Search

Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today's digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application. [ABSTRACT FROM AUTHOR]

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources. [ABSTRACT FROM AUTHOR]

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly. [ABSTRACT FROM AUTHOR]

To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user's waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler's formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes' theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities. [ABSTRACT FROM AUTHOR]

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, "available" and "free." To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm's performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm's performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed "component-based throttled load balancing algorithm" is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. [ABSTRACT FROM AUTHOR]

Increasing scale of task in cloud network leads to problem in load balancing and its improvement in parameters. In this paper, we proposed a hybrid scheduling policy which is hybrid of both Particle Swarm Optimization (PSO) algorithm and actor-critic algorithm named as Hybrid Particle Swarm Optimization Actor Critic (HPSOAC) to solve this issue. This hybrid scheduling policy helps to each agent to improve an individual learning as well as learning through exchanging information among other agents. An experiment is carried out by the help of Python simulator with TensorFlow. Outcome shows that our proposed scheduling policy reduces 5.16% and 10.86% in energy consumption, reduces 7.13% and 10.04% in makespan time, and has marginally better resource utilization over Deep Q-network (DQN) and Q-learning based on Modified Particle Swarm Optimization (QMPSO) algorithm, respectively. [ABSTRACT FROM AUTHOR]

The microservice architecture has many advantages, such as technology heterogeneity, isolation, scalability, simple deployment, and convenient optimization. These advantages are important as we can use diversity and redundancy to improve the resilience of software system. It is necessary to study the method of improving the resilience of software system by diversity implementation and redundant deployment of software core components based on microservice framework. How to optimize the diversity deployment of microservices is a key problem to maximize system resilience and make full use of resources. To solve this problem, an efficient microservice diversity deployment mechanism is proposed in this paper. Firstly, we creatively defined a load balancing indicator and a diversity indicator. Based on this, a diversified microservices deployment model is established to maximize the resilience and the resource utilization of the system. Secondly, combined with load balancing, a microservice deployment algorithm based on load balance and diversity is proposed, which reduces the service's dependence on the underlying mirror by enriching diversity and avoids the overreliance of microservices on a single node through decentralized deployment, while taking into account load balancing. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms other algorithms. [ABSTRACT FROM AUTHOR]

With the 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge node's processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state-of-the-art approaches on a real-world dataset. And the experimental results validate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The central network architecture takes the information content as the processing object and can better meet the user access needs. However, the traditional network node load balancing and caching strategy has been difficult to adapt to the development of the central network architecture. Therefore, this article proposed a load balancing and cache management strategy based on the central network in order to provide a reference for the architecture and application of the central network. Based on the analysis of the existing network architecture, this article expounded the networking form and characteristics of the central network system. By analyzing the load scheduling requirements of network nodes, combined with the transmission characteristics of resource requests in the central network, a cache node load balancing method suitable for the requirements of the central network was proposed based on the existing load balancing algorithms. In addition, by analyzing the cache management strategy and its impact on the performance of the network system, a cache node information forwarding model with resource requests as the object in the central network environment was given. Finally, through experimental test and comparative analysis, the results showed that the method proposed in this article had certain feasibility and effectiveness. Compared with other typical strategies, the load balancing and caching strategy proposed in this article can better meet the user needs of the central network. [ABSTRACT FROM AUTHOR]

In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect. [ABSTRACT FROM AUTHOR]

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree. [ABSTRACT FROM AUTHOR]

Load-balancing optimization in software-defined networking (SDN) has been researched for a long time. Researchers have proposed many solutions to the load-balancing problem but have rarely considered the impact of transmission delay between controllers and switches under high-load network conditions. In this paper, we propose an adaptive load-balancing architecture based on link-state prediction (ALBLP) in SDN that can solve the influence of transmission delay between controllers and switches on network load balancing. ALBLP constructs the prediction model of the network link status, adopts the long-term and short-term memory neural network (LSTM) algorithm to predict the network link-state value, and then uses the predicted value as the Dijkstra weight to calculate the optimal path between network hosts. The proposed architecture can adaptively optimize network load balancing and avoid the empty window period, in which the switch flow table does not exist by actively issuing the flow table. Under the network architecture, we collect the data set of the network link-state by simulating the GÉANT network, and we verify the effectiveness of the proposed algorithm. The experiment results show that the ALBLP proposed in this paper can optimize load balancing in SDN and solve the problem of transmission delay between controllers and switches. It has a maximum load-balancing improvement of 23.7% and 11.7% in comparison with the traditional Open Shortest Path First (OSPF) algorithm and the reinforcement learning method based on Q-Learning. [ABSTRACT FROM AUTHOR]

Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted. [ABSTRACT FROM AUTHOR]

With the development of wireless communication technology, video and multimedia have become an integral part of visual communication design. Designers want higher interactivity, diversity, humanization, and plurality of attributes in the process of visual communication. This makes the process of visual communication have high requirements for the quality and real-time data transmission. To address the problem of transmitting HD video in a heterogeneous wireless network with multiple concurrent streams to improve the transmission rate and thus enhance the user experience, with the optimization goal of minimizing the system transmission delay and the delay difference between paths, the video sender and receiver are jointly considered, and the video transmission rate and the cache size at the receiver are adaptively adjusted to improve the user experience, and a cooperative wireless communication video transmission based on the control model for video transmission based on cooperative wireless communication is established, and video streams with self-similarity and long correlation are studied based on Pareto distribution and P / P / l queuing theory, based on which an adaptive streaming decision method for video streams in heterogeneous wireless networks is proposed. Simulation results show that the proposed multistream concurrent adaptive transmission control method for heterogeneous networks is superior in terms of delay and packet loss rate compared with the general load balancing streaming decision method, in terms of transmission efficiency and accuracy. [ABSTRACT FROM AUTHOR]

The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation-intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle-assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved. [ABSTRACT FROM AUTHOR]

We focus on Partial Differential Equation (PDE)‐based Data Assimilation problems (DA) solved by means of variational approaches and Kalman filter algorithm. Recently, we presented a Domain Decomposition framework (we call it DD‐DA, for short) performing a decomposition of the whole physical domain along space and time directions, and joining the idea of Schwarz's methods and parallel in time approaches. For effective parallelization of DD‐DA algorithms, the computational load assigned to subdomains must be equally distributed. Usually computational cost is proportional to the amount of data entities assigned to partitions. Good quality partitioning also requires the volume of communication during calculation to be kept at its minimum. In order to deal with DD‐DA problems where the observations are nonuniformly distributed and general sparse, in the present work we employ a parallel load balancing algorithm based on adaptive and dynamic defining of boundaries of DD—which is aimed to balance workload according to data location. We call it DyDD. As the numerical model underlying DA problems arising from the so‐called discretize‐then‐optimize approach is the constrained least square model (CLS), we will use CLS as a reference state estimation problem and we validate DyDD on different scenarios. [ABSTRACT FROM AUTHOR]

This article, titled "Research on Load Balancing and Caching Strategy for Central Network," has been retracted by Hindawi due to evidence of systematic manipulation of the publication process. The investigation revealed discrepancies in scope, description of the research, and availability of data, as well as inappropriate citations and incoherent content. Hindawi cannot vouch for the reliability of the article's content and regrets that these issues were not identified before publication. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]