-end Performance Inference Technique (EPIT) that uses a prediction-based approach to map the ingress traffic levels of the P2P network to the end-to-end QoS in the network. Furthermore, by coupling Simulated Annealing (SA) with EPIT, we describe a traffic engineering solution in such a way that the QoS constraints are met while traffic flows into the network are maximized.

In this paper, we present a distributed algorithm to dynamically allocate the available resources of a service-oriented network to delay sensitive network services. We use a utility-based framework to differentiate services based on both their relative profitability and quality-of-service requirements. Our performance metric is the end-to-end delay that a service class experiences in the network. We use network calculus to obtain a deterministic upper bound of this delay and we incorporate this information into our optimization problem formulation. We leverage a moving average control scheme to capture traffic shifts in real time, which makes our solution to react adaptively to traffic dynamics. Finally, we evaluate our system using real traces of instant messaging service traffic.

In this paper, we propose a novel autonomic service delivery platform for service-oriented network environments. The platform enables a self-optimizing infrastructure that balances the goals of maximizing the business value derived from processing service requests and the optimal utilization of IT resources. We believe that our proposal is the first of its kind to integrate several well-established theoretical and practical techniques from networking, microeconomics, and service-oriented computing to form a fully-distributed service delivery platform. The principal component of the platform is a utility-based cooperative service routing protocol that disseminates congestion-based prices amongst intermediaries to enable the dynamic routing of service requests from consumers to providers. We provide the motivation for such a platform and formally present our proposed architecture. We discuss the underlying analytical framework for the service routing protocol, as well as key methodologies which together provide a robust framework for our service delivery platform that is applicable to the next-generation of middleware and telecommunications architectures.

The increasing popularity of wireless fidelity (Wi-Fi) networks at home, in public areas and in the enterprise motivates extensive modeling and analysis of their performance measures, such as network capacity and quality of service (QoS) capabilities. Some of the easier performance problems can be solved by analytical modeling methods, but most of the complicated ones, involving several design parameters from multiple layers, can only be answered through simulation studies that are typically implicit and less suitable for design and optimization. Hence, we believe it is crucial to obtain explicit mathematic models, that are effective in representing system behavior and a basis for performance optimization. Here, we first advocate the application of formal empirical modeling techniques to performance studies of Wi-Fi networks, in order to find usable, if approximate, closed-form mathematical models. Subsequently, by applying these metamodeling techniques, we perform a case study with very useful results: our VoWiFi (voice over Wi-Fi) admission capacity metamodel gives a much tighter bound than those existing in the literature and leads to a more effective admission control scheme. Our work, therefore, points out a new direction for future performance studies of Wi-Fi networks.

In this paper we compare and enhance the three prevailing approaches of IEEE 802.11e performance analysis. Specifically, the first model utilizes a Markov Chain to describe the state of the Backoff Counter, the second is based on a general probabilistic explanation of the standard and the third forms a queuing network. We have injected, in the proposed models, new ideas to cover the latest update of the QoS-enabled 802.11e standard, and compared all the models showing results regarding the accuracy of each approach. Throughput performance is given for various parameters of the medium while including Gaussian error-prone channel in 802.11b/e. Results are also provided regarding the effect of the Block-ACK feature. The comparison is performed both in terms of accuracy and structural possibilities and finally the results are validated via simulations with Opnet Modeler. The proposed comparison mathematical analysis can also be extended to other applications and wireless protocols.

We believe that application-aware networks will be a core component in the development and deployment of emerging network services. However, previous attempts at enabling application-awareness in the network have failed due to issues with security, resource allocation, and cost of deployment. The emergence of the Extensible Markup Language (XML), an open standard that enables data interoperability, along with advances in hardware, software, and networking technologies, serves as the catalyst for the development of service-oriented networking (SON). SON enables network components to become application-aware, so that they are able to understand data encoded in XML and act upon that data intelligently to make routing decisions, enforce QoS or security policies, or transform the data into an alternate representation. This paper describes the motivation behind service-oriented networking, the potential benefits of introducing application-aware network devices into service-oriented architectures, and discusses research challenges in the development of SON-enabled network appliances.

