Load Balancing Scheme Research Articles

Comprehensive review of load balancing in cloud computing system

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

[formula omitted]-LB: Software-defined IDS with UAV Resource Aware Load-Balancing in FANET disaster scenarios

In critical scenarios like natural disasters, the physical infrastructure of the network may be heavily damaged or completely torn down making difficult communications and rescue activities. In such cases, due to their nature, Unmanned Aerial Vehicles (UAVs) are usually employed to provide a temporary support network by forming a Flying Ad-Hoc Network (FANET). However, this network should be carefully monitored and safeguarded to guarantee its stability by deploying network security appliances, like Intrusion Detection Systems (IDSs). In the wake of these considerations, this paper delves into the utilization of IDS functions deployed on UAVs, named IDS-enabled UAVs, in emergencies leveraging the modern Software-Defined Network (SDN) paradigm. Specifically, the study proposes a dynamic approach to promptly activate IDS-enabled UAVs, in an SDN-FANET, responding to network traffic increasing and/or malicious activities taking place within the network. In order to achieve this objective, a Software-defined IDS with UAV Resource Aware Load Balancing strategy – SURA-LB – is proposed and implemented within the SDN controller, leveraging its programmable nature and its holistic view of the network. The SURA-LB strategy equally distributes the load among the set of IDS-enabled UAVs by considering their resources and energy utilization along with the experienced network traffic conditions. Finally, an extensive experimental campaign shows the benefits of the proposed load-balancing strategy in reducing and fairly distributing the workload among the IDS-enabled UAVs outperforming naive and baseline load-balancing strategies like Random and Round-Robin and, therefore, bolstering the resilience and the reliability of the set of IDS-enabled UAVs while the traffic to be monitored and analyzed increases.

Learned load balancing

Effectively balancing traffic in datacenter networks is a crucial operational goal. Most existing load balancing approaches are handcrafted to the structure of the network and/or network workloads. Thus, new load balancing strategies are required if the underlying network conditions change, e.g., due to hard or grey failures, network topology evolution, or workload shifts. While we can theoretically derive the optimal load balancing strategy by solving an optimization problem given certain traffic and topology conditions, these problems take too much time to solve and makes the derived solution stale to deploy. In this paper, we describe a load balancing scheme Learned Load Balancing (LLB), which is a general approach to finding an optimal load balancing strategy for a given network topology and workload, and is fast enough in practice to deploy the inferred strategies. LLB uses deep supervised learning techniques to learn how to handle different traffic patterns and topology changes, and adapts to any failures in the underlying network. LLB leverages emerging trends in network telemetry, programmable switching, and “smart” NICs. Our experiments show that LLB performs well under failures and can be expanded to more complex, multi-layered network topologies. We also prototype neural network inference on smartNICs to demonstrate the workability of LLB.

Deep Reinforcement Learning-Based Task Offloading and Load Balancing for Vehicular Edge Computing

Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this paper, we propose a deep reinforcement learning-based decision scheme for task offloading and load balancing with the optimization objective of minimizing the system cost considering the split offloading of tasks and the load dynamics of edge nodes. First, we model the mutual interaction between mobile vehicles and Mobile Edge Computing (MEC) servers using a Markov decision process. Second, the optimal task-offloading and resource allocation decision is obtained by utilizing the twin delayed deep deterministic policy gradient algorithm (TD3), and server load balancing is achieved through edge collaboration using a server selection algorithm based on the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, we have conducted extensive simulation experiments and compared the results with several other baseline schemes. The proposed scheme can more effectively reduce the system cost and increase the system resource utilization.

Distributed Systems for Real-Time Computing in Cloud Environment: A Review of Low-Latency and Time Sensitive Applications

As a result of its many benefits, including cost-efficiency, speed, effectiveness, greater performance, and increased security, cloud computing has seen a boom in popularity in recent years. This trend has attracted both consumers and businesses. Being able to process and provide data or services in a quick and effective manner while adhering to low latency and time limits is the hallmark of an efficient distributed system that is designed particularly for real-time computing in cloud environments. It is essential to place a high priority on low latency and time sensitivity while developing and putting into action a distributed system for real-time computing in a cloud environment. In order to fulfil the particular requirements of the application or service, consideration must be given to a number of different aspects. In particular, the topic of load balancing will be discussed in this paper. It is possible to ensure a more effective distribution of workload and reduce latency by using load balancers, which distribute incoming traffic over many servers or instances. The throttled algorithm is believed to be the most efficient load balancing strategy for reducing service delivery delay in cloud computing. This research investigates a hybrid method known as Equally Spread Current Execution (ESCE), which is known for its combination with the throttled algorithm.

Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.

