Load Balancing Problem Research Articles

Challenges of Load Balancing Techniques in Cloud Environment: A Review

To highlight issues and challenges of the Load Balancing Algorithm (LBA) in the context of cloud computing. The authors of this work discussed load balancing and the load balancing problems in cloud computing, how to solve them, the basic techniques used to maintain load over the cloud, and the benefits and drawbacks of these algorithms. Furthermore, the static and dynamic load balancing strategies are compared. Load balancing methods in the cloud for data centers must avoid VM overload/low load, which is a challenge in cloud computing. Load balancing mechanisms aid in the management of workloads. The primary goal of load balancing among machines is to spread workloads and computing resources across one or more servers while lowering energy consumption and maximizing resource usage. Cloud computing is a type of computing in which scalable resources are made available on-demand and are frequently virtual, as in an online service. For a variety of reasons, including cost saving, speed, efficiency, performance, and security, cloud computing has recently become a popular choice for individuals and enterprises. Cloud computing uses the Internet to deliver a variety of services to a company's computers and devices. These resources can include data storage, databases, servers, networks, and software, among other tools and applications. In this paper, developers and researchers must design and construct a load balancer e for parallel and distributed cloud systems.

Improvement of tasks scheduling algorithm based on load balancing candidate method under cloud computing environment

In recent years, cloud computing has become an ideal way to provide different types of applications. It can provide the processing, storage, web services, development platforms, and databases to enable the companies to scale their infrastructure and service types quickly. As the demand for higher processing power increases, large amounts of data will cause a significant burden on the cloud computing environment. Therefore, an efficient scheduling algorithm for tasks under the cloud computing environment has become a crucial issue. Some well-known algorithms such as Suffrage, MaxSuffrage, and AMS have been proposed to improve the scheduling performance. However, the above algorithms do not consider the load balancing problem of working nodes. This load balancing issue will limit the overall performance of the network system. Hence, the BCSV scheduling algorithm has been proposed to improve the above problems in this paper. The main idea of BCSV is to use the Smallest Suffrage Value (SSV), Largest Suffrage Value (LSV), and Criteria Suffrage Value (CSV) as the scheduling factor to enhance the performance of the task dispatch. According to the experiment results, the proposed BCSV algorithm can obtain better load balance and makespan than the existed algorithms under the HiHi, HiLo, LoHi, and LoLo heterogeneous network environment. In other words, the proposed BCSV algorithm can get better task scheduling results while considering the load balancing problem.

DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect.

Distributed load-balancing for account-based sharded blockchains

PurposeSharding of blockchains consists of partitioning a blockchain network into several sub-networks called “shards,” each shard processing and storing disjoint sets of transactions in parallel. Sharding has recently been applied to public blockchains to improve scalability through parallelism. The throughput of sharded blockchain is optimized when the workload among the shards is approximately the same. The purpose of this paper is to investigate the problem of balancing workload of account-based blockchains such as Ethereum.Design/methodology/approachTwo known consensus-based distributed load-balancing algorithms have been adapted to sharded blockchains. These algorithms migrate accounts across shards to balance transaction processing times. Two methods to predict transaction processing times are proposed.FindingsThe authors identify some challenging aspects for solving the load-balancing problem in sharded blockchains. Experiments conducted with Ethereum transactions show that the two load-balancing algorithms are challenged by accounts often created to process a single transaction to optimize anonymity, while existing accounts sparsely generate transactions.Originality/valueTests in this work have been conducted on transactions originating from a blockchain platform rather than using artificially generated data distributions. They show the specificity of the load-balancing problem for sharded blockchains, which were hidden in artificial data sets.