The ability to cope with dynamic bandwidth demands will be a particularly important asset for Quality of Service provisioning in networks carrying bandwidth hungry applications. This paper introduces a novel approach based on multi-objective optimization with fuzzy constraints for dynamic bandwidth allocation. This new approach deals with uncertain bandwidth demands more efficiently than an approach based on Classical Optimization Theory and yet supports Quality of Service commitments.

The increasing popularity of Wireless Fidelity (Wi-Fi) networks at home, in public areas and in the enterprise motivates extensive modeling and analysis of their performance measures, such as network capacity, resource requirements and quality of service (QoS) capabilities. Some of the easier performance problems can be solved by analytical modeling methods, but most of the complicated ones, involving too many factors from multiple layers, can only be answered through validated simulation models. However, an explicit mathematic model is always the most effective way to represent the system behavior and the most convenient basis for performance optimization. Here, we first advocate the application of metamodeling techniques to performance studies of Wi-Fi networks, in order to find usable, if approximate, closed-form mathematical models. Subsequently, we formulate a general metamodeling framework for Wi-Fi networks. Our results in two relevant case studies, after applying this framework, support the validity of our metamodeling methodology: our capacity metamodel for 802.11 Distributed Coordination Function (DCF) is validated by a well-known analytical model and displays an interesting log-linear relationship between capacity and number of users; our voice over Wi-Fi admission capacity metamodel gives a much tighter bound than bounds existing in the literature and composes a more practical admission control scheme. Our work, therefore, points out a new direction for future performance studies of Wi-Fi networks.

Numerous routing schemes have been reported to improve network performance over the years. Multi-path routing belongs to one of them and MPLS is an excellent platform for such routing. In this paper, the Shortest Distance Path Based Simulated Annealing (SDPSA) algorithm for finding optimal bandwidth reservation solutions for multi-path routing is developed to improve network performances. The algorithm, which employs the annealing method, is based on previous solutions to find the current sub-optimal solution for multi-path routing. Multiple objectives including balancing traffic load and minimizing network resource consumption are taken into consideration. Finally, the proposed algorithm is applied to a randomly generated network and the NSFNET network. The performance values are compared to a well-known multi-path routing algorithm-HSTwp. The simulation and comparison results show that the proposed SDPSA algorithm is feasible and efficient for the optimization of multi-path IP routing.

The burst loss probability is considered as one of the most important performance indicators of optical burst switching (OBS) networks. Computer simulations are widely used in estimating burst loss probabilities in OBS networks, especially when analytical methods are not possible. However, as the number of wavelengths in an OBS network becomes large, burst losses can become rare events. In such cases, traditional simulation methods may take an exceedingly long time while still not yielding an estimate with acceptable confidence interval. In this paper, we propose a method based on Importance Sampling (IS) to accelerate the simulation. To optimize IS parameters in such an environment, we propose using Simulated Annealing (SA) to directly minimize the variance of IS estimators. The proposed method (SA-ISSC) is easy to use and efficient, while producing very favorable results.

This paper proposes a new traffic engineering approach: importance sampling based traffic engineering (ISTE). ISTE can map the bandwidth of a traffic flow to the quality of service (QoS) it can receive within a network. The proposed ISTE approach does not require extensive knowledge of the network internal details, thus making it applicable to most large and complex networks. It can carry out the end-to-end QoS analysis of a network or carry out the performance analysis of a single network node. Even if there are multiple congested nodes in the network, the ISTE approach remains effective. This paper shows that the ISTE approach, under self-similar (C. Huang, Ph.D. thesis, Carleton University, Canada, 1997; V. Paxson and S. Floyd. IEEE/ACM Trans. on Networking, vol. 3, no. 3, pp. 226-244, 1995) traffic model, is capable of calculating the changes in the network QoS (e.g. probability of buffer overflow) with respect to the changes in the bandwidths of the ingress network traffic flows. In the scenarios where several ingress traffic flows influence the QoS of the network, a more specialized technique called ISTE alternating twisting (ISTE-AT) is proposed. ISTE-AT makes the proposed ISTE approach even more powerful.

