Browsing by Subject "Resource allocation"

Now showing 1 - 20 of 31

Open Access
Age-based vs. stock level control policies for a perishable inventory system
(2001) Tekin, E.; Gürler Ü.; Berk, E.
In this study, we investigate the impact of modified lotsize-reorder control policy for perishables which bases replenishment decisions on both the inventory level and the remaining lifetimes of items in stock. We derive the expressions for the key operating characteristics of a lost sales perishable inventory model, operating under the proposed age-based policy, and examine the sensitivity of the optimal policy parameters with respect to various system parameters. We compare the performance of the suggested policy to that of the classical (Q,r) type policy through a numerical study over a wide range of system parameters. Our findings indicate that the age-based policy is superior to the stock level policy for slow moving perishable inventory systems with high service levels.
Open Access
Analysis of reactive scheduling problems in a job shop environment
(Elsevier, 2000) Sabuncuoğlu, İ.; Bayız, M.
In this paper, we study the reactive scheduling problems in a stochastic manufacturing environment. Specifically, we test the several scheduling policies under machine breakdowns in a classical job shop system. In addition, we measure the effect of system size and type of work allocation (uniform and bottleneck) on the system performance. The performance of the system is measured for the mean tardiness and makespan criteria. We also investigate a partial scheduling scheme under both deterministic and stochastic environments for several system configurations.
Open Access
Analytical loading models in flexible manufacturing systems
(Elsevier, 1993) Kırkavak, N.; Dinçer, C.
It would be difficult to efficiently implement a manufacturing system without solving its design and operational problems. Based on this framework, a system configuration and tooling problem is modeled. The model turns out to be a large mixed integer linear program, so that some alternative optimal seeking and heuristic techniques are used to solve the model for constructing a flow line structured Flexible Manufacturing System. As a result, it may be possible to construct flexible, efficient, simple and easily controllable manufacturing systems. © 1993.
Open Access
Application placement with shared monitoring points in multi-purpose IoT wireless sensor networks
(Elsevier, 2022-11-09) Çavdar, Mustafa Can; Korpeoglu, Ibrahim; Ulusoy, Özgür
The main function of a wireless sensor network (WSN) is to gather data from a certain region and transfer the data to a center or remote locations for further processing. The collected data can be of interest for many applications. Therefore, a physical WSN owned by a single provider can be utilized by many customer applications. Additionally, the data of a particular point or sub-region can satisfy the need of multiple applications. Hence, sensing the data only once in such cases is beneficial to reduce the energy consumption, network traffic and acceptance ratio of the applications. We call this as monitoring point based shared data approach. In this paper, we focus on the placement of applications each of which requires several points to be monitored in an area a WSN covers. We first propose such a monitoring point based shared data approach for WSNs that will serve multiple dynamic applications. We also propose two methods for application placement over a shared physical WSN: one greedy method and one genetic algorithm based method called GABAP. We did extensive simulation experiments to evaluate our algorithms. The results show the effectiveness of our methods in fast and close-to-optimum placement of applications over a single network.
Open Access
An approach to manage connectionless services in connection-oriented networks
(IEEE, 1996) Abdelatı, Muhammed; Arıkan, Erdal
In this work we propose a pricing scheme which serves as an instrument for managing connectionless services in connection-oriented communication networks. The scheme is able to allocate network bandwidth in a Pareto-optimal way that maximizes the total surplus. The key idea is to decompose the service provision procedure among three separate parties whose interactions are governed by a set of competitive pricing mechanisms.
