Dynamic Allocation Mechanism Research Articles

An Improved Polling Mechanism for Dynamic Bandwidth Allocation of XGPON Networks

Abstract: One of the most well-known technologies that offers a large range of services at user’s fingertips without requiring the least amount of infrastructure is passive optical networks. This can be due to a particular component known as the passive optical spiller. In order to facilitate time division access for individual ONUs on a single optical fiber, the appropriate dynamic bandwidth allocation mechanism must be present at the OLT with the advent of user support. For bandwidth assignment by DBA, it is essential to determine the precious upstream bandwidth demand, and can be aided by the more frequent polling mechanism. The more frequent polling mechanism lead to erroneous bandwidth report problem, become more serious when the mechanism is employed in larger coverage area network (specifically larger channel delay). This IBR problem lead increase in upstream delay at higher load condition, it’s because of improper assignment of bandwidth and waste of bandwidth. The IBR issue is resolved by the improved polling and scheduling method presented in this paper. Instead of transmitting the bandwidth allotment in each downstream frame, the OLT uses this scheduling technique to send it all at once to a TCONT (i) of ONU (i) for the duration of the SI period. To counteract the channel delay impact, a unique polling technique is also presented that use the hybrid methodology in which the OLTs sends polling mechanism once per the Service interval but According to the simulation study’s findings, the suggested system reduces mean upstream delay for type 2, 3, and 4 traffic classes by 62% when compared to the EBU scheme and by up to 20% when compared to the XGIANT design.

Study on the Impact of Flow Dynamic Allocation Mechanism on the Balanced Propagation of Multicluster Fractures in Horizontal Wells

Abstract The implementation of the multicluster fracturing technique in horizontal wells is crucial to the economic development of shale gas reservoirs. The stress shadow area generated by them suppresses the effective expansion of fractures, leading to lower construction quality. Numerous well logging and production data indicate that dynamic flow allocation is also an important mechanism restricting oil and gas production. To reveal the mechanism of multifracture synchronous competitive expansion, a multifracture expansion finite element model considering fluid-solid coupling in porous media was established. A wellbore model was built to characterize the fluid pressure drop, incorporating pipe flow and fluid connection elements to emulate fluid flow within the wellbore, and the influence of various factors on the balanced expansion of fractures was investigated. The results show that high porosity, uniform perforation, and high elastic modulus of sweet spots are favorable for the balanced expansion of multiple fractures when using high-volume and low-viscosity fracturing fluids. When the spacing is small and the porosity is low with nonuniform perforation, the threshold generated by perforation friction will dominate the balanced fluid injection and expansion of each fracture. The horizontal well fracturing plan in a certain area of southwest China was optimized using this model. After optimization, when the spacing is less than 15 m, the total number of perforations is 32, and the average injection rate per cluster will be increased by 0.125 m3/min, with a coefficient of variation of 3.9% among clusters. When the perforations are nonuniformly distributed, the average injection rate per cluster will be increased by 0.101 m3/min, the fluid inflow difference coefficient of each cluster is 4.1%, and multiclusters will have a uniformly developed fracture network. This study has certain guiding significance and reference value for the balanced expansion of multifractures in horizontal wells.

Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network

With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.

Leveraging AI‐enabled 6G‐driven IoT for sustainable smart cities

SummaryMany scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low‐latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost‐efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain‐enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in‐depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.

Ultraviolet random access collaborative networking protocol based on time division multiple access

The ultraviolet network, with its advantages of high security, non-line-of-sight communication, and all-weather operation, has broad military application potential. This study proposes an ultraviolet random access collaborative networking protocol based on time division multiple access that combines the benefits of synchronous and asynchronous media access control protocols. Considering the physical layer characteristics, the frame structure of the protocol is constructed based on a cross-layer design. Compared with the time division multiple access protocol, the proposed protocol can achieve higher throughput with a lower delay and packet loss rate by the dynamic timeslot allocation mechanism. Then, the effects of maximum cache capacity and burst traffic on network performance are investigated. Finally, the conducted network experiments prove the cooperative forwarding and random access capabilities of the ultraviolet random access collaborative networking protocol. The proposed protocol provides a reliable and effective method for independent and flexible ultraviolet networks.