Efficient bandwidth allocation has been a popular research topic during the last few years. It is a challenging issue to provide guaranteed quality of service (QoS) for network applications while still obtaining high network utilization. As a promising approach to achieve tradeoff between network utilization and the provisioning of QoS, the concept of effective bandwidth has been widely accepted. However, it requires a full characterization of the underlying process to calculate its effective bandwidth, which is not trivial. It is also well known that the pure effective bandwidth allocation is conservative. To bypass modeling the underlying traffic and overcome the conservative nature of effective bandwidth, we propose a robust adaptive effective bandwidth allocation (AEBA) algorithm. We study the performance of the robust AEBA method under the dynamic weighted round-robin (DWRR) scheduling with a set of simulations using both self-similar traffic and traditional Poisson traffic as input. The simulation results show that our approach allows different QoS requirements to be satisfied while effectively exploiting the statistical multiplexing gain occurring among multiple traffic classes at the same time. The simulation results also show that our approach is robust in that it does not need any assumptions about the underlying traffic.

In this paper we consider calculating blocking probabilities for several bandwidth allocation policies in a multi rate multi class networks. A request can require multiple units of each resource (the multi-rate case) and it does not have to be the same for different requests of the same traffic type. We consider the standard complete-sharing policy, guaranteed-minimum sharing policies and a preemptive policy in a prioritized multi class system. The goal of this paper is to determine the probability of a new customer being admitted with a desired grade of service. We present approximate analytical tools to obtain blocking probabilities in a multi rate multi class system, where users of the same class can have different resource requirements. To validate our approach, we also develop a simulation of the system.

Rapid deployment of IEEE 802.11 wireless local area networks (WLANs) and their increasing quality of service (QoS) requirements motivate extensive performance evaluations of the upcoming 802.11e QoS-aware enhanced distributed coordination function (EDCA). Most of the analytical studies up-to-date have been based on one of the three major performance models in legacy distributed coordination function analysis, requiring a large degree of complexity in solving multidimensional Markov chains. Here, we expose the common guiding principle behind these three seemingly different models. Subsequently, by abstracting, unifying, and extending this common principle, we propose a new unified performance model and analysis method to study the saturation throughput and delay performance of EDCA, under the assumption of a finite number of stations and ideal channel conditions in a single-hop WLAN. This unified model combines the strengths of all three models, and thus, is easy to understand and apply; on the other hand, it helps increase the understanding of the existing performance analysis. Despite its appealing simplicity, our unified model and analysis are validated very well by simulation results. Ultimately, by means of the proposed model, we are able to precisely evaluate the differentiation effects of EDCA parameters on WLAN performance in very broad settings, a feature which is essential for network design.

Performance evaluation of the upcoming 802.11e QoS-aware enhanced distributed channel access (EDCA) in WLANs has drawn extensive attention. The foundations for most of the studies are three major performance models in legacy distributed coordination function (DCF) analysis. In this paper, we first expose the common guiding principle behind these three seemingly different models. Subsequently, we propose a new unified performance model and analysis method to study the saturation throughput and delay performance of EDCA, under the assumption of a finite number of users and ideal channel conditions in a single-hop WLAN. This unified model is easy to understand and apply and helps increase the understanding of the existing performance analysis. In addition to its appealing simplicity, our unified model and analysis are validated well by simulation results. And ultimately, by means of the proposed model, we are able to evaluate the differentiation effects of EDCA parameters on WLAN performance in very broad settings, a feature which is essential for network design.

We develop a framework of using effective bandwidths under dynamic weighted round robin scheduling to study the statistical quality of service assurance issue in self-sizing networks supporting Differentiated Service. A traffic measurement-based adaptive effective bandwidth allocation algorithm aiming at improving the performance of effective bandwidths is proposed. We evaluate our proposed mechanism with a set of simulations that use Poisson and Markov modulated Poisson process sources as input. The simulation results show that the adaptive effective bandwidth allocation allows different quality of service requirements to be satisfied at the same time while overcoming the conservative nature of the pure effective bandwidth allocation.

