Resource Allocation Settings Research Articles

Finding robustly fair solutions in resource allocation

In this study, we consider resource allocation settings where the decisions affect multiple beneficiaries and the decision maker aims to ensure that the effect is distributed to the beneficiaries in an equitable manner. We specifically consider stochastic environments where there is uncertainty in the system and propose a robust programming approach that aims at maximizing system efficiency while guaranteeing an equitable benefit allocation even under the worst scenario. Acknowledging the fact that the robust solution may lead to high efficiency loss and may be over-conservative, we adopt a parametric approach that allows controlling the level of conservatism and present the decision maker alternative solutions that reveal the trade-off between efficiency and the degree of conservatism when incorporating fairness. We obtain tractable formulations, leveraging the results we provide on the properties of highly unfair allocations. We demonstrate the usability of our approach on project selection and shelter allocation applications.

Co-Channel Interference Mitigation in 5g Network using Particle Swarm Optimization

The co-channel interference problem in 5G wireless networks is the subject of this research project. Even with the improvements brought forth by 5G, co-channel interference still degrades network speed and signal quality. Particle Swarm Optimization (PSO) is the method used in this study to minimize this interference and improve network performance by fine-tuning resource allocation settings. PSO-based interference mitigation, network modeling, simulation, an overview of the literature, and comparative analysis are all included in the study. The study is valuable for communication engineers since it explores PSO as a means of improving network quality and expanding our knowledge of wireless technology, even though it is restricted to co-channel interference and PSO. In the sphere of 5G wireless communication, this discovery represents a substantial advancement. The application of particle swarm optimization has shown to be innovative in addressing the complex problem of co-channel interference. Insights into the possibilities of clever optimization strategies for interference reduction can be gained from the applied models and results. With 5G networks set to revolutionize the digital environment, these findings provide a strong basis for future research efforts focused at refining interference avoidance techniques and ultimately boosting the performance and dependability of these networks.

Smart Markets for Real-Time Allocation of Multiproduct Resources: The Case of Shared Electric Vehicles

Motivated by the deep transformation of the transportation sector toward electric and shared mobility, this research develops a decision support system that enables real-time allocation of shared electric vehicles (SEVs) to either the rental market or aggregated as a virtual power plant to the electricity market. The multipurposing of SEVs beyond the traditional scope of mobility services can boost utilization and profitability for fleet operators while also facilitating the integration of variable rate renewable energy sources. This SEV setting embodies the problem of real-time multiproduct resource allocation via smart markets. We develop an approach that leverages highly digitalized real-time markets and intelligent decision making to enable dynamic multiproduct resource allocation. The tool continuously evaluates market attractiveness and strategically places iterative bids on the resource owner’s behalf until all resources have been committed for a specific period. Our key contribution is to provide a blueprint for similar multi-product resource allocation settings, and demonstrating its efficacy in the domain of SEV fleets by developing tailored analytics and machine learning-based solutions.

Fisher markets with linear constraints: Equilibrium properties and efficient distributed algorithms

The Fisher market is one of the most fundamental models for resource allocation. However, Fisher markets are less amenable for resource allocation settings when agents have additional linear constraints beyond the budget constraints of buyers and the capacity constraints of goods. Thus, in this work, we introduce a modified Fisher market, where agents may have additional linear constraints, and study the properties of the resulting equilibria. To set equilibrium prices, we introduce a budget-adjusted social optimization problem (BA-SOP), whose optimal dual variables correspond to the equilibrium prices. Since solving BA-SOP can be computationally intensive and requires centralized knowledge of all agents' utilities, we propose a new class of distributed algorithms based on the Alternating Direction Method of Multipliers (ADMM) to compute equilibrium prices. Our ADMM approach has strong convergence guarantees and provides a general-purpose method for computing market equilibria for Fisher markets with homogeneous linear constraints and classical Fisher markets.

Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework

The optimal management of shared vehicle systems, such as bike-, scooter-, car-, or ride-sharing, is more challenging compared with traditional resource allocation settings because of the presence of spatial externalities—changes in the demand/supply at any location affect future supply throughout the system within short timescales. These externalities are well captured by steady-state Markovian models, which are therefore widely used to analyze such systems. However, using Markovian models to design pricing and other control policies is computationally difficult because the resulting optimization problems are high dimensional and nonconvex. In our work, we design a framework that provides near-optimal policies, for a range of possible controls, that are based on applying the possible controls to achieve spatial balance on average. The optimality gap of these policies improves as the ratio between supply and the number of locations increases and asymptotically goes to zero.