A novel dynamic pricing model for a microgrid of prosumers with photovoltaic systems

Due to the growing demand for electricity and the increasing number of consumers who can produce energy (prosumers) using photovoltaic systems today, energy generated by prosumers can be utilized in the microgrid instead of selling it to the main utility grid. Pricing is one of the most important mechanisms for motivating prosumers to interact with each other in the microgrid. Many works have proposed different pricing models that mostly focus on optimizing prosumers’ behavior and energy usage costs. However, most of the proposed models require constant involvement of the end-user to adjust energy consumption profiles, which is not always possible in a real-world scenario. In this paper, a novel pricing model is presented with the aim of maximizing the utilization of energy generated in the microgrid and reducing the import of energy from the utility grid, whereas ensuring more beneficial prices for energy within the microgrid compared with the utility grid. Mathematical models based on the supply and demand ratio and prosumers’ absolute deviation from the predicted energy usage profiles are developed to determine the internal equilibrium price and the amount of energy each prosumer can buy and sell by interacting with the microgrid. To cover the energy transfer losses in the microgrid, a dynamic loss allocation mechanism is proposed. The proposed pricing model is validated using real energy usage profiles from 100 prosumers. The results show that the total energy usage cost can be decreased, whereas the amount of unused energy that is shared outside the microgrid is minimized.

A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter

Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories.

A dynamic allocation mechanism for formulating the allocation schemes of water resources

Abstract Scientific and effective water allocation schemes are crucial for managing water resources. Traditional allocation schemes are built on an annual basis and fixed. However, because of the drastic spatio-temporal fluctuations in the availability and demand of water resources, fixed annual allocation schemes are impracticable. This work proposed a dynamic allocation mechanism that can divide annual allocation schemes into monthly scales and dynamically correct allocation schemes to make them adapt to the above fluctuations. First, a total quantity allocation model, which focuses on fairness and efficiency, was developed to allocate annual water resources. Secondly, a combination weighting model for a monthly water allocation was built using game theory to make the allocation schemes stricter and more executable. Thirdly, a dynamic allocation mechanism was constructed to dynamically correct the allocation schemes. The results indicate that (1) coupling a total quantity allocation scheme with monthly allocation schemes is more in line with management requirements and (2) compared with static allocation schemes, rolling correction schemes can reduce the deviation between planned and actual water consumption by 10–20%. A dynamic allocation mechanism can make water resource allocation schemes more resilient to spatiotemporal changes in supply and demand of water resources.

Evolution of cooperation in public goods games with dynamic resource allocation: A fairness preference perspective

Exploring the conditions under which cooperation can emerge in populations has long been a subject for humans. Fair preference psychology has been incorporated into the analytical framework of the public goods game. Based on considering the dynamic update of trust-driven investment willingness, we introduce a dynamic payoff allocation mechanism and loners to traditional PGG, explaining how the mechanism related to fairness preferences promotes cooperation. We assume that payoffs are equally allocated among players when the payoffs do not exceed the threshold. Otherwise, the excess portion is proportionally allocated according to the investment willingness of cooperators. The effects of parameter changes on the evolution of cooperation are investigated by conducting Monte Carlo simulations. The results show that introducing dynamic payoff allocation and loners can effectively promote cooperation in a specific range of parameters. Specifically, the existence of loners is a double-edged sword. Loners can prevent players from adopting defection behaviors on a large scale when the payoff of cooperation is unsatisfied and inhibit the emergence and diffusion of cooperative strategies as the payoff of cooperators increases. When the payoff of cooperation is dissatisfactory, the dynamic payoff allocation mechanism can promote the average investment willingness of cooperators to accelerate the spread of cooperation. However, the payoff reallocation coefficient should not be set too high, and a high-level threshold of payoff reallocation may lead to the failure of the dynamic payoff allocation mechanism. There is an optimal payoff reallocation coefficient to maximize the efficiency of the proposed mechanism. Moreover, the optimal payoff reallocation coefficient that can maximize the cooperation level is distinct for the diverse combination of enhancement factor and loss aversion coefficient. However, its value should be set at a low level in general. These results may help researchers better understand the evolution of cooperation in public goods games with dynamic resource allocation from a fairness preference perspective.

A Classy Memory Management System (CyM2S) using an Isolated Dynamic Two-Level Memory Allocation (ID2LMA) Algorithm for the Real Time Embedded Systems

Due to an increased scalability, flexibility, and reduced cost complexity, the dynamic memory allocation models are highly preferred for the real-time embedded systems. For this purpose, the different types of dynamic models have been developed in the conventional works, which are highly focused on allocating the memory blocks with increased searching capability. However, it faced some of the problems and issues related to the factors of complex operations, high time consumption, memory overhead, and reduced speed of processing. Thus, this research work objects to design an advanced and intelligent dynamic memory allocation mechanism for the real-time embedded systems. Here, a Classy Memory Management System (CyM2S) is developed by using an Isolated Dynamic Two-Level Memory Allocation (ID2LMA) algorithm for efficiently allocating the memory blocks with simple searching. The CyM2S helps to reduce the fragmentation rate and time consumption by optimally allocating the memory blocks. In this model, the small buffer has been maintained for surplus pointers, and the allocated blocks comprise the metadata and payload data. During evaluation, the performance of the proposed CyM2S- ID2LMA technique is validated and compared by using the measures of memory allocation time, release time, execution, and processing speed.

