Resource Allocation Process Research Articles

Efficient Scheduling of Jobs and Allocation of Resources in Cloud Computing

Due to the drastic utilization of clouds, a Proper and proficient allocation of resources in dynamically working environment of cloud systems turns into the challenging task. Different promising mechanisms have been created to work on the effectiveness of process of resource allocation. Yet at the same time there is some incompetency as far as resource allocation and job scheduling, when the systems become highly loaded. Hence, an effective algorithm for scheduling of jobs is needed to work on the proficiency of resource allocation activities. In this paper a advanced technique for scheduling of jobs is proposed for effective and unique process of allocation of resources in cloud computing. By making use of prediction-based techniques and mechanism of updating resource tables in dynamic manner, we achieve, better allocation of resources in the form of response time and completion of jobs. The experimental results demonstrate the effective outcomes compared to existing techniques, by achieving exactness in values for resource table updation.

The effect of information-driven resource allocation on the propagation of epidemic with incubation period.

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible–exposed–infected–susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

Power control scheme using particle swarm optimization method in resource allocation process on D2D underlaying communication

Fast growing number of cellular telecommunication technology resulting on the increasing number of the user equipment. This condition increased the eNodeB load. To overcome this problem, the device-to-device (D2D) underlaying communication is introduced. In underlaying scheme, the D2D user equipment (DUE) will do the communication process using the same radio resources with the conventional cellular user equipment (CUE). To avoid a severe interference between these two types of user in the system, a good resource allocation is needed. In this work, a power control scheme using particle swarm optimization (PSO) is proposed, to manage the transmit power on each user on the system. The power control scheme take place after the greedy scheduling algorithm, after all user is given a resource block (RB) to do the communication process. The power transmit for each user is managed to reach a better system capacity, and to reduce the power consumed in one communication process. From the simulation, the PSO power control can improve the sumrate and spectral efficiency up to 12.97% and 3.38% respectively. The PSO power control also can reduce the power consumed by the system up to 8.84%. The fairness happens among the CUEs also can be maintained, despite of the decreasing fairness among DUEs.

Redemptive Resource Sharing and Allocation Scheme for Internet of Things-Assisted Smart Healthcare Systems.

Internet of Things assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. Communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness invalidating multiple resources. IoT's open and ubiquitous nature results in proactive resource sharing, resulting in delayed transmissions. This manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme to address this issue. The available health data is accumulated on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves resource allocation, and reduces transmission complexity. The entire process is managed for transfer learning, data-to-capacity validation, and concurrent recommendation. The first validation knowledge base remains the same/shared for different data accumulation and sharing intervals.

Efficient Management and Application of Human Resources Based on Genetic Ant Colony Algorithm

With the increasing demand of human resources, the cost of staffing and management is increasing, and it is difficult to dynamically allocate and adjust personnel among different parts. It is the key of intelligent management technology to realize efficient application and mining in human resource management. In the aspect of human resource allocation and management, this paper puts forward the efficient management and application of human resource based on the genetic ant colony algorithm. Firstly, this paper describes the process management and parameter application of the genetic algorithm and ant colony algorithm and manages the resource allocation and management process under the two algorithms. Secondly, several current test functions are applied to the genetic algorithm and ant colony algorithm to test the efficiency of the algorithm, which has obvious advantages in convergence efficiency. Finally, the paper uses the efficient management configuration of human resources for comprehensive application and management and applies the system uploading and downloading services on human resumes, respectively. The genetic ant colony algorithm has obvious advantages in efficiency. In human resource data matching, the genetic algorithm is slightly better than the ant colony algorithm in the case of relatively few data in the early stage, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. The ACO-GA algorithm is more consistent with the actual value, which not only ensures the stability but also ensures the accuracy of prediction, which is more in line with the actual needs.

Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.

Decision-Making Framework for Prioritizing the Improvement of Pedestrian Facilities in Urban Areas Using Integrated Delphi, AHP, and TOPSIS Approach

The existing pedestrian facilities in urban areas can be improved by prioritizing the areas based on their infrastructure performance, location, safety, mobility, and liveability aspects. This paper aims to present a combination of Delphi, analytic hierarchy process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing the improvement of pedestrian facilities in an urban area. The approach has been broken into three stages. Firstly, the significant factor assessment was done by the Delphi method. Secondly, AHP was applied to estimate the relative weightage of each factor. Finally, the TOPSIS method was used to rank the alternatives in preparation for the final decision. For better understanding, the proposed method was demonstrated by applying the framework in a case study. The analyses revealed that the proposed method is effective for prioritizing the improvement of pedestrian facilities in an urban area. The method described in this paper is intended to assist decision makers in ranking different sidewalk stretches in the urban areas that need to be improved based on various attributes. This paper makes a major impact by incorporating a novel approach for the decision making of pedestrian facilities improvement, which considered both subjective and objective judgments of multiple decision makers in the procedure at various stages using different multi-criteria decision making techniques. This approach can be combined with a scheduling methodology and optimization technique to increase the efficiency of the resource allocation process.

