Resource Requests Research Articles

Optimized Resource Allocation and Queue Management for Traffic Control in MANET

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks (MANETs). Offering better communication services among the users in a centralized organization is the primary objective of the MANET. Due to the features of MANET, this can directly End-to-End Delay (EED) the Quality of Service (QoS). Hence, the implementation of resource management becomes an essential issue in MANETs. This paper focuses on the efficient Resource Allocation (RA) for many types of Traffic Flows (TF) in MANET. In Mobile Ad hoc Networks environments, the main objective of Resource Allocation (RA) is to process consistently available resources among terminals required to address the service requirements of the users. These three categories improve performance metrics by varying transmission rates and simulation time. For solving that problem, the proposed work is divided into Queue Management (QM), Admission Control (AC) and RA. For effective QM, this paper develops a QM model for elastic (EL) and inelastic (IEL) Traffic Flows. This research paper presents an AC mechanism for multiple TF for effective AC. This work presents a Resource Allocation Using Tokens (RAUT) for various priority TF for effective RA. Here, nodes have three cycles which are: Non-Critical Section (NCS), Entry Section (ES) and Critical Section (CS). When a node requires any resources, it sends Resource Request Message (RRM) to the ES. Elastic and inelastic TF priority is determined using Fuzzy Logic (FL). The token holder selects the node from the inelastic queue with high priority for allocating the resources. Using Network Simulator-2 (NS-2), simulations demonstrate that the proposed design increases Packet Delivery Ratio (PDR), decrease Packet Loss Ratio (PLR), minimise the Fairness and reduce the EED.

An efficient load balancing framework for deploying resource schedulingin cloud based communication in healthcare

In distributed environments, cloud computing is widely used to manage user requests for resources and services. Resource scheduling is used to handle user requests for resources based on priorities within a given time frame. In today's environment, healthcare systems and management rely on smart devices connected to the internet. These devices deal with the massive amounts of data processed and detected by smart medical sensors without sacrificing performance factors like throughput and latency. This has prompted the requirement for load balancing among the smart operational devices to prevent any insensitivity. Load balancing is used to manage large amounts of data in both a centralised and distributed manner. In this paper, a load balancing framework for deploying resource scheduling for cloud-based healthcare environments is presented. We use reinforcement learning algorithms such as GA, SARSA, and Q-learning for resource scheduling. These algorithms are used to predict the optimal solution to manage load in cloud-based healthcare environments. The proposed mechanism is energy efficient, has a low make span, and consumes less latency time. The proposed mechanism is implemented using MATLAB. The performance of the proposed mechanism is analysed using latency, make-span, and throughput performance metrics. In the proposed mechanism, throughput is high, whereas make span is less than in the existing technique.

Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence-Assisted Chatbot: Development Study.

In knowledge transfer for educational purposes, most cancer hospital or center websites have existing information on cancer health. However, such information is usually a list of topics that are neither interactive nor customized to offer any personal touches to people facing dire health crisis and to attempt to understand the concerns of the users. Patients with cancer, their families, and the general public accessing the information are often in challenging, stressful situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of older patients with cancer, to go through the treatment process. Therefore, having someone with professional knowledge who can listen to them and provide the medical information with good will and encouragement would help patients and families struggling with critical illness, particularly during the lingering pandemic. This study created a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This chatbot was created using the IBM Watson Assistant with artificial intelligence and machine learning features. The chatbot or bot was incorporated into a resource that can be easily accessed by the general public. The radiation treatment process in a cancer hospital or center was described by the radiotherapy process: patient diagnosis, consultation, and prescription; patient positioning, immobilization, and simulation; 3D-imaging for treatment planning; target and organ contouring; radiation treatment planning; patient setup and plan verification; and treatment delivery. The bot was created using IBM Watson (IBM Corp) assistant. The natural language processing feature in the Watson platform allowed the bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as intents during development. Therefore, the bot can be trained using the responses received, by recognizing similar responses from the user and analyzing using Watson natural language processing. The bot is hosted on a website by the Watson application programming interface. It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the bot. The bot was tested by potential users, and the overall averages of the identified metrics are excellent. The bot can also acquire users' feedback for further improvements in the routine update. An artificial intelligence-assisted chatbot was created for knowledge transfer regarding radiation treatment process to the patients with cancer, their families, and the general public. The bot that is supported by machine learning was tested, and it was found that the bot can provide information about radiotherapy effectively.