The transmission control protocol (TCP) window adaptation scheme is heuristic in nature. Previous work on such schemes has shown that window size calculations, based on utility-driven and game-theoretic objective functions, lead to better performance in overall throughput. We observe that packet losses are good indicators of network congestion. We utilize the trend in packet losses to propose a predictive utility-based scheme working under imperfect network information. In our work, we record the packet loss observed in the current and previous transmission slots to predict the expected packet loss for the next interval. We then use a simple utility-based scheme to calculate the best window size for the next interval that minimizes the packet loss and maximizes the net utility.

Game-theoretic formulations of the resource allocation problem have existed for a while. However, the issue of perfect knowledge continues to be a significant hurdle on the path to realistic implementations. In this paper, the notion of playing bandwidth allocation games is investigated under imperfect information. Specifically we look at the case of connection oriented networks regulated by resource pricing. We devise a distributed adaptive control strategy based on dynamic estimation in order to cope up with the uncertainty of noise and delay. Simulation results illustrate the scalability and accuracy of the algorithms under multiple scenarios. Potential applications include teletraffic and optical networks, as well as ad hoc wireless networks, enabling users to partition bandwidth without the need of a centralized synchronizing entity.

The concept of effective bandwidth can be utilized to estimate the amount of bandwidth that should be allocated to a source in order to meet a QoS requirement. Several different effective bandwidth estimators have been defined in the literature; however it is necessary to ensure that these estimators are practically implementable and feasible in realistic network environments. This necessity serves as our motivation to implement several estimators in a realistic network in order to evaluate the use of online measurement-based resource allocation schemes. In this paper, we describe our implementation of three resource allocation schemes within the realtime traffic flow measurement architecture. We compare our results of emulation to previous simulation results in order to compare the accuracy and performance of the schemes. Finally, we demonstrate that these schemes are feasible to be implemented in network hardware to be utilized in self-sizing high-speed networks.

We consider the problem of extending the network "self-sizing" framework to locally controlled networks, in which resource allocation decisions are made at the node level. Schemes for self-sizing and adaptive resource optimization have been proposed in the past for globally controlled networks. We show that by performing online resource allocation at each node based on their local knowledge, we can achieve considerable bandwidth savings, congestion reduction and also satisfy QoS at the packet level. Online traffic measurement based on effective bandwidths has been used as the tool for estimating real time bandwidth allocation, which implicitly guarantees the QoS. Simulation results also suggest that by making some of the nodes aware of their neighbors resource availability, higher self-sizing gains can be attained.

Efficient and reliable bandwidth allocation remains an important open issue in the management of networks that aim to offer a guaranteed quality of service (QoS). Guaranteeing QoS to incoming requests means that users explicitly specify certain requirements such as throughput, loss or delay. It also means that the provider needs to be prepared to compensate for transient outages or overloads and re-allocate resources according to priorities. Dynamic bandwidth allocation can be used to assure that high priority requests can be always routed through relatively favorable paths within a differentiated services environment. In this paper we introduce a utility-based QoS model and a generalized bandwidth allocation scheme which accounts for the users of QoS requirements. We analyze the dynamic and static bandwidth allocation policies in the presence of four types of traffic each described with its utility value when the network experiences overloads. Our approach allows a service provider to differentiate between different types of customers based on their priority or the service charges that they pay, in order to offer real time services, to provide QoS guarantees for multimedia traffic and to guarantee stability even in overloaded conditions.

The enhanced distributed coordination function (EDCF) is one of the main features of 802.11e quality of service (QoS) enhancement over legacy 802.11 distributed coordination function (DCF). In order to control network performance, a network designer needs to understand how the EDCF parameters affects the QoS performance explicitly. However, we are unaware of any publications regarding EDCF analysis that includes all of the protocol details. Most evaluations only use numerical results. We propose a simple but extremely accurate analytical model to compute the saturation throughput performance of 802.11e EDCF, with the assumption of ideal channel conditions and same different inter frame space (DIFS). The analysis consists of two cases. The first case demonstrates how different nodes can achieve differentiate access in uplink. In the second scenario, there are multiple flows in one node, and they compete for the channel in downlink. With the help of the analysis, we show how to design a distributed MAC weighted round robin (WRR) packet scheduler to emulate an ideal network layer scheduler. Simulation comparison indicates that the MAC layer scheduler can achieve higher utilization, smaller delay than the network layer WRR and bandwidth ratio as wanted.

