Numerical Representation of Directed Acyclic Graphs for Efficient Dataflow Embedded Resource Allocation

Purpose Using the example of community access programs (CAPs), the purpose of this paper is to describe resource allocation and policy decisions related to providing health services for the uninsured in the USA and the organizational values affecting these decisions. Design/methodology/approach The study used comparative case study methodology at two geographically diverse sites. Researchers collected data from program documents, meeting observations, and interviews with program stakeholders. Findings Five resource allocation or policy decisions relevant to providing healthcare services were described at each site across three categories: designing the health plan, reacting to funding changes, and revising policies. Organizational values of access to care and stewardship most frequently affected resource allocation and policy decisions, while economic and political pressures affect the relative prioritization of values. Research limitations/implications Small sample size, the potential for social desirability or recall bias, and the exclusion of provider, member or community perspectives beyond those represented among participating board members. Practical implications Program directors or researchers can use this study to assess the extent to which resource allocation and policy decisions align with organizational values and mission statements. Social implications The description of how healthcare decisions are actually made can be matched with literature that describes how healthcare resource decisions ought to be made, in order to provide a normative grounding for future decisions. Originality/value This study addresses a gap in literature regarding how CAPs actually make resource allocation decisions that affect access to healthcare services.

Social inequalities are worsened by the decision about how resources are to be allocated. Considering that children and adolescents are likely to become future leaders and decision-makers, understanding which principles they follow regarding resource allocation in situations of inequality is crucial. This paper aims at presenting a protocol for systematic review of the relationship between perception of resource inequality and allocation decisions made by children and adolescents. Method: A systematic search will be conducted in the PsycINFO, PsycArticles, Web of Science (Core Collections), Scopus, ERIC and LILACS databases. The paper will also include empirical studies from the authors’ personal files, studies obtained through contact with the main authors of the articles included, and through backward and forward snowballing. The studies’ quality will be assessed using the Quality Assessment with Diverse Studies (QuADS). Finally, a narrative synthesis will be conducted to review and synthesize data. This protocol was outlined following the PRISMA-P guidelines. The protocol is registered at the Open Science Framework (OSF) under the code DOI: https://doi.org/10.17605/OSF.IO/D96MY. The systematic review will follow the Joanna Briggs Institute guidelines, and will be reported according to the PRISMA 2020 reporting guidelines. Discussion: The results of this review will be relevant to policy makers, decision-makers, educators, and researchers.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

We develop an optimal control model for optimally allocating effort between detection and correction processes, during testing phase of a software development life cycle (SDLC). The objective is to propose a resource allocation plan to minimise the total testing cost with the reliability criteria using flexible software reliability growth model considering testing effort under dynamic environment. Furthermore, we have incorporated the learning curve effect in correction cost function due to its importance in cost estimation, and resource allocation decisions. A technique based on optimal control theory is proposed for the software managers to optimise the allocation of the limited resources, while genetic algorithm is used to find the approximate value of detection and correction effort. An illustrative example is presented. The optimal release policy under reliability criteria is also discussed.

Since the existing terrestrial fifth generation (5G) network has limited coverage, it is difficult to meet the growing demand for seamless network connection. Meanwhile, current network resource allocation methods mainly research on how to improve system performance only from the perspective of resource utilization, but rarely take users’ specific needs for network content into consideration. This brings severe challenges to efficient network service and flexible resource allocation. Therefore, we construct the content service-oriented resource allocation model for space–air–ground integrated sixth generation networks (SAGIN 6G), and formulate the three-sided matching issue among the space–air–ground integrated network equipment (SAGINE), content sources, and users. In this model, users request to establish connection with SAGIN which forwards users’ request to content service provider (CSP). CSP manages the creation of content data, and finally returns the requested content to users through SAGIN. For the content service-oriented resource allocation in SAGIN, finding the optimal stable three-sided matching with the largest cardinality is an NP-complete problem. Therefore, to efficiently solve the above issue, we design some reasonable restrictions and convert it to a restricted three-sided matching problem with size and cycle preferences. We further develop the content-oriented resource allocation algorithm (COR2A) and the user-oriented resource allocation algorithm (UOR2A) in a distributed manner. Extensive simulations verify our approach outperforms traditional benchmark resource allocation schemes in terms of system throughput, CSP revenue, and user experience.

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

Airport scheduling has an important optimization value in air transportation, and its complexity and multiple constraints make it difficult for existing methods to deal with it comprehensively. This paper is a review paper, that systematically summarizes the application of genetic algorithms and machine learning in the scheduling of flights, the scheduling of airport resource allocation, and the scheduling of airport external resource allocation. Through the analysis of many studies, this paper summarizes the advantages and disadvantages of different algorithms in various scheduling scenarios and points out the advantages of genetic algorithms in dealing with complex multi-objective optimization problems. In addition, this paper explores the potential of combining machine learning with genetic algorithms to further enhance the effect of scheduling optimization. This paper provides an important reference for future research and helps the innovation and practical application of methods in the field of airport scheduling.