A cyclic-track decomposition method for 3D MOC neutron transport simulation

The method of characteristics (MOC) is a promising numerical technique for solving the neutron transport equation. This method has been parallelized to accommodate its high computing power and memory requirements. Angle decomposition is widely used by parallel MOC codes to distribute MOC tracks over CPU cores, but it may lead to load imbalance. Based on modular ray tracing, we present a cyclic-track decomposition method along with a load balancing strategy to address the limitations of angle decomposition for parallel MOC on 3D axially extruded geometries. It has been implemented as the top-level decomposition in the ANT-MOC code to demonstrate its effectiveness and to show its potential as an alternative to domain decomposition. We evaluated the cyclic-track decomposition method using a generalized performance model and some neutron transport benchmarks. Our evaluation showed that the cyclic-track decomposition method generated a more balanced load distribution than the angle decomposition method, and ANT-MOC with the new strategy for load balancing achieved a good performance of over 40% parallel efficiency as it scales from 10,240 to 98,304 cores on the ORISE supercomputer.

Data Communication Algorithm of HPDB Parallel Database System Based on Computer Network

High-Performance Database (HPDB) is designed to meet the demands of today’s modern data-driven world, as massive volumes of information need to be accessed and analyzed with minimal latency. A pivotal aspect of their operation lies in efficient data communication among nodes in a computer network, which is essential for parallel database systems. HPDBs may involve distributed architectures and parallel database systems to store and process data across multiple nodes or servers in a network. Hence, an algorithm called Hybridized Partitioning Strategy (HPS)-based Communication (C) for achieving HPDB has been proposed to facilitate data transmission and coordination across a computer network using the Message Passing Interface (MPI) protocol. The proposed HPS-C-HPDB technique includes partitioning and distributing data, query routing, and load balancing strategies to achieve high-performance levels. The (HPS) combines hash and range partitioning methods for effective processing and retrieval to balance data distribution, reduce communication overhead in parallel databases, and improve the system’s performance. In parallel database systems, query routing effectively routes requests into the optimal nodes or partitions based on the query’s conditions and the data’s placement and guarantees efficient data processing and retrieval. The proposed scheme is evaluated using various performance metrics like throughput, response time, speedup, and communication overhead analysis.

Research on Image Processing Resource Reconstruction Based on Load Balancing Strategy

With the development of intelligent vehicles, the vehicle image processing system has put forward increasing demand for computing resource utilization efficiency and real-time processing. However, the traditional information processing method that binds software and hardware severely restricts the efficient use of image processing resources. In order to solve this problem, this paper proposes a resource reconstruction scheme based on a load balancing strategy, which can realize unified management and dynamic allocation of image processing resources by establishing a system resource view. Then, this paper builds a physical verification platform and constructs a comparative verification experiment to verify the effectiveness of the resource reconstruction scheme. Experimental results prove that this resource reconstruction scheme can effectively improve the resource utilization efficiency of multi-core digital signal processing (DSP), realize software-defined hardware functions, and optimize the real-time performance of parallel processing.

A literature study and performance gaps on Centralized server-based load balancing and routing strategies under cloud-IoT-WSN

ABSTRACT Wireless Sensor Network (WSN) infrastructure has to be balanced for maintaining the Quality of Service (QoS) in the network. However, load balancing technology is complex for meeting the requirements of high flexibility and adaptability due to the conventional WSN architecture. The major complication for developing distributed techniques for these models is load balancing that is the major aim of this study. These distributed methodologies use routing decisions that must be efficient without the need for more information about the system, where one of the efficient techniques is probabilistic routing. This paper plans to conduct a critical literature review on a diverse centralised server for controlling the load balancing in the cloud and routing strategies in WSN as well as linked with an IoT systems. This study reviews a set of contributions regarding load balancing in the cloud and routing strategies in WSN with clear algorithmic analysis. The simulation tools and recorded performance measures are also analysed and sorted out. Finally, the existing research gaps and the future directions are expedited that to be adaptable to attain better protocol performance with a centralised server in balancing node traffic, load balancing in the cloud and improving the throughput of the entire network.

QoS‐enhanced load balancing strategies for metaverse‐infused VR/AR in engineering education 5.0

AbstractThe evolution of augmented reality (AR) and virtual reality (VR) technologies has ushered in a new era of immersive experiences, with applications ranging from entertainment to education. The proposed framework introduces a fog layer with an innovative, improved geographic load‐balancing algorithm. It optimizes load distribution and provides quality of service (QoS) parameters that are important for enhancing user experiences for AR/VR applications. The iFogSim toolkit experimentally validates the framework in electroencephalogram‐based VR/AR gaming applications. Also, the proposed framework is tested in a diverse range of scenarios. Results show that the proposed algorithm improves QoS for AR/VR applications with noticeable improvements on average latency, jitter, and packet loss. Future research should aim to address these limitations for a more comprehensive understanding of the proposed framework's practicality and effectiveness. The development of immersive technologies will continue to expand in multiple sectors, and future research will focus on energy efficiency, security, and real‐world applications.

Dynamic load balancing in IoT-enabled WSNs using fuzzy logic-based control

The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications.

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

Load balancing and topology dynamic adjustment strategy for power information system network: a deep reinforcement learning-based approach

As power information systems play an increasingly critical role in modern society, higher requirements are placed on the performance and reliability of their network infrastructure. In order to cope with the growing data traffic and network attack threats in the power information system, we select the power information system data center network as the research object and design an overall system solution based on software defined network, including the application layer, control layer and infrastructure layer. A typical fat tree network topology is simulated and analyzed. We define the load balancing and network topology dynamic adjustment problem as a Markov decision process, and design a data flow path acquisition method based on breadth-first search to construct the action space of each host. Then, a deep reinforcement learning algorithm based on deep Q-network, priority experience replay and target network is introduced to provide solutions for optimizing the performance of power information systems and responding to network attacks. Simulation results show that the proposed method is better than the traditional equal-cost multi-path algorithm in terms of average bandwidth utilization, average jitter and average packet loss, and can reduce the probability of network nodes being attacked by more than 11%.

Retracted: Research on Load Balancing and Caching Strategy for Central Network

Retracted: Research on Load Balancing and Caching Strategy for Central Network

Dynamic optimization scheme for load balancing and energy efficiency in software-defined networks utilizing the krill herd meta-heuristic algorithm

Software-Defined Networks (SDN) represent an efficient approach for managing large-scale networks, aiming to enhance resource use, performance, cost efficiency, and quality of service for dynamic services. By segregating the control plane and utilizing a centralized software controller, SDN offers notable capabilities in software development, programmability, and flexibility. Nevertheless, balancing energy efficiency and processing power remains a key challenge. This paper introduces an interactive scheme within the SDN controller, employing the krill herd meta-heuristic algorithm for optimization-based programming. The proposed method emphasizes the interaction between load balancing (LB) and energy efficiency, leveraging control plane data and virtual machine (VM) updates to derive optimal solutions. Simulation results illustrate an average 13.5 % enhancement in SDN load balancing, a 25.9 % improvement in energy efficiency, and an approximate 20 % performance boost, showcasing the effectiveness of the proposed approach.

Load Balancing in Blockchain Networks: A Survey

Load Balancing (LB) in networking attempts to minimize the under-utilization and over-utilization of network resources by distributing the network flows evenly in the communication network. Blockchain includes a string of joined blocks that are built into the preservation of the non-tampering nature and safeguarding the verifiability. According to our felt experience, we are the initiators to review on blockchain-driven LB, where we organize the blockchain-driven LB concept under 7 categories and interpret thoroughly their performance in relation to LB techniques, BC-driven variables, LB approach, network variables, and similar things. We accumulated an early sample of 76 paper citations by handpicking articles for eligibility requirements scrutinized from web repositories, deploying a thorough and continuous method. Relying on this assessment, blockchain has been deployed in network LB for protecting data fidelity, trustworthiness, confidentiality of data transfers, deploying blockchain consensus for LB, deploying smart contracts for LB, deploying with secure resource trading, access control, and as a coordinator in the system of LB. Moreover, LB involves balancing loads in the blockchain transactions themselves. Thorough interpretation depicts that from blockchaindriven LB schemes, 37.5% deploy blockchain transaction LB, 90% deploy conventional blockchain architecture, 30% deploy generic consensus, 97.5% deploy decentralized LB, 97.5% deploy dynamic LB, 52.5% deploy deterministic LB, and the majority target generic networks. Finally, we go over the potential and predicaments of the notion of blockchain-driven LB and then provision ideas to defeat them.

Optimal EV Charging Strategy for Distribution Networks Load Balancing in a Smart Grid Using Dynamic Charging Price

Optimal EV Charging Strategy for Distribution Networks Load Balancing in a Smart Grid Using Dynamic Charging Price

A New Throttled Adapted Load Balancing (TALB) Strategy for Dynamic VM Allocations in Cloud Datacenters

A New Throttled Adapted Load Balancing (TALB) Strategy for Dynamic VM Allocations in Cloud Datacenters

A new throttled adapted load balancing strategy for dynamic VM allocations in cloud datacentres

A new throttled adapted load balancing strategy for dynamic VM allocations in cloud datacentres