Fast adversarial training (FAT) aims to enhance the robustness of models against adversarial attacks with reduced training time, however, FAT often suffers from compromised robustness due to insufficient exploration of adversarial space. In this paper, we develop a loss function to mitigate the problem of degraded robustness under FAT. Specifically, we derive a quadratic upper bound (QUB) on the adversarial training (AT) loss function and propose to utilize the bound with existing FAT methods. Our experimental results show that applying QUB loss to the existing methods yields significant improvement of robustness. Furthermore, using various metrics, we demonstrate that this improvement is likely to result from the smoothened loss landscape of the resulting model.

Object detection has become an increasingly important application for mobile devices. However, state-of-the-art object detection relies heavily on deep neural network, which is often burdensome to compute on mobile devices. To this end, we develop a layering framework for joint video frame drop and object detection task offloading. In the lower layer, by invoking Lyapunov optimization, we devise an algorithm for partitioning and offloading the computation tasks of deep neural networks. This algorithm also specifies the flow control for admitting the application traffic into the network. In the upper layer, we use the flow control as a form of guidance in the action space in order to develop a reinforcement learning (RL) algorithm that selectively drops video frames with object detection performance in consideration. By the nature of design, this Lyapunov-guided RL guarantees the network stability. We show through simulations that our Lyapunov-guided RL drops video frames with reasonable object detection performance and reduced latency while keeping the network stable. We also implemented our algorithm on the remote-controlled (RC) car equipped with microprocessor and GPU, and demonstrate the applicability of our algorithm to real-time object detection tasks from the video stream generated as the RC car moves.

Offloading techniques are considered one of the key enablers of deep neural network (DNN)-based artificial intelligence (AI) services on end devices with limited computing resources. However, offloading DNN layers involves hard combinatorial problems. To this end, we develop a deep reinforcement learning (DRL)-based offloading algorithm for computing DNN layers with minimum end-to-end inference latency. We combine long short-term memory (LSTM) and graph neural network for state embedding that can exploit spatial correlation over the network to accelerate training, and temporal correlation over time to reduce the overhead of state monitoring. With this embedding, our DRL algorithm can draw multiple actions from a single state observation and adapt, without retraining, to new environments unseen in the training phase. We show through extensive simulations that our algorithm outperforms the existing ones in terms of both latency and robustness to feedback delay which is inevitable in practice, in particular, achieving a performance enhancement of up to 29.6% in some scenarios.

We propose a novel practical access point (AP) deployment technique for 6G ultra-reliable industrial Internet-of-things (IIoT) networks. In particular, a cell-free massive multiple-input multiple-output (CF -mMIMO) architecture is adopted to improve reliability with macro-diversity, where a station (STA) is simultaneously associated with and served by multiple APs. We first mathematically formulate an integer linear programming (ILP)-based optimization problem that minimizes the number of required APs while satisfying a certain quality-of-service (QoS) of IIoT networks, such as the minimum number of concurrent communication links and the minimum required signal-to-noise ratio (SNR). However, the optimal technique requires significant computational complexity and time, unfortunately. Hence, we propose a low-complexity AP deployment algorithm based on parallel search methods, named PRADA. The proposed algorithm sufficiently reduces the number of required APs in a computationally efficient manner while satisfying the QoS constraints.

Computational resources are increasingly virtualized to enable computational tasks to be offloaded to remote facilities along the route between the source and destination. The principle that underlies traditional routing, i.e., that only networking resources need to be considered, may no longer be true in a virtualized environment. In this paper, we propose a framework for the efficient utilization of multi-resource infrastructures in which computational resources can be used via the network. Such a framework intrinsically calls for the joint consideration of networking and computational resources. In particular, we focus on unifying the controls in dynamic service chaining and multiple resource management, which are the key technologies in an integrated SDN/NFV architecture. We formulate a multi-path problem for choosing the resources to use in different services. The problem can be viewed as variational inequality using the Lagrange duality and saddle point theory. Based on this, we develop an extragradient-based algorithm that controls and splits the sending rate of each service. We prove that the algorithm converges to the optimal, minimizing the system cost while maximizing service utility. Simulations for diverse scenarios demonstrate that our algorithm achieves high QoS while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