RRH-Sector selection and load balancing based on MDP and dynamic RRH-Sector-BBU mapping in C-RAN

Cloud radio access network (C-RAN) is a paradigmatic architecture that supports the tremendous increase in mobile network traffic. Despite the novel advancements that C-RAN offers, a time-varying traffic environment can cause load imbalances, resulting in inefficient resource utilization. Consequently, the network performance can degrade in terms of the blocked users, the number of unnecessary handovers, and the power consumption. This paper presents an RRH-Sector pair selection for new connections and network load-balancing framework that optimizes the network performance and operator reward without affecting the users’ Quality of Service (QoS) in C-RAN. Firstly, the proposed approach selects the best RRH-Sector pair for each new connection demand by considering the user and operator objectives jointly. This decision is based on an algorithm derived from the Markov decision process (MDP). Secondly, the load-balancing problem is addressed via optimization of the RRH-Sector-BBU dynamic mapping formulated as a linear integer-based constrained optimization problem. We compare solutions for this problem obtained by several evolutionary algorithms. Simulation results show that the proposed RRH-Sector selection scheme provides significant gains when compared with the traditional method based on the received signal strength (RSS). Furthermore, the RRH-Sector-BBU mappings obtained by the evolutionary algorithms are compared with the optimal exhaustive search method. The results show that in most cases the proposed models reach the optimum solution for the number of blocked users, the number of handovers, and the BBU power consumption.

Resource Management for Intelligent Vehicular Edge Computing Networks

To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs’ workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can be also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs’ workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

Resource Operation Intelligent Scheduling Algorithm and System Based on Integrated 5G Public Private Network Communication Network

With the development of the economy, users’ demands for communication systems continue to increase. At the same time, due to the development of technologies such as the Internet of Things, big data, and cloud computing, Internet users’ requirements for communication systems are also increasing. Network application scenarios such as in-vehicle networking, transmission of ultra-high-definition video, and real-time data transmission are emerging one after another. The breakthrough development of 5G communication technology has opened a chapter in the 5G era and has become the key to solving the problem of cellular communication technology. However, in the architecture of advanced networks, it is still a big problem to solve the problem of load balancing and realize the reasonable scheduling of network resources. At the same time, it has also become a hot spot of current research. This paper studies the resource allocation problem in the 5G communication network. Based on the analysis of its communication principle, the communication simulation system in the corresponding scenario is built and improved. At the same time, in order to solve the resource allocation under the new SDN architecture in the core network To solve the problem, a slice resource allocation scheme based on reinforcement learning is designed, and a slice resource allocation algorithm based on DQN (Deep Q-Network) is proposed. By testing the system functions, the superiority of the proposed algorithm in relevant scenarios is verified.

Nash Social Welfare in Selfish and Online Load Balancing

In load-balancing problems there is a set of clients, each wishing to select a resource from a set of permissible ones to execute a certain task. Each resource has a latency function, which depends on its workload, and a client’s cost is the completion time of her chosen resource. Two fundamental variants of load-balancing problems are selfish load balancing (a.k.a. load-balancing games ), where clients are non-cooperative selfish players aimed at minimizing their own cost solely, and online load balancing , where clients appear online and have to be irrevocably assigned to a resource without any knowledge about future requests. We revisit both problems under the objective of minimizing the Nash Social Welfare , i.e., the geometric mean of the clients’ costs. To the best of our knowledge, despite being a celebrated welfare estimator in many social contexts, the Nash Social Welfare has not been considered so far as a benchmarking quality measure in load-balancing problems. We provide tight bounds on the price of anarchy of pure Nash equilibria and on the competitive ratio of the greedy algorithm under very general latency functions, including polynomial ones. For this particular class, we also prove that the greedy strategy is optimal, as it matches the performance of any possible online algorithm.

Adrestus: Secure, scalable blockchain technology in a decentralized ledger via zones

Nowadays, an increasing number of blockchain architectures provide well-promising protocols for pseudonymous online payments via proposed cryptocurrencies. Most of them suffer from a number of extensibility and scalability issues, as their capacity regarding the number of transactions they are capable of processing per second is limited. Security is also a challenge for this kind of architectures. This paper presents the design and implementation of the Adrestus system, a blockchain-based transaction system with a novel consensus mechanism that is able to tolerate Byzantine faults and is designed to scale without compromising system security. One of the main components of the Adrestus design is a consistent hashing mechanism for the efficient assignment of transactions on parallel regions, called zones, and for solving load balancing problems. We claim that the Adrestus blockchain system scales linearly without compromising system security and achieves its goals without introducing the unnecessary overhead and by eliminating energy and computational waste. Preliminary theoretical simulations and results reflect that Adrestus exceeds the average throughput of the most well-known cryptocurrencies like Bitcoin, and thus, it achieves a higher performance. In this paper, we present this proposed approach along with simulation results and examine the conditions for the proposed fault-tolerant system to meet safety and liveness.

