Generalized Resource Allocation Research Articles

Metabolic model-based ecological modeling for probiotic design.

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

On the Egalitarian–Utilitarian spectrum in stochastic capacitated resource allocation problems

In this paper, we study a generalized resource allocation problem where a limited resource is to be allocated to a set of agencies with stochastic receiving capacities while taking into account the decision-maker’s preference towards equity in allocation. Taking the empiric distributional nature of the capacities into account, we formulate the problem in a chance-constrained based Mixed-Integer Programming framework with user-specified reliability and tolerance levels as well as equity preference level. We show that the problem can be conveniently reduced to a linear equivalent with adjusted capacity constraints. Deriving the tightest lower and upper bounds corresponding to the utilitarian and egalitarian perspectives, respectively, we give the closed-form optimal solutions for such cases. Using real data, we test the behavior of our model with a view towards the inherent equity-efficiency as well as reliability-efficiency trade-offs. We further characterize the cost and value of information in a detailed analysis.

Optimizing resource allocation in service systems via simulation: A Bayesian formulation

The service sector has become increasingly important in today's economy. To meet the rising expectation of high‐quality services, efficiently allocating resources is vital for service systems to balance service qualities with costs. In particular, this paper focuses on a class of resource allocation problems where the service‐level objective and constraints are in the form of probabilistic measures. Further, process complexity and system dynamics in service systems often render their performance evaluation and optimization challenging and relying on simulation models. To this end, we propose a generalized resource allocation model with probabilistic measures, and subsequently, develop an optimal computing budget allocation (OCBA) formulation to select the optimal solution subject to random noises in simulation. The OCBA formulation minimizes the expected opportunity cost that penalizes based on the quality of the selected solution. Further, the formulation takes a Bayesian approach to consider the prior knowledge and potential performance correlations on candidate solutions. Then, the asymptotic optimality conditions of the formulation are derived, and an iterative algorithm is developed accordingly. Numerical experiments and a case study inspired by a real‐world problem in a hospital emergency department demonstrate the effectiveness of the proposed algorithm for solving the resource allocation problem via simulation.

Dynamical Analysis and Optimization of a Generalized Resource Allocation Model of Microbial Growth

Gaining a better comprehension of the growth of microorganisms is a major scientific challenge, which has often been approached from a resource allocation perspective. Simple mathematical self-replicator models based on resource allocation principles have been surprisingly effective in accounting for experimental observations of the growth of microorganisms. Previous work, using a three-variable resource allocation model, predicted an optimal resource allocation scheme for the adaptation of microbial cells to a sudden nutrient change in the environment. We here propose an extended version of this model considering also proteins responsible for basic housekeeping functions, and we study their impact on predicted optimal strategies for resource allocation following changes in the environment. A full dynamical analysis of the system shows there is a single globally attractive equilibrium, which can be related to steady-state growth conditions of bacteria observed in experiments. We then explore the optimal allocation strategies using optimization and optimal control theory. We show that the solutions to this dynamical problem have a complicated structure that includes a second-order singular arc given in feedback form, and characterized by i) Fuller's phenomenon, and ii) the turnpike effect, producing a very particular asymptotic behaviour towards the solution of the static optimization problem. Our work thus provides a generalized perspective on the analysis of microbial growth by means of simple self-replicator models.

Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems: A Tutorial Overview

Over the last decade, simultaneous wireless information and power transfer (SWIPT) has become a practical and promising solution for connecting and recharging battery-limited devices due to significant advances in low-power electronics technology and wireless communications techniques. To realize the promised potentials, advanced resource allocation design plays a decisive role in revealing, understanding, and exploiting the intrinsic rate–energy tradeoff capitalizing on the dual use of radio frequency (RF) signals for wireless charging and communication. In this article, we provide a comprehensive tutorial overview of SWIPT from the perspective of resource allocation design. The fundamental concepts, system architectures, and RF energy harvesting (EH) models are introduced. In particular, three commonly adopted EH models, namely, the linear EH model, the nonlinear saturation EH model, and the nonlinear circuit-based EH model, are characterized and discussed. Then, for a typical wireless system setup, we establish a generalized resource allocation design framework that subsumes conventional resource allocation design problems as special cases. Subsequently, we elaborate on relevant tools from optimization theory and exploit them for solving representative resource allocation design problems for SWIPT systems with and without perfect channel state information (CSI) available at the transmitter, respectively. The associated technical challenges and insights are also highlighted. Furthermore, we discuss several promising and exciting future research directions for resource allocation design for SWIPT systems intertwined with cutting-edge communication technologies, such as intelligent reflecting surfaces, unmanned aerial vehicles, mobile edge computing, federated learning, and machine learning.

Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization

Evolutionary multitasking optimization (EMTO) is an emerging paradigm for solving several problems simultaneously. Due to the flexible framework, EMTO has been naturally applied to multi-objective optimization to exploit synergy among distinct multi-objective problem domains. However, most studies barely take into account the scenario where some problems cannot converge under restrictive computational budgets with the traditional EMTO framework. To dynamically allocate computational resources for multi-objective EMTO problems, this article proposes a generalized resource allocation (GRA) framework by concerning both theoretical grounds of conventional resource allocation and the characteristics of multi-objective optimization. In the proposed framework, a normalized attainment function is designed for better quantifying convergence status, a multi-step nonlinear regression is proposed to serve as a stable performance estimator, and the algorithmic procedure of conventional resource allocation is refined for flexibly adjusting resource allocation intensity and including knowledge transfer information. It has been verified that the GRA framework can enhance the overall performance of the multi-objective EMTO algorithm in solving benchmark problems, complex problems, many-task problems, and a real-world application problem. Notably, the proposed GRA framework served as a crucial component for the winner algorithm in the Competition on Evolutionary Multi-Task Optimization (Multi-objective Optimization Track) in IEEE 2020 World Congress on Computational Intelligence.

On the optimization of two-class work-conserving parameterized scheduling policies

Numerous scheduling policies are designed to differentiate quality of service for different applications. Service differentiation can in fact be formulated as a generalized resource allocation optimization towards the minimization of some important system characteristics. For complex scheduling policies, however, optimization can be a demanding task, due to the difficult analytical analysis of the system at hand. In this paper, we study the optimization problem in a queueing system with two traffic classes, a work-conserving parameterized scheduling policy, and an objective function that is a convex combination of either linear, convex or concave increasing functions of given performance measures of both classes. In case of linear and concave functions, we show that the optimum is always in an extreme value of the parameter. Furthermore, we prove that this is not necessarily the case for convex functions; in this case, a unique local minimum exists. This information greatly simplifies the optimization problem. We apply the framework to some interesting scheduling policies, such as Generalized Processor Sharing and semi-preemptive priority scheduling. We also show that the well-documented \(c\mu \)-rule is a special case of our framework.

Are All the Subproblems Equally Important? Resource Allocation in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. A naïve way to distribute computational effort is to treat all the subproblems equally and assign the same computational resource to each subproblem. This paper proposes a generalized resource allocation (GRA) strategy for decomposition-based MOEAs by using a probability of improvement vector. Each subproblem is chosen to invest according to this vector. An offline measurement and an online measurement of the subproblem hardness are used to maintain and update this vector. Utility functions are proposed and studied for implementing a reasonable and stable online resource allocation strategy. Extensive experimental studies on the proposed GRA strategy have been conducted.

A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN

Network Virtualization (NV) allows multiple heterogeneous architectures to simultaneously coexist in a shared infrastructure. Embedding multiple virtual networks (VNs) in a shared substrate deals with efficient mapping of virtual resources in the physical infrastructure and is referred to as the Virtual Network Embedding problem (VNE-problem). Although there is recently a number of research work in the area of network virtualization based on the Software-Defined Networking (SDN) technology, virtual network embedding in SDN remains challenging from both theoretical and practical points of view.This article focuses on virtual network embedding strategies and related issues for Infrastructure-as-a-Service (IaaS) paradigms under the constraint of fixed virtual node locations. Special considerations are given to the problems related to resource allocation and link sharing of multi-layer virtual networks on top of the physical substrate. Firstly, a heuristic virtual network embedding algorithm is proposed that can improve the mapping acceptance ratio and resource efficiency in the IaaS context. Secondly, REsource reSERvation in generalized Virtual NETworks (ReServNet), a Software-Defined Networking platform designed for embedding multi-level virtual networks in physical infrastructures is developed. By defining new softwarized logical functions, ReServNet allows network administrators to create and manage multiple virtual networks on top of the physical network and allocate bandwidth resources to them accordingly. Moreover, the ReServNet framework allows for designing, prototyping, benchmarking and evaluating the performance of different network embedding algorithms easily in real SDN virtualization environments. Different issues related to virtual network embedding on SDN-based physical substrate are also analyzed and discussed in detail.