In this paper, we model users of next generation services by means of utility differentiated classes. Noncooperative game theory is employed to explain user behavior with respect to the network price. Rate control algorithms for attaining such a noncooperative Nash equilibrium are then presented. We extend our previous model of a single link fed by Poisson traffic to encompass a generic network and non-Poisson traffic. These results can be applied to a wide variety of future networks ranging from LSPs in MPLS networks to wavelength paths in WDM networks.

Active queue management (AQM) techniques are designed to detect incipient network congestion and proactively drop packets so as to avoid congestion later. Most AQM techniques use the exponentially weighted moving average (EWMA) of the queue length as a measure of congestion. More recent efforts, like adaptive virtual queue, have concentrated on rate-based packet marking. In this paper we analyze the robustness of a pure rate based packet marking scheme and compare it with our proactive AQM (PAQM) scheme. The PAQM proposed in this paper extends the calculation of the EWMA queue length by including a term that represents the future traffic intensity. To simplify the analysis, the AQM techniques were studied as a combination of a measurement module and a control module. Robustness, throughput and end-to-end delay jitter were chosen as performance metrics for various combinations of predictors and controllers. We have the following observations for a given network (i) performance of AQM schemes is sensitive to both the weight and interval of predicted future observations and prediction need not always perform better, (ii) the performance of more robust AQM schemes in terms goodput was found to be better than less robust ones.

Bandwidth allocation is a fundamental problem in communication networks, especially where bandwidth is reserved for requests to guarantee a certain quality of service (QoS). Connection preemption, coupled with the capability to reroute connections, provides available and reliable services to high-priority connections when a network is heavily loaded and connection request arrival patterns are unknown, or when the network experiences transient overloads or faults that reduce the available capacity or routes. Preemption becomes more a more attractive strategy in a differentiated services scenario, especially when using DiffServ-aware traffic engineering approach. However, the complexity of such connection preemption algorithms is a very important performance criterion for implementation in real networks. In this paper, we analyze two simple and efficient preemption policies with random selection and examine their performance in a dynamic setting. To compare the dynamic performance of the new algorithms to the existing ones, we conduct complexity analysis and simulation studies.

In this paper, a comprehensive analytical model to predict the bandwidth achieved by aggregates of TCP flows in a DiffServ network is presented. The model predicts achieved bandwidth in three different cases: an over-provisioned network, an under-provisioned network, and a near-provisioned network. In developing the model, we ensure that all parameters are measurable using standard tools and information available from routers and network management tools in today's networks. Simulation was used to establish the validity of the model and understand its scope of applicability and limitations. Using the model, we explain why achieved excess bandwidth is based on factors such as RTT, packet size, and CIR. Finally, we present a novel extension of the model to predict the bandwidth of TCP flows in a Diffserv network with multiple congested nodes.

This paper focuses on the initial value of TCP's retransmission timeout (RTO) timer. We make a three-fold contribution to the work in this area. Firstly, to motivate this work, we conduct a study of Internet traffic to determine loss rates for those packets that rely on timeouts with the initial RTO value to recover. Secondly, we experiment with TCP stacks in nine different operating systems to understand the initial RTO values used. Finally, we conduct simulations with HTTP traffic models to study the impact of different initial RTO values on the transfer delay of Web objects. The experiments show that average transfer delay can be impacted by as much 70% on bottleneck links with loss rates above 6%. Further, TCP flows with timer granularity below 100 ms or round trip times (RTT) below 250 ms can suffer similar performance degradation. The results suggest that TCP's initial RTO value can be reduced from its current recommended value of 3s to between 500 ms and 1s.