Modern wireless systems are evolving toward a heterogeneous environment where multiple radio access technologies (RATs) coexist. Most of the RATs in current use operate independently of each other, however, there is an increasing demand for coordination among different RATs in order to support exponentially growing wireless traffic. Our goal in this paper is to design a super-MAC that integrates multiple RATs in a tightly coupled manner. To this end, we propose an architecture for super-MAC, and develop an algorithm that splits IP traffic into multiple RATs and controls each RAT to exploit multi-RAT diversity. We prove the asymptotic optimality of the super-MAC algorithm by introducing a novel reference system that enables the analysis of the multi-RAT system where there is a mixture of slotted (such as cellular) and unslotted (such as WiFi) RATs. Through extensive simulations we show that the super-MAC algorithm can enhance the quality of service including throughput, energy efficiency, and fairness.

In spectrum-sharing networks, primary users have the right to preempt secondary users, which can significantly degrade the performance of underlying secondary users. In this paper, we use backup channels to provide reliability guarantees for secondary users. In particular, we study the optimal white channel assignment that minimizes the amount of recovery capacity (i.e., bandwidth of backup channels) needed to meet a given reliability guarantee, where both deterministic and probabilistic requirements are considered. This problem is shown to be coupled by two NP-hard objectives. We characterize the structure of the optimal assignment and develop bi-criteria approximation algorithms. Moreover, we investigate the scaling of the recovery capacity as the network size becomes large. It is shown that the recovery capacity is negligible as compared to the total traffic demands in a large-scale network.

Mobile computation offloading is emerging as a promising technology to enhance the computation power of mobile devices by borrowing processing resources from the cloud. However, using cloud resource is a double-edged sword because of the potentially enormous network energy consumption of mobile devices. In this paper, we study the mobile device resource management problem for application throughput fairness and energy efficiency in computation offloading environment. Our problem seeks to optimize task arrival rates, scheduling for local processing and offloading, CPU clock speed, and network interface selection, so as to maximize the energy-utility efficiency defined as achievable utility per unit energy consumption. The efficiency metric has a fractional form that is hard to deal with in general. To address this difficulty, we modify a general Lyapunov optimization technique and derive a series of short-term problems that change over time with respect to an unknown objective parameter. Then, we derive an offloading algorithm and prove that the algorithm maximizes the long-term energy-utility efficiency. Trace-driven simulations demonstrate that our algorithm achieves high-energy efficiency while maintaining throughput fairness among applications running on a mobile device.

Network function virtualization (NFV) and Computation offloading (CO) are state-of-the-art technologies for flexible utilization of networking and processing resources. These two technologies are closely related in that they enable multiple physical entities to process a function provided in a service, and the service (or end host) chooses which resources to use. In this paper, we propose a generalized dual-resource system, which unifies NFV service and CO service frameworks, and formulate a multi-path problem for choosing resources to use in NFV and CO services. The problem is reformulated as a variational inequality by using Lagrange dual theory and saddle point theory. Based on this formulation, we propose an extragradient-based algorithm that controls and splits the sending rate of a service. We prove that the algorithm converges to an optimal point where system cost minus service utility is minimized. Simulations under diverse scenarios demonstrate that our algorithm achieves high quality of service while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