Open Access
Contextual multi-armed bandits with structured payoffs
(2020-09) Qureshi, Muhammad Anjum
Multi-Armed Bandit (MAB) problems model sequential decision making under uncertainty. In traditional MAB, the learner selects an arm in each round, and then, observes a random reward from the arm’s unknown reward distribution. In the end, the goal is to maximize the cumulative reward by learning to select optimal arms as much as possible. In the contextual MAB—an extension to MAB—the learner observes a context (side-information) in the beginning of each round, selects an arm, and then, observes a random reward whose distribution depends on both the arriving context and the chosen arm. Another MAB variant, called unimodal MAB, assumes that the expected reward exhibits a unimodal structure over the arms, and tries to locate the arm with the “peak” reward by learning the direction of increase of the expected reward. In this thesis, we consider an extension to unimodal MAB called contextual unimodal MAB, and demonstrate that it is a powerful tool for designing Artificial Intelligence (AI)- enabled radios by utilizing the special structure of the dependence of the reward to contexts and arms of the wireless environment. While AI-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources, allocating resources over the mmWave band is extremely challenging due to rapidly-varying channel conditions. We consider several resource allocation problems in this thesis under various design possibilities for mmWave radio networks under unknown channel statistics and without any channel state information (CSI) feedback: i) dynamic rate selection for an energy harvesting transmitter, ii) dynamic power allocation for heterogeneous applications, and iii) distributed resource allocation in a multi-user network. All of these problems exhibit structured payoffs which are unimodal functions over partially ordered arms (transmission parameters) as well as unimodal or monotone functions over partially ordered contexts (side-information). Structure over arms helps in reducing the number of arms to be explored, while structure over contexts helps in using past information from nearby contexts to make better selections. We formalize dynamic adaptation of transmission parameters as a structured MAB, and propose frequentist and Bayesian online learning algorithms. We show that both approaches yield logarithmic in time regret. We also investigate dynamic rate and channel adaptation in a cognitive radio network serving heterogeneous applications under dynamically varying channel availability and rate constraints. We formalize the problem as a Bayesian learning problem, and propose a novel learning algorithm which considers each rate-channel pair as a two-dimensional action. The set of available actions varies dynamically over time due to variations in primary user activity and rate requirements of the applications served by the users. Additionally, we extend the work to cater to thescenario when the arms belong to a continuous interval as well as the contexts. Finally, we show via simulations that our algorithms significantly improve the performance in the aforementioned radio resource allocation problems.
Open Access
Customer order scheduling problem: a comparative metaheuristics study
(Springer, 2007) Hazır, Ö.; Günalay, Y.; Erel, E.
The customer order scheduling problem (COSP) is defined as to determine the sequence of tasks to satisfy the demand of customers who order several types of products produced on a single machine. A setup is required whenever a product type is launched. The objective of the scheduling problem is to minimize the average customer order flow time. Since the customer order scheduling problem is known to be strongly NP-hard, we solve it using four major metaheuristics and compare the performance of these heuristics, namely, simulated annealing, genetic algorithms, tabu search, and ant colony optimization. These are selected to represent various characteristics of metaheuristics: nature-inspired vs. artificially created, population-based vs. local search, etc. A set of problems is generated to compare the solution quality and computational efforts of these heuristics. Results of the experimentation show that tabu search and ant colony perform better for large problems whereas simulated annealing performs best in small-size problems. Some conclusions are also drawn on the interactions between various problem parameters and the performance of the heuristics.
Open Access
A data mining approach for location prediction in mobile environments
(Elsevier, 2005) Yavaş G.; Katsaros, D.; Ulusoy, Özgür; Manolopoulos, Y.
Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods. © 2004 Elsevier B.V. All rights reserved.
Open Access
The discrete resource allocation problem in flow lines
(Institute for Operations Research and the Management Sciences (INFORMS), 1995) Karabati, S.; Kouvelis, P.; Yu, G.
In this paper we address the discrete resource allocation problem in a deterministic flow line. We assume that the processing times are convex and noningcreasing in the amount of resources allocated to the machines. We consider the resource allocation problem for a fixed sequence of jobs for various performance criteria (makespan, weighted sum of completion times, cycle time for cyclic schedules) and develop a formulation of the problem as a convex program, where the number of constraints grows exponentially with the number of jobs and machines. We also present a generalization of the formulation for resource allocation problems in a cyclic directed graphs. We demonstrate that the problem is NP-complete in the strong sense and present an effective solution procedure. The solution procedure is an implicit enumeration scheme where a surrogate relaxation of the formulation is used to generate upper and lower bounds on the optimal objective function value. Finally, we address the simultaneous scheduling and resource allocation problem, and we present an approximate and iterative solution procedure for the problem.
Open Access
Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters
(2018-01) Urul, Gökalp
With tremendous increase in Internet capacity and services, the demand for cloud computing has also grown enormously. This enormous demand for cloud based data storage and processing forces cloud providers to optimize their platforms and facilities. Reducing energy consumption while maintaining service level agreements (SLAs) is one of the most important issues in this optimization effort. Dynamic virtual machine allocation and migration is one of the techniques to achieve this goal. This technique requires constant measurement and prediction of usage of machine resources to trigger migrations at right times. In this thesis, we present a dynamic virtual machine allocation and migration method utilizing CPU usage prediction to improve energy efficiency while maintaining agreed quality of service levels in cloud datacenters. Our proposed method, called LRAPS, tries to estimate short-term CPU utilization of hosts based on their utilization history. This estimation is then used to detect overloaded and underloaded hosts as part of live migration process. If a host is overloaded, some of the VMs running on that host are migrated to other hosts to avoid SLA violations; if a host is underloaded, all of the VMs in that host are tried to be migrated to other machines so that the host can be powered off. We did extensive simulation experiments using CloudSim to evaluate the efficiency and effectiveness of our proposed method. Our simulation experiments show that our method is feasible to apply and can signi cantly reduce power consumption and SLA violations in cloud systems.