We consider the network design and optimization of high-speed multiservice networks. Meeting different service requirements is facilitated by dividing the network into virtual bands. A band corresponds to a service type. To achieve high transport efficiency, bands should be reconfigured frequently to track the varying traffic. Our work aims at developing a self-sizing system which can allocate network capacity automatically and adaptively using on-line traffic data. We study the optimization problem for band partitioning for high-speed multiservice networks. A two-step optimization approach is presented. An optimization model is developed to partition bandwidth among bands to minimize total system cost under capacity constraints while the QoS at call level and cell level are guaranteed. An online traffic measurement system allows the network to automatically detect the amount of bandwidth necessary to satisfy the QoS requirements at cell level. To this end our system exploits the notion of effective bandwidth. To meet the requirement of frequent band partitioning, a fast algorithm based on simulated annealing is presented to solve the model. Simulation results are reported to demonstrate the effectiveness of the optimization approach.

We model and simulate stochastic traffic based on two established statistics: marginal distribution and autocorrelation function. The objective or this paper is two-fold: firstly to investigate the issue of modelling input network traffic in an automated way using the QTES methodology. In order to capture both the autocorrelation structure and the marginal distribution simultaneously and secondly to examine the use of different traffic models such as transform expanded sample (TES) models, spatial renewal process, distorted Gaussian model in the output queueing analysis of a system.

We analyze the accuracy of four methods for measuring effective bandwidths (EBs). We point out advantages and drawbacks of the four estimators. We find that for finite time realizations of a process the measured effective bandwidth differs considerably from its analytical counterpart. We also show that increasing the trace length has little impact on the accuracy of the measurements. We explain this behavior as a consequence of the intrinsic characteristics of the observed trace. We subsequently introduce the notion of "empirical effective bandwidth" (EEB) as a measure of performance tailored to the actual values. We derive properties of the EEB which capture its behavior in the parameter space and we contrast these properties with the ones obtained for analytical effective bandwidths. Finally, we comment on the use of EEBs in the context of connection admission control.

A key feature of integrated services networks is their ability to provide a variety of quality of service (QoS) guarantees to different applications. To this end scheduling systems can be employed. A primary QoS parameter is the cell loss probability (CLP), whose typical values are very small and difficult to estimate by means of standard simulation schemes. We propose an application of the importance sampling (IS) technique to efficiently estimate the CLP of an ideal two-queue generalized processor sharing (GPS) scheduling discipline. We subsequently apply this algorithm to simulate a realistic scheme, namely the packet-by-packet generalized processor sharing (PGPS). We model input traffic as Markov arrival processes (MAPs). The algorithm we present is based on large deviation results, which provide the asymptotic decay rate of per-session queue length tail distributions.

We propose a minimum cost method for traffic shaping in the context of QoS-based networks. Given the user's desired QoS and the network's resource availability, our procedure determines the least-cost parameters for a traffic shaper which still guarantees access to the network whilst satisfying the QoS constraints. We illustrate our scheme using on-off sources and formulate the QoS constraints by effective bandwidths.

The objective of this paper is to investigate a modeling methodology called QTES (quantized transform-expand-sample) which can be used to model network traffic taking into consideration both the marginal and the autocorrelation function of the empirical data. An effort is made towards an algorithmic procedure rather than a heuristic search, thereby largely automating QTES modeling.

In this paper, DS/CDMA S-ALOHA systems with finite buffer capacity and finite user population are analyzed using the tagged user approach (TUA) proposed in Wan and Sheikh. The data are assumed to arrive in the user buffer in the form of a message containing one or more packets. An arriving message is accepted into the user buffer if the buffer has enough space to hold the whole message, otherwise it is rejected. The queueing analysis for the system is developed in this paper. The analysis is verified by simulation.

This paper addresses the performance of packet-based Internet data services over CDMA cellular/PCS wireless networks. Packet-based services are provided to bursty information sources modeled as on-off processes. For these services, transmission is discontinued at the end of data burst (on-period) as no information is generated during the unpredictable off-intervals. TCP (transmission control protocol) performance degrades in a wireless channel due to its vulnerability to interference which causes more packet losses than due to congestion in a network. We refer to the IS-99 standard, in particular to the CDMA data communications protocol stack that proposes a radio link protocol (RLP) for recovery of packet losses. The focus of this work is to identify the network parameters that significantly impact the performance, and more specifically, tune the TCP against wireless channel losses. In particular, the research investigates the following: 1) the way RLP interacts with the TCP for Internet traffic in achieving the overall TCP/RLP throughput; and 2) the role of the buffer space available at the mobile receiver in enhancing the performance for Internet applications.

An efficient and easy technique to generate fractional Gaussian noise traffic based on the spatial renewal process is developed and demonstrated. The synthetically generated trace reproduces the desired marginal, autocorrelation and Hurst parameters well. The model is particularly suitable for use in the discrete-event simulation of queueing systems involving VBR compressed video and aggregated LAN traffic.

This paper discusses an ATM congestion control mechanism that introduces a leaky bucket control scheme based on fuzzy logic principles. Network congestion is described linguistically by introducing a fuzzy rule and appropriate fuzzy variables, and is treated mathematically via fuzzy set manipulations. With the application of fuzzy logic the complex mathematical treatment of classical feedback control is avoided, and the "hard" bound effect in the traditional LB is also eliminated in favor of "soft" bound membership functions. In order to evaluate the effectiveness of the fuzzy LB, the performance of the ATM network with a non-fuzzy adaptive LB mechanism is also investigated. Network parameters which affect the performance are identified and optimized for the two control schemes and a comparison of the two approaches is carried out under the same network condition and optimal parameters. Finally, the performance of the fuzzy LB and adaptive LB are also evaluated under self-similar traffic load. The performance analysis in this paper is based on simulation combined with numerical optimization method. Our results indicate that the fuzzy leaky bucket mechanism leads to significant improvement to the system performance.

Examines the transmission of variable bit-rate (VBR) H.261 video over a mixed traffic (video/inter-LAN) integrated services frame relay (FR) network. We introduce a modified H.261 codec that produces VBR output and show that parsing of the video bit stream at group of blocks (GOB) boundaries produces variable length FR packets well suited to the network. We demonstrate that GOB-level video traffic requires a more sophisticated statistical model of the resulting data stream than the frame-level models. The transform expand sample (TES) method is used to obtain an accurate model of the autocorrelation and the marginal probability distribution of the bit-rate variations at the GOB level. A simple and effective methodology is introduced for capturing the periodic components that are present in the GOB-level autocorrelation. The methodology is extended to permit simulations of VBR codecs in which the codec quantization step size is adjusted in response to prevailing network conditions. We show that the quality of service requirements of VBR video can be met by using the FR backward explicit congestion notification (BECN) facility in conjunction with a modified H.261 codec whose rate is controlled by the congestion notification. We also show that the performance of the control mechanism is significantly influenced by a subset of network threshold and codec control parameters which are identified using 2/sup k/ factorial analysis techniques. We obtain optimal ranges of values for these parameters using mean field annealing. Variable quantization rate control may be more effective for this purpose than variable frame rate control.

Variable bit rate (VBR) compressed video is expected to become one of the major loading factors in high-speed packet networks such as ATM-based B-ISDN. However, recent measurements based on long empirical traces (complete movies) revealed that VBR video traffic possesses self-similar (or fractal) characteristics, meaning that the dependence in the traffic stream lasts much longer than traditional models can capture.In this paper, we present a unified approach which, in addition to accurately modeling the marginal distribution of empirical video records, also models directly both the short and the long-term empirical autocorrelation structures. We also present simulation results using synthetic data and compare with results based on empirical video traces.Furthermore, we extend the application of efficient estimation techniques based on importance sampling that we had used before only for simple fractal processes. We use importance sampling techniques to efficiently estimate low probabilities of packet losses that occur when a multiplexer is fed with synthetic traffic from our self-similar VBR video model.