Pupil-Linked Arousal Response Reveals Aberrant Attention Regulation among Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a developmental disorder that is characterized by difficulties with social interaction and interpersonal communication. It has been argued that abnormal attentional function to exogenous stimuli precedes and contributes to the core ASD symptoms. Notably, the locus ceruleus (LC) and its noradrenergic projections throughout the brain modulate attentional function, but the extent to which this locus ceruleus-norepinephrine (LC-NE) system influences attention in individuals with ASD, who frequently exhibit dysregulated alerting and attention orienting, is unknown. We examined dynamic attention control in girls and boys with ASD at rest using the pupil dilation response (PDR) as a noninvasive measure of LC-NE activity. When gender- and age-matched neurotypical participants were passively exposed to an auditory stream, their PDR decreased for recurrent stimuli but remained sensitive to surprising deviant stimuli. In contrast, children with ASD showed less habituation to recurrent stimuli as well as a diminished phasic response to deviants, particularly those containing social information. Their tonic habituation impairment predicts their phasic orienting impairment, and both impairments correlated with the severity of ASD symptom. Because of the fact that these pupil-linked responses are observed when individuals passively listen without any task engagement, our findings imply that the intricate and dynamic attention allocation mechanism, mediated by the subcortical LC-NE system, is impaired in ASD.SIGNIFICANCE STATEMENT Autistic individuals show attentional abnormalities to even simple sensory inputs, which emerge even before formal diagnosis. One possible mechanism behind these abnormalities is a malfunctioning pacemaker of their attention system, the locus ceruleus-norepinephrine pathway. Here we found, according to the pupillary response (a noradrenergic activity proxy), autistic children are hypersensitive to repeated sounds but hyposensitive to surprising deviant sounds when compared with age-matched controls. Importantly, hypersensitivity to repetitions predicts hyposensitivity to deviant sounds, and both abnormalities positively correlate to the severity of autistic symptoms. This provides strong evidence that autistic children have faulty noradrenergic regulation, which might underly the attentional atypicalities previously evidenced in various cortical responses in autistic individuals.

A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking

As a new network architecture, information-centric networking (ICN) decouples the identifiers and locators of network entities and makes full use of in-network cache technology to improve the content distribution efficiency. For reliable multicast, ICN in-network cache can help reduce the loss recovery delay. However, with the development of applications and services, a multicast tree node often serves multiple reliable multicast groups. How to reasonably allocate cache resources for each multicast group will greatly affect the performance of reliable multicast. In order to improve the overall loss recovery performance of reliable multicast, this paper designs a dynamic cache allocation mechanism (DCAM). DCAM considers the packet loss probability, the node depth of the multicast tree, and the multicast transmission rate of multicast group, and then allocates cache space for multicast group based on the normalized cache quota weight. We also explore the performance of three cache allocation mechanisms (DCAM, AARM, and Equal) combined with four cache strategies (LCE, CAPC, Prob, and ProbCache), respectively. Experimental results show that DCAM can adjust cache allocation results in time according to network changes, and its combinations with various cache strategies outperform other combinations. Moreover, the combination of DCAM and CAPC can achieve optimal performance in loss recovery delay, cache hit ratio, transmission completion time, and overhead.

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

In this paper, we investigate a resource allocation problem for a Cellular Vehicle to Everything (C-V2X) network to improve energy efficiency of the system. To address this problem, self-organizing mechanisms are proposed for joint and disjoint subcarrier and power allocation procedures which are performed in a fully distributed manner. A multi-agent Q-learning algorithm is proposed for the joint power and subcarrier allocation. In addition, for the sake of simplicity, it is decoupled into two sub-problems: a subcarrier allocation sub-problem and a power allocation sub-problem. First, to allocate the subcarrier among users, a distributed Q-learning method is proposed. Then, given the optimal subcarriers, a dynamic power allocation mechanism is proposed where the problem is modeled as a non-cooperative game. To solve the problem, a no-regret learning algorithm is utilized. To evaluate the performance of the proposed approaches, other learning mechanisms are used which are presented in fig.8. Simulation results show the multi-agent joint Q-learning algorithm yields significant performance gains of up to about 11% and 18% in terms of energy efficiency compared to proposed disjoint mechanism and the third disjoint Q-learning mechanism for allocating the power and subcarrier to each user; however, the multi-agent joint Q-learning algorithm uses more memory than disjoint methods.

