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(The final version of the chapter does not involve an abstract. Introduction is provided below.) Wireless access has been increasingly popular recently due to portability and low cost of wireless terminals and equipment. The emerging technologies for wireless local area networks (WLANs) are defined by the IEEE 802.11n standard, where physical layer data rates exceeding 200 Mbps are provisioned with multiple input multiple output antenna techniques. However, actual throughput to be experienced by WLAN users is considerably lower than the provided physical layer data rates, despite the link efficiency is enhanced via the frame aggregation concept of 802.11n. In a multi user communication system, scheduling is the mechanism that determines which user should transmit/receive data in a given time interval. Opportunistic scheduling algorithms maximize system throughput by making use of the channel variations and multi user diversity. The main idea is favouring users that are experiencing the most desirable channel conditions at each scheduling instant, i.e. riding the peaks. While maximizing capacity, such greedy algorithms may cause some users to experience unacceptable delays and unfairness, unless the users are highly mobile. In order to remedy this problem, we combine aggregation and opportunistic scheduling approaches to further enhance the throughput of next generation WLANs. We argue that aggregation can dramatically change the scheduling scenario: A user with a good channel and a long queue may offer a higher throughput than a user with better channel conditions but shorter queue. Hence, the statement that always selecting the user with the best channel maximizes throughput is not valid anymore. In this work, we first present our queue aware scheduling scheme that take into account the instantaneous channel capacities and queue sizes simultaneously, named as Aggregate Opportunistic Scheduling (AOS). Detailed simulations results indicate that our proposed algorithm offers significant gains in total system throughput, by up to 53%, as compared to opportunistic schedulers while permitting relatively fair access. We also improve AOS with the principle of relayed transmissions and show the improvements of opportunistic relaying. Later on, we propose another scheduler, which aims to maximize the network throughput over a long time scale. For this purpose, we estimate the statistical evolution of queue states and model the 802.11n MAC transmissions using queuing theory by extending the bulk service model. Utilizing the outcomes of the queuing model, we design Predictive Scheduling with time-domain Waterfilling (P-WF) algorithm. P-WF further improves the performance of our queue aware schedulers, as the throughput is maximized by applying the water filling solution to time allocations. This chapter includes an overview of existing literature on opportunistic scheduling for wireless networks in general and presents our proposed algorithms with comparative detailed performance analysis as they are applied into the next generation WLANs.

In the bounded delay buffer management problem unit size packets arrive online to be sent over a network link. The objective is to maximize the total weight of packets sent before their deadline. In this paper we are interested in the two-valued variant of the problem, where every packet has either low (1) or high priority weight ( α > 1 ). We show that the optimal randomized competitive ratio against an oblivious adversary is 1 + ( α − 1 ) ∕ ( α 2 + α ) .

This paper focuses on the design of provably efficient online link scheduling algorithms for multi-hop wireless networks. We consider single-hop traffic and the one-hop interference model. The objective is twofold: 1) maximizing the throughput when the flow sources continuously inject packets into the network, and 2) minimizing the evacuation time when there are no future packet arrivals. The prior work mostly employs the link-based approach, which leads to throughput-efficient algorithms but often does not guarantee satisfactory evacuation time performance. In this paper, we propose a novel Node-based Service-Balanced (NSB) online scheduling algorithm. NSB aims to give scheduling opportunities to heavily congested nodes in a balanced manner, by maximizing the total weight of the scheduled nodes in each scheduling cycle, where the weight of a node is determined by its workload and whether the node was scheduled in the previous scheduling cycle(s). We rigorously prove that NSB guarantees to achieve an efficiency ratio no worse (or no smaller) than 2/3 for the throughput and an approximation ratio no worse (or no greater) than 3/2 for the evacuation time. It is remarkable that NSB is both throughput-optimal and evacuation-time-optimal if the underlying network graph is bipartite. Further, we develop a lower-complexity NSB algorithm, called LC-NSB, which provides the same performance guarantees as NSB. Finally, we conduct numerical experiments to elucidate our theoretical results., Comment: This paper was accepted for publication in IEEE Transactions on Mobile Computing. A preliminary version of the paper was presented at IEEE INFOCOM 2016

Despite extensive studies on optimal power allocation, how to design an efficient joint user scheduling and power allocation scheme for uplink multiuser networks remains largely unexplored. This study investigates joint opportunistic user scheduling and power allocation in uplink multiuser networks to maximise user throughput subject to the power and resource sharing constraints . By exploiting the cumulative distribution function-based scheduling method, the authors first characterise the optimal power allocation subject to both long-term and short-term power constraints. Instead of calculating the transmit power in an iterative and central manner, users can independently decide their instantaneous transmit power in the proposed scheme, which facilitates the algorithm implementation for each user in uplink networks. The closed-form throughput of the proposed scheme is also derived, which can provide an efficient way to estimate and evaluate user performance. Numerical results reveal that compared with several benchmark schemes, the proposed scheme improves throughput performance significantly.

Network navigation is a promising paradigm for providing location awareness in wireless environments, where nodes estimate their locations based on sensor measurements and prior knowledge. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements. The procedure of selecting node pairs at different times for inter-node measurements, referred to as network scheduling, affects the evolution of the localization errors. Thus, it is crucial to design efficient scheduling strategies for network navigation. This paper introduces situation-aware scheduling that exploits network states to select measurement pairs, and develops a framework to characterize the effects of scheduling strategies and of network settings on the error evolution. In particular, both sufficient and necessary conditions for the boundedness of the error evolution are provided. Furthermore, opportunistic and random situation-aware scheduling strategies are proposed, and bounds on the corresponding time-averaged network localization errors are derived. These strategies are shown to be optimal in terms of the error scaling with the number of agents. Finally, the reduction of the error scaling by increasing the number of simultaneous measurement pairs is quantified.

This paper presents a framework for balancing fairness and efficiency in the collaborative networks (CNs) of enterprises. In any CN, the collaboration process often leads to a dilemma: the need to choose between fairness and efficiency. The objective of this research is to propose an algorithm that attempts to increase optimal weights of fairness while maintaining efficiency. In this research, two concepts are utilized to distinguish the balance between fairness and efficiency in CNs: 1) the generalized α-fair concept; and 2) Jain's fairness index. The performance of the proposed algorithm has been tested with conceptual heterogeneous and homogeneous CNs (HeCNs and HoCNs, respectively) based on the enterprise capacity. The experimental results indicate that a balance between efficiency and fairness in CNs is possible while forming a network and obtaining mutual benefits fairly among the enterprises. In addition, the proposed algorithm can minimize the deviation between most and least beneficial enterprises in CN in terms of total profit, lost sale cost, and inventory cost.

Device-to-device (D2D) communication can improve coverage, spectrum efficiency, and energy efficiency based on the current cellular network architecture. The fair scheduling for D2D communication in the orthogonal frequency division multiple access-based cellular network is studied in this paper. Based on the proportional fairness criteria, the optimization objective is formulated to maximize the sum of the relative achievable rate of both cellular users (CUs) and D2D pairs. It is a hybrid optimization problem since it involves in both the resource reuse and the transmit power control. A practical proportional fairness scheduling (PFS) algorithm is proposed. Firstly, the admissible area is decided according to the transmit power limitation of terminals and the quality of service (QoS) requirement. Next, we seek the optimal transmit power combination for the CU and D2D pair in the admissible area and it is demonstrated that the optimal value can be selected from several points on the border of the admissible area. Lastly, when multiple subchannels can be employed for scheduling, based on the generated bipartite graph, Hungarian algorithm is adopted to realize maximum matching, i.e., select reusable CUs and D2D pairs to maximize the sum of the relative achievable rates. The simulation results show that PFS algorithm can guarantee the excellent performance of both throughput and fairness.

© 1967-2012 IEEE. Multicasting is emerging as an efficient method to deliver the same data to a group of users, thereby saving network resources. The fairness between different multicast groups is an important quality-of-service (QoS) indication, but it has not been given significant attention. In this paper, we propose a normalized signal-To-noise ratio (SNR)-based fair scheduling for multiple multicast groups in multicast systems. The system fairness and capacity are then analyzed and compared for both fair scheduling and greedy scheduling over independent but non-identically distributed (i.n.d.) fading channels. Closed-form expressions in terms of the system spectral efficiency, outage probability, system fairness, and average bit error rate (BER) are derived in an uncoded/coded M-Ary quadrature amplitude modulation based adaptive transmission multicast system over i.n.d. Rayleigh fading channels. Numerical results show that compared with greedy scheduling, fair scheduling achieves considerably high fairness at the cost of slight system capacity loss, regardless of the number of multicast groups. Our focus is on the physical layer without rate loss, but we also briefly discuss applications of the proposed scheduling in a cross-layer design subject to the loss rate QoS constraint.

Recent results have made a promising case for offering oversubscribed wired data center networks (DCN) with extreme costs. Inter-rack wireless networks are drawing intensive attention to augment such wired DCNs with a few wireless links. Inspired by the promise of easy deployment and plug-and-play, we present VLCcube, a novel inter-rack wireless solution that extends the design of wireless DCN into three further dimensions: (1) all inter-rack links are wireless; (2) there is no imposition of any infrastructure-level alteration on wired production data centers; and (3) it should be plug-and-play, without any need of additional mechanical or electronic control operations. This vision, if realized, will lead to increased flexibility, reduced reconstructing cost, simplified configuration and usage, and outstanding compatibility with existing wired DCNs. Previous proposals, however, are opposed to the last two design rationales. To achieve this vision, the proposed VLCcube augments Fat-Tree, a representative DCN in production data centers, by organizing all racks into a wireless Torus structure via the emerging visible light links. We further present the topology design, hybrid routing, and flow scheduling schemes for VLCcube. Extensive evaluations indicate that VLCcube outperforms Fat-Tree significantly under the existing ECMP flow scheduling scheme, irrespective of the undergoing traffic pattern. Moreover, the performance of VLCcube can be significantly promoted by our congestion-aware flow scheduling scheme. More precisely, compared to ECMP, our flow scheduling scheme makes VLCcube achieve $\times 1.50$ throughput under batched flows, $\mathrm{\times}2.21$ and $\times 2.59$ throughput under two different kinds of online flows.

Due to the rapid development in the transmission of Wireless Network technologies, the need for rate evaluation using network coding demands urgent attention. This is solely dependent on how to improve on high performance communication channels for user’s interest. In this paper, an approach of using queuing theory in a wireless network is presented. The idea of network coding in a wireless environment address the transmission of packets not only in unicast form but also address the conflict in packet movement across a channel. Network coding also contributes greatly in computer communication system and other computer based electronic appliances. This means that a source is transmitting packets to a particular destination according to the user’s needs in a communication system. Packet transmission is enhanced within the wireless network using queuing theory. In this approach, the first routers are set to be the user access router and another directly connected router is set to be the source router. The user access router is set to connect 11 number of other routers where mainly the network coding is conducted. The results show an improvement in transmission rate by applying network coding as compared to other scenarios where networks coding are not utilized or the conventional routing either.

We consider the impact of delayed state information on the performance of centralized wireless scheduling algorithms. Since state updates must be collected from throughout the network, they are inevitably delayed, and this delay is proportional to the distance of each respective node to the controller. In this paper, we analyze the optimal controller placement resulting from this delayed state information. We propose a dynamic controller placement framework, in which the controller is relocated using delayed queue length information at each node, and transmissions are scheduled based on channel and queue length information. We characterize the throughput region under such policies, and find a policy that stabilizes the system for all arrival rates within the throughput region.

Single-ISA asymmetric multicore processors (AMPs), which combine high-performance big cores with low-power small cores, were shown to deliver higher performance per watt than symmetric CMPs (Chip Multi-Processors). Previous work has highlighted that this potential of AMP systems can be realizable via OS scheduling. To date, most existing scheduling schemes for AMPs have been designed to optimize the system throughput, but they are inherently unfair. Although fairness-aware schedulers have been also proposed, they fail to effectively deal with user priorities and do not always ensure that equal-priority applications experience a similar slowdown. To overcome these limitations, we propose ACFS, an asymmetry-aware completely fair scheduler that seeks to optimize fairness while ensuring acceptable throughput. Our evaluation on real AMP hardware and using scheduler implementations in the Linux kernel demonstrates that ACFS achieves an average 23% fairness improvement over two state-of-the-art schemes, while providing higher system throughput. Throughput and fairness are largely conflicting optimization goals on AMPs.Previous asymmetry-aware schedulers fail to effectively deal with user priorities.ACFS achieves an average 23% fairness improvement over state-of-the-art schemes.Predicting cross-core performance on current AMPs is one of the major challenges.

In this paper, we link the throughput gains, due to transceiver adaptation, in a point-to-point transmission link to the expected gains in a mesh network. We calculate the maximum network throughput for a given topology as we vary the length scale. We show that the expected gain in the network throughput due to transceiver adaptation is equivalent to the gain in a point-to-point link with a length equal to the mean length of the optical paths across the minimum network cut. We also consider upper and lower bounds on the variation of the gain in the network throughput due to transceiver adaptation, where integer-constrained channel bandwidth assignment and quantized adaptations are considered. This bounds the variability of results that can be expected and indicates why some networks can give apparently optimistic or pessimistic results. We confirm the results of previous authors that show finer quantization steps in the adaptive control lead to an increase in the throughput since the mean loss of throughput per transceiver is reduced. Finally, we consider the likely network advantage of digital nonlinear mitigation and show that a significant tradeoff occurs between the increase in the signal-to-noise ratio for larger mitigation bandwidths and the loss of throughput when routing fewer large-bandwidth superchannels.

Often one would like to allocate shared resources in a fair way. A common and well-studied notion of fairness is Max-Min Fairness , where we first maximize the smallest allocation, and subject to that the second smallest, and so on. We consider a networking application where multiple commodities compete over the capacity of a network. In our setting, each commodity has multiple possible paths to route its demand (for example, a network using Multiprotocol Label Switching (MPLS) tunneling). In this setting, the only known way of finding a max-min fair allocation requires an iterative solution of multiple linear programs. Such an approach, although polynomial time, scales badly with the size of the network, the number of demands, and the number of paths, and is hard to implement in a distributed environment. More importantly, a network operator has limited control and understanding of the inner working of the algorithm. In this article we introduce Upward Max-Min Fairness, a novel relaxation of Max-Min Fairness, and present a family of simple dynamics that converge to it. These dynamics can be implemented in a distributed manner. Moreover, we present an efficient combinatorial algorithm for finding an upward max-min fair allocation. This algorithm is a natural extension of the well-known Water Filling Algorithm for a multiple path setting. We test the expected behavior of this new algorithm and show that on realistic networks upward max-min fair allocations are comparable to the max-min fair allocations both in fairness and in network utilization.

There have been some rapid advances on the design of full duplex (FD) transceivers in recent years. Although the benefits of FD have been studied for single-hop wireless communications, its potential on throughput performance in a multi-hop wireless network remains unclear. As for multi-hop networks, a fundamental problem is to compute the achievable end-to-end throughput for one or multiple communication sessions. The goal of this paper is to offer some fundamental understanding on end-to-end throughput performance limits of FD in a multi-hop wireless network. We show that through a rigorous mathematical formulation, we can cast the multi-hop throughput performance problem into a formal optimization problem. Through numerical results, we show that in many cases, the end-to-end session throughput in a FD network can exceed $2 \times$ of that in a half duplex (HD) network. Our finding can be explained by the much larger design space for scheduling that is offered by removing HD constraints in throughput maximization problem. The results in this paper offer some new understandings on the potential benefits of FD for end-to-end session throughput in a multi-hop wireless network.

In Long-Term Evolution (LTE) and beyond systems, radio resource scheduling mechanism plays one of the main roles in system performance maximization. From this perspective, due to the conflicting quality requirements of different traffic types, providing a compromise among all performance targets for heterogeneous traffic is difficult. Moreover, the centralized scheduling mechanism for the ever growing number of users along with the massive variety of services, especially in overload states, is infeasible due to the extensive cost of information acquisition and computations. In this paper, we design resource scheduling policies for supporting the efficient delivery of heterogeneous traffic in overload states of a cell. To this end, we cast the class-based bearer-level resource distribution problem as a Proportional Fractional Knapsack model. The objective of the formulated problem is to meet Quality of Service (QoS) requirements and provide fairness for all standardized service classes. Since the solution of this problem is computationally expensive, due to the uncertainty and limited information on network and user operation, we develop a Gaussian-based analytical model and drive a formula for simplified computation of the weight of service bearers. Then, we propose Proportional Fractional Knapsack algorithm for guaranteeing effective utilization of resources for heterogeneous traffic. Finally, performance evaluation results are provided and demonstrate that the proposed scheduling approach can provide a significant level of fairness, in balance with the QoS and throughput performance targets, comparable with optimal ones.

