Dynamic Weighted Fairness with Minimal Disruptions

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 the Cellular Long-Term Evolution (LTE) downlink, the smallest radio resource unit a Scheduler can assign to a user is a Resource Block (RB). Each RB consists of twelve (12) adjacent Orthogonal Frequency Division Multiplexing (OFDM) sub-carriers with inter-subcarrier spacing of 15 kHz. Over the years, researchers have investigated the problem of radio resource allocation in cellular LTE downlink and have made useful contributions. In an earlier paper for example, we proposed a deterministic dynamic programming based technique for optimal allocation of RBs in the downlink of multiuser Cellular LTE System. We found that this proposed methodology optimally allocates RBs to users at every transmission instant, but the computational time associated with the allocation policy was high. In the current work, we propose a truncated dynamic programming based technique for efficient and optimal allocation of radio resource. This paper also addresses uncertainty emanating from users’ mobility within a Cell coverage area. The objective is to significantly reduce the computational time and dynamically select applicable modulation scheme (i.e., QPSK, 16QAM, or 64QAM) in response to users’ mobility. We compare the proposed scheme with the Fair allocation and the earlier proposed dynamic programming based techniques. It is shown that the proposed methodology is more efficient in allocating radio resource and has better performance than both the Fair Allocation and the deterministic dynamic programming based techniques.

Recently fair division theory has emerged as a promising approach for the allocation of multiple computational resources among agents. While in reality agents are not all present in the system simultaneously, previous work has studied static settings where all relevant information is known upfront. Our goal is to better understand the dynamic setting. On the conceptual level, we develop a dynamic model of fair division, and propose desirable axiomatic properties for dynamic resource allocation mechanisms. On the technical level, we construct two novel mechanisms that provably satisfy some of these properties, and analyze their performance using real data. We believe that our work informs the design of superior multiagent systems, and at the same time expands the scope of fair division theory by initiating the study of dynamic and fair resource allocation mechanisms.

The fair allocation of scarce resources is relevant to a wide field of applications. For example, cloud resources, such as CPU, RAM, disk space, and bandwidth, have to be shared. This paper presents a mechanism to find fair allocations of multiple divisible resources, which, contrary to other mechanisms, is applicable to but not limited to the example above. Wide applicability of the mechanism is achieved by designing it (1) to scale with the number of consumers and resources, (2) to allow for arbitrary preference functions of consumers, and (3) to not rely on monetary compensation. The mechanism uses a mathematical definition of greediness to balance resources consumers receive and thereby to compute a fair allocation.

Determining whether an allocation is equitable requires ethical and empirical analyses, specifically answers to 3 questions: (1) What is the ethical standard for fair allocation? (2) What is the quantitative equity metric for this standard? and (3) What do the empirical data demonstrate? Two ethical standards for assessing the fair allocation of scarce medical resources have been delineated: the COVID-19 Vaccines Global Access initiative's population-based standard and the COVID-19 health-burden standard. The equity metric for the population-based standard is vaccine per person in each country, and for the health-burden standard it is vaccine per excess deaths per country. When using excess deaths data from the World Health Organization and vaccine data from the United Nations Children's Fund, the health-burden standard showed that middle-income countries with high excess deaths (e.g., Indonesia, Peru, Mexico, Egypt, South Africa) were treated inequitably: not provided enough vaccines given their high excess deaths. The right standard to assess fair and equitable allocation is a health-burden assessment. According to this standard, contrary to assumptions and initial claims, low-income countries were not treated inequitably in COVID-19 vaccine allocation, and middle-income countries with high excess deaths were treated inequitably. (Am J Public Health. 2025;115(7):1085-1094. https://doi.org/10.2105/AJPH.2025.308077).