Open Access
Fair resource allocation: Using welfare-based dominance constraints
(Elsevier, 2022-03-01) Argyris, N.; Karsu, Özlem; Yavuz, Mirel
In this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision is acceptably fair. In contrast to existing literature, we incorporate fairness in the form of welfare dominance, ensuring that the resultant distribution of benefits to beneficiaries is at least as good as some reference distribution with respect to a set of social welfare functions that satisfy commonly accepted efficiency and fairness related axioms. We introduce a practical means to parameterize the problem, which allows for excluding welfare functions that are deemed insufficiently or overly sensitive to inequality. This allows for analyzing the impact of changes in inequality aversion on efficiency, thus revealing the trade-off between efficiency and fairness. We develop tractable reformulations for the resulting non-linear multi-level optimization problems. We then extend this approach for cases where resources are allocated to groups of individuals with different sizes. We demonstrate the potential use of the suggested framework on two case studies: a workload allocation problem and a healthcare provisioning problem.
Open Access
Fast learning for dynamic resource allocation in AI-Enabled radio networks
(IEEE, 2020) Qureshi, Muhammad Anjum; Tekin, Cem
Artificial Intelligence (AI)-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources. However, allocating resources over the mmWave band is extremely challenging due to rapidly-varying channel conditions. We consider several resource allocation problems for mmWave radio networks under unknown channel statistics and without any channel state information (CSI) feedback: i) dynamic rate selection for an energy harvesting transmitter, ii) dynamic power allocation for heterogeneous applications, and iii) distributed resource allocation in a multi-user network. All of these problems exhibit structured payoffs which are unimodal functions over partially ordered arms (transmission parameters) as well as over partially ordered contexts (side-information). Unimodality over arms helps in reducing the number of arms to be explored, while unimodality over contexts helps in using past information from nearby contexts to make better selections. We model this as a structured reinforcement learning problem, called contextual unimodal multi-armed bandit (MAB), and propose an online learning algorithm that exploits unimodality to optimize the resource allocation over time, and prove that it achieves logarithmic in time regret. Our algorithm's regret scales sublinearly both in the number of arms and contexts for a wide range of scenarios. We also show via simulations that our algorithm significantly improves the performance in the aforementioned resource allocation problems.
Open Access
Finding robustly fair solutions in resource allocation
(2022-07) Elver, İzzet Egemen
In this study, we consider resource allocation problems 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 (measured by the total expected benefit) 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 the total expected benefit 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.
Open Access
The general behavior of pull production systems: the allocation problems
(Elsevier, 1999) Kırkavak, N.; Dinçer, C.
The design of tandem production systems has been well studied in the literature with the primary focus being on how to improve their e ciency. Considering the large costs associated, a slight improvement in e ciency can lead to very signi®cant savings over its life. Division of work and allocation of bu er capacities between workstations are two critical design problems that have attracted the attention of many researchers. In this study, ®rst an understanding into how the system works is to be provided. Except for the integration of two allocation problems, the basic model utilized here is essentially the same as the previous studies. Theoretical results that characterize the dynamics of these systems may also provide some heuristic support in the analysis of large-scale pull production systems. Ó 1999 Elsevier Science B.V. All rights reserved.
Open Access
Generic resource allocation metrics and methods for heterogeneous cloud infrastructures
(Elsevier, 2019) Mergenci, Cem; Körpeoğlu, İbrahim
With the advent of cloud computing, computation has become a commodity used by customers to access computing resources with no up-front investment, but as an on-demand and pay-as-you-go basis. Cloud providers make their infrastructure available to public so that anyone can obtain a virtual machine (VM) instance that can be remotely configured and managed. The cloud infrastructure is a large resource pool, allocated to VM instances on demand. In a multi-resource heterogeneous cloud, allocation state of the data center needs to be captured in metrics that can be used by allocation algorithms to make proper assignments of virtual machines to servers. In this paper, we propose two novel metrics reflecting the current state of VM allocation. These metrics can be used by online and offline VM placement algorithms in judging which placement would be better. We also propose multi-dimensional resource allocation heuristic algorithms showing how metrics can be used. We studied the performance of proposed methods and compared them with the methods from the literature. Results show that our metrics perform significantly better than the others and can be used to efficiently place virtual machines with high success rate.