Fixed-Parameter Algorithms for Unsplittable Flow Cover

The Unsplittable Flow Cover problem (UFP-cover) models the well-studied general caching problem and various natural resource allocation settings. We are given a path with a demand on each edge and a set of tasks, each task being defined by a subpath and a size. The goal is to select a subset of the tasks of minimum cardinality such that on each edge e the total size of the selected tasks using e is at least the demand of e. There is a polynomial time 4-approximation for the problem (Bar-Noy et al. STOC 2001) and also a QPTAS (Höhn et al. ICALP 2018). In this paper we study fixed-parameter algorithms for the problem. We show that it is W[1]-hard but it becomes FPT if we can slighly violate the edge demands (resource augmentation) and also if there are at most k different task sizes. Then we present a parameterized approximation scheme (PAS), i.e., an algorithm with a running time of \(f(k)\cdot n^{O_{\epsilon }(1)}\) that outputs a solution with at most (1 + 𝜖)k tasks or asserts that there is no solution with at most k tasks. In this algorithm we use a new trick that intuitively allows us to pretend that we can select tasks from OPT multiple times. We show that the other two algorithms extend also to the weighted case of the problem, at the expense of losing a factor of 1 + 𝜖 in the cost of the selected tasks.

Resource Allocation and Sharing in URLLC for IoT Applications Using Shareability Graphs

The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet-of-Things (IoT) application scenarios. Since the problem of realizing ultrareliable low-latency communications (URLLCs) is becoming more and more important, it has attracted the attention of researchers, and new efficient resource allocation algorithms are necessary. In this article, we consider a challenging scenario where the available spectrum might be fragmented across nonadjacent portions of the band, and channels are differently affected by interference coming from surrounding networks. Furthermore, channel state information (CSI) is assumed to be unavailable, thus requiring an allocation of resources-based only on topology information and channel statistics. To address this challenge in a dense smart factory scenario, where devices periodically transmit their data to a common receiver, we present a novel resource allocation methodology based on a graph-theoretical approach originally designed to allocate mobility resources in on-demand, shared transportation. The proposed methodology is compared with two benchmark allocation strategies, showing its ability of increasing spectral efficiency of as much as 50% with respect to the best performing benchmark. Contrary to what happens in many resource allocation settings, this increase in spectrum efficiency does not come at the expense of fairness, which is also increased as compared to benchmark algorithms.

Smart Markets for Real-Time Allocation of Multi-Product Resources: The Case of Shared Electric Vehicles

Motivated by the deep transformation of the transportation sector toward electric and shared mobility, this research develops a decision support tool that enables real-time allocation of shared electric vehicles (SEVs) to either the rental market, or - aggregated as a virtual power plant (VPP) - to the electricity market. This multi-purposing of SEVs beyond the traditional scope of mobility services can boost utilization and profitability for fleet operators while also facilitating the integration of variable rate renewable energy sources. We abstract this setting to the general problem of real-time multi-product resource allocation via smart markets. Due to a lack of suitable techniques in the literature, we develop our own general approach that leverages highly digitized real-time markets and intelligent agents to enable dynamic multi-product resource allocation. Agents continuously evaluate which markets are most attractive and strategically and iteratively place bids in the respective markets on the resource owner's behalf until all resources have been committed for a specific period. We contribute a general framework that provides a blueprint for similar multi-product resource allocation settings and demonstrate its efficacy in the domain of SEV fleets by developing tailored analytics and machine learning based solution approaches along the dimensions of this framework.

Children’s Inequity Aversion in Procedural Justice Context: A Comparison of Advantageous and Disadvantageous Inequity

There are two forms of unfairness widely studied in resource allocation settings: disadvantageous inequity (DI) in which one receives less than the partner and advantageous inequity (AI) in which one receives more than the other. We investigated children’s aversion to AI and DI in a procedural justice context. Children of 4-, 6-, and 8- years old were asked to spin a wheel (procedure) to decide how to allocate two different rewards with others. In each condition, they chose between a fair procedure providing equal chances for the two parties to get the bigger reward, and an unfair procedure (either a disadvantageous procedure in the DI condition, or an advantageous procedure in the AI condition). Results showed that children in the two younger age groups had a preference for the unfair procedure that would maximize their own profit in AI, but a greater aversion to the unfair procedure that would disadvantage them in DI. Eight-year-olds, however, had a greater preference for the fair procedure in AI than the 6-year-olds. In addition, the discrepancy between aversion to AI and DI disappeared in the 8-year-olds. The findings indicate children’s development of other-oriented concerns such as fairness concern and altruism in procedural justice, consistent with previous findings in distributive justice.

