A Unified Scheduling Framework Based on Virtual Timers for Selfish-Policy Shared Spectrum

In this paper, we address the uplink resource scheduling problem in single carrier frequency-domain multiple access systems. In particular, we focus on the efficiency and fairness trade-offs in scheduling and resource allocation for wireless cellular networks. We present an efficient implementation method that translates these scheduling problems into set partitioning problems that are well-studied in the literature. Then, we discuss a family of utility functions that enable us to investigate the performance of different frequency domain schedulers such as the sum-rate maximization, proportional fair, and max-min fair schedulers. We use the price of fairness as a metric to analytically quantify these trade-offs. Based on the intuition that fairness of resource allocation in cellular radio networks corresponds to the prioritization of cell-edge user rates, we demonstrate that the proportional fair scheduler significantly improves fairness among users, and increases the rates offered to the cell-edge and median users when compared to the sum-rate maximization scheduler. This comes at the cost of reducing the cell-center user rates and the aggregate user rate. We present the steps on how to take into account the practical implementation constraints, in particular, those related with the discrete Fourier transform implementation, in the problem formulation. Simulation results that illustrate these trade-offs are also presented. We conclude that this type of analysis can provide guidelines for the network operators to control the efficiency and fairness trade-off as the data traffic grows.

In modern data intensive computing, it is increasingly common for jobs to be executed in a distributed fashion across multiple machine clusters or datacenters to take advantage of data locality. This paper studies fair resource allocation among jobs requiring distributed execution. We extend conventional max-min fairness for resource allocation in a single machine or machine cluster to distributed job execution over multiple sites and define Aggregate Max-min Fairness (AMF) which requires the aggregate resource allocation across all sites to be max-min fair. We show that AMF satisfies the properties of Pareto efficiency, envy-freeness and strategy-proofness, but it does not necessarily satisfy the sharing incentive property. We propose an enhanced version of AMF to guarantee the sharing incentive property. We present algorithms to compute AMF allocations and propose an add-on to optimize the job completion times under AMF. Experimental results show that compared with a baseline which simply requires the resource allocation at each site to be max-min fair, AMF performs significantly better in balancing resource allocation and in job completion time, particularly when the workload distribution of jobs among sites is highly skewed.

In modern, competitive, and dynamic access networks the underlying bandwidth distribution mechanism has to be capable of understanding user requirements, meeting stringent quality of service (QoS) demands, and satisfying a broad spectrum of user traffic dynamics. Undoubtedly, optical fiber is the dominant transmission medium enabling practical and cost-effective optical infrastructures in the last mile. Passive optical networks (PONs) represent one of the most promising player towards the fiber to the home (FTTH) vision allowing users to experience high quality, demanding multimedia services and applications. The 10-gigabit-capable passive optical network (XG-PON), one of the latest PON standard, incorporates a set of profound conditions a contemporary PON should ensure. Fairness provisioning constitutes one of the most critical features a PON should provide. However, ensuring fairness in an access network with numerous different users, requesting multiple traffic flows in any time, is not a straightforward task. In this work, we focus on the fairness issue by devising an adaptive, efficient, and fair dynamic bandwidth allocation (DBA) scheme called Insistent FAIr STrategy prOcesS (IFAISTOS). IFAISTOS investigates and maintains user traffic profiles. Overloaded users are carefully treated by gaining greater granting windows than other users; however bandwidth monopolization is prevented. Fairness is ensured for all users in terms of traffic load and average delay. A steering, adaptive mechanism records user traffic profiles by changing and defining bandwidth weights proportional to individual traffic needs. Extensive simulation results reveal the efficacy of the proposed DBA in terms of fairness and average packet delay.

Fair and efficient resource allocation in IEEE 802.11ah WLAN with heterogeneous data rates

Dynamic and fair resource allocation in a distributed ring-based WDM-PON architectures

Multi-connectivity is considered as a 5G key technique to improve both the user performance and the overall resource utilization. In this paper, we examine a resource allocation problem under multi-connectivity in evolved LTE and propose a utility proportional fair (UPF) resource allocation that preserves users quality-of-service (QoS) considering backhaul capacity limitations. The proposed policy is compared with proportional fair (PF) resource allocation through extensive simulations. Presented results show that multi-connectivity outperforms single-connectivity in terms of network aggregated rate and users QoS satisfaction in different network case studies, i.e., empty and loaded cell scenarios with fixed and variable backhaul capacity.

To support any services in all-IP networks, it is necessary to allocate both computing resource and network resource (bandwidth) simultaneously and effectively. The authors proposed the joint resource allocation method for multiple resource types, assuming the non-delay resource allocation. Although the proposed method can achieve an efficient use of resources, it may result in an unfair use of resources in which resources may be monopolized by a specific service. Therefore, the authors proposed the fair joint multiple resource allocation method which tries to equalize the total amount of resource allocated to each service. This paper proposes to enhance the fair joint resource allocation method, assuming three or more services and unbalanced generation traffic. It is demonstrated by basic simulation evaluations that the enhanced fair joint resource allocation method could enable fair resource allocation among multiple services without a large decline in resource efficiency.

