Resource Allocation Procedures Research Articles

Moral Injury in Times of COVID-19.

Health care providers are facing increased risk of moral distress during the COVID-19 pandemic because of changes in clinical practice and resource allocation procedures. Health care systems that employ a proactive approach in mitigating the lasting effects of moral distress will fare better in the long run. Psychologists are well poised to apply timely psychological first aid to the crisis needs of patients, colleagues, and even themselves. Key interventions are detailed, including creating and promoting safety, calmness, connectedness, self-efficacy, and hope.

Joint power-efficient traffic shaping and service provisioning for metro elastic optical networks

Considering the time-averaged behavior of a metro elastic optical network, we develop a joint procedure for resource allocation and traffic shaping to exploit the inherent service diversity among the requests for power-efficient network operation. To support the quality of service diversity, we consider minimum transmission rate, average transmission rate, maximum burst size, and average transmission delay as the adjustable parameters of a general service profile. The work evolves from a stochastic optimization problem, which minimizes the power consumption subject to stability, physical, and service constraints. The optimal solution of the problem is obtained using a complex dynamic programming method. To provide a near-optimal fast-achievable solution, we propose a sequential heuristic with a scalable and causal software implementation, according to the basic Lyapunov iterations of an integer linear program. The heuristic method has a negligible optimality gap and a considerably shorter runtime compared to the optimal dynamic programming, and reduces the consumed power by 72% for an offered traffic with a unit variation coefficient. The adjustable trade-offs of the proposed scheme offer a typical 10% power saving for an acceptable amount of excess transmission delay or drop rate.

Reliability Analysis of a Hypersonic Vehicle Panel with Spatio-Temporal Variability

Coupled aerothermoelastic analysis is required to predict the response of a hypersonic vehicle panel. The required computational effort of the coupled multidisciplinary analysis, interactions between multiple failure modes, and the spatio-temporal variability of the response create significant challenges to the reliability analysis of such structures. To overcome these challenges, this paper presents an adaptive surrogate modeling framework for the reliability analysis of a hypersonic vehicle panel with spatio-temporal variability. The kriging surrogate modeling method in conjunction with singular value decomposition is first employed to emulate the original disciplinary simulation models in a low-dimensional space. Because of the limited computational resources, the initial surrogate models trained with a limited number of multidisciplinary simulations may not accurately represent the original physics models, which results in errors in the reliability analysis of the vehicle panel. A methodology is developed to analyze the effects of surrogate model uncertainty on the results of reliability analysis, and to improve the accuracy of reliability analysis by adaptively allocating the computational resources to individual disciplinary simulations. A computational resource allocation procedure is developed to identify the discipline, the response, and the time instant for which the surrogate model needs improvement and to select the corresponding training inputs. The methodology is illustrated for the reliability assessment of a panel subjected to the failure modes of deformation and overheating.

A Permutation-Based Model for Analysis of Resource Allocation Overheads in Vehicular Ad Hoc Networks

This paper considers the theoretical limits of resource allocation overheads in vehicular ad hoc networks (VANETs), where the resource allocation procedure may consume extensive system resources. A permutation-based model is proposed to analyze the overhead limits where the unordered resource allocation patterns are considered as a permutation process. The minimum bits to represent the resource allocation information can be derived based on the permutation-based model. The bipartite graph is applied to describe the resource reuse in the respective domains for practical scenarios, and the dynamicity is taken into consideration. The proposed permutation-based model is applied to analyze the resource allocation overheads of two typical resource allocation schemes in VANETs: time-division multiple access (TDMA) and cluster-based algorithms. The proposed methodology provides new insights in a tradeoff analysis between overheads and system efficiency when analyzing the capacity of the VANETs.

Dynamic Resource Allocation in Metro Elastic Optical Networks Using Lyapunov Drift Optimization

Consistent growth in the volume and dynamic behavior of traffic mandates new requirements for fast and adaptive resource allocation in metro networks. We propose a dynamic resource allocation technique for adaptive minimization of spectrum usage in metro elastic optical networks. We consider optical transmission as a service specified by its bandwidth profile parameters, which are minimum, average, and maximum required transmission rates. To consider random traffic events, we use a stochastic optimization technique to develop a novel formulation for dynamic resource allocation in which service level specifications and network stability constraints are addressed. Next, we employ the elegant theory of Lyapunov optimization to solve the stochastic optimization problem and derive a fast integer linear program, which is periodically solved to create an adaptation between available resources and the dynamic network state. To quantize the performance of the proposed technique, we report its spectral efficiency as a function of the peak-to-average traffic ratio and Lyapunov penalty coefficient. Simulation results show that the dynamic resource allocation procedure can improve spectral efficiency by a factor of 3.3 for a peak-to-average traffic ratio of 1.37 and a Lyapunov penalty coefficient of 103 in comparison with fixed network planning. There is also a trade-off between transmission delay and spectrum utilization in the proposed technique, which can be adjusted by a Lyapunov penalty coefficient.