Open Access
Improving performance of sparse matrix dense matrix multiplication on large-scale parallel systems
(Elsevier BV, 2016) Acer, S.; Selvitopi, O.; Aykanat, Cevdet
We propose a comprehensive and generic framework to minimize multiple and different volume-based communication cost metrics for sparse matrix dense matrix multiplication (SpMM). SpMM is an important kernel that finds application in computational linear algebra and big data analytics. On distributed memory systems, this kernel is usually characterized with its high communication volume requirements. Our approach targets irregularly sparse matrices and is based on both graph and hypergraph partitioning models that rely on the widely adopted recursive bipartitioning paradigm. The proposed models are lightweight, portable (can be realized using any graph and hypergraph partitioning tool) and can simultaneously optimize different cost metrics besides total volume, such as maximum send/receive volume, maximum sum of send and receive volumes, etc., in a single partitioning phase. They allow one to define and optimize as many custom volume-based metrics as desired through a flexible formulation. The experiments on a wide range of about thousand matrices show that the proposed models drastically reduce the maximum communication volume compared to the standard partitioning models that only address the minimization of total volume. The improvements obtained on volume-based partition quality metrics using our models are validated with parallel SpMM as well as parallel multi-source BFS experiments on two large-scale systems. For parallel SpMM, compared to the standard partitioning models, our graph and hypergraph partitioning models respectively achieve reductions of 14% and 22% in runtime, on average. Compared to the state-of-the-art partitioner UMPa, our graph model is overall 14.5 ï¿½ faster and achieves an average improvement of 19% in the partition quality on instances that are bounded by maximum volume. For parallel BFS, we show on graphs with more than a billion edges that the scalability can significantly be improved with our models compared to a recently proposed two-dimensional partitioning model.
Open Access
Investing in quality under autonomous and induced learning
(Taylor & Francis, 2003) Serel, D. A.; Dada, M.; Moskowitz, H.; Plante, R. D.
The reduction of variability in product performance characteristics is an important focus of quality improvement programs. Learning is intrinsically linked to process improvement and can assume two forms: (i) autonomous learning; and (ii) induced learning. The former is experientially-based, while the latter is a result of deliberate managerial action. Our involvement in quality and capacity planning with several major corporations in different industries suggested that it would be instructive to devise a model that would prescribe an optimal combination of autonomous and induced learning over time to maximize process improvement. We thus propose such a model to investigate the optimal quality improvement path for a company given that quality costs depend on both autonomous and induced types of learning experienced on a number of quality characteristics. Several properties of an optimal investment path are developed for this problem. For example, it is shown that decisions maximizing short-term gains may actually lead to suboptimal resource utilization decisions when total costs associated with a longer planning horizon are taken into account. Numerical examples are used to assess the sensitivity of the optimal investment plan with respect to changes in several model parameters.
Open Access
Marginal allocation algorithm for nonseparable functions
(Taylor & Francis, 1999) Yüceer, Ü.
Marginal allocation algorithm is implemented to discrete allocation problems with nonseparable objective functions subject to a single linear constraint. A Lagrangian analysis shows that the algorithm generates a sequence of undominated allocations under the condition of discretely convex objective functions and Lagrangian functions. The case of separable functions is proven to be a special case. An application is provided to illustrate the method and various size randomly chosen problems are run to demonstrate the efficiency of the marginal allocation algorithm.
Open Access
Network hub location problems: The state of the art
(Elsevier, 2008) Alumur, S.; Kara, B. Y.
Hubs are special facilities that serve as switching, transshipment and sorting points in many-to-many distribution systems. The hub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in order to route the traffic between origin-destination pairs. In this paper we classify and survey network hub location models. We also include some recent trends on hub location and provide a synthesis of the literature.
Open Access
Online balancing two independent criteria
(Springer, 2008-10) Tse, Savio S.H.
We study the online bicriteria load balancing problem in this paper. We choose a system of distributed homogeneous file servers located in a cluster as the scenario and propose two online approximate algorithms for balancing their loads and required storage spaces. We first revisit the best existing solution for document placement, and rewrite it in our first algorithm by imposing some flexibilities. The second algorithm bounds the load and storage space of each server by less than three times of their trivial lower bounds, respectively; and more importantly, for each server, the value of at least one parameter is far from its worst case. The time complexities for both algorithm are O(logM). © 2008 Springer Berlin Heidelberg.