Decentralized joint resource allocation and path selection in multi-hop integrated access backhaul 5G networks

In a distributed stream processing system, elastic resource provisioning/scheduling is the main factor that affects system performance and limits system applications. However, in the data stream computing platform, resource allocation is often suboptimal due to the large fluctuations of the data stream rate, which creates a performance bottleneck for the cluster. In this paper, we propose a data stream prediction strategy (Dp-Stream) for elastic computing system to mitigate the resource allocation issue. First, we establish a back propagation (BP) neural network prediction model based on genetic simulated annealing algorithm to predict the trend of the data stream rate in the next time window of the cluster; second, according to the time latency, the estimation model adjusts the resources allocated to the critical operations of the critical path in the Directed Acyclic Graph (DAG) and finally, the resource communication cost is optimized. We evaluate the prediction accuracy and system latency of the proposed scheduling strategy in Storm. The experimental results prove the feasibility and effectiveness of the proposed strategy.KeywordsData stream predictionResource schedulingStream computingBack propagation neural networkStorm

The work reports the developed planning algorithms for optimum resource allocation over a dynamic 5G infrastructure, addressing all involved types of system resources, from the radio access domain to the MEC domain through the Optical Distribution Network (ODN). A modular modelling design is adopted considering these three domains. In that manner, allocation is composed of three optimization phases, which are executed sequentially and ensure optimal end-to-end allocation of resources. The first allocation phase focuses on the over-the-air resources, which are directly assigned to the 5G mobile users. The key entities for radio access are the Remote Radio Heads (RRHs), which are placed at the edge of the network fronthaul and provide wireless communication slots to the User Equipment (UE). Beamforming capabilities are also assumed allowing each RRH to support simultaneous and independent transmission of multiple directed beams to provide high quality targeted coverage. The resource allocation scheme is responsible for assigning subchannels within specific beams to address the bandwidth requirements of the requested services. The translation from the requested data-rate and the allocated bandwidth is based on the characteristics of 3GPP New Radio (NR) standard. Furthermore, the radio resource allocation process is responsible for assigning different bands for optimal frequency reuse within the available spectrum and avoids interference. The formulated ILP optimization problem, which is solvable in close to real-time due to its low computational complexity, allows dynamic adaptation to requests and resources, with a target to minimize energy consumption. The latter is achievable through the aggregation of wireless resources into individual beams and RRHs. In order to further enhance this process, an RRH prioritization algorithm is introduced for weighting the candidate allocations. With the completion of the first allocation phase, the optical resource allocation phase is initiated. The considered architecture follows that of the EU funded blueSPACE project and consists of a fully flexible ODN exploiting the cutting-edge capabilities of Spectrally Spatially Flexible Optical Networks (SS-FONs). In this context, the allocator considers the spectral and/or spatial multiplexing features of the optical nodes for performing elastic switching. The aim is to establish complete optical routes (lightpaths) from the radio access domain to the Central Office (CO). In particular, the output of this allocation phase is utilized to determine end-to-end fiber-based connections between BBUs and beams. The formulated optimization problem (a low complexity ILP) is able to model any type of network topology and provide optimal paths to dynamic demands according to the available optical domain resources. Power saving is achieved by minimizing the number of the optical elements used for building the required lightpaths. The last allocation phase assigns MEC resources within the blueSPACE virtual Content Delivery Network (vCDN) and Evolved Packet Core (vEPC). The services request MEC resources to offload computationally demanding tasks (such as real-time multimedia processing) and the allocator is responsible to make optimal decisions for the hosting of the required VM instances. The formulated ILP problem provides such a fast decision-making mechanism, while averting the scattering of allocated resources to numerous physical servers in order to conserve energy. With the termination of this third phase, all required network and computing resources are fully allocated for serving dedicated enhanced Mobile Broadband (eMBB) slices. The aforementioned resource allocation algorithms are implemented and simulated in MATLAB. For the evaluation process, two real-world simulation scenarios are considered to assess the allocation effectiveness and energy conservation capabilities for changing traffic requests. One scenario simulates a 5G deployment over a football stadium and the second scenario a deployment over a city park. Due to their coverage requirements the two scenarios lead to different RRH location and connectivity properties. Results show that in the “Stadium” scenario we achieve 50% savings in the number of activated RRHs and beams, while in the “City Park” scenario this metric reaches 67%. With respect to the allocation of optical networking resources, our optimization approach achieves conservation of up to 11% of optical ports and 14% of optical cores for the stadium scenario, while the corresponding results for the “City Park” scenario are 9% and 16%, respectively. Lastly, regarding the MEC resource allocation, the conducted simulations in the context of the “Stadium” scenario have shown 22% average reduction of the activated physical MEC servers (reaching up to 38%), while in the “City Park” scenario these savings average even higher to 68% (reaching up to 91%).