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Owing to the nonoverlapping spectrum, light fidelity (LiFi) and WiFi technologies can coexist and form a heterogeneous LiFi WiFi network (HLWN). The performance of HLWN significantly depends upon the load balancing strategies. Since load balancing of HLWN is a nonconvex mixed-integer nonlinear programming optimization problem, it is mathematically intractable, and therefore, the conventional optimization methods fail to provide an optimal global solution. Although an optimal solution can be obtained using the exhaustive search method, it would be computationally complex. Therefore, in this article, a reinforcement learning (RL)-based algorithm is explored for solving the load balancing problem for the downlink HLWN at reasonably low complexity and near optimal performance. We have proposed three different reward functions for RL; the first and second reward functions work toward maximizing average network throughput and user satisfaction, respectively. The third reward function is designed to maximize the long-term system throughput and ensure at least 50% user’s satisfaction for all users. In order to study the effects of link aggregation on the system performance, this article considers two different types of receiver schemes, namely, single access point (SAP) and link aggregation (LA) scheme. While the SAP allows the user to receive data only from an SAP, the LA scheme allows the user to receive data simultaneously from both LiFi and WiFi AP. This article also includes effect of random orientation of the receiver device and handover overhead. Furthermore, concepts of domain knowledge have been included in this article to reduce the computational complexity of the algorithm. The proposed system performance is compared with the the following two benchmarks: received signal strength (RSS) and exhaustive search based on the computational complexity, average system throughput, and user satisfaction. It is shown that the proposed RL scheme outperforms the RSS scheme in average system throughput and user satisfaction. The RL scheme with an appropriate reward function provides a matching performance to the exhaustive search at reasonably low complexity.

Demand Response Application as a Service: An SDN-Based Management Framework

With an increase in the utilization of appliances, meeting the energy demand of consumers by traditional power grids is an important issue. The success of Demand Response (DR) depends conclusively on real-time data communication between the consumers and the suppliers. Hence, a scalable and programmable communication network is required to handle the data generated. We prove that the problem of DR global load balancing includes energy and data constraints is NP-hard. So, a dynamic and self-configurable network technology known as Software-defined Networking (SDN) can be an efficient solution. In order to handle DR communication challenges, an SDN-enabled framework for DR flow management is designed in this paper. This framework is based on two-tier cloud computing and manages energy and data traffic seamlessly. We also equip this framework with Network Functions Virtualization (NFV) technology. The proposed framework is implemented on a practical testbed, which includes Open vSwitch, Floodlight controller, and OpenStack. Its performance is appraised by comprehensive experiments and scenarios. Based on the results, it achieves low delay, a high throughput, and improves Peak to Average Ratio (PAR) by balancing the energy and data on the entire DR network.

Load Flow Analysis on IEEE 14 Bus System

Abstract: This article presents a load flow analysis of an IEEE14 BUS system using the Newton-Raphson method, which simplifies the analysis of load balancing problems. The software used for the programming platform is MATLAB. This paper gives an overview of the electrical performance and power flows (real and reactive) under a steady state. There are various methods for load flow computations. The gauss-seidel method is more popular in smaller systems because of less computational time. In the case of larger systems computation time increases in this condition, the Newton-Raphson method is preferred. This project aims to develop a MATLAB program to calculate voltages and active and reactive power at each bus for IEEE 14 bus systems. The MATLAB program is executed with the input data and results are compared. Keywords: load flow studies, Newton-Raphson method, IEEE 14 bus system.

Research on the Correlation between Multisource Big Data Virtual Assisted Preschool Education and the Development of Children's Innovative Ability.

Objective Innovation ability is an important part of children's core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children's innovative ability. Method In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children's innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children's innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children's innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children's innovation ability.