Resource Allocation on Blockchain Enabled Mobile Edge Computing System

Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.

Collusion, Shading, and Optimal Organization Design

Abstract We introduce a behavioral contract theory idea, “shading” (Hart and Moore (2008). “Contracts as Reference Points.” Quarterly Journal of Economics 123 (1): 1–48)) as a component of ex-post haggling (addressed by Coase (1937. “The Nature of the Firm.” Economica 4 (16): 386–405) and Williamson (1975. Markets and Hierarchies: Analysis and Antitrust Implications. New York: Free Press)) into the collusion model à la Tirole (1986. “Hierarchies and Bureaucracies: On the Role of Collusion in Organizations.” Journal of Law, Economics, and Organization 2: 181–214, 1992. “Collusion and the Theory of Organizations.” In Advances in Economic Theory: The Sixth World Congress, edited by J. J. Laffont. Cambridge: Cambridge University Press), thereby constructing a new model of hierarchical organization. By integrating the two ideas, i.e. collusion and shading, we enrich the existing collusion model, thereby obtaining a new result for Collusion-proof versus Equilibrium Collusion. The basic idea is that the increase in shading pressure strengthens the incentive for collusion, thereby making it difficult to implement collusion-proof incentive schemes, which leads to the Equilibrium Collusion. In addition, we also provide a micro-foundation for ex-post haggling costs, where we view rent-seeking associated with collusive behavior and ex-post haggling generated from aggrievement and shading as the two sources of the costs. This model is used to examine the optimal organizational design problem as an optimal response to the trade-off between gross total surplus and ex-post haggling costs, and to take a step further the idea of efficient organization design (Milgrom (1988. “Employment Contracts, Influence Activities and Efficient Organization Design.” Journal of Political Economy 96: 42–60)). We believe that our model could help provide a deep understanding of resource allocation and decision processes in hierarchical organizations.

Lung Ultrasound to Assist ICU Admission Decision-Making Process of COVID-19 Patients With Acute Respiratory Failure.

OBJECTIVES:There is only low-certainty evidence on the use of predictive models to assist COVID-19 patient’s ICU admission decision-making process. Accumulative evidence suggests that lung ultrasound (LUS) assessment of COVID-19 patients allows accurate bedside evaluation of lung integrity, with the added advantage of repeatability, absence of radiation exposure, reduced risk of virus dissemination, and low cost. Our goal is to assess the performance of a quantified indicator resulting from LUS data compared with standard clinical practice model to predict critical respiratory illness in the 24 hours following hospital admission.DESIGN:Prospective cohort study.SETTING:Critical Care Unit from University Hospital Purpan (Toulouse, France) between July 2020 and March 2021.PATIENTS:Adult patients for COVID-19 who were in acute respiratory failure (ARF), defined as blood oxygen saturation as measured by pulse oximetry less than 90% while breathing room air or respiratory rate greater than or equal to 30 breaths/min at hospital admission. Linear multivariate models were used to identify factors associated with critical respiratory illness, defined as death or mild/severe acute respiratory distress syndrome (Pao2/Fio2 < 200) in the 24 hours after patient’s hospital admission.INTERVENTION:LUS assessment.MEASUREMENTS AND MAIN RESULTS:One hundred and forty COVID-19 patients with ARF were studied. This cohort was split into two independent groups: learning sample (first 70 patients) and validation sample (last 70 patients). Interstitial lung water, thickening of the pleural line, and alveolar consolidation detection were strongly associated with patient’s outcome. The LUS model predicted more accurately patient’s outcomes than the standard clinical practice model (DeLong test: Testing: z score = 2.50, p value = 0.01; Validation: z score = 2.11, p value = 0.03).CONCLUSIONS:LUS assessment of COVID-19 patients with ARF at hospital admission allows a more accurate prediction of the risk of critical respiratory illness than standard clinical practice. These results hold the promise of improving ICU resource allocation process, particularly in the case of massive influx of patients or limited resources, both now and in future anticipated pandemics.

Exploring the efficiency of primary health care provision in rural and sparsely populated areas: a case study from Mongolia.

