Dynamic Resource Allocation Approach Research Articles

DYNAMIC RESOURCE ALLOCATION IN EDGE COMPUTING ENVIRONMENTS: A MACHINE LEARNING APPROACH

Edge computing is a promising paradigm that addresses the limitations of traditional cloud-centric architectures by placing computation near the data source. The main challenge is efficient resource allocation for various applications while optimizing resource utilization. This paper proposes a machine learning-based approach for dynamic resource allocation in edge computing environments. It uses machine learning algorithms to analyze real-time data traffic patterns, application demands, and edge node capabilities to dynamically allocate resources like compute, storage, and bandwidth. By continuously acquiring historical data and adjusting to varying workloads, the system improves overall system performance and user experience. Experimental evaluation and simulations show the effectiveness and efficiency of this approach in enhancing resource utilization, lowering latency, and improving scalability in edge computing environments.

Optimizing Scalable Online Services: A Dynamic Resource Allocation Approach Utilizing Artificial Intelligence

Optimizing Scalable Online Services: A Dynamic Resource Allocation Approach Utilizing Artificial Intelligence

Towards an SDN-based Dynamic Resource Allocation in 5G Networks

The rapid evolution of mobile technologies has ushered in a new era of connectivity, particularly with the advent of the Fifth Generation (5G) technology, which promises low latency and high throughput. However, with the exponential growth of applications and services, resource allocation is becoming essential to ensure optimal quality of service. In this paper, we examine the limitations of traditional approaches and propose a new dynamic resource allocation approach based on Software-Defined Networking (SDN) for flexible and intelligent management of 5G networks. Our model aims to improve network efficiency by using resources intelligently and to guarantee a superior user experience.

Dynamic Resource Allocation Approach for Interference Mitigation based on NOMA in Network Operating in Both Uplink and Downlink

The new notion of Internet of Vehicles (IoV) supports Vehicle-to-Everything (V2X) communications, which play a very important role in 5G systems. Ultra-Dense-Network (UDN) and the new scheme Non-orthogonal multiple access (NOMA) are two important concepts to meet the requirements of V2X services with their remarkable capacity. However, the concept of NOMA technology, which allows multiple vehicles to be served by the same resource, can lead to interference between different active users. In addition, the extensive placement of small base stations (SBS) in UDN causes severe interference and degrades the network performance. To solve these problems, interference mitigation is widely used to avoid interference in NOMA-based UDN. In this document, we suggested a dynamic resource allocation method named Intra-cell-Inter-cell Interference Mitigation for V2X communication for UDN in uplink and downlink sense (Intra-Inter IMVN). Our algorithm aims to achieve five principals objectives, namely applying the same algorithm for both downlink and uplink modes, reducing the degree of interference, improving network performance in aspects like error rates and throughput, minimizing cost and delay when using this alternative, combining dynamic metrics to support many V2X scenarios and requirements, and dealing with two categories of interference such as inter-cell interference and intra-cell interference. Numerical results demonstrate that our suggested Intra-Inter IMVN approach offers higher performance compared to other existing algorithms as a function of various parameters.

Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches

In today's era of rapid smart equipment development and the Industrial Revolution, the application scenarios for Internet of Things (IoT) technology are expanding widely. The combination of IoT and industrial manufacturing systems gives rise to the Industrial IoT (IIoT). However, due to resource limitations such as computational units and battery capacity in IIoT devices (IIEs), it is crucial to execute computationally intensive tasks efficiently. The dynamic and continuous generation of tasks poses a significant challenge to managing the limited resources in the IIoT environment. This paper proposes a collaborative approach for optimal offloading and resource allocation of highly sensitive industrial IoT tasks. Firstly, the computation-intensive IIoT tasks are transformed into a directed acyclic graph. Then, task offloading is treated as an optimization problem, taking into account the models of processor resources and energy consumption for the offloading scheme. Lastly, a dynamic resource allocation approach is introduced to allocate computing resources to the edge-cloud server for the execution of computation-intensive tasks. The proposed joint offloading and scheduling (JOS) algorithm creates its DAG and prepare a offloading queue. This queue is designed using collaborative q-learning based reinforcement learning and allocate optimal resources to the JOS for execution of tasks present in offloading queue. For this machine learning approach is used to predict and allocate resources. The paper compares conventional and machine learning-based resource allocation methods. The machine learning approach performs better in terms of response time, delay, and energy consumption. The proposed algorithm shows that energy usage increases with task size, and response time increases with the number of users. Among the algorithms compared, JOS has the lowest waiting time, followed by DQN, while Q-learning performs the worst. Based on these findings, the paper recommends adopting the machine learning approach, specifically the JOS algorithm, for joint offloading and resource allocation.