TLRO based modeling of alternative commands and its application to generalized distributed resource allocation problem

There are systems where alternative constructs need to be used for its description. In weakest precondition calculus disjunction of the post conditions of constituent guarded commands is used as the post condition of an alternative construct. Such representation is not suitable because it does not express a one to one correspondence between a guard and the relevant component of the characterized post condition. In this paper Temporal Logic Related to Observation (TLRO) is used to solve this problem. Each guarded command is expressed as a TLRO rule by using its strongest post condition and the corresponding precondition. List of these representations for all the constituent guarded commands is the required model. Technique is illustrated by considering a common sense scenario. The scheme is then applied to a generalized resource allocation algorithm which is a weaker version of Drinking Philosophers problem.

Resource Allocation in Multicast OFDM Systems: Lower/Upper Bounds and Suboptimal Algorithm

This letter studies generalized resource allocation in multiple description coding multicast (MDCM) orthogonal frequency division multiplexing (OFDM) systems, where many multicast groups are served simultaneously by base station (BS). Due to non-tractable nature, the problem is reformulated to weighted-capacity maximization by resource distribution among virtual multicast groups (VMGs). Then, the relaxation of exclusively-used constraints allows us to provide both upper and lower bounds for the original problem. A suboptimal algorithm is also proposed based on Newton's method to reduce the computational complexity. Simulation results show the proposed algorithm approximately reaches the optimal capacity within 2 iterations for multicast waterfilling, and MDCM is more bandwidth-efficient for multicast services than conventional multicast (CVM).

Decision model and analysis for investment interest expense deduction and allocation

Abstract Investment income tax planning requires informed, strategic choices. One must determine the amount of qualified dividends and net long-term capital gain to be included in investment income (against which investment interest expense can be deducted). This choice also determines the residual qualified dividends and net long-term capital gain which enjoy a reduced tax rate. Another important decision is whether all or some of this interest expense should be deducted in the current year or carried forward. This paper puts forward a new approach to formulate these questions as a generalized resource allocation problem which permits analysis of the interdependence between, and the tax consequences of, the above decisions. The commonly used approach – deducting investment interest expense sooner rather than later – we consider myopic since the benefit of deferring some of the deduction is not leveraged. Presented here is a tax planning guideline (a necessary and sufficient condition for optimality) to realize a more forward-looking strategy. We also show that, for certain income structures, the tax savings by deducting a one-dollar investment interest expense may be more than the tax rate on the dollar of investment income that is offset.

Resource allocation optimization for quantitative service differentiation on server clusters

The goal of service differentiation is to provide different service quality levels to meet changing system configuration and resource availability and to satisfy different requirements and expectations of applications and users. In this paper, we investigate the problem of quantitative service differentiation on cluster-based delay-sensitive servers. The goal is to support a system-wide service quality optimization with respect to resource allocation on a computer system while provisioning proportionality fairness to clients. We first propose and promote a square-root proportional differentiation model. Interestingly, both popular delay factors, queueing delay and slowdown, are reciprocally proportional to the allocated resource usage. We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then study the problem of service differentiation provisioning from an important relative performance metric, slowdown. We give a closed-form expression of the expected slowdown of a popular heavy-tailed workload model with respect to resource allocation on a server cluster. We design a two-tier resource management framework, which integrates a dispatcher-based node partitioning scheme and a server-based adaptive process allocation scheme. We evaluate the resource allocation framework with different models via extensive simulations. Results show that the square-root proportional model provides service differentiation at a minimum cost of system delay. The two-tier resource allocation framework can provide fine-grained and predictable service differentiation on cluster-based servers.

Near-Optimal Solution of Generalized Resource Allocation Problems with Large Capacities

We consider problems of allocating resources to activities where the allocation to each activity is restricted to a general set of admissible values, the objective function is additively-separable but not necessarily concave nor differentiable, and each activity uses at most one resource. We develop a simple algorithm, based on a nonsmooth convex relaxation, that generates a near-optimal solution whenever each allocation is a small fraction of resource capacity.

Planning models in higher education: Historical review and survey of currently available models

The use of planning models in higher education has been receiving increasing attention in recent years. The article summarizes the planning model literature from its earliest use to the present, emphasizing the generalized resource allocation models. An organizational chart is presented which allows a more specific categorization of models. Several examples of each category are cited from the current literature. Conceptual issues such as implementation, acceptance, suitability and effectiveness are discussed in terms applicable to models in general. The article concludes with an in-depth analysis of the six significant resource allocation models: HELP/PLANTRAN, RRPM, SEARCH, CAMPUS, TRADES, and EFPM.