We consider energy efficient base station (BS) sleeping and clustering problems in cooperative cellular networks, where clusters of base stations jointly transmit to users. Our key idea of energy saving is to exploit spatio-temporal fluctuation of traffic demand, and use minimal energy to provide achievable data rate only slightly greater than varying traffic demand. However, it is highly challenging to design traffic-aware algorithms without the future traffic demand information. To overcome this difficulty, we develop joint BS sleeping and clustering algorithms using queue instead of the future traffic information. The queue length information captures spatio-temporal mismatch between traffic demand and offered data rate. For BS clustering problem, we propose an optimal algorithm under given BS sleep mode state that has polynomial complexity. We integrate the optimal clustering solution into the sleeping problem, which is a complex combinatorial problem, and develop a joint optimal clustering and sleeping algorithm with reduced complexity compared to the exhaustive search. We also develop a greedy algorithm that finds a near-optimal clustering and sleeping solution with polynomial complexity. Through extensive simulations, we show that the proposed algorithms can save significant energy when traffic load is low.

In this paper, we study dynamic bandwidth slicing and resource allocation problems to support a mixture of IoT (Internet of Things) and video streaming services. By employing Lyapunov optimization method with time-scale separation approach, we develop algorithms for long time-scale bandwidth slicing, and short time-scale IoT device scheduling, power allocation (for IoT service) and quality decision (for video streaming service). We show through simulations that proposed dynamic bandwidth slicing and resource allocation algorithms outperform the static bandwidth slicing and resource allocation policies in terms of average total cost and average total delay.

In spectrum-sharing networks, primary users have the right to preempt secondary users, which significantly degrades the performance of underlying secondary users. In this paper, we use backup channels to provide reliability guarantees for secondary users. In particular, we study the optimal white channel assignment that minimizes the amount of recovery capacity (i.e., bandwidth of backup channels) needed to meet a given reliability guarantee. This problem is shown to be coupled by two NP-hard objectives. We characterize the structure of the optimal assignment and develop bi-criteria approximation algorithms. Moreover, we investigate the scaling of the recovery capacity as the network size becomes large. It is shown that the recovery capacity is negligible as compared to the total traffic demands in a large-scale network.

We consider energy efficient base station sleeping and clustering problems in cooperative cellular networks where clusters of base stations jointly transmit to users. Our key idea of energy saving is to exploit a spatio-temporal fluctuation of traffic demand, which is to use minimal energy to provide capacity only slightly greater than varying traffic demand. Then, energy saving is possible without capacity loss. However, it is highly challenging to design traffic-aware algorithms without the future traffic demand information. To overcome this, we develop algorithms using queue instead of the future traffic information. For BS clustering problem, we propose an optimal algorithm that has polynomial complexity. For BS sleeping problem, which is a complex combinatorial problem, we propose two algorithms; One finds an optimal solution with reduced complexity compared to the exhaustive search, and the other finds a near-optimal solution with polynomial complexity. Through extensive simulations we show that the proposed algorithms can save significant energy when traffic load is low.

This paper presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over provisioning. We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing among backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for large-scale networks and present simulation results that verify our analysis and approach.

We consider the problem of providing inter-access-point (AP) fairness guarantee in dense AP deployments where starvation can occur. In particular, we develop a framework for providing robust minimum throughput guarantee for each AP under the uncertainty of user distributions. Our framework consists of an AP throughput provisioning scheme and a distributed CSMA algorithm. The throughput provisioning scheme computes a robust feasible minimum AP throughput vector based on a random AP-level conflict graph and chance-constrained optimization. By incorporating the minimum throughput vector, we develop a distributed CSMA algorithm that fulfills the minimum requirement for each AP and is compatible with the IEEE 802.11 standard. We show through extensive simulations that our framework addresses the AP starvation problem by guaranteeing minimum throughput for each AP.

We develop a rate allocation scheme for two-sided networks where both contents providers (CPs) and end users (EUs) participate in deciding data rate. For rate allocation, our scheme splits the network congestion price into network usage prices by CP and EU, respectively. Using this property, we show that our scheme maximizes the aggregate utility of CPs and EUs, while maximizing the net utility of each CP and EU.

We study the secondary user power allocation in OFDM-based cognitive radio networks with underlay mode where secondary user is allowed to share the spectrum licensed to primary user, provided that limited interference is generated to primary user. While this problem has been studied extensively in the literature, most of the previous works in this context assume that secondary user has the exact information of how much interference it generates to primary user, which may not be the case in practice. We assume that the interference channel gains are known to secondary user with uncertainty, and develop a power allocation algorithm that can keep interference under a desired level. In particular, we formulate the power allocation problem as a chance-constrained program that finds a power allocation that guarantees limited interference with high probability. The formulation is non-convex in general, and we apply techniques from robust optimization theory to convexify the formulation. We show through simulations that our approach finds a power allocation that remains feasible under time-varying channel conditions.

We study the coordinated transmission problem in cooperative cellular networks where a cluster of base stations forms a virtual cell to serve a mobile station (MS). The performance of such an MS-centric virtual cell network is dictated by the beamformer that enables to suppress interference; however, designing a beamformer is highly challenging due to the coupled nature of interference and desired signals under arbitrarily formed virtual cells. We develop a new formulation of the beamforming problem for sum-rate maximization in virtual cell networks and analyze the structure of its optimal solutions. Based on this analysis, we develop a beamforming algorithm that can balance between desired signal maximization and interference minimization, so as to maximize the sum-rate. We show through extensive simulations that our balanced beamforming algorithm mitigates edge user effect and outperforms existing algorithms in various scenarios where virtual cells are allowed to overlap.

We study the reliability maximization problem in wavelength division multiplexing (WDM) networks with random link failures. Reliability in these networks is defined as the probability that the logical network is connected, and it is determined by the underlying lightpath routing, network topologies, and the link failure probability. By introducing the notion of lexicographical ordering for lightpath routings, we characterize precise optimization criteria for maximum reliability in the low failure probability regime. Based on the optimization criteria, we develop lightpath routing algorithms that maximize the reliability, and logical topology augmentation algorithms for further improving reliability. We also study the reliability maximization problem in the high failure probability regime.

We develop a distributed throughput-optimal power allocation algorithm in wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems that prohibits an efficient solution even in a centralized setting. By generalizing the randomization framework originally proposed for input queued switches to SINR rate-based interference model, we characterize the throughput-optimality conditions that enable efficient and distributed implementation. Using gossiping algorithm, we develop a distributed power allocation algorithm that satisfies the optimality conditions, thereby achieving (nearly) 100 percent throughput. We illustrate the performance of our power allocation solution through numerical simulation.

This paper addresses the problem of logical topology design for optical backbone networks subject to stochastic traffic demands. The network design problem is broken into three tasks: traffic routing, capacity allocation, and link placement. While the routing and capacity allocation subproblem can be formulated using convex optimization, the link placement component is prohibitive due to its nonlinearity. To address this issue, we develop a linear formulation for the routing and capacity allocation subproblem by extending tools from robust optimization to Gaussian random variables. We show that this linear formulation performs comparably to the optimal nonlinear formulation. Our formulation can then be used to solve the link-placement subproblem for stochastic traffic.

We study the reliability maximization problem in WDM networks with random link failures. Reliability in these networks is defined as the probability that the logical network is connected, and it is determined by the underlying lightpath routing and the link failure probability. We show that in general the optimal lightpath routing depends on the link failure probability, and characterize the properties of lightpath routings that maximize the reliability in different failure probability regimes. In particular, we show that in the low failure probability regime, maximizing the ``cross-layer" min cut of the (layered) network maximizes reliability, whereas in the high failure probability regime, minimizing the spanning tree of the network maximizes reliability. Motivated by these results, we develop lightpath routing algorithms for reliability maximization.

This paper presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over-provisioning. We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing amongst backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for largescale networks, and present simulation results that verify our analysis and approach.