A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms

Natural resource allocation is a complex problem that entails difficulties related to the nature of real world problems and to the constraints related to the socio-economical aspects of the problem. In more detail, as the resource becomes scarce relations of trust or communication channels that may exist between the users of a resource become unreliable and should be ignored. In this sense, it is argued that in multi-agent natural resource allocation settings agents are not considered to observe or communicate with each other. The aim of this paper is to study multi-agent learning within this constrained framework. Two novel learning methods are introduced that operate in conjunction with any decentralized multi-agent learning algorithm to provide efficient resource allocations. The proposed methods were applied on a multi-agent simulation model that replicates a natural resource allocation procedure, and extensive experiments were conducted using popular decentralized multi-agent learning algorithms. Experimental results employed statistical figures of merit for assessing the performance of the algorithms with respect to the preservation of the resource and to the utilities of the users. It was revealed that the proposed learning methods improved the performance of all policies under study and provided allocation schemes that both preserved the resource and ensured the survival of the agents, simultaneously. It is thus demonstrated that the proposed learning methods are a substantial improvement, when compared to the direct application of typical learning algorithms to natural resource sharing, and are a viable means of achieving efficient resource allocations.

Conflicting Congestion Effects in Resource Allocation Games

We study strategic resource allocation settings, where jobs correspond to self-interested players who choose resources with the objective of minimizing their individual cost. Our framework departs from the existing game-theoretic models mainly in assuming conflicting congestion effects, but also in assuming an unlimited supply of resources. In our model, a job's cost is composed of both its resource's load (which increases with congestion) and its share in the resource's activation cost (which decreases with congestion). We provide results for a job-scheduling setting with heterogeneous jobs and identical machines. We show that if the resource's activation cost is shared equally among its users, a pure Nash equilibrium (NE) might not exist. In contrast, the proportional sharing rule induces a game that admits a pure NE, which can also be computed in polynomial time. As part of the algorithm's analysis, we establish a new, nontrivial property of schedules obtained by the longest processing time algorithm. We also observe that, unlike in congestion games, best-response dynamics (BRD) are not guaranteed to converge to a Nash equilibrium. Finally, we measure the inefficiency of equilibria with respect to the minimax objective function, and prove that there is no universal bound for the worst-case inefficiency (as quantified by the “price of anarchy” measure). However, the best-case inefficiency (quantified by the “price of stability” measure) is bounded by 5/4, and this is tight. These results add another layer to the growing literature on the price of anarchy and stability, which studies the extent to which selfish behavior affects system efficiency.

A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling

The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed. In addition, BoTs may be executed in potentially heterogeneous sets of Cloud resources, which may be either previously allocated for a different and fixed number of hours or dynamically reallocated as needed. In this paper, a family of 14 scheduling heuristics for concurrently executing BoTs in Cloud environments is proposed. The Cloud scheduling heuristics are adapted to the resource allocation settings (e.g., 1-hour time slots) of Clouds by focusing on maximizing Cloud resource utilization based on the remaining allocation times of Cloud resources. Cloud scheduling heuristics supported by information about BoT tasks (e.g., task size) and/or Cloud resource performances are proposed. Additionally, scheduling heuristics that require no information of either Cloud resources or tasks are also proposed. The Cloud scheduling heuristics support the dynamic inclusion of new Cloud resources while scheduling and executing a given BoT without rescheduling. Furthermore, an elastic Cloud resource allocation mechanism that autonomously and dynamically reallocates Cloud resources on demand to BoT executions is proposed. Moreover, an agent-based Cloud BoT scheduling approach that supports concurrent and parallel scheduling and execution of BoTs, and concurrent and parallel dynamic selection and composition of Cloud resources (by making use of the well-known contract net protocol) from multiple and distributed Cloud providers is designed and implemented. Empirical results show that BoTs can be (i) efficiently executed by attaining similar (in some cases shorter) makespans to commonly used benchmark heuristics (e.g., Max–min), (ii) effectively executed by achieving a 100% success execution rate even with high BoT execution request rates and executing BoTs in a concurrent and parallel manner, and that (iii) BoTs are economically executed by elastically reallocating Cloud resources on demand.

Admission Control Algorithms for Revenue Optimization with QoS Guarantees in Mobile Wireless Networks

We propose and analyze call admission control algorithms integrated with pricing for revenue optimization with QoS guarantees to serve multiple service classes in mobile wireless networks. Traditional admission control algorithms make acceptance decisions for new and handoff calls to satisfy certain QoS constraints such as the dropping probability of handoff calls and the blocking probability of new calls being lower than a pre-specified threshold. We analyze a class of partitioning and threshold-based admission control algorithms that make acceptance/rejection decisions not only to satisfy QoS requirements but also to optimize the revenue of the system by taking prices and arrival/departure information of service calls into account. We show that for a "charge-by-time" pricing scheme, there exist optimal resource allocation settings under which the partitioning and threshold-based admission control algorithms would produce the maximum revenue obtainable by the system without sacrificing QoS requirements. Further, we develop a new hybrid admission control algorithm which outperforms both partitioning and threshold-based admission control algorithms over a wide range of input parameters characterizing the operating environment and service workload conditions. Methods for utilizing of the analysis results for realtime admission control for revenue optimization with QoS guarantees are described with numerical data given to demonstrate the applicability.