Scheduling tasks close to their data and optimising resources utilisation are both crucial for the efficiency of MapReduce system. On the other hand, there is a conflict between fairness and efficiency. In this study, an efficient and dominant resource held time fairness (EHTF) scheduler is presented, in which the efficient utilisation of resources, data locality and fairness are addressed simultaneously. In EHTF scheduler, the authors introduce the concept of ‘coarse-grained fairness’ to improve the efficiency of MapReduce system. For each scheduling, several tasks from different jobs can be assigned to the free slot without violating the coarse-grained fairness doctrine. To determine the best task from these several tasks in each scheduling step, a score model is proposed by taking into consideration both resources utilisation and data locality. The authors describe the design and implementation of EHTF scheduler. The authors’ experimental results show that EHTF achieves more fairness and better throughput than Fair and Quincy schedulers.

In multihop wireless ad hoc networks, it is important to maintain the outcome fairness of throughput and to maximize the throughput. In this paper, we propose a novel opportunistic scheduling framework by considering outcome fairness and throughput simultaneously. Since the data rate fluctuates intensively due to channel errors, we first devise a data rate estimation method with an adaptive sliding window to accurately and adaptively estimate the data rate. Then, we present a framework together with two mechanisms. The first proposed mechanism is ROSA-WOM, with the weighted objective function method to configure the data rate. The other one is ROSA-MGCF, and it is a maximum total goodput method under a constrained fairness index. The proposed mechanisms are able to schedule the flows fairly even when the data rate of all flows is more than the channel capacity. We establish a testbed to evaluate these two mechanisms. The experiment results show that our proposed mechanisms can not only trade off with different fairness and throughput requirements, but also effectively provide robust service isolation, outcome fairness, and high throughput in the presence of channel errors.

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal.

As systems scale toward exascale, many resources will become increasingly constrained. While some of these resources have historically been explicitly allocated, many—such as network bandwidth, I/O bandwidth, or power—have not. As systems continue to evolve, we expect many such resources to become explicitly managed. This change will pose critical challenges to resource management and job scheduling. In this paper, we explore the potential of relaxing network allocation constraints for Blue Gene systems. Our objective is to improve the batch scheduling performance, where the partition-based interconnect architecture provides a unique opportunity to explicitly allocate network resources to jobs. This paper makes three major contributions. The first is substantial benchmarking of parallel applications, focusing on assessing application sensitivity to communication bandwidth at large scale. The second is three new scheduling schemes using relaxed network allocation and targeted at balancing individual job performance with overall system performance. The third is a comparative study of our scheduling schemes versus the existing scheduler on Mira, a 48-rack Blue Gene/Q system at Argonne National Laboratory. Specifically, we use job traces collected from this production system.

Packets that traverse more hops to the destination will get lower throughput and result in the inter-flow fairness issue.We propose a virtual queue management scheme that does not require the modification of any communication protocol.According to the number of flows, our scheme adjusts the parameters to achieve each flow's fair sharing of channel resource.Our proposed scheme can mitigate the inter-flow fairness problem and effectively improves the quality of video transmission. Due to intra-flow and inter-flow interference problems, the throughput performance decreases dramatically in a multi-hop wireless network. These two kinds of bandwidth unfair sharing problems could cause serious collisions and congestion, hence affecting the performance of multi-hop wireless networks. That is to say, data packets that need to traverse more hops to arrive at the destination will get lower throughput and result in the inter-flow fairness problem. Furthermore, the quality of video transmission is especially poor in traditional multi-hop wireless network environments. Therefore, in this paper, we propose a virtual queue management scheme that does not require the modification of any communication protocol. According to the number of flows, it adjusts the queue management scheme to achieve each flow's fair sharing of channel resource. It also improves the quality of video transmission. Through NS2 simulations, the results show that our proposed scheme can mitigate the inter-flow fairness problem and effectively improves the quality of video transmission. Display Omitted

Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to-grid technology, the scheduler controls the electricity loads of EVs considering fairness in the residential distribution network. We propose contribution-based fairness, in which EVs with high contributions have high priorities to obtain charge energy. The contribution value is defined by both the charge/discharge energy and the timing of the action. EVs can achieve higher contribution values when discharging during the load peak hours. However, charging during this time will decrease the contribution values seriously. We formulate the fair energy scheduling problem as an infinite-horizon Markov decision process. The methodology of adaptive dynamic programming is employed to maximize the long-term fairness by processing online network training. The numerical results illustrate that the proposed EV energy scheduling is able to mitigate and flatten the peak load in the distribution network. Furthermore, contribution-based fairness achieves a fast recovery of EV batteries that have deeply discharged and guarantee fairness in the full charge time of all EVs.