Reticular chemistry for the rational design of mechanically robust mesoporous merged-net metal-organic frameworks

Reticular chemistry for the rational design of mechanically robust mesoporous merged-net metal-organic frameworks

How to pivot and make any Magnet® site visit successful.

How to pivot and make any Magnet® site visit successful.

Temporal Computing Resource Allocation Scheme With End Device Assistance

With the development of the Industrial Internet of Things (IIoT) and edge computing technology, edge servers have been used to allocate computing resources to IIoT devices for alleviating the huge computing needs of IIoT applications. Aiming at the problems of unbalanced distribution and inefficient use of computing resources during computing resource allocation, we propose a temporal computing resource allocation scheme with end device assistance. First, by establishing the temporal interest degree which uses the long-term interest accumulation and short-term interest change, IIoT devices are divided into different interest queues, the end devices with similarly temporal interest to the resource request device are sensed. Second, a social RippleNet based on a temporal knowledge graph is constructed. By analyzing the temporal characteristics and propagation process of social attributes in RippleNet, the temporally social similarity between the end devices and the resource request device is extracted, then the final computing resource allocation decision is generated by using the computing resource selection algorithm we proposed based on temporally social similarity. The simulation results show that the scheme we proposed can effectively improve the efficiency of computing resource allocation and balance the distribution of computing resources in the IIoT environment.

Reinforcement learning based monotonic policy for online resource allocation

This research aims to design an optimal and strategyproof mechanism for online resource allocation problems. In such problems, consumers randomly arrive with their resource requests in an arbitrary manner. As a result, there is uncertainty in the future resource demands. In addition, the allocation and payment decisions depend on the providers’ past experiences. To address these challenges, we propose a novel reinforcement learning algorithm for optimising the resource allocation policy. The proposed algorithm adopts a novel monotonic reward shaping function that uses a dominant-resource multi-label classification technique. Finally, a critical payment value is calculated in order to maintain the strategyproofness in the online environment. The experimental evaluations show that the proposed mechanism achieves results that are within 96% of the optimal social welfare while outperforming the other mechanisms that use fixed pricing.

Multistage Cloud-Service Matching and Optimization Based on Hierarchical Decomposition of Design Tasks

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.

Admission Control and Resource Reservation for Prioritized Slice Requests With Guaranteed SLA Under Uncertainties

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.

Credibility-based chance-constrained multimode resource-constrained project scheduling problem under fuzzy uncertainty

This paper investigates a multimode resource-constrained project scheduling problem under hybrid uncertain environment. With the activity durations are fuzzy variables, the resource requests change with the durations. The goal is to minimize the project duration and the cost associated with the required resources. Considering the stochastic duration and resource demand, a chance constrained programming model based on credibility is established. In order to solve the model, the chance constraints and fuzziness are treated by introducing the credibility distribution function and its inverse function. And, a hybrid algorithm combining particle swarm optimization (PSO) and genetic algorithm (GA) is proposed. It has shown fine performance in solving problems with 30, 60, and 120 activities. We also analyze the sensitivity of confidence, fuzziness, number of activities, number of modes, and resource unit cost. Meanwhile, fuzzy value-at-risk is used as a risk measure, and a model based on it is constructed. These numerical results show that the developed algorithm can obtain the near optimal solution of large-scale projects in a reasonable calculation time.

An improved pricing approach based on combinatorial auction method in cloud environment

Cloud pricing models are essential in the context of the increasing availability of cloud service providers across the Internet and the willingness of users to use high-quality and affordable services. The double-sided combinatorial auction model has been used extensively due to its inherent characteristics. This paper presents a cost-effective pricing method that considers both the forward market and spot market. Moreover, resource requests are workflow or hybrid to support scalability and improve the number of federal cloud response requests. The evaluation results indicate more profit for both buyers and vendors simultaneously. Furthermore, the proposed algorithm reduces the waiting time and the number of pending demands, increasing users' satisfaction relative to cloud services.