Optimizing the Resource Allocation of Periodic Messages With Different Sizes in LTE-V2V

Recently, 3GPP has added new features to long term evolution (LTE) that allow vehicles to communicate directly with each other and with surrounding objects. These short-range communications will play a key role in the so-called cellular vehicle-to-anything (C-V2X). As a particular service, which will be the basis of most applications for automated and connected cars, each vehicle will periodically broadcast information on its identity, status, and movements through short-range vehicle-to-vehicle (V2V) communications. Given the importance of this service, great attention has been given to the associated resource allocation procedures and the number of vehicles that can be simultaneously communicating in the network. However, little attention has been paid to handling messages of different sizes, which is actually foreseen in practice in order to transmit static information with a reduced periodicity. With this in mind, we evaluate the number of vehicles that can be allocated when parameters are optimized for messages of different sizes. This objective is pursued taking into account the numerous constraints imposed by the standard, through the formalization and solution of a combinatorial optimization problem. Example results, based on 3GPP indications, show that, with respect to the optimization of the allocation based on the larger packets, it is possible to obtain an increase that varies between 10% and 30%, depending on the conditions, with peaks above 150% in specific cases.

Scaling-up strategic purchasing: analysis of health system governance imperatives for strategic purchasing in a free maternal and child healthcare programme in Enugu State, Nigeria

BackgroundSignificant knowledge gaps exist in the functioning of institutional designs and organisational practices in purchasing within free healthcare schemes in low resource countries. The study provides evidence of the governance requirements to scale up strategic purchasing in free healthcare policies in Nigeria and other low-resource settings facing similar approaches.MethodsThe study was conducted at the Ministry of Health and in two health districts in Enugu State, Nigeria, using a qualitative case study design. Semi-structured interviews were conducted with 44 key health system actors (16 policymakers, 16 providers and 12 health facility committee leaders) purposively selected from the Ministry of Health and the two health districts. Data collection and analysis were guided by Siddiqi and colleagues’ health system governance framework. Data were analysed using a framework approach.ResultsThe key findings show that supportive governance practices in purchasing included systems to verify questionable provider claims, pay providers directly for services, compel providers to procure drugs centrally and track transfer of funds to providers. However, strategic vision was undermined by institutional conflicts, absence of purchaser-provider split and lack of selective contracting of providers. Benefit design was not based on stakeholder involvement. Rule of law was limited by delays in provider payment. Benefits and obligations to users were not transparent. The criteria and procedure for resource allocation were unclear. Some target beneficiaries seemed excluded from the scheme. Effectiveness and efficiency was constrained by poor adherence to purchasing rules. Accountability of purchasers and providers to users was weak. Intelligence and information is constrained by paper-based system. Rationing of free services by providers and users’ non-adherence to primary gate-keeping role hindered ethics.ConclusionWeak governance of purchasing function limits potential of FMCHP to contribute towards universal health coverage. Appropriate governance model for strengthening strategic purchasing in the FMCHP and possibly free healthcare interventions in other low-resource countries must pay attention to the creation of an autonomous purchasing agency, clear framework for selective contracting, stakeholder involvement, transparent benefit design, need-based resource allocation, efficient provider payment methods, stronger roles for citizens, enforcement of gatekeeping rules and use of data for decision-making.

Resource Allocation in HS-OFDM-based PLC Systems: A Tutorial

This work organizes and discusses important information on resource allocation in power line communication (PLC) systems based on the Hermitian symmetric orthogonal frequency-division multiplexing scheme. In the context of this work, the term resource refers to bits and transmission power, which can be optimally or sub-optimally allocated to subcarriers on the basis of the knowledge of the normalized signal-to-noise ratio (nSNR) of power line subchannels. In this sense, we thoroughly discuss the formulation of the resource allocation for PLC systems and accordingly derive expressions for the nSNR as well as bit and transmission power allocation in all subchannels. Additionally, we precisely detail greedy algorithms to assist practitioners and researchers in the implementation of the resource allocation procedure. Considering a frequency-selective PLC channel corrupted by additive colored Gaussian noise as well as M-ary pulse amplitude modulation and quadrature amplitude modulation as modulation schemes, the influence of the gap from the Shannon capacity curve on symbol error rate probability is discussed. On top of the carried out discussion, numerical results are presented in order to emphasize the usefulness of a proper comprehension of this subject.

Time-aware service-classified spectrum defragmentation algorithm for flex-grid optical networks

Abstract By employing sophisticated routing and spectrum assignment (RSA) algorithms together with a finer spectrum granularity (namely frequency slot) in resource allocation procedures, flex-grid optical networks can accommodate diverse kinds of services with high spectrum-allocation flexibility and resource-utilization efficiency. However, the continuity and the contiguity constraints in spectrum allocation procedures may always induce some isolated, small-sized, and unoccupied spectral blocks (known as spectrum fragments) in flex-grid optical networks. Although these spectrum fragments are left unoccupied, they can hardly be utilized by the subsequent service requests directly because of their spectral characteristics and the constraints in spectrum allocation. In this way, the existence of spectrum fragments may exhaust the available spectrum resources for a coming service request and thus worsens the networking performance. Therefore, many reactive defragmentation algorithms have been proposed to handle the fragmented spectrum resources via re-optimizing the routing paths and the spectrum resources for the existing services. But the routing-path and the spectrum-resource re-optimization in reactive defragmentation algorithms may possibly disrupt the traffic of the existing services and require extra components. By comparison, some proactive defragmentation algorithms (e.g. fragmentation-aware algorithms) were proposed to suppress spectrum fragments from their generation instead of handling the fragmented spectrum resources. Although these proactive defragmentation algorithms induced no traffic disruption and required no extra components, they always left the generated spectrum fragments unhandled, which greatly affected their efficiency in spectrum defragmentation. In this paper, by comprehensively considering the characteristics of both the reactive and the proactive defragmentation algorithms, we proposed a time-aware service-classified (TASC) spectrum defragmentation algorithm, which simultaneously employed proactive and reactive mechanisms in suppressing spectrum fragments with the awareness of services’ types and their duration times. By dividing the spectrum resources into several flexible groups according to services’ types and limiting both the spectrum allocation and the spectrum re-tuning for a certain service inside one specific spectrum group according to its type, the proposed TASC defragmentation algorithm cannot only suppress spectrum fragments from generation inside each spectrum group, but also handle the fragments generated between two adjacent groups. In this way, the proposed TASC algorithm gains higher efficiency in suppressing spectrum fragments than both the reactive and the proactive defragmentation algorithms. Additionally, as the generation of spectrum fragments is retrained between spectrum groups and the defragmentation procedure is limited inside each spectrum group, the induced traffic disruption for the existing services can be possibly reduced. Besides, the proposed TASC defragmentation algorithm always re-tunes the spectrum resources of the service with the maximum duration time first in spectrum defragmentation procedure, which can further reduce spectrum fragments because of the fact that the services with longer duration times always have higher possibility in inducing spectrum fragments than the services with shorter duration times. The simulation results show that the proposed TASC defragmentation algorithm can significantly reduce the number of the generated spectrum fragments while improving the service blocking performance.

Low Complexity Resource Allocation for Massive Carrier Aggregation

Optimal resource allocation (RA) in massive carrier aggregation scenarios is a challenging combinatorial optimization problem whose dimension is proportional to the number of users, component carriers (CCs), and OFDMA resource blocks per CC. Toward scalable, near-optimal RA in massive CA settings, an iterative RA algorithm is proposed for joint assignment of CCs and OFDMA resource blocks to users. The algorithm is based on the principle of successive geometric programming approximations and has a complexity that scales only linearly with the problem dimension. Although its derivation is based on a relaxed formulation of the RA problem, the algorithm is shown to converge to integer-valued RA variables with probability 1 under mild assumptions on the distribution of user utilities. Simulations demonstrate improved performance of the proposed algorithm compared to commonly considered heuristic RA procedures of comparable complexity.

Downlink Scheduling and Resource Allocation for 5G MIMO-Multicarrier: OFDM vs FBMC/OQAM

The definition of the next generation of wireless communications, so-called 5G networks, is currently underway. Among many technical decisions, one that is particularly fundamental is the choice of the physical layer modulation format and waveform, an issue for which several alternatives have been proposed. Two of the most promising candidates are: 1) orthogonal frequency division multiple (OFDM), a conservative proposal that builds upon the huge legacy of 4G networks and 2) filter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM), a progressive approach that in frequency selective channels sacrifices subcarrier orthogonality in lieu of an increased spectral efficiency. The comparative merits of OFDM and FBMC/OQAM have been well investigated over the last few years but mostly, from a purely physical layer point of view and largely neglecting how the physical layer performance translates into user-relevant metrics at the upper-layers. This paper aims at presenting a comprehensive comparison of both modulation formats in terms of practical network indicators, such as goodput, delay, fairness, and service coverage, and under operational conditions that can be envisaged to be realistic in 5G deployments. To this end, a unifying cross-layer framework is proposed that encompasses the downlink scheduling and resource allocation procedures and that builds upon a model of the queueing process at the data-link control layer and a physical layer abstraction that can be chosen to model either OFDM or FBMC/OQAM. Extensive numerical results conclusively demonstrate that most of the a priori advantages of FBMC/OQAM over OFDM do indeed translate into improved network indicators, that is, the increase in spectral efficiency achieved by FBMC/OQAM makes up for the distortion caused by the loss of orthogonality.

Enhanced uplink multi-users scheduling for future 802.11ax networks: wait-to-pick-as-available enhanced

One of the key enablers of the upcoming IEEE 802.11ax standard is the inclusion of uplink multi-users (UL MU) transmission model, which is performed using multiple-input multiple-output (MIMO) and beamforming techniques. So far, UL MU-MIMO has not been standardized in any of IEEE 802.11 amendments, because of technical issues facing its definition. One of these significant issues is the scheduling of UL MU-MIMO transmissions, which refers to selection rules and resource allocation procedures performed before simultaneous data transmissions. Indeed, simultaneous transmitters and receiver should exchange some information to correctly transmit the parallel data frames. However, this information exchange adds overheads, leading to reduce network performance. In this regard, we have proposed, in a previous work, a novel 802.11ax medium access control protocol aiming at reducing elapsed time in managing the establishment of an UL -MU communication called Wait-to-Pick-As-Available (W2PAA). Our contribution in this paper is twofold. First, we introduce an analytical model based on semi-Markov Chains to evaluate the performance of W2PAA; taking into a detailed behavior of the backoff counter (i.e. freezing period). Second, we propose an enhanced version of W2PAA aiming at improving both system and user-oriented performances. Aiming at validating the analytical model and comparing the performance of both versions of W2PAA, we used computer simulation. Obtained results, validate the analytical model in one hand, and clearly indicated the gain of both W2PAA versions by report to the basic UL-Single User on the other hand. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient and generic downlink resource allocation procedure for pre-5G networks

This paper addresses the downlink resource allocation problem in pre-5G (LTE-B) networks. At each time slot, the problem is to share the radio resources between users in order to maximize a given objective function. We expressed this problem considering the LTE standard constraints, which are rarely considered in the literature. Mainly, for any given user, the base station is constrained to transmit with a single Modulation and Coding Scheme (MCS). We show that this problem is NP-Hard, and therefore, we propose a generic approximation algorithm which covers a large variety of objectives. This algorithm is composed of three routines that enable an effective resource sharing procedure. The first routine computes a solution for a relaxation of our main problem, while the second routine selects the most suitable MCS for each user. Finally, the last routine effectively distributes the unallocated resources. For the Max Rate policy, simulation results show that our algorithm outperforms other existing algorithms in terms of capacity, and remains close to the optimal. Under the Proportional Fairness policy, our solution also provides a very good fairness while maintaining a near-optimal capacity.

Improved critical chain buffer management framework considering resource costs and schedule stability

The critical chain scheduling and buffer management (CC/BM) methodology has proven to be a favorable approach to schedule resource-constrained projects and to offer a valuable control tool for monitoring projects under uncertainty. The previous studies on CC/BM seem to have neglected the cost performance, which might render its wider applications to the modern economic activities that are mostly performed in a project way. This paper presents an improved CC/BM framework that allows additional resource allocation/reallocation to bring forward activity starting times based on cost and schedule stability criteria. In the planning phase, the decision is made concerning the regular resource availability period in order to minimize the expected resource costs. In the execution phase, a scheduled order repair method for rescheduling along with two reactive resource allocation procedures as the corrective action whenever delays are beyond a certain buffer threshold are presented and examined in order to exhibit a comprehensive project schedule/cost control system that is adaptive to the CC/BM management logic. Finally, our computational experiment demonstrates the benefits of the proposed reactive methods under different cost or availability parameters.