At present the Internet is being transformed to a sophisticated system where network researchers are exploring new ways to dynamically program network switches, routers to accelerate network innovation. This trend introduces the concept of Active and programmable networks. The goal is to simplify the deployment of network services, leading to networks that explicitly support the process of service creation and deployment. To use such technology safely and efficiently, individual nodes must provide mechanisms to enforce resource lim- its to the contending flows. In active and programmable networks, the packet scheduling schemes should consider multiple resources such as CPU and memory in addition to bandwidth to achieve overall fairness. Maintaining fairness of one resource allocation does not ensure the fair allocation of other resources automatically. The dynamic nature of network load, and the unpredictability of processing times of active packets pose another significant challenge in providing fairness guarantees between contending flows. This chapter presents a fair resource allocation mechanism for a programmable/active node.

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

In wireless networks many nodes contend for available resources creating a challenge in resource allocation. With shared resources, fairness in allocation is a serious issue. Fairness metrics have been defined to measure the fairness level of resource decisions in allocations. Therefore, fairness metrics significantly influence network and node performances emphasizing the need for due diligence to fairness metrics. It can be seen that fairness metrics in many of the allocation strategies and algorithms in the literature are not analyzed in depth to show the overall fairness of allocations from different perspectives. Hence, we propose a series of fairness metrics for resource allocation in wireless networks, which evaluate individual, system, short and long term fairness. Our metrics are general enough to be adapted in either multiple or single resource sharing scenarios. Algorithms using these metrics for channel allocation problem in peer-to-peer wireless regional area network is proposed and simulated, and the results show that our metrics in conjunction with smart channel allocation strategies guarantee both the fairness and performances.

Max-min fair allocation for resources with hybrid divisibilities

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale.

The Petri net (PN) formalism is a suitable tool for modeling parallel systems due to its basic characteristics, such as synchronization. The extension of PN, the Colored Petri Nets (CPN) allows the incorporation of more details of the real system into the model (for example, contention for shared resources). The CPNs have been widely used in a variety of fields to produce suitable models. One of their biggest strengths is that their overall philosophy is quite similar to the philosophy of the object-oriented paradigm. In this regard, the CPN models can be used to implement simulators in a rather straightforward way. In this paper, the CPN framework is employed to implement a new resource allocation simulator, which is used to verify the performance of our previous work, where we proposed a fair resource allocation scheme with flow control and maximum utilization of the system’s resources.

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.

For construction activities, timely resource allocation is crucial to avoid unnecessary waiting time of resources and delay of activities, especially under the condition of limited supply of resources. Timely resource allocation, i.e. determination of an activity that has the highest priority to obtain resources at that instant, is a dynamic decision-making process dependent on real-time information during a construction process. With the consideration of operational and stochastic characteristics of construction operations and the fuzziness of multiple-decision objectives for an appropriate allocation policy (due to imprecision or subjectivity in decision criteria), a fuzzy dynamic resource allocation (FDRA) based on the fuzzy decision-making approach is developed. In order to model the timely resource allocation decisions, the FDRA is built into a discreteevent simulation system with an activity scanning strategy. The benefit of FDRA on construction productivity is analysed through simulation experimentation by which comparisons among different allocation policies are made.

We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness considerations in mind. To address this problem, we propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning. This framework can be applied to evaluate the fairness properties of a historical policy, as well as to impose constraints in the design of new (counterfactual) allocation policies. Our work culminates with a set of incompatibility results that investigate the interplay between the different fairness metrics we propose. Notably, we demonstrate that: 1) fairness in allocation and fairness in outcomes are usually incompatible; 2) policies that prioritize based on a vulnerability score will usually result in unequal outcomes across groups, even if the score is perfectly calibrated; 3) policies using contextual information beyond what is needed to characterize baseline risk and treatment effects can be fairer in their outcomes than those using just baseline risk and treatment effects; and 4) policies using group status in addition to baseline risk and treatment effects are as fair as possible given all available information. Our framework can help guide the discussion among stakeholders in deciding which fairness metrics to impose when allocating scarce resources.

Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a new server-based approach ; each server allocates resources by maximizing a per-server utility function . We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposed Per-Server Dominant Share Fairness ). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones.