Improve Load Balancing Research Articles

Cloud Replica Management-Based Hybrid Optimization

This research addresses the challenges in cloud-based replica management by proposing a novel strategy employing a Genetically Implied Greywolf with Oppositional Learning (GIGOL) hybrid optimization technique. This approach optimizes multi-objectives such as response time, load balancing, availability, replication cost, and energy consumption, ensuring cost-effectiveness and energy efficiency. The GIGOL model integrates Genetic Algorithm, opposition learning, and Grey Wolf Optimization, aiming to achieve optimal replica placement. The study emphasizes resolving overhead issues through machine learning techniques for efficient cloud-based replica management. Performance evaluation showcases improvements in response time, load balancing, availability, replication cost, and energy consumption, highlighting the effectiveness of the proposed approach within budget constraints and management policies.

PPB-MCTS: A novel distributed-memory parallel partial-backpropagation Monte Carlo tree search algorithm

Monte-Carlo Tree Search (MCTS) is an adaptive and heuristic tree-search algorithm designed to uncover sub-optimal actions at each decision-making point. This method progressively constructs a search tree by gathering samples throughout its execution. Predominantly applied within the realm of gaming, MCTS has exhibited exceptional achievements. Additionally, it has displayed promising outcomes when employed to solve NP-hard combinatorial optimization problems. MCTS has been adapted for distributed-memory parallel platforms. The primary challenges associated with distributed-memory parallel MCTS are the substantial communication overhead and the necessity to balance the computational load among various processes. In this work, we introduce a novel distributed-memory parallel MCTS algorithm with partial backpropagations, referred to as Parallel Partial-Backpropagation MCTS (PPB-MCTS). Our design approach aims to significantly reduce the communication overhead while maintaining, or even slightly improving, the performance in the context of combinatorial optimization problems. To address the communication overhead challenge, we propose a strategy involving transmitting an additional backpropagation message. This strategy avoids attaching an information table to the communication messages exchanged by the processes, thus reducing the communication overhead. Furthermore, this approach contributes to enhancing the decision-making accuracy during the selection phase. The load balancing issue is also effectively addressed by implementing a shared transposition table among the parallel processes. Furthermore, we introduce two primary methods for managing duplicate states within distributed-memory parallel MCTS, drawing upon techniques utilized in addressing duplicate states within sequential MCTS. Duplicate states can transform the conventional search tree into a Directed Acyclic Graph (DAG). To evaluate the performance of our proposed parallel algorithm, we conduct an extensive series of experiments on solving instances of the Job-Shop Scheduling Problem (JSSP) and the Weighted Set-Cover Problem (WSCP). These problems are recognized for their complexity and classified as NP-hard combinatorial optimization problems with considerable relevance within industrial applications. The experiments are performed on a cluster of computers with many cores. The empirical results highlight the enhanced scalability of our algorithm compared to that of the existing distributed-memory parallel MCTS algorithms. As the number of processes increases, our algorithm demonstrates increased rollout efficiency while maintaining an improved load balance across processes.

HybOff: a Hybrid Offloading approach to improve load balancing in fog environments

Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments.

Multitier scalable clustering wireless network design approach using honey bee ratel optimization

Wireless sensor networks (WSNs) have emerged as a one of the most fascinating areas of lookup in the past few years principally due to its massive quantity of workable applications. However limited energy, node deployment strategy, routing techniques has considerable impact on the overall performance of network. This paper proposes sustainable honey bee ratel based hybrid wireless sensor network (SHHWSN) layering framework for improving load balancing and scalability issues in network. In SHHWSN tessellated hexagonal shape clusters are formed to cover entire geographical region without any gaps. Hybrid routing schema constrained with redundant data identification policy used in this, seeks to optimize energy utilization. The proposed SHHWSN was evaluated and validated by simulation, and the results were compared with earlier schematics where mostly one sided stochastic node deployments were followed. The experimental results show that SHHWSN has superior performance in terms of network alive nodes, delay, energy and throughput. The proposed SHHWSN obtained values of 50 nodes is 3, 0.061, 0.14, 0.89, 100 nodes is 3,0.037, 0.19, 0.551, 150 nodes is 13, 0.081, 0.23, 0.415, and 200 nodes is 23, 1.01, 0.25, 0.356, which is significantly more effective than current techniques.

Adaptive thresholds for improved load balancing in mobile edge computing using K-means clustering

Adaptive thresholds for improved load balancing in mobile edge computing using K-means clustering

Tarazu: An Adaptive End-to-end I/O Load-balancing Framework for Large-scale Parallel File Systems

The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu , an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art.

Microservice instances selection and load balancing in fog computing using deep reinforcement learning approach

Fog-native computing is an emerging paradigm that makes it possible to build flexible and scalable Internet of Things (IoT) applications using microservice architecture at the network edge. With this paradigm, IoT applications are decomposed into multiple fine-grained microservices, strategically deployed on various fog nodes to support a wide range of IoT scenarios, such as smart cities and smart farming. Nonetheless, the performance of these IoT applications is affected by their limited effectiveness in processing offloaded IoT requests originating from multiple IoT devices. Specifically, the requested IoT services are composed of multiple dependent microservice instances collectively referred to as a service plan (SP). Each SP comprises a series of tasks designed to be executed in a predefined order, with the objective of meeting heterogeneous Quality of Service (QoS) requirements (e.g., low service delays). Different from the cloud, selecting the appropriate service plan for each IoT request can be a challenging task in dynamic fog environments due to the dependency and decentralization of microservice instances, along with the instability of network conditions and service requests (i.e., change quickly over time). To deal with this challenge, we study the microservice instances selection problem for IoT applications deployed on fog platforms and propose a learning-based approach that employs Deep Reinforcement Learning (DRL) to compute the optimal service plans. The latter optimizes the delay of application requests while effectively balancing the load among microservice instances. In our selection process, we carefully address the plan-dependency to efficiently select valid service plans for every request by introducing two distinct approaches; an action masking approach and an adaptive action mapping approach. Additionally, we propose an improved experience replay to address delayed action effects and enhance our model training efficiency. A series of experiments were conducted to assess the performance of our Microservice Instances Selection Policy (MISP) approach. The results demonstrate that our model reduces the average failure rate by up to 65% and improves load balance by up to 45% on average when compared to the baseline algorithms.

A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming demand surges and resource outages. Numerous comprehensive methods still overlook certain aspects, such as the inclusion of multiple objectives, addressing local minimum problems, and optimizing resource utilization to its fullest potential. Our research focus is on exploring cloud-deployed three-tier applications to address these challenges. This paper presents a new approach to load balancing, introducing a novel weight assignment algorithm. The algorithm effectively distributes tasks among virtual machines using three carefully optimized algorithms. To begin with, we convert the user tasks into the directed acyclic graph (DAG) structured workflow, organizing them according to their execution sequence. The iterative critical path search algorithm, implemented with a layer-based priority assigning technique, addresses the critical path challenge. This process involves finding a critical path, dividing tasks into distinct layers, classifying them based on their computational complexity and priorities, and distinguishing between critical and non-critical tasks. Following the classification process, the tasks are organized and prioritized using an improved Moth Flame Optimization approach based on multiple criteria, which consider criticality, priority, and makespan. This approach significantly reduces scheduling latency and delays. Finally, task allocation is proposed using the high convergence rock hyrax optimization algorithm with a divide-and-conquer strategy, enabling effective exploration and exploitation of overcrowded resources for improved load balancing. The CloudSim simulation tool is used to carry out the simulation, where it assesses various performance aspects such as latency, bandwidth, completion time, throughput, energy usage, instances of not meeting QoS violations, and overhead.

Load Balancing using Weight Based scheme in AWS

This paper presents research on load balancing methods for cloud computing platforms. Load balancing is an important element of cloud systems since it allows for optimal resource utilization and consistent performance levels. The study investigates several load balancing technologies and evaluates their effectiveness in coping with the dynamic and heterogeneous nature of cloud workloads. The study conducts experiments and analyses to discover significant parameters influencing load balancing performance, such as workload allocation, server capacity, and network latency. With the weight-based technique, different servers are dynamically given requests based on their weights, which are determined by the processing capacities of the servers. By distributing the work proportionately, this approach aims to decrease user request response times and avoid server overload. The effectiveness of the proposed approach is evaluated via simulations, demonstrating its potential to improve system performance and scalability relative to traditional load balancing methods. Generally speaking, this paper presents an optimized load distribution approach that raises the efficiency and stability of cloud computing systems. The findings enable to improve load balancing methods matched to the particular requirements and issues of cloud computing, eventually enhancing system scalability, reliability, and user

A hybrid MGO-JAYA based clustered routing for FANETs

In recent years, Flying Ad-hoc Networks (FANETs) have gained significant attention among researchers due to the widespread applications and increasing popularity of Unmanned Aerial Vehicles (UAVs). As technology advances and more research is undertaken, FANETs are expected to become a vital aspect of modern times, allowing for more effective and creative applications in different domains. However, FANETs also face several challenges, including high mobility, dynamic topology, energy constraints, and communication reliability. Addressing these challenges is essential to unlock the full potential of FANETs and to ensure reliable and timely delivery of data. In this paper, we propose HMGOC, a novel clustered routing model for FANETs, utilizing a hybrid approach that combines the Mountain Gazelle Optimizer (MGO) and Jaya Algorithms. The dynamic flying behavior of UAVs demands an adaptive and efficient clustering strategy to maintain network stability and ensure robust and reliable communication among UAVs. In this context, MGO, one of the most recent swarm-based optimization methods, is enhanced and employed for FANET clustering process. Also, we design a routing mechanism based on conditional Bayes' theorem which adapts to changing network conditions, reduces packet losses, and ensures timely data delivery. HMGOC offers several advantages over other competitive techniques, including improved load balancing, minimized energy consumption and latency, and enhanced network throughput and lifespan. The simulation results demonstrate that the HMGOC technique beats the existing methods in terms of enhanced cluster stability and lifetime, increased packet deliverability, energy efficiency, reduced latency, and minimized overhead.

Improving Load Balance in Fog Nodes by Reinforcement Learning Algorithm

Improving Load Balance in Fog Nodes by Reinforcement Learning Algorithm

CMOS Wireless Hybrid Transceiver Powered by Integrated Photodiodes for Ultra-Low-Power IoT Applications

In this article, a communication platform for a self-powered integrated light energy harvester based on a wireless hybrid transceiver is proposed. It consists of an optical receiver and a reconfigurable radio frequency (RF) transmitter. The hybrid optical/RF communication approach improves load balancing, energy efficiency, security, and interference reduction. A light beam for communication in the downlink, coupled with a 1 MHz radio frequency signal for the uplink, offers a small area and ultra-low-power consumption design for Smart Dust/IoT applications. The optical receiver employs a new charge-pump-based technique for the automatic acquisition of a reference voltage, enabling compensation for comparator offset errors and variations in DC-level illumination. On the uplink side, the reconfigurable transmitter supports OOK/FSK/BPSK data modulation. Electronic components and the energy harvester, including integrated photodiodes, have been designed, fabricated, and experimentally tested in a 0.18 µm triple-well CMOS technology in a 1.5 × 1.3 mm2 chip area. Experiments show the correct system behavior for general and pseudo-random stream input data, with a minimum pulse width of 50 µs and a data transmission rate of 20 kb/s for the optical receiver and 1 MHz carrier frequency. The maximum measured power of the signal received from the transmitter is approximately −18.65 dBm when using a light-harvested power supply.

Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time.

Intelligent Cloudlet Scheduling for Optimized Execution Time in Cloud Computing Environments

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalable and flexible resources. However, efficient resource management, particularly cloudlet scheduling, presents a significant challenge due to its NP-hard nature. This paper introduces a novel heuristic-based cloudlet scheduling algorithm aimed at minimizing execution time and improving load balancing in cloud computing environments. We detail the development and implementation of the algorithm, along with a simulation setup using the CloudSim toolkit to evaluate its performance against existing methods. Results from extensive simulations demonstrate that the proposed algorithm consistently reduces turnaround times, thus optimizing resource allocation. The findings suggest that our approach can significantly impact cloud computing efficiency, paving the way for improved service provider offerings and user satisfaction. The implications of these advancements are discussed, alongside potential directions for future research in dynamic cloud environments.

Simulation modeling of Zoom traffic on a campus network: A case study

In this paper, we develop a synthetic workload model for the Zoom network application based on empirical Zoom traffic measurements from a campus network. We then use this model in a simulation study of Zoom network traffic at the campus scale. The simulation results show that hybrid learning places a substantial load on the campus network. Additional simulation experiments investigate the potential benefits of locally-hosted Zoom infrastructure, improved load balancing strategies for Zoom servers, and multicast delivery for Zoom network traffic. The simulation results show that the multicast approach offers the greatest potential benefit for improving Zoom performance on our campus network.