A Bioinspired Fair Resource-Allocation Algorithm for TDMA-Based Distributed Sensor Networks for IoT

Many studies on distributed resource-allocation algorithms have been conducted recently because of the increasing number of network nodes and the rapidly changing network environments in the Internet of Things (IoT). In this paper, we propose the multihop DESYNC algorithm, which is a bioinspired Time Division Multiple Access- (TDMA-) based distributed resource-allocation scheme for distributed sensor networks. We define a detailed frame structure for the proposed multihop DESYNC algorithm and a firing message, which acts as a reference for resource allocation. In addition, operating procedures for resource allocation and collision detection avoidance under multihop DESYNC are explained. Simulations show that multihop DESYNC effectively resolves the hidden-node problem and that it fairly shares resources among nearby nodes in multihop networks. Moreover, it achieves better performance than the CSMA/CA algorithm in terms of channel reuse gain and average throughput.

Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment

Cloud computing is aimed at delivering computing services as a utility. One of the Cloud computing services is Workflow as a Service (WFaaS). Workflow scheduling is a vital area in WFaaS. The objective of this paper is to propose a scheduling algorithm that meets the Quality of Service constraints such as Makespan and Cost. The proposed algorithm Level Based Task Prioritization (LBTP) follows the list scheduling strategy. The LBTP algorithm alters task prioritization phase and the resource selection phase, where three different algorithms like Earliest Finish Time, Parent Resource Allocation and Round Robin are applied. The LBTP algorithm is tested for small workflows. The performance analysis is done by varying the Communication to Computation Ratio and number of tasks. The scheduling metrics, Makespan and Cost of the proposed algorithm are compared with the Customer Facilitated Cost based Scheduling (CFCSC) algorithm. The experimental results show that Earliest Finish Time resource selection procedure outperforms CFCSC algorithm with respect to Makespan for communication intensive graphs. For computation intensive graphs, the parent resource allocation procedure produces better Makespan. With respect to Cost parameter, irrespective of total number of tasks in the Direct Acyclic Graph, all the resource selection procedures have incurred minimum cost. The proposed algorithm helps the Cloud Provider to select the scheduling algorithm in accordance with the Quality of Service parameters. Further, the LBTP algorithm can be tested with Regular Scientific workflows like Montage and Cybershake.

Adaptive and Generic Scheduling Scheme for LTE/LTE-A Mobile Networks

Schedulers for multi-carrier wireless networks are a central element of cellular systems and are subject to extensive research. However, state-of-the-art schedulers are hardly implementable in a real system such as long term evolution advanced (LTE-A), which imposes additional constraints on how resources are allocated. To address this problem, we first propose adaptive and generic scheduling scheme (AGSS) a generic resource allocation procedure that enables the implementation of state-of-the-art scheduling policies for building a LTE scheduler. In a second step, we propose our own scheduling policy called opportunistic PDOR aware (OPA) that both optimizes the use of the radio spectrum and provides the quality of service actually expected by applications. We show how to implement this policy using our generic scheduling scheme. We then compare the performance of AGSS to the classic scheme in a LTE environment when used with a given policy. We show that the proposed scheme outperforms the classic scheme whatever the policy. We also establish that OPA offers the best performances in terms of capacity and quality of service compared to state-of-the-art policies.

Socially Aware Heterogeneous Wireless Networks.

The development of smart cities has been the epicentre of many researchers’ efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users’ locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

A derivative-free approach for a simulation-based optimization problem in healthcare

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs, developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital’s ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation-based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation-based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete-event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative-free optimization algorithm to modify the given setting. We report results for a real-world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach.

Reputation-guided evolutionary scheduling algorithm for independent tasks in inter-clouds environments

Self-adaptation provides software with flexibility to different behaviours (configurations) it incorporates and the (semi-)autonomous ability to switch between these behaviours in response to changes. To empower clouds with the ability to capture and respond to quality feedback provided by users at runtime, we propose a reputation guided genetic scheduling algorithm for independent tasks. Current resource management services consider evolutionary strategies to improve the performance on resource allocation procedures or tasks scheduling algorithms, but they fail to consider the user as part of the scheduling process. Evolutionary computing offers different methods to find a near-optimal solution. In this paper we extended previous work with new optimisation heuristics for the problem of scheduling. We show how reputation is considered as an optimisation metric, and analyse how our metrics can be considered as upper bounds for others in the optimisation algorithm. By experimental comparison, we show our techniques can lead to optimised results.