We consider network reliability in layered networks where the lower layer experiences random link failures. In layered networks, each failure at the lower layer may lead to multiple failures at the upper layer. We generalize the classical polynomial expression for network reliability to the multilayer setting. Using random sampling techniques, we develop polynomial-time approximation algorithms for the failure polynomial. Our approach gives an approximate expression for reliability as a function of the link failure probability, eliminating the need to resample for different values of the failure probability. Furthermore, it gives insight on how the routings of the logical topology on the physical topology impact network reliability. We show that maximizing the min cut of the (layered) network maximizes reliability in the low-failure-probability regime. Based on this observation, we develop algorithms for routing the logical topology to maximize reliability.

In layered networks, a single failure at a lower layer may cause multiple failures in the upper layers. As a result, traditional schemes that protect against single failures may not be effective in multilayer networks. In this paper, we introduce the problem of maximizing the connectivity of layered networks. We show that connectivity metrics in layered networks have significantly different meaning than their single-layer counterparts. Results that are fundamental to survivable single-layer network design, such as the Max-Flow Min-Cut Theorem, are no longer applicable to the layered setting. We propose new metrics to measure connectivity in layered networks and analyze their properties. We use one of the metrics, Min Cross Layer Cut, as the objective for the survivable lightpath routing problem and develop several algorithms to produce lightpath routings with high survivability. This allows the resulting cross-layer architecture to be resilient to failures between layers.

We consider network reliability in layered networks where the lower layer experiences random link failures. In layered networks, each failure at the lower layer may lead to multiple failures at the upper layer. We generalize the classical polynomial expression for network reliability to the multi-layer setting. Using random sampling techniques, we develop polynomial time approximation algorithms for the failure polynomial. Our approach gives an approximate expression for reliability as a function of the link failure probability, eliminating the need to resample for different values of the failure probability. Furthermore, it gives insight on how the routings of the logical topology on the physical topology impact network reliability. We show that maximizing the min cut of the (layered) network maximizes reliability in the low failure probability regime. Based on this observation, we develop algorithms for routing the logical topology to maximize reliability.

We develop diverse routing schemes for dealing with multiple, possibly correlated, failures. While disjoint path protection can effectively deal with isolated single link failures, recovering from multiple failures is not guaranteed. In particular, events such as natural disasters or intentional attacks can lead to multiple correlated failures, for which recovery mechanisms are not well understood. We take a probabilistic view of network failures where multiple failure events can occur simultaneously, and develop algorithms for finding diverse routes with minimum joint failure probability. Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for modeling correlated failures. In this context, we formulate the problem of finding two paths with minimum joint failure probability as an integer nonlinear program (INLP) and develop approximations and linear relaxations that can find nearly optimal solutions in most cases.

We develop diverse routing schemes for dealing with multiple, possibly correlated, failures. While disjoint path protection can effectively deal with isolated single link failures, recovering from multiple failures is not guaranteed. In particular, events such as natural disasters or intentional attacks can lead to multiple correlated failures, for which recovery mechanisms are not well understood. We take a probabilistic view of network failures where multiple failure events can occur simultaneously, and develop algorithms for finding diverse routes with minimum joint failure probability. Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for modeling correlated failures. In this context, we formulate the problem of finding two paths with minimum joint failure probability as an Integer Non-Linear Program (INLP), and develop approximations and linear relaxations that can find nearly optimal solutions in most cases.

We consider throughput-optimal power allocation in multi-hop wireless networks. The study of this problem has been limited due to the non-convexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach to deal with this difficulty. To this end, we generalize the randomization framework originally proposed for input queued switches to an SINR rate-based interference model. Further, we develop distributed power allocation and comparison algorithms that satisfy these conditions, thereby achieving (nearly) 100% throughput. We illustrate the performance of our proposed power allocation solution through numerical investigation and present several extensions for the considered problem.