Fairness is an important property of computer networks and systems. In a wide range of these systems such as distributed multi-hop wireless networks, multihomed networks, and cloud computing, each user may be allocated a number of system resources; this resembles a many-to-many relationship between the sets of users and resources, which raises the problem of system-wide fair resource allocation. In this paper, we assume that each user/node can be allocated a number of resources, which could be either in its neighborhood or far from it. As a key difference with previous works and through incorporating the concept of “flow”, we model near/far resources allocated to nodes. To attain fair flow control in such systems, first we model this situation by introducing a new multi-server system called multi-ring , in which a server that represents a resource, can serve only a subset of either neighboring or far nodes in the system. Then, we define a centralized optimization problem to attain weighted proportional fairness among all nodes meaning that the sum of allocated capacities from all servers to each node (while considering its flow) is fair. We evaluate fairness properties of multi-ring networks and provide conditions on system parameters under which a system can have a fair resource allocation. Moreover, we present a distributed method to attain fairness in distributed environments, and its stability/convergence is evaluated by non-linear discrete dynamical systems. We present conditions under which the system is stable, and through numerical analysis, we show how to obtain stable system parameters for large systems. The effectiveness of the presented method is studied through extensive numerical evaluation. Results show the success of our method in attaining fairness for various topologies and system parameters, and confirm our stability analysis. A number of systems with fairness issues are also studied as potential applications of our model.

Wireless link scheduling is one of the major challenging issues in multi-hop wireless networks when they need to be designed in distributed fashion. This paper improves the general randomized scheduling method by using learning automata based framework that allows throughput optimal scheduling algorithms to be developed in a distributed fashion. A distributed scheduling algorithm that operates on more realistic conflict graph was proposed based on the physical interference model. This model uses a combination of a distributed learning automata based on the pick algorithm and an algorithm that compares successive scheduling solutions. Comparison was made by creating spanning tree on the conflict graph of the two consecutive schedules. Briefly, a distributed scheduling schemes was proposed, that: (i) is throughput optimal, (ii) intelligently choose links for new schedule, and (iii) message and time complexity is in O(n3).

Delay-tolerant networks are sparse mobile ad hoc networks in which there is typically no complete path between the source and destination. Although many routing schemes for delay-tolerant networks have been proposed, they do not address fairness issues in terms of the workload/traffic handled at each node and the share of throughput among different destination nodes. In this paper, we propose a socially aware routing strategy that optimizes both fairness and throughput. A relay node is selected based on the multi-hop delivery probability and its queue length. The effect of queue length control is to divert traffic away from highly connected nodes and allows nodes to explore less-congested paths to the destination. This helps balance the network loads, thus achieving workload fairness. Furthermore, to achieve throughput fairness, we sort arriving messages into different destination-based queues. Messages are then scheduled following a two-level forwarding strategy that optimizes throughput fairness using round-robin and delivery ratio using priority scheduling. Extensive real-trace-driven simulation results show that our scheme outperforms existing algorithms in terms of the delivery ratio. Furthermore, our scheme achieves a high throughput fairness, while distributing the network loads more evenly, with the top 10% of network nodes handling only 22% of the forwardings. Copyright © 2016 John Wiley & Sons, Ltd.

We study an one-hop device-to-device (D2D) assisted wireless caching network, where popular files are randomly and independently cached in the memory of end-users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through an one-hop D2D transmission, or from the base station (BS). We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio (SINR) and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of arts., Comment: This manuscript has been submitted to TCOM

The concept of heterogeneous wireless networks (HWNs) is based on the coexistence and interoperability of different types of radio access technologies (RATs) such as long term evolution (LTE) and wireless local area network (WLAN) in a unified wireless heterogeneous platform. Guaranteeing the quality of service (QoS) is an important issue for the next generation wireless networks which are characterized by providing different types of services. To schedule different types of service in HWN, distinct scheduling algorithms have been studied intensively in the literature. Thus in our research work, we focus on a common scheduling algorithm for the HWN where the traffic streams are classified into different categories, and each category has its own set of QoS parameters such as data rate and delay. In this article, we propose a new dynamic scheduling algorithm for HWN. The proposed solution introduces a new approach in scheduling packets while maintaining performance in wireless networks. The scheduling scheme is mainly based on transmission links' condition from the media independent handover (MIH) module, type of call (handoff call prioritization) and classes of service. In order to study the performance of the proposed scheme, we use simulation analysis and compare the performance of our scheme with a competing reference scheme called NSA (new scheduling algorithm) for wireless mesh networks in order to reveal its ability to adapt to the specific service and channel conditions. Simulation results show that under large number of users, the proposed algorithm has lower packet loss and blocking calls ratio while offers allowable average packet delay. Distinct scheduling algorithms have been studied in this work.We focus on a common scheduling algorithm for the heterogeneous wireless networks.We propose a new dynamic scheduling algorithm.The scheduling scheme is based on transmission links' condition from the MIH module.The scheduling scheme is based on type of call and the classes of service.

It is well known that IEEE 802.11 based MAC provides max---min fairness to all nodes even in a multi-rate WLAN. However, the max---min fairness may not always be the preferred fairness criteria as it significantly reduces overall system throughput. In this paper, we explore the proportional fairness and the time fairness. First, we obtain a condition that must be satisfied by the attempt probabilities to achieve proportional fairness. Using this condition, we propose a stochastic approximation based on-line algorithm that tunes attempt probabilities to achieve proportional fairness. The proposed algorithm can be implemented in a distributed fashion, and can provide optimal performance even when node uses a rate adaptation scheme. Next, we show that the time fairness is a special case of weighted max---min fairness with the weight for a node is equal to its transmission rate. Thus, the existing algorithms to achieve weighted max---min fairness can be used to achieve time fairness as well. This exposition also demonstrates that the proportional fairness and the time fairness are not the same contrary to what was conjectured. Performance comparison of various fairness criteria is done through ns-3 simulations. Simulation results show that time fair schemes achieve the highest throughput, and the sum of logarithm of individual node's throughputs under the time fairness is close to that under a proportionally fair scheme.