Adaptive passenger-finding recommendation system for taxi drivers with load balancing problem

In the literature, much effort has been devoted to providing taxi drivers with recommendations on how to find passengers efficiently. However, the load balancing problem is not taken seriously, leading to a situation where taxis gather and compete at some points while passengers wait a long time in other places due to low taxi supply. Besides, there are multiple passenger-finding strategies, and recommending appropriate strategies according to time and location helps to improve drivers’ performance. This paper proposes a global load balancing approach that can be applied to different passenger-finding strategies; specifically, a new measure dealing with load balance between taxis and passengers is introduced, and an adaptive passenger-finding recommendation method is proposed. These two problems are integrated into a single coherent framework using a multi-objective optimisation approach to maximise drivers’ potential benefits while minimising supply-demand imbalance. Extensive experiments on real-world taxi data from New York City and San Francisco verify that the proposed method improves upon the performance of existing methods in terms of both drivers’ net profit and passengers’ waiting time.

Conservation with moving meshes over orography

Adaptive meshes have the potential to improve the accuracy and efficiency of atmospheric modelling by increasing resolution where it is most needed. Mesh re-distribution, or r-adaptivity, adapts by moving the mesh without changing the connectivity. This avoids some of the challenges with h-adaptivity (adding and removing points): the solution does not need to be mapped between meshes, which can be expensive and introduces errors, and there are no load balancing problems on parallel computers. A long standing problem with both forms of adaptivity has been changes in volume of the domain as resolution changes at an uneven boundary. We propose a solution which achieves exact local conservation and maintains a uniform scalar field while the mesh changes volume as it moves over orography. This is achieved by introducing a volume correction parameter which tracks the cell volumes without using expensive conservative mapping.A finite volume solution of the advection equation over orography on moving meshes is described and results are presented demonstrating improved accuracy for cost using moving meshes. Exact local conservation and maintenance of uniform scalar fields is demonstrated and the correct mesh volume is preserved.We use optimal transport to generate meshes which are guaranteed not to tangle and are equidistributed with respect to a monitor function. This leads to a Monge-Ampère equation which is solved with a Newton solver. The superiority of the Newton solver over other techniques is demonstrated in the appendix. However the Newton solver is only efficient if it is applied to the left hand side of the Monge-Ampère equation with fixed point iterations for the right hand side.

A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing

Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources is desirable for improved usage and service delivery. In this paper, we reviewed various resource management techniques embraced in literature. We concentrated majorly on investigating game-theoretic submission for the management of required resources, as a potential solution in modeling the resource allocation, scheduling, provisioning, and load balancing problems in cloud computing. This paper presents a survey of several game-theoretic techniques implemented in cloud computing resource management. Based on this survey, we presented a guideline to aid the adoption and utilization of game-theoretic resource management strategy.

Randomness analysis of end-to-end delay in random forwarding networks.

Random forwarding networks play a significant role in solving security and load balancing problems. As a random quantity easily obtained by both sender and receiver, the end-to-end delay of random forwarding networks can be utilized as an effective random source for cryptography-related applications. In this paper, we propose a mathematical model of Random forwarding networks and give the calculation method of end-to-end delay distribution. In exploring the upper limit of the randomness of end-to-end delay, we find that the end-to-end delay collision of different forwarding routes is the main reason for the decrease of end-to-end delay randomness. Some of these collisions can be optimized by better network deployment, while others are caused by some interesting network topology, which is unavoidable. For further analysis, we propose an algorithm to calculate the inevitable collision in random forwarding networks skillfully by using Symbol Matrix, and we give the optimal node forwarding strategy with the maximum randomness of the end-to-end delay for a given number of middle forwarding nodes and forwarding times. Finally, we introduce a specific application of generating symmetric keys by using the randomness of the end-to-end delay.

Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform

The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020.

The Improved Nonparametric Regression Model for the IoT Link Load Balancing Control Algorithm

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.

A load-balancing problem for distributed bulk-service queues with size-dependent batch processing times

A load-balancing problem for distributed bulk-service queues with size-dependent batch processing times