Mongolia is facing serious challenges in the health sector and the macro-economic sphere that have important implications for health financing and the completion of universal health coverage. In this context, improving the efficiency of primary health care facilities is a critical issue. We study the efficiency of Soum Health Centres (SHCs) that provide primary care in rural Mongolia. Based on activity and resources data collected for all SHCs of Mongolia in 2017 and 2018 we estimate bias-adjusted efficiency scores. A double bootstrap truncated regression procedure is then used to study the factors associated with SHCs' efficiency. On average, SHCs could potentially engage in the same activity while reducing overall resource use by ∼23%. A comparatively higher population density and dependency ratio in the districts where they are located tend to favour SHCs' efficiency. Conversely, the higher the poverty rate in the soum, the lower the efficiency. We find a positive association between SHCs' efficiency and the proportion of doctors in the health workforce. The human resources allocation process and the capitation formula currently used to pay SHCs should be adjusted based on the size and socioeconomic/demographic characteristics of the population living in the catchment area of SHCs.

Public legitimacy of healthcare resource allocation committees: lessons learned from assessing an Israeli case study

BackgroundThe National Health Insurance Law enacted in 1995 stipulates a list of health services to which all Israeli residents are entitled. For the past 20 years, the list has been updated annually, as a function of a predetermined budget, according to recommendations from the Public National Advisory Committee (PNAC), which evaluates and prioritizes candidate technologies. We assessed the legitimacy of this resource-allocation process as reflected in Israeli public discourse and its congruence with the accountability for reasonableness (A4R) framework.MethodsA qualitative analysis of public discourse documents (articles in the print media, court rulings and parliamentary debates (N = 119) was conducted to assess the perceived legitimacy by the Israeli public of the PNAC. Further content analysis of these documents and semi-structured interviews with stakeholders (N = 70) revealed the mainstays and threats to its legitimacy. Based on these data sources, on governmental documents specifying PNAC's procedures, and on data from participant observations, we assessed its congruence with A4R’s four conditions: publicity, relevance, revision and appeals, regulation.ResultsThe PNAC enjoys ongoing support for its legitimacy in Israeli public discourse, which stem from its perceived professional focus and transparency. These strengths are consistent with the A4R’s emphasis on the publicity and the relevance conditions. The three major threats to PNAC's legitimacy pertain to: (1) the composition of the committee; (2) its operating procedures; (3) its guiding principles. These perceived shortcomings are also consistent with incongruencies between PNAC's work model and A4R. These findings thus further support the empirical validity of the A4R.ConclusionThe analysis of the fit between the PNAC and A4R points to refinements in all four conditions that could make the A4R a more precise evaluative framework. Concurrently, it highlights areas that the PNAC should improve to increase its legitimacy, such as incorporating cost-effectiveness analyses and including patient representatives in the decision-making process.Hebrew and Arabic abstracts for this article are available as an additional file.

The MIND Dietary Pattern Is Selectively Related to Cognitive Processing Speed

ObjectivesGiven that previous research has disproportionately focused on individual nutrients, knowledge of diet patterns and cognitive health is limited. Thus, we investigated the relationship between different diet indices (Mediterranean, Dietary Approaches to Stop Hypertension [DASH], Healthy-Eating-Index-2015 [HEI-2015], and Mediterranean-DASH Intervention for Neurodegenerative Delay [MIND]) and attentional inhibition and neuroelectric function.MethodsAdults aged 34.1 ± 6.0 years old (N = 207) completed the Dietary History Questionnaire II (DHQII, Past Year and Past Month with Portion Size) to assess adherence to different diet indices. Attentional inhibition was assessed using a modified Eriksen Flanker task while event-related potentials (ERPs) were recorded. The amplitude and latency of the P3 ERP component were used to index attentional resource allocation and information processing speed, respectively. Stepwise linear regression modeling was used to assess the influence of each dietary index on behavioral performance and neuroelectric function following adjustment for significant covariates (e.g., age, sex, intelligence quotient, and body mass index).ResultsGreater adherence to the MIND diet was associated with an earlier incongruent P3 peak latency (∆R2 = 0.02, β = −0.14, p = 0.04) but not congruent peak latency (∆R2 = 0.02, β = −0.12, p = 0.07). Adherence to the Mediterranean, DASH, and HEI-2015 patterns was not correlated with P3 latency (p > 0.05). No associations were observed between the diet indices and attentional inhibition at the behavioral level (i.e., accuracy or reaction time) or P3 amplitude (all p's > 0.05).Conclusions Greater adherence to the MIND diet was selectively related to faster information processing speed. This relationship was only evident during incongruent trials, suggesting that the influence of greater MIND diet adherence is particularly beneficial when upregulation of cognitive control is required. Future MIND diet intervention trials are warranted to help inform dietary recommendations for healthy cognitive aging.Funding SourcesThis work was funded by the Department of Kinesiology and Community Health at the University of Illinois, Division of Nutritional Sciences, and Hass Avocado Board.