Throughput and energy efficiency in fading channels are studied in the presence of randomly arriving data and statistical queuing constraints. In particular, Markovian arrival models, including discrete-time Markov, Markov fluid, and Markov-modulated Poisson sources, are considered. Employing the effective bandwidth of time-varying sources and the effective capacity of time-varying wireless transmissions, maximum average arrival rates in the presence of statistical queuing constraints are characterized. For the two-state (ON/OFF) source models, throughput is determined in a closed form as a function of the source statistics, channel characteristics, and quality of service (QoS) constraints. Throughput is further studied in certain asymptotic regimes. Furthermore, energy efficiency is analyzed by determining the minimum energy per bit and a wideband slope in the low signal-to-noise ratio regime. Overall, the impact of source characteristics, QoS requirements, and channel fading correlations on the throughput and energy efficiency of wireless systems is identified.

Several distributed scheduling policies have been proposed with the objective of attaining the maximum throughput region or a guaranteed fraction throughput region. These policies consider only the theoretical throughput and do not account the lost in throughput due to the time complexity of implementing an algorithm in practice. Therefore, we propose a novel concept called effective throughput to characterize the actual throughput by taking into account the time complexity. Effective throughput can be viewed as the actual transmitted data without including the control message overhead. Numerical results demonstrate that in practical scheduling, time complexity significantly affects throughput. The performance of throughput degrades when the time complexity is high.

Visible Light Communication (VLC) using Light Emitting Diodes (LEDs) within the existing lighting infrastructure can reduce the implementation cost and may gain higher throughput than radio frequency (RF) or Infrared (IR) based wireless systems. Current indoor VLC systems may suffer from poor downlink resource allocation problems and small system throughput. To address these two issues, we propose an algorithm called a conflict graph scheduling (CGS) algorithm, including a conflict graph and a scheme that is based on the conflict graph. The conflict graph can ensure that users are able to transmit data without interference. The scheme considers the user fairness and system throughput, so that they both can get optimum values. Simulation results show that the proposed algorithm can guarantee significant improvement of system throughput under the premise of fairness.

Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the ((PF)2S) Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.

 Abstract—This paper aims at the design of a centralized-controlled TDMA scheduling algorithm in the single channel Wireless Sensor Network (WSN) environments. We consider the network scenario where packets generated by each WSN device are destined for the common Gateway. A Top-down scheduling mechanism is proposed to assign wireless devices near the Gateway with high priority for transmitting packets. Through simulations, this paper compares and analyzes the scheduling efficiency and packet delay performance achieved by the proposed mechanism and other related works under various network topologies and scenarios. We also investigate the capability of these algorithms in providing differentiated service to those packets with multiple frequency generation patterns and different priority orders. Furthermore, the lossy wireless channel with PDR

In order to fully utilize spectrum resource in wireless mesh networks (WMNs), we propose a combination of some popular communication techniques, including link scheduling, spatial reuse, power and rate adaptation and network coding (NC), to activate as many transmission links as possible during one scheduling period, so that the total scheduling length can be minimized and network throughput can be maximized. Different from previous studies, we consider the interplay among these techniques and present an optimal NC-aware link scheduling mechanism in multi-rate WMNs, which relies on the enumeration of all possible schedules. Due to the high computational complexity of our proposed model, we utilize a column generation (CG)-based method to resolve the optimization problem and decompose it into a master problem (MP) and a pricing problem (PP). Furthermore, we present a distributed power control algorithm for PP, by which the computational complexity of the CG-based scheme can be largely reduced. Simulation results demonstrate the superiority of our method under various network situations.

In this paper, we consider a network in which lower power nodes (LPNs) are deployed jointly within macrocells. However, there are significant differences between the transmit power levels, coverage areas, and deployment densities of these two types of base stations. Such disparities lead to an unfair load distribution, as well as a lower throughput for picocells’users equipments (UEs). A good solution to such issues is the exploitation of the cell range expansion (CRE) technique. Although CRE has widely proven its effectiveness, it may degrade the network capacity if the cell bias is not chosen properly. In fact, it may generate severe intercell interference at extended region cell (ERC) UEs, which leads to a deterioration of their throughput. We thus propose a downlink coordinated cell range expansion for mobility management (CCREMM) strategy that analytically computes the joint optimal bias at picocells and macrocells. CCREMM mitigates the interference at ERC-UEs by accounting for their maximum tolerable interference. Moreover, CCREMM reaches the load balancing and the UE QoS satisfaction by accounting for additional parameters. It will be proven that our strategy which is associated with the maximum throughput scheduling technique, results in a cell load-balancing improvement, fairness, and a 50–90% UE throughput enhancement. These performance figures are shown to surpass those achieved by alternative approaches proposed in the existing literature.