FedRec: Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers (FCPs) to Cloud Users (CU) has fuelled significant innovations in cloud provisioning models. Owing to the massive traffic, massive CU resource requests are sent to FCPs, and appropriate service recommendations are sent by FCPs. Currently, the Fourth-Generation (4G)-Long Term Evolution (LTE) network faces bottlenecks that affect end-user throughput and latency. Moreover, the data is exchanged among heterogeneous stakeholders, and thus trust is a prime concern. To address these limitations, the paper proposes a Blockchain (BC)-leveraged rank-based recommender scheme, FedRec, to expedite secure and trusted Cloud Service Provisioning (CSP) to the CU through the FCP at the backdrop of base 5G communication service. The scheme operates in three phases. In the first phase, a BC-integrated request-response broker model is formulated between the CU, Cloud Brokers (BR), and the FCP, where a CU service request is forwarded through the BR to different FCPs. For service requests, Anything-as-a-Service (XaaS) is supported by 5G-enhanced Mobile Broadband (eMBB) service. In the next phase, a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender (RBR) model based on the CU requests. In the final phase, based on the matching recommendations between the CU and the FCP, Smart Contracts (SC) are executed, and resource provisioning data is stored in the Interplanetary File Systems (IPFS) that expedite the block validations. The proposed scheme FedRec is compared in terms of SC evaluation and formal verification. In simulation, FedRec achieves a reduction of 27.55% in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks. For the IPFS, we have achieved a bandwidth improvement of 17.91%. In the RBR models, the maximum obtained hit ratio is 0.9314 ​at 200 million CU requests, showing an improvement of 1.2% in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services. The obtained results indicate the viability of the proposed scheme against traditional approaches.

QoE-DEER: A QoE-Aware Decentralized Resource Allocation Scheme for Edge Computing

With the increasing prevalence of online services mounted on IoT devices, edge computing gains significant momentum over conventional cloud-centric architecture. Edge servers are geographically deployed in a distributed manner nearby IoT devices, which not only frees online services from the high hardware requirement but also sharply reduces network latency experienced by IoT users. Recent works have extensively studied the problem of edge resource management and request scheduling to achieve high Quality of Service (QoS) with low latency, but there has been little focus on Quality of Experience (QoE) that an edge resource allocation scheme brings about. In this article, we study the Edge Resource Allocation (ERA) problem across multiple service requests with the objective of overall QoE maximization, which has non-polynomial computational complexity. To attack the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hardness of solving the ERA problem, we adopt a game-theoretic approach to formulate the ERA problem as a potential game ERAGame which admits a Nash Equilibrium (NE). Then, we novelly present a decentralized algorithm namely QoE-DEER to find an NE solution which equivalently maximizes the overall QoE as the ERA problem. Finally, the performance and convergence of our algorithm is evaluated both theoretically and experimentally, which indicates its significant advantages over the state-of-the-art approaches.

The Path of Digital Government and University Asset Intelligence Value-Added Service Driven by Block Chain Technology

At present, the government and universities mainly adopted the centralized management mode in the sharing of asset digital resources, which not only had low work efficiency, but also cannot make full use of resources. Block chain technology played an important role in enterprise asset data resource data sharing, but there was less research on asset data sharing services applied to digital government and universities. Therefore, this paper proposed the research on the path of government and university asset value-added service driven by block chain technology, in order to provide reference for improving the utilization rate of state-owned assets and better meeting the needs of users. Based on the research on the problems existing in the digital resource management of relevant government agencies and university departments at home and abroad, combined with the application advantages of block chain technology in relevant fields, this paper analyzed the composition of block chain and the application technical characteristics of value-added services. Starting from the current situation of asset data information management by the government and universities and the needs of users for resources, this paper expounded the important impact of block chain technology on digital government and university asset information management. By analyzing the elements of block chain value-added service, this paper put forward the block chain-driven asset intelligent value-added service mode. In order to track and manage users’ transaction information and meet users’ demand for resources, the application path of asset intelligent value-added service driven by block chain technology was constructed based on the asset value-added service mode, and combined with the characteristics of users’ request for resources, the implementation method of asset intelligent value-added service path was proposed. The experimental results showed that the method proposed in this paper had more advantages than the existing data sharing service system, and can effectively provide technical support for asset value-added services.

Enhancing Response Time of Cloud Resources Through Energy Efficient Cloud Scheduling Algorithm

Cloud Computing is becoming a dominant trend in providing information technology (IT) services. The cloud comprises many hardware and software resources today, and more people are switching to such services. Users' requests for cloud resources must incur a minimum amount of load on the system while getting a rapid response. In the cloud today, there is too much computational power. Load balancing makes it possible for various components of the cloud computing environment to work efficiently. To balance client requests to available resources so that the system is not overloaded, and the requested resources are delivered as quickly as possible, an effective load balancing strategy is essential. In this research article, we have presented a critical analysis of various existing cloud load balancing and scheduling algorithms. Several task scheduling approaches have been proposed in the literature review, but there appears to be a lack of scheduling algorithms for real-time task works based on historical scheduling records (HSR). The proposed algorithm uses information available in HSR to efficiently distributes incoming user requests to available virtual machines. The proposed scheduling algorithm uses the scaleup and scale down resource algorithm which helps in achieving maximum resource utilization. The algorithm tries to balance the load on VMs by scaling up and down cloud resources. WorkflowSim is used to analyze the performance of the algorithm proposed. The simulation results are compared with the existing scheduling algorithm which shows the proposed algorithm outperforms existing scheduling algorithms in terms of makespan.

Resource identifier interoperability among heterogeneous IoT platforms

Many standards, projects, and platforms are being developed as the Internet of Things (IoT) is adopted in a wide range of fields. However, because each IoT platform is based on a different resource identifier (ID), it is difficult to identify each device and use the service among heterogeneous IoT platforms. To solve this problem, we propose an interoperability framework that includes an IoT resource name system (RNS) based on analysis of the resource IDs (i.e., device ID and resource request formats) of five selected IoT platforms: oneM2M, Oliot, Watson IoT, IoTivity, and FIWARE. The IoT RNS converts a specific resource path into a resource request format for each platform. The converted resource path is shared among IoT RNSs for each platform, and users can request services from other platforms using converted resource paths. We also present an example of interoperability scenario among heterogeneous IoT platforms using the proposed IoT RNS in a smart city. The scenario includes each stage, such as resource registration and deletion, sharing mapping tables, converting resource addresses, and service requests. Furthermore, to prove the aims of the proposed approach, we implemented the resource interoperability scenario between oneM2M and FIWARE. In the experiments, resources can interwork in the two platforms through resource path conversion. Based on the results, we performed a qualitative evaluation of the IoT RNS with the current studies. In conclusion, our proposal overcomes the issues of building an existing integrated platform or specific central ontology and duplicating resources inside the platform. In addition, we separate the functions of the root and local IoT RNSs to solve communication traffic and memory capability issues.

Dynamic changes of VN resource requests research on dynamic VN mapping algorithms for increasing demand for resources

Abstract The research of optical network virtualization technology is gradually deepening among optical network virtualization for network cloud service is one of the main research directions of optical network in the future. The dynamic VN mapping algorithm, the most critical part of the virtual network layer, was not studied. As mentioned earlier, optical networks for cloud-oriented services face the challenge of how to respond to the dynamic changes in VN resource requests during virtualization.

A Multiserver Configuration and Request Distribution Framework for Profit Maximization in a Three-Tier Cloud Service Architecture

The rapid development of cloud computing has generated numerous cloud service providers. In a three-tier cloud service architecture, maximizing the cloud service profit is not only a goal pursued by cloud service providers but also a potent force that drives the continuous development of cloud computing technology. The traditional methods of maximizing cloud service profits are either limited to cloud resource management or service request distribution. Few work contributes to maximizing the profit of cloud service providers while considering both resource management and request distribution. Besides, the heterogeneity of servers is also an important concern when configuring the multiserver system. Therefore, this paper proposes a multiserver configuration and request distribution framework oriented to a three-tier cloud service architecture for maximizing the profit of cloud service providers. Compared with the benchmark method, extensive numerical simulation experiments show that our proposed framework can not only maximize the profit of cloud service providers, but also has a well-time efficiency.

Universities and CSR Teaching: New Challenges and Trends

This paper aims to analyse how universities are currently improving CSR teaching, pointing out some new challenges and trends emerging from a context in which CSR issues have become increasingly important. The research provides a longitudinal study of the literature for a period of 20 years. After screening the retrieved documents, we based our analysis on 31 main scientific documents. Our findings highlight how universities are improving CSR teaching by utilising the development of new curricula and new teaching methods, such as case methods, service learning, problem-based learning, and action and experiential learning. The paper is novel because it explores how the request for human resources with CSR skills forced universities to widen their teaching programs. Our findings suggest valuable and useful insights for the academic and professional community.

Innovations in Fatality Management During the COVID-19 Pandemic.

Innovations in Fatality Management During the COVID-19 Pandemic.