Several services to be supported by emerging high-speed networks are expected to result in highly bursty (autocorrelated) traffic streams. A typical example is variable bit-rate (VBR) compressed video. Therefore, traffic modeling and performance evaluation techniques geared towards autocorrelated streams are extremely important for the design of practical networks.The TES (Transform - Expand - Sample) technique has emerged as a general methodology for modeling autocorrelated random processes with arbitrary marginal distributions. Because of their generality and practical applicability, TES models can be readily used to accurately characterize bursty traffic streams in ATM networks.Although TES models can be easily implemented for simulation studies, the need still exists for analytical results on the performance of queueing systems driven by autocorrelated traffic. Of particular interest are the tails of the waiting time distribution in queues driven by TES-modeled bursty traffic. Such tail probabilities, when they become exceedingly small, may be difficult to obtain via conventional simulation.In order to extend existing results, based on Large Deviations theory, to TES processes, the main difficulty is posed by the continuous state-space of the TES time-series. In this paper, we develop a general result concerning exponential bounds for the waiting time under continuous state-space Markov arrivals. We apply this result to TES/GI/1 queues, show numerical examples, and compare our bound with simulation results. Accurate estimates of extremely low probabilities are obtained by employing fast simulation techniques based on importance sampling.

Importance sampling (IS) techniques offer the potential for large speed-up factors for bit error rate (BER) estimation using Monte Carlo (MC) simulation. To obtain these speed-up factors, the IS parameters specifying the simulation probability density function (PDF) must be carefully chosen. With the increased complexity in communication systems, analytical optimization of the IS parameters can be virtually impossible. We present a new IS optimization algorithm based on stochastic gradient techniques. The formulation of the stochastic gradient descent (SGD) algorithm is more general and system-independent than other existing IS methodologies, and its applicability is not restricted to a specific PDF or biasing scheme. The effectiveness of the SGD algorithm is demonstrated by two examples of communication systems where the IS techniques have not been applied before. The first example is a communication system with diversity combining, slow nonselective Rayleigh fading channel, and noncoherent envelope detection. The second example is a binary baseband communication system with a static linear channel and a recursive least square (RLS) linear equalizer in the presence of additive white Gaussian noise (AWGN).

Two importance sampling (IS) methodologies for Monte Carlo simulation of communication links characterized by time-varying channels and adaptive equalizers are presented. One methodology is denoted as the twin system (TS) method. A key feature of the TS method is that biased noise samples are input to the adaptive equalizer, but the equalizer is only allowed to adapt to these samples for a time interval equal to the memory of the system. In addition to the TS technique, the IA method, a statistically biased, but simpler, technique for using IS with adaptive equalizers that is based on the independence assumption between equalizer input and equalizer taps is presented. Experimental results show run-time speedup factors of two to seven orders of magnitude for a static linear channel with memory, and of two to almost five orders of magnitude for a slowly-varying random linear channel with memory for both the IA and TS methods.>

Importance sampling is recognized as a potentially powerful method for reducing simulation runtimes when estimating the bit error rate (BER) of communications systems using Monte Carlo simulation. Analytically, minimizing the variance of the importance sampling (IS) estimator with respect to the biasing parameters has typically yielded solutions for systems for which the BER could be found analytically. A technique for finding an asymptotically optimal set of biasing parameter values, in the sense that as the resolution of the search and the number of runs used both approach infinity, the algorithm converges to the true optimum, is proposed. The algorithm determines the amount of biasing that minimizes a statistical measure of the variance of the BER estimate and exploits a theoretically justifiable relationship, for small sample sizes, between the BER estimate and the amount of biasing. The translation biasing scheme is considered, although the algorithm is applicable to other parametric IS techniques. Only mild assumptions are required of the noise distribution and system. Experimentally, improvement factors ranging from two to eight orders of magnitude are obtained for a number of distributions for both linear and nonlinear systems with memory.>

Importance sampling (IS) is a powerful method for reducing simulation run times when estimating the probabilities of rare events in communication systems using Monte Carlo simulation and is made feasible and effective for the simulation of networks of queues by regenerative techniques. However, using the most favorable IS settings very often makes the length of regeneration cycles infinite or impractically long. To address this problem, a methodology that uses IS dynamically within each regeneration cycle to drive the system back to the regeneration state after an accurate estimate has been obtained is discussed. A statistically based technique for optimizing IS parameter values for simulations of queueing systems, including complex systems with bursty arrival processes, is formulated. A deterministic variant of stochastic simulated annealing (SA), called mean field annealing (MFA), is used to minimize statistical estimates of the IS estimator variance. The technique is demonstrated by evaluating blocking probabilities.>