Fair Resource Research Articles

Proportional Fair Scheduling in Wireless Networks With RF Energy Harvesting and Processing Cost

In this letter, we apply the notion of proportional fair (PF) scheduling to wireless powered communication networks, which consist of a base station that broadcasts radio-frequency energy over the downlink [energy harvesting (EH) phase] and EH users that transmit information over the uplink [information transmission (IT) phase]. The doubly near fair problem hinders the fair resource allocation in these networks, but can be successfully overcome by an opportunistic policy for scheduling of a single EHU per time-division multiple access (TDMA) frame. It turns out that the optimal PF scheduling is realized when each TDMA frame consists of either an EH or an IT phase. The proposed online solution is asymptotically optimal but also applicable for a finite number of transmission epochs.

Fair Resource Allocation With Interference Mitigation and Resource Reuse for LTE/LTE-A Femtocell Networks

Joint consideration of interference, resource utilization, fairness, and complexity issues is generally lacking in existing resource allocation schemes for Long-Term Evolution (LTE)/LTE-Advanced femtocell networks. To tackle this, we employ a hybrid spectrum allocation approach whereby the spectrum is split between the macrocell and its nearby interfering femtocells based on their resource demands, whereas the distant femtocells share the entire spectrum. A multiobjective problem is formulated for resource allocation between femtocells and is decomposed using a lexicographic optimization approach into two subproblems. A greedy algorithm of reasonably low complexity is proposed to solve these subproblems sequentially. Simulation results show that the proposed scheme achieves substantial throughput and packet loss improvements in low-density femtocell deployment scenarios while performing satisfactorily in high-density femtocell deployment scenarios with substantial complexity and overhead reduction. The proposed scheme also performs nearly as well as the optimal solution obtained by exhaustive search.

Enhancing the Flexibility of TCP in Heterogeneous Network.

Due to a set of constant initial values, the performance of conventional TCP drops significantly encountering heterogeneous network, showing low throughput and unfairness. This paper firstly demonstrates the chaotic character of TCP congestion control in heterogeneous network, especially the sensitivity to initial value. Inspired by merit of nature-inspired algorithm, a novel structure of TCP congestion control (IPPM, Internet Prey-Predator Model) is proposed. Parameters such as available link capacity(C), congestion window (W) and queue length (Q) are collected by IPPM, which calculates the max value of C according to the interacting relationship existing in C, W and Q, and IPPM initiates the TCP ssthresh with the calculated value. Plenty of simulation results show that the modified TCP can effectively avoid network congestion and packet loss. Besides, it holds high resource utilization, convergence speeds, fairness and stability.

Patient Requests for Off-Label Bioprediction of Dementia.

In 2012, the FDA approved for the differential diagnosis of Alzheimer's disease a brain-imaging technology, Amyvid-PET (aka florbetapir-PET), capable of non-invasively estimating the burden of amyloid plaques; this approval for one indication renders the technology a candidate for off-label use for another indication according to a physician's judgment. What should a physician do if an educated, pro-active, and concerned patient requests off-label use of Amyvid-PET to help her estimate the likelihood that her mild memory complaints are "just normal aging" or are likely to profoundly worsen? I consider reasons that a physician might justify denial of such a request, including concerns of safety, uncertain benefit, and fair resource allocation, but cautiously conclude that there may be certain cases where off-label bioprediction would be permissible.

A Fair and Efficient Resource Allocation Scheme for Multi-Server Distributed Systems and Networks

Maintaining efficiency and fairness is a challenging problem in distributed systems and networks. In this paper, we focus on distributed multi-server systems and networks in which each user may be allocated resources by different servers. Reemphasizing polling systems as abstractions of resource sharing systems, in this paper, first we introduce a multi-server polling system in which each server (resource) can poll (be allocated to) only a subset of queues (users) in the system to model a wide range of multi-server systems such as multihomed networks and cloud computing. Then, to obtain a fair resource allocation vector to queues, a network utility maximization problem with a general utility function is defined. Depending on the type of the utility function, the presented scheme can attain different kinds of fairness such as weighted proportional and max-min fairness. Although maintaining fairness is important in many applications, providing efficiency is also crucial. Hence, we present an efficient algorithm to convert the obtained fair resource allocation vector into a Markovian routing matrix to determine the polling order of queues. This algorithm is capable of improving performance measures such as delay variance and mitigating short-term unfairness by minimizing the probability of consecutive polling of the same queue. Two distributed schemes are presented to obtain fairness and efficiency in even highly dynamic and distributed environments. The effectiveness of the presented schemes is also studied through simulation and numerical evaluation. Our results show their success in attaining fairness and efficiency in dynamic multi-server distributed systems and networks.