Multi-connectivity is emerging as promising solution to provide reliable communications and seamless connectivity at the millimeter-wave frequency range. Due to the obstacles that cause frequent interruptions at such high frequency range, connectivity to multiple cells can drastically increase the network performance in terms of throughput and reliability by coordi- nation among the network elements. In this paper, we propose an algorithm for the link scheduling optimization that maximizes the network throughput for multi-connectivity in millimeter-wave cellular networks. The considered approach exploits a centralized architecture, fast link switching, proactive context preparation and data forwarding between millimeter-wave access points and the users. The proposed algorithm is able to numerically approach the global optimum and to quantify the potential gain of multi-connectivity in millimeter-wave cellular networks. Funding agencies: European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [643002]; CENIIT

A typical data and energy integrated communication network (DEIN) conceives a conventional base station, which is capable of simultaneously transmitting the data and energy to user equipments (UEs) during the downlink (DL) transmissions by invoking the time-division-multiple-access (TDMA) protocol in the medium access control (MAC) layer. Several UEs operating in this DEIN are capable of harvesting the energy from the DL transmissions by adopting the power splitting (PS) technique and they are also capable of exploiting the harvested energy for powering their uplink (UL) data transmissions by invoking the TDMA protocol in the MAC layer. Both of the UL sum-throughput and the UL fair-throughput of the DEIN is maximized by deciding the duration of each time-slot during the DL/UL transmissions and by determining the optimal PS factor for each UE. Both of these optimization problems are finally solved by the classic method of Lagrange multipliers in close-form. An interesting observation shows that supporting low-throughput data services during the DL transmissions does not degrade the wireless energy transfer and hence does not reduce the throughput of the UL transmissions.

Large-scale data centers are the key infrastructures for hosting and running a variety of applications. Besides traditional L2/L3 devices, middleboxes are widely deployed in data centers and perform many important functions, e.g., the intrusion detection and firewall. Middleboxes are equipped with multiple kinds of resources, such as CPU and memory. Data flows undergoing different functions have heterogeneous processing time requirements on diverse resources. Researchers are in a dilemma as to how to provide fair service for flows and efficiently utilize those scarce resources. To address this problem, we propose a novel packet scheduling method, active time fairness queuing (ATFQ), for multi-resource environments. Prior packet scheduling methods usually focus on pursuing the fairness among flows, resulting in enormous waste of those scarce resources. ATFQ overcomes this essentially by redefining the fairness and can maximize the resource utilization with the guarantee of fairness. We conduct extensive simulations to evaluate the performance of ATFQ. The evaluation results demonstrate that flows get better service in many aspects under ATFQ. Meanwhile, the resource utilization rises up by about 10% than the traditional DRFQ, which is one of the mainstream involved methods.

Fair resource is a key building block of any shared computing system. Recently fair division theory has emerged as a promising approach for the allocation of multiple computational resources among agents. Recent research has discussed efficiency and fairness requirements and identified a number if desirable scheduling objectives including so-called dominant resource fairness(DRF). DRF is not good between fair and resource utilization. A new allocation model Balancing Fairness and Efficiency with BottleneckAware Allocation(BAA) find good appropriate balance between fairness to the clients and maximizing system utilization. But BAA without taking into account the weight value and maximum number of tasks which are users need to run. We propose the IBAA fair allocation mechanism. IBAA has lots of good properties, it satisfies DSI, PE and EF. We construct IBAA mechanisms that provably satisfy properties, and analyze the performance. We believe that our work informs the design of superior multiusers system, and at the same time expands the scope of fair division theory by initiating the study of dynamic and fair resource allocation mechanisms.

Example of the improvement in channel utilization over time.Display Omitted We propose a cooperative channel assignment and packet forwarding protocol.Our protocol reduces the transmission times for the low-data rate direct link users.Our protocol selects multi-hop path with min transmit time even direct path exists.Results show that our protocol notably improves network throughput over IEEE 802.11. Typical MAC protocols for IEEE 802.11-based ad hoc networks employ a direct transmission strategy whenever the transmitter can directly reach the receiver (one-hop transmission). While such a design enjoys simplicity, it limits the number of admitted transmissions in a given neighborhood. Due to the performance anomaly of 802.11-based wireless networks, transmission with low data rates occupy the shared medium for longer periods of time. Occupying the transmission medium for a longer period of time results in less available transmission time for other nodes, which consequently reduces the number of transmitted packets during the same period of time, leading to a reduction in network throughput. To improve the overall network throughput, we present a cooperative multi-channel MAC protocol for single-hop wireless mobile ad hoc networks that attempts at computing the path with the minimum required transmission time for a given source-destination pair (including the channel assignment along that path). The proposed protocol attempts at improving network performance by means of cooperative communications. According to our approach, if the one-hop (direct) path between communicating nodes supports low data rate (requires longer transmission time), the source selects a multi-hop path to the destination such that the total amount of required transmission time is minimized. Through simulations, we show that our proposed protocol achieves significant throughput and fairness improvement compared to the standard IEEE 802.11-based protocol.

Multi-user multi-input multi-output (MU-MIMO) system has attracted the 4 th generation wireless network as one of core technique for performance enrichment. In this system rate control is a challenging problem and another problem is optimization. Proper scheduling can resolve these problems by deciding which set of user and at which rate the users send their data. This paper proposes a new multi-parameter based scheduling (MPS) for downlink multi-user multiple-input multiple-output (MU-MIMO) system under space-time block coding (STBC) transmissions. Goal of this MPS scheme is to offer improved link level performance in terms of a low average bit error rate (BER), high packet delivery ratio (PDR) with improved resource utilization and service fairness among the user. This scheme allows the set of users to send data based on their channel quality and their demand rates. Simulation compares the MPS performance with other scheduling scheme such as fair scheduling (FS), normalized priority scheduling (NPS) and threshold based fair scheduling (TFS). The results obtained prove that MPS has significant improvement in average BER performance with improved resource utilization and fairness as compared to the other scheduling scheme.

