Load Balancing Mechanism Research Articles

Artificial neural network based load balancing scheme for top of rack switches in optical data centers

Abstract Data centers serve as dedicated facilities for housing computer systems and their related components, including telecommunications and storage systems. They typically have high levels of security and environmental controls to ensure that the equipment housed within them functions optimally. Data center networks (DCNs) often employ load balancing algorithms to handle large volumes of traffic and ensure that all servers and switches are utilized equally, keeping the network running smoothly. However, as load on the server varies, therefore dynamic traffic management systems that can adjust traffic flow in real-time based on the current traffic state is required. This study presents an artificial neural network-based load balancing method. By training a feed-forward artificial neural network (ANN) using a back propagation (BP) learning algorithm, it evenly distributes workload over all of the nodes. Simulation results are also presented to prove the usefulness of the proposed load balancing mechanism. It is found that the load balancing scheme can reduce the packet blocking probability (PBP) by 10 folds and delay by about nearly 11 percent.

Parallel distributed productivity‐aware tree‐search using Chapel

AbstractWith the recent arrival of the exascale era, modern supercomputers are increasingly big making their programming much more complex. In addition to performance, software productivity is a major concern to choose a programming language, such as Chapel, designed for exascale computing. In this paper, we investigate the design of a parallel distributed tree‐search algorithm, namely P3D‐DFS, and its implementation using Chapel. The design is based on the Chapel's DistBag data structure, revisited by: (1) redefining the data structure for Depth‐First tree‐Search (DFS), henceforth renamed DistBag‐DFS; (2) redesigning the underlying load balancing mechanism. In addition, we propose two instantiations of P3D‐DFS considering the Branch‐and‐Bound (B&B) and Unbalanced Tree Search (UTS) algorithms. In order to evaluate how much performance is traded for productivity, we compare the Chapel‐based implementations of B&B and UTS to their best‐known counterparts based on traditional OpenMP (intra‐node) and MPI+X (inter‐node). For experimental validation using 4096 processing cores, we consider the permutation flow‐shop scheduling problem for B&B and synthetic literature benchmarks for UTS. The reported results show that P3D‐DFS competes with its OpenMP baselines for coarser‐grained shared‐memory scenarios, and with its MPI+X counterparts for distributed‐memory settings, considering both performance and productivity‐awareness. In the context of this work, this makes Chapel an alternative to OpenMP/MPI+X for exascale programming.

An adaptive context-aware vertical handover decision algorithm for heterogeneous networks

In a heterogeneous network, vertical handovers occur frequently between different access technologies to meet the requirements of mobile users. The selection of an access technology for a mobile user for handover largely depends on the contextual information of the user and network at that point of time. In this work, we have proposed an adaptive, context-aware vertical handover decision algorithm for heterogeneous networks. Both static and dynamic contextual information about the user and the network are given as inputs to our proposed algorithm. They are provided proactively by the networks to the mobile nodes on a timely basis. An optimal network scanning procedure is introduced as part of the handover process to minimize the energy consumption of the nodes. We also propose a utility function-based cell load balancing mechanism to improve overall resource utilization. In addition, a delay-sensitive handover traffic-based hybrid channel allocation mechanism is proposed. The simulation results clearly suggest that the proposed work significantly enhanced the overall performance of the system as compared to some existing work in terms of call blocking probability, channel utilization rate, consumed energy, and handover delays.

Optimized load balancing mechanism in parallel computing for workflow in cloud computing environment

Cloud computing gives on-demand access to computing resources in metered and powerfully adapted way; it empowers the client to get access to fast and flexible resources through virtualization and widely adaptable for various applications. Further, to provide assurance of productive computation, scheduling of task is very much important in cloud infrastructure environment. Moreover, the main aim of task execution phenomena is to reduce the execution time and reserve infrastructure; further, considering huge application, workflow scheduling has drawn fine attention in business as well as scientific area. Hence, in this research work, we design and develop an optimized load balancing in parallel computation aka optimal load balancing in parallel computing (OLBP) mechanism to distribute the load; at first different parameter in workload is computed and then loads are distributed. Further OLBP mechanism considers makespan time and energy as constraint and further task offloading is done considering the server speed. This phenomenon provides the balancing of workflow; further OLBP mechanism is evaluated using cyber shake workflow dataset and outperforms the existing workflow mechanism.