F2C: Enabling Fair and Fine-Grained Resource Sharing in Multi-Tenant IaaS Clouds

This paper presents F2C , a cooperative resource management system for Infrastructure-as-a-Service (IaaS) clouds. Inspired by group-buying mechanisms in real product and service markets, F2C advocates a group of cloud tenants (called tenant coalition ) to buy resource capacity in bulk and share the resource pool in the form of virtual machines (VMs). Tenant coalitions leads to vast opportunities for fine-grained resource sharing among multiple tenants. However, resource sharing, especially for multiple resource types, poses several challenging problems in pay-as-you-use cloud environments, such as sharing incentive, free-riding, lying and economic fairness. To address those problems, we propose Reciprocal Resource Fairness (RRF) , a novel resource allocation mechanism to enable fair sharing on multiple resource types within a tenant coalition. RRF is implemented in two complementary and hierarchical mechanisms: inter-tenant resource trading and intra-tenant weight adjustment. RRF satisfies several highly desirable properties to ensure fairness. We implement F2C in Xen platform. The experimental results show F2C is promising for both cloud providers and tenants. For cloud providers, F2C improves VM density and cloud providers’ revenue by 2.2X compared to the current IaaS cloud models. For tenants, F2C improves application performance by 45 percent and guarantees 95 percent economic fairness among multiple tenants.

Hierarchical distributed management clustering protocol for wireless sensor networks

In recent years, there has been a marked increase in the use of wireless sensor networks in various environments such as crisis areas, military operations, and monitoring systems. These networks do not use a fixed network infrastructure and therefore they are a popular choice for highly dynamic environments. One of the main concerns in these networks is the topology management issue, which the clustering method is a subfield for that. The main objective of clustering methods is optimizing the energy consumption. This paper proposes a new clustering protocol, which uses many parameters such as the activity history of each node, local and general state of nodes and their resources condition to determine the best cluster heads and members of each cluster that can increase the network lifetime, fair resource consumption and network coverage.

Virtual network embedding with pre‐transformation and incentive convergence mechanism

SummaryEfficient and fair resource allocation for multitudinous virtual networks running cloud‐based applications is crucial to archive dynamic resources multi‐tenancy in cloud computing. In order to solve the problem, we propose a novel virtual network embedding (VNE) algorithm to increase revenue and utilization of substrate network as well as to improve acceptance fairness of virtual networks. First, we present a virtual topology pre‐transformation mechanism leveraging reusable technology to reduce topology difference and achieve acceptance fairness. Then, because of the Non‐deterministic polynomial‐time (NP)‐hard characteristics of VNE, we model the problem as an integer linear programming problem and solve the VNE problem with a discrete particle swarm optimization‐based algorithm. The operations and parameters of particles are well redefined according to the VNE context. Finally, an incentive convergence mechanism is proposed to reduce mapping complexity, which can be used to accelerate convergence and to save more bandwidth by exploiting individual candidate nodes' lists. Simulation results prove that our proposed method is superior to the existing similar algorithms in terms of physical resource utilization, acceptance fairness, revenue/cost ratio, and searching efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

On the Sum Rate of Fair Resource Allocation With Selective Feedback

Opportunistic scheduling exploits the multiuser diversity to improve the performance of wireless communications. The scheduling gain is enabled by the channel feedback sent from the receiver to the transmitter. In this paper, we consider the so-called opportunistic cumulative distribution function (CDF) scheduling with threshold-based selective feedback in the orthogonal frequency division multiple access downlink system. Among opportunistic scheduling techniques, the CDF scheduling is known to provide multiuser diversity gain while maintaining fair radio resource sharing among users. We first derive the exact and asymptotic average sum rates assuming antenna selection at transmitters. The expressions are valid for arbitrary number of inter-cell interference and number of transmit antennas. Moreover, a closed-form approximation for the user rate distribution is calculated using the exact moments of the user sum rate. The approximation is shown to provide an accurate estimate for the rate distribution, and the results can be utilized in rate adaptation at the transmitter.

A Fast-and-Fair Collision Resolution Protocol Tailored for Ad Hoc Wireless Networks

Wireless technologies have permeated a significant number of devices over the years. On the other hand, Medium Access Control (MAC) protocols strive to deal with an increasing number of contending nodes on crowded frequency bands. This work presents an efficient medium access control resolution protocol. The proposed protocol, termed Collision Resolution Protocol (CRP), uses pulse/tone signaling that works in both Collision Detection (CD) and Non-Collision Detection (NCD) environments. Given n>1 contending nodes, CRP selects a single transmitting node in 16n and 40n time slots, respectively, for the CD and the NCD scenarios, with probability of at least 1-2-1.5n. Furthermore, CRP does not require an exact number, or estimation, of the contending nodes. Extensive simulations on the OMNeT++ shows that, for n = 256 and considering CD-capability, CRP successfully grants channel access in 0.03% of the time required by the IEEE 802.11 standard while improving the number of transmissions per second in 100%. In addition, CRP provides fair resource allocation and reduced channel access latency.