A significant trend caused by cloud computing is to aggregate applications for sharing resources. Thus, it is necessary to provide fair resources and performance among applications, especially for the network, which is provided in the best-effort manner in current clouds. Although many studies have made efforts for provisioning fair bandwidth, they are not sufficient for network fairness. In fact, for interactive applications, response time is more sensitive than bandwidth, and users expect a fair response time not just bandwidth. In this study, the authors want to investigate whether the traditional methods of sharing bandwidth can help the fairness of response time. They show that: (1) bandwidth has little relationship to response time, and adjusting bandwidth hardly affects response time in most cases. Thus, the traditional methods cannot help the fairness of response time much; and (2) the fairness between components is different from the one between transactions, and many prior studies only consider the former while ignoring the latter. Thus, the authors cannot help much for multitier applications consisting of multiple transactions either. As a result, they construct a model with two metrics to evaluate the fairness status of the network sharing, while considering the applications' characteristics on both the response time and throughput. Based on the model, they also propose a mechanism to improve the fairness status. The evaluation results show that the authors' mechanism improves the fairness status by 26.5%–52.8%, and avoids performance degradation compared to some practical mechanisms.

Today’s giant data centers are power hungry. Data center energy saving not only helps control the operational cost, but also benefits the sustainable growth of cloud services. Due to the adoption of much more switches in modern data centers as well as the mature server-side power management techniques, energy saving for the data center network is becoming increasingly important. Most previous works on saving data center network energy focus on aggregating flows to as few switches as possible. However, in this paper we argue that this method may not work for network-limited flows, the throughputs of which are elastic based on the competing flows. To save the network energy consumed by this kind of elastic flows, we propose a flow scheduling approach called Willow , which takes both the number of switches involved and their active working durations into consideration. We formulate this problem by programming and design a greedy approximate algorithm to schedule flows in an online manner. Simulations based on MapReduce traces show that Willow can save up to 60 percent network energy compared with ECMP scheduling in typical settings, and outperforms other classical heuristic algorithms such as simulated annealing and particle swarm optimization. Testbed Experiments demonstrate that this kind of dynamic energy-efficient flow scheduling causes negligible impact on upper-layer applications.

We consider the concurrent transmission scheduling problem in a rate-adaptive wireless personal area network (WPAN). In such networks, the physical layer can adaptively change modulation and coding schemes based on the interference level in the environment and accordingly change the data rate. The scheduling problem is to assign users to time slots so that the total throughput is maximized. The challenge is that the achieved data rate of one flow is limited by the interference from other flows in the same slot, which is unknown until the schedule is known. We propose to discretize data rate into several distinct levels supported by the PHY layer and then use a linear programming model to find the highest rate level a flow can achieve. The same model is extended to consider a mixture of omni-directional antennas and directional antennas with heterogeneous transmitting power. The simulation results show that the proposed algorithms outperform the previous work for adaptive-rate transmission scheduling in both throughput and fairness.

This paper proposes a utility-based scheduling framework for efficient differentiation of users’ Quality-of-Service (QoS) in a broadband wireless access system involving heterogeneous mixed traffic flows. The utility-based scheduling framework, called Maximum QoS Satisfaction (MQS), is based on three novel Radio Resource Allocation (RRA) techniques; delay-based scheduling policy for Real-Time (RT), minimum-rate-based scheduling policy for Non-Real-Time (NRT) and a throughput-based scheduling policy for Best-Effort (BE) services. Simulation study shows that MQS achieves superior performances in terms of average system throughput and user satisfaction both in single and heterogeneous mixed traffic scenarios, when compared to some existing ones.

As a major supplement to LTE-Advanced, D2D communications underlaying cellular networks have proven efficient in offloading network infrastructures and improving network performance. The scheduling mechanism plays a key role in providing better user experience in D2D communications. However, controlled by operators, D2D communications pose specific problems that do not exist in available wireless networks. Therefore, mature scheduling mechanisms devised for cellular networks or ad hoc networks are not directly applicable to D2D communications. In this article, we first review recent research on scheduling mechanisms for D2D communications, and discuss the design considerations and implementation challenges. Then we propose an on-demand scheduling mechanism, DO-Fast, which can provide QoS differentiation capabilities. Next, we provide performance evaluations based on simulations and an experimental testbed. Finally, we conclude this article and point out possible directions for future research.

Communication system design for wireless networked control systems (WNCSs) is very challenging since the strict timing and reliability requirements of control systems should be met by the wireless communication systems that introduce non-zero packet error probability and non-zero delay at all times. Particularly, the scheduling algorithms for WNCSs should be designed to provide maximum level of adaptivity accommodating packet losses and changes in network topology while exploiting periodic nature of the sensor node transmissions. Creating such a schedule has been previously studied for an Ultra Wide Band (UWB) based WNCS. In this paper, we extend the joint optimization problem of power control, rate adaptation and scheduling with the objective of providing maximum adaptivity for general WNCSs employing continuous rate transmission model in which Shannon's channel capacity formulation is used for the achievable transmission rate. Upon proving the NP-hardness of the problem, we provide a framework for the design of a heuristic algorithm for scheduling and propose an optimal polynomial time algorithm for the power control and rate adaptation problem following the derivation of the optimality conditions. We demonstrate via extensive simulations that the proposed algorithms outperform the existing algorithms with performance close to optimal solution and average runtime admissible for practical WNCSs.

We propose a two-level scheduler for a frequency reuse-1 multi-cell wireless network satisfying inter- and intra-cell weighted temporal fairness constraints. As opposed to hard partitioning of the entire frequency band to different cell patterns in frequency reuse- $M$ systems $(M>1) $ , we propose sharing this band opportunistically in time by these patterns. Through numerical examples, we show notable gains in overall network throughput due to improved multi-user diversity in comparison with a conventional frequency reuse-3 system.