Energy Efficient Load-Balancing Mechanism in Integrated IoT–Fog–Cloud Environment

The Internet of Things (IoT) and cloud computing have revolutionized the technological era unabatedly. These technologies have impacted our lives to a great extent. The traditional cloud model faces a variety of complications with the colossal growth of IoT and cloud applications, such as network instability, reduced bandwidth, and high latency. Fog computing is utilized to get around these problems, which brings IoT devices and cloud computing closer. Hence, to enhance system, process, and data performance, fog nodes are planted to disperse the load on cloud servers using fog computing, which helps reduce delay time and network traffic. Firstly, in this article, we highlight the various IoT–fog–cloud models for distributing the load uniformly. Secondly, an efficient solution is provided using fog computing for balancing load among fog devices. A performance evaluation of the proposed mechanism with existing techniques shows that the proposed strategy improves performance, energy consumption, throughput, and resource utilization while reducing response time.

Asymmetry-Aware Load Balancing With Adaptive Switching Granularity in Data Center

Datacenter networks provide large bisection bandwidth by load balancing traffic over rich parallel paths in multi-rooted tree topologies. Nevertheless, production datacenters operate under various path diversities caused by traffic dynamics, hardware failures and heterogeneous switching equipment. Therefore, the load balancing schemes in data center should be resilient to network asymmetry. Prior fine-grained schemes such as RPS and Presto are prone to experience packet reordering problem under asymmetric topology since they split flows into small units which are spread across all parallel paths. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily leading to under-utilization of multiple paths. To solve these problems, we propose a load balancing mechanism called AG, which adaptively adjusts switching granularity according to the asymmetric degree of multiple paths. AG increases switching granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing switching granularity to obtain high link utilization under small degrees of topology asymmetry. Moreover, we design a switch-based scheme which measures the difference of one-way delay of multiple paths to obtain accurate state of topology asymmetry with low overhead. AG is a practical switch-based solution without modification at end hosts. The experimental results of NS2 simulations and real implementation show that AG reduces the average and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99^{th}$</tex-math> </inline-formula> flow completion time by up to 54% and 65% compared with the state-of-the-art load balancing schemes, respectively.

Design, static analysis, and dynamic system modeling of 3-PPRS parallel manipulator with load-balancing UPS leg

AbstractAs a heavy load is applied to the parallel manipulators, it causes inaccuracies while positioning the end-effector or unbalanced dynamic forces in the legs. Various load-balancing techniques overcome this. However, the disadvantage of most load-balancing mechanisms is that they add inertia to the assembly and decrease the speed of motion. This article studies a new load-balancing method (a passive damper mechanism). The passive balancing mechanism is proposed to negate the inertia effects while countering the static inaccuracies in the parallel mechanism. This is verified by the structural analysis of the mechanism. The impact of the damper element on the dynamics of the mechanism is unknown. Hence, a complete mathematical model for the balancing mechanism has been developed to study its impact on the dynamics of the entire structure. Laplace transformations characterize the system response. The inclusion of a passive damper in a 3-prismatic-prismatic-revolute-spherical system was examined and found to be stable and critically damped. Such a passive damper was envisaged to facilitate additional force transmission for the actuators, and the DC gain from the system response validates the torque support for the actuators.

AN ENHANCED RESOURCE ALLOCATION SCHEME FOR LONG TERM EVOLUTION ADVANCED NETWORK

The emerging Long-Term Evolution Advanced (LTE-A) is an extended version of Long-Term Evolution (LTE) fourth-generation (4G) wireless broadband technologies that supports high speed transmission of multimedia applications, like video streaming, multimedia online game, Voice over IP (VoIP), browsing over the web etc. The Third Generation Partnership Project (3GPP) does not define a standard packet scheduling strategy in LTE-A. Therefore, it is expected for researchers and network service providers to design efficient schemes. This paper proposed an Enhanced Resource Allocation Scheme (E-RAS) that improves the packet scheduling strategy of LTE-A by addressing issues related to delay and fairness of users with both high and low channel conditions, for both Real Time (RT) and Non-Real Time (NRT) flows. The aim was achieved by using a fair spectrum allocation mechanism that consider the priority of RT flows of cell-centre users, without neglecting the throughput requirements of edge-centre users. Moreover, the proposed algorithm introduces an efficient traffic load balancing mechanism to efficiently regulate traffic load in LTE-A network. To evaluate the proposed scheme, simulation was conducted using the MATLAB programming with LTE-A system level simulator. The result obtained shows that the proposed scheme achieves better delay for RT and NRT traffics by 7% and 11% respectively as against the Resource Allocation Scheme to Optimize the Throughput (RASOT) scheme.