Fair Flow Control and Fairness Evaluation in Computer Networks and Systems

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.

Time-Based Bandwidth Allocation for Heterogeneous Storage

Providing fairness and system efficiency are important, often conflicting, requirements when allocating shared resources. In a hybrid storage system the problem is complicated by the high variability in request service times, caused by speed differences between heterogeneous devices and workload-specific variations in access time within a device. This paper describes a model for fair time-based resource allocation in a hybrid storage system that has better fairness and efficiency than traditional IOPS-based schemes. An analytical model is developed and an optimal algorithm for fairly allocating device times to applications while maximizing the system throughput or utilization is presented. The results are validated using Linux implementations.

Towards Multi-Resource Fair Allocation with Placement Constraints

Multi-resource fair schedulers have been widely implemented in compute clusters to provide service isolation guarantees. Existing multi-resource sharing policies, notably Dominant Resource Fairness (DRF) and its variants, are designed for unconstrained jobs that can run on all machines in a cluster. However, an increasing number of datacenter jobs specify placement constraints and can only run on a particular class of machines meeting specific hardware/software requirements (e.g., GPUs or a particular kernel version). We show that directly extending existing policies to constrained jobs either compromises isolation guarantees or allows users to gain more resources by deceiving the scheduler. It remains unclear how multi-resource fair sharing is defined and achieved in the presence of placement constraints. We address this open problem by a new sharing policy, called Task Share Fairness (TSF), that provides provable isolation guarantees and is strategy-proof against gaming the allocation policy. TSF is shown to be envy-free and Pareto optimal as well.

Fair Resource Allocation in a Volatile Marketplace

We consider the setting where a seller must allocate a collection of goods to budgeted buyers, as exemplified by online advertising systems where platforms decide which impressions to serve to various advertisers. Such resource allocation problems are challenging for two reasons: (a) the seller must strike a balance between optimizing her own revenues and guaranteeing fairness to her (repeat) buyers and (b) the problem is inherently dynamic due to the uncertain, time-varying supply of goods available with the seller. We propose a stochastic approximation scheme akin to a dynamic market equilibrium. Our scheme relies on frequent re-solves of an Eisenberg-Gale convex program, and does not require the seller to have any knowledge about how goods arrival processes evolve over time. The scheme fully extracts buyer budgets (thus maximizing seller revenues), while at the same time provides a 0.47 approximation of the proportionally fair allocation of goods achievable in the offline case, as long as the supply of goods comes from a wide family of (possibly non-stationary) Gaussian processes. We then extend our results to a more general family of metrics called α-fairness. Finally, we deal with a multi-objective problem where the seller is concerned with both the proportional fairness and efficiency of the allocation, and propose a hybrid algorithm which achieves a 0.27 bi-criteria guarantee against fairness and efficiency.

On Fair Resource Sharing in Downlink Coordinated Multi-Point Systems

An opportunistic scheduling scheme is proposed for the downlink of a coordinated multi-point (CoMP) system, which exploits both spatial multiplexing and multiuser diversity gains while maintaining fair resource sharing among users. The proposed scheduling scheme consists of two steps in each time slot: selecting a base station (BS) at each user, and then selecting a user at each BS. Although the proposed scheme selects users in a distributed manner, the channels among the selected users are proved to be orthogonal when the number of users approaches infinity. Simulation results demonstrate that the proposed scheme yields a better throughput than the existing scheduling schemes designed for fair resource sharing in CoMP systems.

Adaptive WHTS-Assisted SDMA-OFDM Scheme for Fair Resource Allocation in Multi-User Visible Light Communications

This paper proposes applying Walsh-Hadamard transform spreading (WHTS) in conjunction with space-division multiple access (SDMA) in an orthogonal frequency-division multiplexing (OFDM) system to provide enhanced multi-user downlink capability in visible light communication (VLC) scenarios. VLC environments are characterized by strong interference from the emissions of adjacent lamps. Thus, SDMA-OFDM schemes can be applied to enable multi-user capability while ensuring a highly efficient use of the limited available bandwidth. However, these SDMA-OFDM systems can only provide multiple access for a small number of users whenever there is enough channel diversity to separate the users' information. This is the case, for example, when each single user is associated with a specific emitting lamp and angle-diversity receiving techniques are applied. However, to accommodate a greater number of users requires a user's distribution in the frequency domain. In this sense, the proposed Walsh-Hadamard transform spreading scheme provides a fair assignment of the available subcarriers among the different users leading to an identical throughput and bit error rate (BER) for users allocated to the same emitting lamp. This BER is also controllable by means of an adaptive throughput mechanism. The numerical results show that the proposed adaptive WHTS-assisted SDMA-OFDM system in a full-user scenario presents a similar performance to that of an adaptive SDMA-OFDM system irrespective of the number of users per lamp, while providing enhanced multi-user capability. Finally, an adaptive algorithm for resource allocation is evaluated, demonstrating a high degree of satisfaction (regarding demanded data rates) when large numbers of users are simultaneously served.