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

Performance evaluation of CoAP proxy virtualisation in cloud‐assisted sensor networks

In this study, the authors have modelled the performance of a scalable proxy virtualisation framework to support an appropriate level of quality of service (QoS) for delay sensitive applications in cloud-based large-scale sensor networks. The proposed model leverages the functionalities of a constrained application protocol (CoAP) to expose a standard interface for transparently decoupling of the applications from servers and ensuring the implementation of the custom QoS metrics through virtualisation techniques. The model, combined with the prediction-based dynamic resource allocation approach, enables the computation of the number of requests that can be supported for a given service rate and required QoS. It also features the evaluation of different performance metrics, particularly, the probability distribution of the response time and the ability to identify the optimal balance between the resource cost and the service performance. The numerical results obtained from the proposed analytical model can be used to help design and optimise industrial cloud-based sensing systems.

Improved differential search algorithm based dynamic resource allocation approach for cloud application

The performance of a service-based system (SBS) in a cloud environment may not satisfy service-level agreement (SLA) constraints when the system load changes. To improve the profits of resource providers and satisfy the global SLA, it is necessary to dynamically allocate proper resource for SBS based on the forecasted system load. By analyzing the complex workflow of the SBS, this paper proposes improved differential search algorithm-based dynamic resource allocation approach which adopts an active mechanism to respond to the change of system load so as to ensure the timely response to change. The dynamic resource allocation model based on costs optimization and SLA constraint is then proposed. The improved differential search algorithm is designed to solve the dynamic resource allocation model. This paper proposes a load forecasting approach based on deep belief networks (DBNs) in order to accurately forecast the load to support dynamic resource allocation. Experimental results show that the approach performs well in terms of the quality of the solution compared with other related approaches.

An Intelligent Economic Approach for Dynamic Resource Allocation in Cloud Services

With Inter-Cloud, distributed cloud and open cloud exchange (OCX) emerging, a comprehensive resource allocation approach is fundamental to highly competitive cloud market. Oriented to infrastructure as a service (IaaS), an intelligent economic approach for dynamic resource allocation (IEDA) is proposed with the improved combinatorial double auction protocol devised to enable various kinds of resources traded among multiple consumers and multiple providers at the same time enable task partitioning among multiple providers. To make bidding and asking reasonable in each round of the auction and determine eligible transaction relationship among providers and consumers, a price formation mechanism is proposed, which is consisted of a back propagation neural network (BPNN) based price prediction algorithm and a price matching algorithm. A reputation system is proposed and integrated to exclude dishonest participants from the cloud market. The winner determination problem (WDP) is solved by the improved paddy field algorithm (PFA). Simulation results have shown that IEDA can not only help maximize market surplus and surplus strength but also encourage participants to be honest.

Joint Dynamic Resource Allocation and Waveform Adaptation for Cognitive Networks

This paper investigates the issue of dynamic resource allocation (DRA) in the context of multi-user cognitive radio networks. We present a general framework adopting generalized signal expansion functions for representation of physical-layer radio resources as well as for synthesis of transmitter and receiver waveforms, which allow us to join DRA with waveform adaptation, two procedures that are currently carried out separately. Based on the signal expansion framework, we develop noncooperative games for distributed DRA, which seek to improve the spectrum utilization on a per-user basis under both transmit power and cognitive spectral mask constraints. The proposed DRA games can handle many radio platforms such as frequency, time or code division multiplexing (FDM, TDM, CDM), and even agile platforms with combinations of different types of expansion functions. To avoid the complications of having too many active expansion functions after optimization, we also propose to combine DRA with sparsity constraints. Generally, the sparsity-constrained DRA approach improves convergence of distributed games at little performance loss, since the effective resources required by a cognitive radio are in fact sparse. Finally, to acquire the channel and interference parameters needed for DRA, we develop compressed sensing techniques that capitalize on the sparse properties of the wideband signals to reduce the number of samples used for sensing and hence the sensing time.

Intelligent scheduling and optimisation for resource-constrained networks

In control network with resources constraints, there is an unavoidable tradeoff between the quality of control (QoC) and requirement of bandwidth (RoB) in order to optimise the performance of control system. To address the impact of this contradiction, two intelligent and optimal scheduling strategies, which are respectively based on fuzzy logic control technique and neural network, are employed by a bandwidth scheduler according to corresponding dynamic resource allocation approach. In order to guarantee the system's stability, the lower and upper bounds of the assignable bandwidth are evaluated in terms of linear matrix inequalities and the resource constraints, respectively. In addition, the normalisable criterions of QoC and RoB are also defined, which can estimate the performance of the whole networked control systems. Preliminary simulations show that the proposed strategies can save significant bandwidth and simultaneously improve overall control performance in comparison with the fixed bandwidth allocation and optimal bandwidth allocation in the literatures.