Invulnerability Improvement of Multipath Protocols in Different Scenarios

Developments in computer communications and networks enabled the deployment of exciting new areas. Meanwhile, Efficient network and communication technologies also promote the development of routing protocols. AODV(Ad hoc On-Demand Distance Vector Routing, AODV) routing protocol has invulnerability to different mobile attributes of agent nodes and network damage scenarios. However, this invulnerability performance can not meet the needs of mobile multi-agent cooperative communication. Therefore, this paper proposes an improved responsive multipath routing protocol MD_AODV(Multipath Destroy-resistance AODV, MD_AODV). This routing protocol is based on AODV routing protocol. It is mainly improved in three aspects: multichannel concurrency mechanism, node load balancing mechanism and shortest path maintenance mechanism. Firstly, in this research, an improved multipath routing protocol is modeled and implemented, which is based on OPNET Network simulation platform. Then, network models with different degrees of impairment are built. Combines the mobile attributes of mobile multi-agent task coordination to carry out invulnerability simulation experiments . These experiments study the invulnerability of the improved multipath routing protocol. Finally, this paper evaluates and compares the routing invulnerability of MD_AODV, AODV and AOMDV(Ad-hoc On-Demand Multipath Distance Vector, AOMDV). The evaluation results show that MD_AODV routing has better invulnerability when the network is severely damaged

LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

SummaryRecently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively.

A Novel QoS Guaranteed Joint Resource Allocation Framework for 5G NR with Supplementary Uplink Transmission

In 5G scenarios, the dynamic resource allocation of network slicing is crucial for quality-of-service (QoS) guaranteed under fluctuating traffic demands in rapidly changing communication environments. In this paper, we propose a novel QoS guaranteed joint resource allocation framework for NR with supplementary uplink (SUL) called QGJRA-SUL, where three parameters of SUL admission, TDD pattern, and band slicing scheme are jointly optimized. The framework is driven by a well-designed deep reinforcement learning agent. By combining the activation functions tanh and softmax, the agent can jointly optimize three parameters at the same time. Under the original problem of QoS satisfaction rate maximization, we introduce the load unbalance degree of slices into the reward function as a penalty term. The simulation results show that the framework can guarantee the QoS satisfaction rate well and balance the load of slices. QGJRA-SUL can accommodate 15% more user equipments (UEs) with the same QoS satisfaction rate than that of a traditional single-band solution without SUL, and achieve a 73% increase in the performance of load balancing than that without a load balancing mechanism near the full load.

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

AbstractCloud computing is the most rapidly growing technology that facilitates virtualized computer resources to the users through its service providers. Load balancing and task scheduling is the important concern in the cloud computing scenario from the service provider point of view. The process of load balancing is responsible for allocating the dynamic workload among the cloud systems, such that equal sharing of the resources without the virtual machines (VMs) being overloaded or underloaded. This load balancing process not only needs to allocate the tasks to suitable VMs but also need to maintain trade‐off among VMs. Thus, a potential dynamic and optimized load balancing strategy is essential for improving resource utilization, services, reliability, and throughput by maintaining equilibrium between VMs with minimized makespan. In this article, hybrid dingo and whale optimization algorithm‐based load balancing mechanism (HDWOA‐LBM) is proposed for effective load balancing those aids in maximized throughput, reliability, and resource utilization in the clouds. This HDWOA‐LBM is proposed by mimicking the hunting characteristics of dingo (equivalent to tasks) and VMs as their prey to be hunted. It inherited the process of exploration and exploitation through the chasing and approaching, encircling, and harassing inherent with dingo optimization algorithm (DOA) to determine the optimal allocation of incoming tasks to the suitable VM. In specific, it utilized the merits of whale optimization for improving the exploitation phase of DOA to balance the trade‐off between local and global search. The simulation experiments of the proposed HDWOA‐LBM conducted using CloudSim confirmed better throughput of 21.28%, maximized reliability of 25.42%, minimized makespan of 22.98%, and improved resource allocation of 20.86%, on par with the competitive intelligent load balancing schemes used for investigation.

Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows

Distributed computing services in cloud environments are easily accessible to end users. These services are delivered to end users via a subscription-based model. The “infrastructure as a service” (IaaS) cloud model is one of the best cloud environment models for running data- and computing-intensive applications. Real-world scientific applications are the best examples of data and computing intensiveness. For their implementation, scientific workflow applications need high-performance computational resources and a large volume of storage. The workflow tasks are linked based on computational and data interdependence. Considering the high volume and variety of scientific workflows (SWs), the resources of the IaaS cloud model require managing energy efficiently and without failure or loss. Therefore, in order to address the issues of power consumption and task failure for real-world SWs, this research work proposes a replication-based dynamic energy-aware resource provisioning (R-DEAR) strategy for SWs in an IaaS cloud environment. The proposed strategy, R-DEAR, is a resource- and service-provisioning strategy that implements a replication-based fault-tolerant and load-balancing mechanism. The proposed R-DEAR strategy schedules the tasks of a scientific workflow with a replication-based fault-tolerant mechanism. The proposed R-DEAR strategy also manages the power consumption of IaaS cloud resources dynamically through a load-sharing process. Simulation results show that the proposed R-DEAR strategy reduces energy consumption, execution cost, and execution time by 9%, 15%, and 18%, respectively, as compared with the existing state-of-the-art strategy.

Load balancing and service discovery using Docker Swarm for microservice based big data applications

Big Data applications require extensive resources and environments to store, process and analyze this colossal collection of data in a distributed manner. Containerization with cloud computing provides a pertinent remedy to accommodate big data requirements, however requires a precise and appropriate load-balancing mechanism. The load on servers increases exponentially with increased resource usage thus making load balancing an essential requirement. Moreover, the adjustment of containers accurately and rapidly according to load as per services is one of the crucial aspects in big data applications. This study provides a review relating to containerized environments like Docker for big data applications with load balancing. A novel scheduling mechanism of containers for big data applications established on Docker Swarm and Microservice architecture is proposed. The concept of Docker Swarm is utilized to effectively handle big data applications' workload and service discovery. Results shows that increasing workloads with respect to big data applications can be effectively managed by utilizing microservices in containerized environments and load balancing is efficiently achieved using Docker Swarm. The implementation is done using a case study deployed on a single server and then scaled to four instances. Applications developed using containerized microservices reduces average deployment time and continuous integration.

Deep Active Learning Intrusion Detection and Load Balancing in Software-Defined Vehicular Networks

Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and improve resource utilization. In this paper, we propose a load balancing algorithm to map sensor data, vehicles and data centers performing tasks. A dynamic convergence method is proposed to help identify vehicle system load factors and compare their termination criteria. We also propose a packet-level intrusion detection model. After all load balancing, the model can track the attack on the network. The proposed model further combines the entropy-based active learning and the attention-based model to efficiently identify the attacks. Experiments are then conducted on the standard KDD data to validate the developed models with and without an attention-based active learning mechanism. Our experimental results show that the load balancing mechanism is able to achieve more performance gains than previous techniques. Moreover, the results show that the developed model can improve the decision boundary by using a pooling strategy and an entropy uncertainty measure.

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized to get the benefits of cloud computing. Other essential features include portability, recovery after failure, and, most importantly, creating the core mechanism for load balancing. Several study results have been reported in enhancing load-balancing systems employing stochastic or biogenetic optimization methods. It examines the underlying issues with load balancing and the limitations of present load balance genetic optimization approaches. They are criticized for using higher-order probability distributions, more complicated solution search spaces, and adding factors to improve decision-making skills. Thus, this paper explores the possibility of summarizing load characteristics. Second, this study offers an improved prediction technique for pheromone level prediction over other typical genetic optimization methods during load balancing. It also uses web-based third-party cloud service providers to test and validate the principles provided in this study. It also reduces VM migrations, time complexity, and service level agreements compared to other parallel standard approaches.