This paper revisits equal power allocation from the viewpoint of asymptotic network utility maximization (NUM) problem in multi-carrier systems. It is a well-known fact that the equal power allocation is near optimal to the sum capacity maximization problem in high SNR (signal-to-noise ratio) regime, i.e., optimal water-filling approximates to equal power allocation in that case. Due to this property together with its simplicity, the equal power allocation has been adopted in several researches, but its performance in other problems has not been clearly understood. We evaluate the suitability of equal power allocation in NUM problem which turns into various resource sharing policies according to utility functions. Namely, our conclusion is that in frequency selective channels, the equal power allocation is near optimal for efficiency-oriented resource sharing policy, but when fairness is emphasized, its performance is severely degraded and thus frequency-selective power allocation is necessary. For this, we develop a suboptimal subcarrier and frequency-selective power allocation algorithm for asymptotic NUM problem using the gradient-based scheduling theory and compare the performance of equal power allocation and the developed algorithm. Extensive simulation results are presented to verify our arguments.

This paper revisits burst contention resolution problems in optical burst switching (OBS) networks from the viewpoint of network utility maximization. Burst collision occurs when two or more bursts access the same wavelength simultaneously, and the occurrence becomes more frequent as the offered load increases. In particular, when the network is overloaded, no contention resolution scheme would effectively avoid the collision without the help of congestion control. We formulate a joint optimization problem where two variables, the length and the time at which each burst is injected into the network, are jointly optimized in order to maximize aggregate utility while minimizing burst loss. A distributed algorithm is also developed, which explicitly reveals how burst contention resolution and congestion control must interact. The simulation results show that the joint control decouples throughput performance from burst loss performance so that burst loss ratio does not increase as network throughput increases. This is not the case in conventional contention resolution schemes where burst loss ratio increases as network throughput increases so that achievable network throughput is limited. Our work is the first attempt to the joint design of congestion and contention control and might lead to an interesting development in OBS research.

In this paper, we study how the cooperative relaying can improve both capacity and fairness in cellular network. The capacity and fairness have a trade-off relationship, so increasing cell throughput deteriorates fairness and vice versa. First, we show that the achievable average throughput region can be enlarged by using the cooperative relaying. This enlarged region means that capacity and fairness can be improved at the same time with an adequate scheduling algorithm. Thus, secondly we propose a generalized scheduling algorithm for cooperative relaying. The proposed scheduling algorithm can improve both capacity and fairness at the expense of cooperation among users. From simulations, we show that the trade-off relationship can be surpassed and the unfairness problem in the heterogeneous channel condition can be solved by the opportunistic relaying.

Most of up-to-date wireless data systems are based on the OFDM technology and employ the opportunistic scheduling to exploit channel itself fully. Thus, feedback reduction in multiuser multi-carrier systems becomes more of an issue due to the large amount of feedback required to pass to the base station. In this paper, we propose a novel feedback reduction scheme preserving the essential of multi-user diversity. In our proposed scheme, active users determine their feedback amount based on the feedback efficiency factor in a distributed manner. The objective is to reduce the feedback load remarkably while achieving almost the same performance to the full feedback condition. Our proposed scheme offers several advantages over existing ones. First, it does not distort the property of the scheduling policies. Second, total feedback load can be explicitly controlled to a target level regardless of the number of users in a system by adjusting each user's feedback load adaptively.

A scheduling problem is considered in the cellular network where there exist CBR (constant bit rate) users requiring exact minimum average throughput and delay guarantee, and EMG (elastic with minimum guarantee) users requiring minimum average throughput and delay guarantee and more throughput if possible. We first define a new utility function of average queue length. Based on the new queue utility function and the throughput utility function proposed before, we design a 2-dimensional weight function that will be used in our scheduling algorithm, and suggest a source control algorithm that can work with the scheduling algorithm. We show through simulations that the proposed algorithm guarantees the QoS (quality of service) requirements of CBR and EMG users.