Multi-Timescale Coordinated Control With Optimal Network Reconfiguration Using Battery Storage System in Smart Distribution Grids

This paper focuses on the global optimality of the relaxed solutions of a multi-timescale co-optimization problem. The proposed co-optimization framework involves the multi-timescale co-optimization of distribution feeder reconfiguration with the optimal dispatch of traditional voltage regulating devices and utility-scale distributed energy resources. The on-load tap changer (OLTC) is scheduled on an hourly basis while the potential of fast-acting battery energy storage system and photovoltaic inverters is exploited by dispatching them on a 20-min basis. The optimal switching plan is computed on a daily basis. The proposed multi-timescale co-optimization model is formulated as a mixed-integer second-order cone program to achieve global optimum. The objective is to reduce power losses and improve load balancing among feeders. The proposed co-optimization framework satisfies the grid security constraints by employing the accurate DistFlow branch equations and exact linearization of the OLTC model. To ensure radiality, the limitation of widely used spanning tree constraints is addressed by combining them with single-commodity flow constraints. The simulation results demonstrate the feasibility (hence global optimality) of the relaxed solutions computed by the co-optimization framework.

A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions.

The expectation for communication systems beyond 5G/6G is to provide high reliability, high throughput, low latency, and high energy efficiency services. The integration between systems based on radio frequency (RF) and visible light communication (VLC) promises the design of hybrid systems capable of addressing and largely satisfying these requirements. Hybrid network design enables complementary cooperation without interference between the two technologies, thereby increasing the overall system data rate, improving load balancing, and reducing non-coverage areas. VLC/RF hybrid networks can offer reliable and efficient communication solutions for Internet of Things (IoT) applications such as smart lighting, location-based services, home automation, smart healthcare, and industrial IoT. Therefore, hybrid VLC/RF networks are key technologies for next-generation communication systems. In this paper, a comprehensive state-of-the-art study of hybrid VLC/RF networks is carried out, divided into four areas. First, indoor scenarios are studied considering lighting requirements, hybrid channel models, load balancing, resource allocation, and hybrid network topologies. Second, the characteristics and implementation of these hybrid networks in outdoor scenarios with adverse conditions are analyzed. Third, we address the main applications of hybrid VLC/RF networks in technological, economic, and socio-environmental domains. Finally, we outline the main challenges and future research lines of hybrid VLC/RF networks.

Adaptive Image Size Padding for Load Balancing in System-on-Chip Memory Hierarchy

The conventional address map often incurs traffic congestion in on-chip memory components and degrades memory utilization when the access pattern of an application is not matched with the address map. To reduce traffic congestion and improve the memory system performance, we propose an adaptive image size padding technique for a given address mapping and a hardware configuration. In the presented software approach, the system can adaptively determine the image pad size at the application-invoke time to enhance the load balancing across the on-chip memory hierarchy. Mainly targeting a high-bandwidth image processing application running in a device accelerator of an embedded system, we present the design, describe the algorithm, and conduct the performance experiment. As a result, the experiments indicate the presented design can improve load balancing up to 95% and performance up to 35%, with insignificant memory footprint overheads.

HARMONIC: Shapley values in market games for resource allocation in vehicular clouds

Real-time allocation of resources to fulfill service requests from road vehicles is becoming increasingly complex, for two main reasons: the continuous increase in the number of Internet-connected vehicles on roads all over the world, and the emergence of complex and resource-greedy applications that require fast execution, often under limited availability of computational resources. While many resource allocation solutions to this problem have been proposed recently, these solutions rely on unrealistic scenarios and constraints that limit their practical use.This paper presents HARMONIC, a Game Theory-based coalition game that aims to maximize resource utilization and dynamically balance resource usage across multiple Vehicular Clouds (VCs). HARMONIC employs a Shapley value-based strategy to determine the order of task allocation to available resources. It is built upon our proposed Market Game model, specifically designed to address resource allocation challenges in dynamic VCs. We conduct a comparative analysis with existing literature solutions under various scenarios and resource constraints to evaluate HARMONIC’s performance. Our simulation results demonstrate that HARMONIC achieves resource allocation in fewer rounds and with fewer failures. Furthermore, it effectively distributes tasks to more VCs, improving load balancing and overall system efficiency.

Clustering based EO with MRF technique for effective load balancing in cloud computing

PurposeCloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.Design/methodology/approachVMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.FindingsThe proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.Originality/valueUser’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.