In this paper, a cross-layer resource allocation mechanism is proposed for wireless multimedia service.In particular, a game theory based on quality of service (QoS) for multimedia users is introduced to deal with the fairness of network resource allocation in wireless networks.Moreover, the channel states of wireless users are additionally regarded under the cross-layer design in WiMAX environment.In details, the bargaining solution is adopted to discover the efficient and fair resource allocation strategy for multimedia service in considering QoS in the peak signal-to-noise ratio (PSNR) and the channel states in the carrier-to-interface ratio (CINR).The proposed mechanism is illustrated and evaluated by simulation results of transmitting video sequences in WiMAX environment.

Max-min fair allocation for resources with hybrid divisibilities

Balancing energy efficiency and throughput fairness in IEEE 802.11 WLANs

We consider a multi-channel cognitive radio network (CRN) where multiple secondary users share a single channel and multiple channels are simultaneously used by a single secondary user (SU) to satisfy their rate requirements. In this competitive CRN, our interest is in determining optimal power and rate distribution choices for each SU while maintaining fairness in “quality of experience” across all SUs. Unlike prior approaches that focus on resource allocation based on instantaneous quality of service (QoS), our approach to fairness encompasses both current and prior history of user experience with respect to QoS. Specifically, we quantify user experience over time by introducing dynamic fairness weights for each SU in the resource allocation framework. The dynamics of the weights are governed by the Homo Egualis (HE) society model. We consider Jain system level fairness index as a measure of fairness in resource allocation. Simulation results show that the weighted resource allocation scheme provide a better system level fairness index relative to the unweighted allocation scheme.

Localized single carrier frequency division multiple access (L-SC-FDMA) has been selected as the uplink transmission scheme in 3GPP Long Term Evolution (LTE) due to its low Peak to Average Power Ratio (PAPR) over OFDMA in general and high rate-sum capacity over Interleaved SC-FDMA in particular. This paper presents virtual clusters based proportional fairness and resource allocation scheme. Virtual cluster-based proportional fairness (VCPF) scheduler exploits the link adaptation information available at MAC layer to form virtual clusters. The distributed proportional fairness scheduler ensures a minimum throughput for all users in the coverage area by assigning contiguous RBs, proportional to the throughput and the number of users in a particular cluster or group. Simulations have been performed using practical scenario of uniformly distributed users in Rayleigh faded coverage area and design formulas have been devised for network planning to get the best possible fairness with promising level of quality of service (QoS).

Fair resource allocation for end-to-end flows is an important yet challenging problem in multi-hop wireless networks. Recent research on fair resource allocation mainly focuses on two directions: 1) theoretical methods for joint optimization of scheduling and rate allocation, which are inherently hard to implement; 2) heuristic rate allocation solutions, which are practical for implementation, but simplify the scope of resource sharing regions using different neighborhood models. One key question that remains unanswered is how well these heuristic neighborhood-aware rate allocation solutions approximate the fair point defined in the theoretical optimal resource allocation framework. To evaluate the impact of different neighborhood models on end-to-end fairness, we first establish a baseline fairness model (i.e., proportional fairness) using the price-based resource allocation framework. In this framework, price represents the cost of the resource usage incurred by unit flow. The rate of a flow is directly linked to its price, which is the aggregated price of the links it traverses. The link price is the sum of the prices of all the resource sharing regions which it belongs to. Obviously, when the resource sharing regions are approximated by different neighborhood models in the heuristic solutions, link prices will manifest as different values. To quantify the deviations of these heuristic neighborhood-aware solutions from the baseline proportional fairness model, we introduce a normalized fairness index model. Six different neighborhood models are evaluated. Our study makes two significant observations: 1) symmetric knowledge on the construction of a neighborhood is important in achieving fairness. Such a knowledge has not been considered in any of the existing neighborhood-aware rate allocation algorithms; 2) while 1-hop neighborhood information brings noticeable gain in fairness, the knowledge of 2-hop neighborhood information does not necessarily bring additional benefit.

As a “hometown of non-ferrous metals”, Hunan Province is rich in mineral resources, and some mineral reserves occupy a considerable proportion in China, but the supply and demand of various minerals are different. How to take into account the efficiency and fairness of mineral resource allocation has become the subject of this thesis. In order to achieve sustainable development in an all-round way, it is necessary to fully and effectively allocate rare mineral resources. With the least resources, the greatest benefits can be generated and the greatest benefits can be obtained. In the context of the new normal economy, the era of allocating resources based on demand has ceased to exist and is replaced by an effective supply-side structure. This article analyzes the allocation of mineral resources from the supply-side structure, including the international trade status of mineral resources, the tax system of mineral resources, and focuses on the relationship between the supply and demand of mineral resources in Hunan Province. Suggestions for optimal configuration are presented.

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.