Wireless Network Security Using Load Balanced Mobile Sink Technique

Real-time applications based on Wireless Sensor Network (WSN) technologies are quickly increasing due to intelligent surroundings. Among the most significant resources in the WSN are battery power and security. Clustering strategies improve the power factor and secure the WSN environment. It takes more electricity to forward data in a WSN. Though numerous clustering methods have been developed to provide energy consumption, there is indeed a risk of unequal load balancing, resulting in a decrease in the network’s lifetime due to network inequalities and less security. These possibilities arise due to the cluster head’s limited life span. These cluster heads (CH) are in charge of all activities and control intra-cluster and inter-cluster interactions. The proposed method uses Lifetime centric load balancing mechanisms (LCLBM) and Cluster-based energy optimization using a mobile sink algorithm (CEOMS). LCLBM emphasizes the selection of CH, system architectures, and optimal distribution of CH. In addition, the LCLBM was added with an assistant cluster head (ACH) for load balancing. Power consumption, communications latency, the frequency of failing nodes, high security, and one-way delay are essential variables to consider while evaluating LCLBM. CEOMS will choose a cluster leader based on the influence of the following parameters on the energy balance of WSNs. According to simulated findings, the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability, improves the network’s lifetime, decreases data latency, and balances network capacity.

Retracted: Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm.

[This retracts the article DOI: 10.1155/2022/3120883.].

Implementing ML Models in Load Balancing to Improve Application Performance

In modern distributed systems, load balancing plays a critical role in ensuring optimal performance and user experience. However, traditional static load balancing mechanisms often fail to adapt to dynamic traffic patterns, leading to performance degradation, increased latency, and inefficient resource utilization. This paper presents a novel approach that leverages machine learning (ML) models to enhance load balancing by predicting traffic fluctuations and intelligently distributing workloads in real time. By training ML models on historical traffic data and application performance metrics, we enable the system to make proactive decisions about resource allocation. This approach improves the ability to handle traffic surges during peak periods, minimizes latency, and optimizes infrastructure usage. The research outlines the implementation of various ML techniques, such as reinforcement learning and neural networks, into a microservices-based architecture, demonstrating how these models enhance both load balancing and auto-scaling capabilities. Empirical results from the study reveal that ML-driven load balancing reduces latency by up to 40%, improves resource efficiency, and lowers infrastructure costs by 30%, compared to traditional methods. The paper concludes by discussing the technical challenges, future possibilities of using more advanced ML algorithms, and the broader implications for cloud-native application performance.

Well-behaved online load balancing against strategic jobs

In the online load balancing problem on related machines, we have a set of jobs (with different sizes) arriving online, and the task is to assign each job to a machine immediately upon its arrival, so as to minimize the makespan, i.e., the maximum completion time. In classic mechanism design problems, we assume that the jobs are controlled by selfish agents, with the sizes being their private information. Each job (agent) aims at minimizing its own cost, which is its completion time plus the payment charged by the mechanism. Truthful mechanisms guaranteeing that every job minimizes its cost by reporting its true size have been well studied (Aspnes et al. JACM 44(3):486–504, 1997, Feldman et al. in: EC, 2017). In this paper, we study truthful online load balancing mechanisms that are well-behaved (machine with higher speed has larger workload). Good behavior is important as it guarantees fairness between machines and implies truthfulness in some cases when machines are controlled by selfish agents. Unfortunately, existing truthful online load balancing mechanisms are not well behaved. We first show that to guarantee producing a well-behaved schedule, any online algorithm (even non-truthful) has a competitive ratio at least \(\varOmega (\sqrt{m})\), where m is the number of machines. Then, we propose a mechanism that guarantees truthfulness of the online jobs and produces a schedule that is almost well-behaved. We show that our algorithm has a competitive ratio of \(O(\log m)\). Moreover, for the case when the sizes of online jobs are bounded, the competitive ratio of our algorithm improves to O(1). Interestingly, we show several cases for which our mechanism is actually truthful against selfish machines.