Cooperative and fair MAC protocols for cognitive radio ad-hoc networks

A secondary user (SU) in multichannel cognitive radio ad hoc network (CRAHN) has a limited transmission range, which may raise a hidden multichannel sensing problem. In addition, CRAHNs can be deployed ubiquitously, and SUs from any CRAHNs could co-exist utilizing the spectrum. This situation leads to the fairness issue of spectrum resource sharing between the SUs. Both cooperative and fairness issues are important to CRAHN performance. In this paper, a cooperative and a non-cooperative multichannel (MC)-MAC protocol is proposed. In order to address the fairness issue, a fair multichannel (FMC)-MAC protocol for CRAHN is proposed, which orientates to the fairness in resource sharing. In this FMC-MAC, the SU keeps the current backoff (CB) counter when a PU appears to claim the intended channel. These proposed MAC protocols are simulated using NS2 and compared with other protocols. In addition, a mathematical model using Markov chain is constructed for FMC-MAC and the performance measures are derived. From results, the MC-MAC protocol has enhanced the network utilization and the cooperative scheme has significantly enhanced the packet delivery ratio and decreased the end-to-end delay of SUs in high traffic. The cooperative protocol enhances packet delivery ratio up to 15 % and decreases end-to-end delay down to 32 %, compared to the non-cooperative one. The FMC-MAC protocol with other two existing protocols. From the comparison results, a higher fairness has been shown by FMC-MAC CB while still maintaining a high throughput.

Jammer-Assisted Resource Allocation in Secure OFDMA With Untrusted Users

In this paper, we consider the problem of resource allocation in the orthogonal frequency division multiple access system with single source and $M$ untrusted users in presence of a friendly jammer. The jammer is used to improve either the weighted sum secure rate or the overall system fairness. The formulated optimization problem in both the cases is a mixed integer non-linear programming problem, belonging to the class of NP-hard. In the sum secure rate maximization scenario, we decouple the problem and first obtain the subcarrier allocation at source and the decision for jammer power utilization on a per-subcarrier basis. Then, we do joint source and jammer power allocation using primal decomposition and alternating optimization framework. Next, we consider fair resource allocation by introducing a novel concept of subcarrier snatching with the help of jammer. We propose two schemes for jammer power utilization, called proactively fair allocation (PFA) and on-demand allocation (ODA). PFA considers equitable distribution of jammer power among the subcarriers, while ODA distributes jammer power based on the user demand. In both cases of jammer usage, we also present suboptimal solutions that solve the power allocation at a highly reduced complexity. Asymptotically optimal solutions are derived to benchmark optimality of the proposed schemes. We compare the performance of our proposed schemes with equal power allocation at source and jammer. Our simulation results demonstrate that the jammer can indeed help in improving either the sum secure rate or the overall system fairness.

CDF-Based Scheduling Algorithm for Proportional Throughput Fairness

Cumulative distribution function (CDF)-based scheduling (CS) with resource-based fairness is well-known as an efficient algorithm that satisfies fair resource sharing among users while accommodating exploitation of multiuser diversity. In this letter, we extend CS to satisfy proportional throughput fairness. In particular, the proposed CS with proportional throughput fairness (CSPT) exploits the properties of CS to intelligently calculate the channel access ratio (CAR) for each user that satisfies proportional throughput fairness. Then, user selection is performed under CS with the obtained CARs. Moreover, the throughput upper-bound of schedulers satisfying proportional throughput fairness is analyzed and, through asymptotic analysis and simulations for various scenarios, we show that the performance of our proposed CSPT is close to this upper-bound.

Options of partners improve carbon for phosphorus trade in the arbuscular mycorrhizal mutualism.

The mutualism between plants and arbuscular mycorrhizal fungi (AMF) is widespread and has persisted for over 400million years. Although this mutualism depends on fair resource exchange between plants and fungi, inequality exists among partners despite mechanisms that regulate trade. Here, we use (33) P and (14) C isotopes and a split-root system to test for preferential allocation and reciprocal rewards in the plant-AMF symbiosis by presenting a plant with two AMF that differ in cooperativeness. We found that plants received more (33) P from less cooperative AMF in the presence of another AMF species. This increase in (33) P resulted in a reduced (14) C cost per unit of (33) P from less cooperative AMF when alternative options were available. Our results indicate that AMF diversity promotes cooperation between plants and AMF, which may be an important mechanism maintaining the evolutionary persistence of and diversity within the plant-AMF mutualism.