Welfare Effects of Dynamic Matching: An Empirical Analysis

Abstract Allocating resources without monetary payments is expected to yield inefficient allocations. Theory suggests that introducing rationing when resources are allocated repeatedly over time can mitigate this issue, while the magnitude of the resulting efficiency gains is an empirical question in most settings. We study a dynamic assignment mechanism used by the Michigan Department of Natural Resources to allocate bear hunting licenses and find that it yields a more efficient allocation than static mechanisms, allocating participants to types of resources for which they have a higher value without crowding out participants with a high overall value for hunting. Our empirical analysis also highlights the importance of heterogeneity across participants and across allocated resources for determining the efficiency of a dynamic allocation mechanism.

A truthful ascending auction mechanism for dynamic allocation of LSA spectrum blocks

A truthful ascending auction mechanism for dynamic allocation of LSA spectrum blocks

Task Duplication-Based Scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

Face Image Publication Based on Differential Privacy

As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one‐dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort‐SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the ε‐differential privacy. Therefore, this paper proposes a sorting method‐SAS method, which satisfies the ε‐differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort‐SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1‐sroce is improved by 55.62% and 16.55%.

Collaborative Flow Control in the DARPA Spectrum Collaboration Challenge

Wireless network technologies are becoming more and more popular. Because of this, important parts of the wireless spectrum become overloaded. Static spectrum allocation, which has been the norm for decades, is not suitable anymore. To maintain the high demand for spectrum and the continuous development of new wireless technologies, there is a need for an intelligent, dynamic spectrum allocation mechanism, where different network technologies collaboratively optimize the spectrum usage. New wireless network paradigms, such as Neutral Host Networks (NHNs) and private 5G, require a smart, spectrum-footprint-aware flow control algorithm to overcome the spectrum scarcity in collaborative way. This article presents a strategy, vision and flow control mechanism to implement collaboration in a Quality of Service (QoS)-driven way. The solution in this article is based on policies which may activate depending on its current and neighbor’s network states. Through a flow ordering and selection strategy, these policies optimize the spectrum footprint, based on the performance and QoS-requirements of the own and surrounding networks. The proposed algorithm is tested extensively and validated on a large scale during the DARPA Spectrum Collaboration Challenge (SC2) competition. The results of the SC2 final event and intermediate scrimmages showed that the proposed approach increased the score, indicating increased inter-network collaboration was achieved.

Auction Mechanism for Dynamic Bandwidth Allocation in Multi-Tenant Edge Computing

Inspired by the shared infrastructure of colocation data centers and the growth of Mobile Edge Computing (MEC), colocation MEC businesses have thrived to offer an economical and low latency solution for MEC based tenants. In colocation MEC, a colocation service provider leases spaces at the base stations (BSs) to tenants for housing their servers. Although the tenants fully manage their own servers, they still need to purchase bandwidth from the network provider to via bandwidth purchasing market to serve their users. As a result, tenants should consider the trade-off between delay and energy consumption, which are related to bandwidth and server computing speed, respectively. In this paper, an auction framework is used to model the interaction between the network provider and tenants in the bandwidth market. The network provider determines the bandwidth amount and corresponding payment by tenants based on their submitted bids, in which the valuation of the bandwidth amount in each bid is based on the coupling between bandwidth and server computing speed. The networking provider also seeks winners in the social welfare maximization problem, which is NP-hard. To solve this problem, we propose a solution based on the randomized auction mechanism, which is proved to be approximately truthful, individually rational, and computationally efficient. Simulation results verify the effectiveness of our proposed randomized auction scheme in bandwidth trading.

Optimization of System Framework for Secure Communication and Data Virtualization on Cloud Computing.

The consequent deployment of vital infrastructure to provide the secure communication and virtualization not only rectifies the challenges and difficulties but also benefits with saving the process of digitization. With a bang in evolution of cloud computing the uniqueness of every organization affects the virtualization of its information communication technology and applications. The answer for such organizations is to stipulate the precise cloud computing model equivalent to the deliberated and operational goals. This research article emphasized at budding and optimizing a framework for virtual infrastructure and cloud computing model to support the organization stakeholders. Moreover it also improves the cloud security with a protected virtual infrastructure and communication model with better performance. On account of data storage security necessities and distinctiveness of cloud computing environment, a method for secured data & storage based on dynamic allocation and access control mechanism is presented. Dynamic resource allocation is applied for resource utilization that focused on data virtualization and memory virtualization, for protection and access control a modified KPABE algorithm is integrated. The combination of these methods resulting on the optimize resource use, centralizing of storage. The implementation and comparison of results revealed that the proposed modified KP-ABE has performed effectively than the other security standards and especially for resource utilization. The proposed method serves for efficient data storage, access control solutions and computation in cloud environment.