In this paper, dynamic subcarrier and power allocation problem is considered in the context of asymptotic utility maximization in multi-carrier systems. Using gradient-based resource allocation, we formulate an optimization problem involving subcarrier and transmit power allocation for each time slot, and propose an optimal algorithm solving the problem. Since the optimal algorithm is impractical due to its complexity, a simple suboptimal algorithm is also proposed based on generalized Benders decomposition. Furthermore, we identify the performance of equal power allocation policy by showing that equal power allocation is not always near optimal in general utility maximization problem and characterizing the optimality condition of equal power allocation. This result not only justifies the use of our dynamic power allocation, but also generalizes the performance of equal power allocation, which is well-known to be approximately optimal to the sum capacity maximization problem in high SNR (signal-to-noise ratio) regime. Our algorithms and analysis are verified through extensive simulations.

In this paper, a scheduling problem is considered in the cellular network where there exist CBR (constant bit rate) users requiring exact minimum average throughput guarantee, and EMG (elastic with minimum guarantee) users requiring minimum average throughput guarantee and more if possible. We propose a combined scheduling and call admission control algorithm that exactly guarantees the minimum requirements of CBR and EMG users, and then allocates the leftover capacity to EMG users. The proposed algorithm is developed using utility maximization problem without minimum throughput constraints and newly defined utility functions. In the algorithm, it is easy to give priority to particular users so that their requirements are guaranteed prior to any other user. Moreover, the priority structure enables the proposed measurement-based call admission control algorithm to perform admission trial without affecting the minimum required throughput of ongoing users. We verify the performance of our algorithm through mathematical analysis and simulations.

In this paper, a scheduling problem in wireless networks is considered when there simultaneously exist CBR (constant bit rate) users requiring exact minimum average throughput guarantee and EMG (elastic with minimum guarantee) users requiring minimum average throughput guarantee and more if possible. By exploiting utility maximization problem without minimum throughput constraint and newly defined utility functions, we propose a combined scheduling and call admission control scheme that exactly guarantees the minimum requirements of CBR and EMG users and then allocates the leftover capacity to EMG users. In the proposed scheme, it is easy to give priority to particular users so that they are guaranteed their requirements prior to any other user. Moreover, the priority structure enables the proposed measurement-based call admission control algorithm to perform admission trial without affecting the minimum performance of existing users. We show through mathematical analysis and simulations that our scheme works as designed.

A fair allocation of utility (application-layer performance) is essential in providing QoS (quality of service) guarantee. However, there have been few researches in the literature of utility-fair network resource allocation scheme. In this paper, we propose a distributed utility max-min flow control algorithm which accommodates application diversity in that it does not require the concavity of utility functions, and is scalable in that it does not require any per-flow operation in the network. The algorithm is proved to be convergent under the assumption that there exists a single congested node and the communication delay between any two nodes in the network is bounded. Although the convergence of the algorithm is analyzed for the case of a single congested node, we show through simulations that the proposed algorithm works as designed for the case of multiple congested nodes.

We present a distributed algorithm to compute the bandwidth max-min fair rates in a multi-rate multicast network. The significance of the algorithm, compared to previous algorithms (Sakar, S. and Tassiulas L., 1999, 2000; Kar, K. et al., 2001), is that it is more scalable (it does not require each link to maintain the saturation status of all sessions and virtual sessions travelling through it) it is more stable (it converges asymptotically to the desired equilibrium, satisfying the minimum plus max-min fairness, even in the presence of heterogeneous round-trip delays) and it has explicit link buffer control (the buffer occupancy of every bottlenecked link in the network asymptotically converges to the pre-defined value). In addition, we propose an efficient feedback consolidation algorithm which is computationally simpler than its hard-synchronization based counterpart and eliminates unnecessary consolidation delay by preventing it from awaiting backward control packets (BCPs) that do not directly contribute to the session rate. Through simulations, we verify the performance of the proposed multi-rate multicast flow control scheme based on these two algorithms.