Heterogeneous Markov Decision Process Model for Joint Resource Allocation and Task Scheduling in Network Slicing Enabled Internet of Vehicles

In the Internet of Vehicles (IoV) with network slicing functions, both the inter-slice resource allocation and the intra-slice task scheduling have received extensive attention. However, the joint optimization of these two problems has not been fully studied. This letter focuses on the complexity issue of the joint resource allocation and task scheduling problem in the IoV with both ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) slices. A heterogeneous Markov decision process (HMDP) which models the resource allocation process and the task scheduling process in two different layers is proposed. Benefiting from the two-layer structure of the HMDP, the size of action space is significantly reduced, and the asynchronous decisions of different sub-MDPs are enabled. The corresponding layered deep reinforcement learning (DRL) architecture is also designed to solve the HMDP-based optimization problem. Simulation results show that the layered DRL-based algorithm outperforms other methods.

Exploring the context, role and impact of public health nursing during COVID-19: A multiple case study protocol.

AimThe purpose of this study is to: (a) describe public health nursing roles over the course of the COVID‐19 pandemic in Ontario, Canada; (b) describe the contextual factors that influence public health nursing role implementation; and (c) describe nurses' perceived impact of their roles on client outcomes and professional/personal nursing practice.DesignDescriptive multiple case study.MethodsRecruitment of public health nurses (PHNs), working in direct service or administrative leadership positions, in an Ontario public health unit will be conducted through purposive and snowball sampling. Nursing roles will be compared and contrasted across three cases differentiated by geographic setting: urban, urban–rural, northern. In each geographic case, a priori estimates of sample size will include 10 PHNs providing direct care and at least five nurses in an administrative leadership role; with an overall estimated study sample size of 45 individuals. Demographic data will be collected using an online anonymous survey. Individual semi‐structured interviews with PHNs, and focus groups with nursing administrators will be conducted via telephone and audio‐recorded. Individual interviews and focus groups will be transcribed verbatim. Reflexive thematic analysis will be used to generate emergent themes in each case and cross‐case synthesis will be used to compare and contrast patterns across geographic cases.DiscussionExpected findings will provide an in‐depth analysis of the rapidly evolving roles and functions of PHNs throughout the COVID‐19 pandemic and their impact on individuals, families and communities. As well, findings will provide a new understanding about the contextual barriers and facilitators of PHN role implementation in their working environments.ImpactStudy findings can support decision‐making in relation to funding, resource allocation and supportive work structures and processes at a public health system and/or individual public health organization level.

Seasonal mobility of populations and allocation of police resources in Finland

This study analyses the impact of the seasonal mobility of populations on the emergency response times (ERT) of the police. The concept of multi-locality increases the mobility of people but, however, the public service structures of the society and different administrative systems do not sufficiently account for that. The results of the mixed-model regression demonstrate the challenges in the police resources allocation when people are acting in different areas than expected. Emergency response times was lengthened as the seasonal population increased and spatially dispersed over the regions during the seasons. We suggest that the seasonal mobility should be involved the future planning process of police resource allocation.

Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest

Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathematical model of resource allocation and cost function. Generation of random forests and prediction of target objects are based on historical data. Resource allocation steps are based on predictive structure. The invention provides a resource allocation method that satisfies task completion degree constraints and includes a resource allocation algorithm based on random forest with a high probability of finding an optimal solution. It also addresses the issue that intelligent optimization algorithms such as genetic algorithms are prone to fall into local optimum.

5G Converged Network Resource Allocation Strategy Based on Reinforcement Learning in Edge Cloud Computing Environment.

Aiming at the problem that computing power and resources of Mobile Edge Computing (MEC) servers are difficult to process long-period intensive task data, this study proposes a 5G converged network resource allocation strategy based on reinforcement learning in edge cloud computing environment. n order to solve the problem of insufficient local computing power, the proposed strategy offloads some tasks to the edge of network. Firstly, we build a multi-MEC server and multi-user mobile edge system, and design optimization objectives to minimize the average response time of system tasks and total energy consumption. Then, task offloading and resource allocation process is modeled as Markov decision process. Furthermore, the deep Q-network is used to find the optimal resource allocation scheme. Finally, the proposed strategy is analyzed experimentally based on TensorFlow learning framework. Experimental results show that when the number of users is 110, final energy consumption is about 2500 J, which effectively reduces task delay and improves the utilization of resources.

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security.

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies.

Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless Access Networks.

Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5–2.7% higher than DQN and Double DQN, but the convergence is faster.