Dynamic Load Balancing Method Research Articles

An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods.

A Review Load balancing algorithms in Fog Computing

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.

Adaptive Load Balancing Using RR and ALB: Resource Provisioning in Cloud

Cloud Computing context, load balancing is an issue. With a rise in the number of cloud-based technology users and their need for a broad range of services utilizing resources successfully or effectively in a cloud environment is referred to as load balancing, has become a significant obstacle. Load balancing is crucial in storage systems to increase network capacity and speed up response times. The main goal is to present a new load-balancing mechanism that can balance incoming requests from users all over globally who are in different regions requesting data from remote data sources. This method will combine effective scheduling and cloud-based techniques. A dynamic load balancing method was developed to ensure that cloud environments have the ability to respond rapidly, in addition to running cloud resources efficiently and speeding up job processing times. Applications' incoming traffic is automatically split up across a number of targets, including Amazon EC2 instances, network addresses, and other entities by elastic load balancing. Elastic load balancing offers three distinct classifications of load balancer, and each one provides high availability, intelligent scalability, and robust security to guarantee the error-free functioning of your applications. Application load balancing and round robin are the two load balancing mechanisms in database cloud that are focus of this research study.

In Improved Algorithm for Load Balancing techniques Over Cloud Computing

The term "cloud computing" describes the services and programmes that data centres make available to users all over the world. Online access is provided for all services and programmes. Virtualization is a crucial component of the cloud computing environment, and virtual machine migration is one of its key components. Virtual machine migration is the process of moving an operational virtual machine to a destination computer where it continues to function normally with all of its installed programmes and operating system. Making server balancing easier. By dispersing the workload among available servers, this boosts performance. There are several load balancing algorithms, which may be divided into static and dynamic load balancing methods.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

Research on Detection and Early Warning Mechanism of Emergency Public Health Medical Education System Based on Internet of Things Technology.

Sudden public health and medical education events have tested the stability of society to a great extent. The government need to strengthen capacity building, make use of system dynamic supervision, warn public health events in advance, and minimize the impact scope and related harmfulness of events. This not only facilitates the rapid mobilization of resources by the later government but also facilitates the comprehensive and detailed deployment and arrangement of decision-makers. As we all know, the Internet of Things is used by all walks of life because of its outstanding advantages of low power consumption, low cost, and wide range. Therefore, this article takes the Internet of Things as the technical basis of the system. According to the actual demand and resource design, it includes two system functions: detection and early warning. The results show that: (1) considering the practical principle, the evaluation system interface found that the scores of font size and color style are all below 80%, which need to be optimized and adjusted; the overall interface basically meets the needs of users. (2) The throughput of the three methods is different. The CoAP-E has superior throughput. (3) With the increase in packet loss rate, the request success rate of the CoAP method decreases in a “drop” manner. The CoAP-E method in this article has the best performance. (4) When the packet loss rate is 25%, the network adaptability of this method is the strongest, and the retransmission rate is less than 18%; the CoAP method is as high as 35%. (5) When the number of concurrent requests is less than 2500, there is no obvious difference between the three methods; the optimal performance of the dynamic load balancing method is 10.1 s. (6) The system comprehensively considers various factors of five site selections. The highest comprehensive score of Final Site, 5 is 8.7, which can be used as the resettlement place of emergency rescue facilities. This article starts from the characteristics and needs of public emergencies, and the final set of the system runs well. It can quickly reflect public health emergencies and medical education events. Use the most effective system functions for risk control, and maximize the analysis, organization, and coordination of events. The follow-up optimization of system details needs to be studied.

Dynamic load balancing with over decomposition in plasma plume simulations

An electric propulsion plasma plume simulation employs the Particle-in-Cell (PIC) model for heavy species (i.e. ions and neutral atoms) and yields a highly non-uniform particle distribution at steady state. For parallel simulations, partitioning the computational domain uniformly and evenly assigning the subdomains to MPI processes would never lead to properly balanced loads across MPI processes. For this reason, a patch-based dynamic load balancing method with over-decomposition and Hilbert space-filling curve has been implemented into the Thermophysics Universal Research Framework (TURF). The data transfer is accomplished by utilizing TURF's serialization and deserialization feature, in which the tree hierarchical object structure is copied into a contiguous memory block of data, and the tree hierarchy is reconstructed after the data transfer. This generalized dynamic load balancing approach allows for the same routine to be used for any models besides the PIC model in TURF (e.g. fluid, continuum kinetic, hybrid models, etc). This paper first introduces individual pieces of TURF that enable dynamic load balancing when they are combined. Then, the dynamic load balancing method is demonstrated in three different particle simulations, showing performance gains when the number of subdomains is larger than that of MPI processes. The benefit is even more notable in simulations with highly non-uniform particle distributions at steady state, commonly seen in plasma plume simulations.

Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications

As demands of renewable energy and more energy storages in power systems promote, the digital twin technology for multi-energy system draws attention from the researchers in power system and energy area. The load balancing algorithm of the digital twin server cluster significantly impacts the computing performance of the computing nodes. Optimal complete matching of a weighted bipartite graph is proposed to allocate computing task blocks to each computing node evenly. In the proposed algorithm, the computation times of simulation computing task blocks are estimated and the computation time of one single step in the task blocks are used as dynamic load indicators of the computing nodes. The indices are fed back to the scheduling server in real time. With the total steps of the simulation computing task blocks and the computation time of each single step for the task blocks, a fully weighted bipartite graph of computing nodes and simulation computing task blocks is constructed, greedy expansion of the Hungarian algorithm is used to optimize the complete matching of this weighted bipartite graph. Based on the optimization results, the task blocks are allocated to the computing nodes correspondingly in real time. Simulation-driven model parameter correction function, the representative simulation computing applications of digital twin for multi-energy system was used to test the proposed algorithm and the results show a significant improvement of the load balancing effect at the computing nodes.

Towards Deadline Guaranteed Cloud Storage Services

More and more organizations move their data and workload to commercial cloud storage systems. However, the multiplexing and sharing of the resources in a cloud storage system present unpredictable data access latency to tenants, which may make online data-intensive applications unable to satisfy their deadline requirements. Thus, it is important for cloud storage systems to provide deadline guaranteed services. In this paper, to meet a current form of service level objective (SLO) that constrains the percentage of each tenant’s data access requests failing to meet its required deadline below a given threshold, we build a mathematical model to derive the upper bound of acceptable request arrival rate on each server. We then propose a Deadline Guaranteed storage service (called DGCloud ) that incorporates three basic algorithms. Its deadline-aware load balancing scheme redirects requests and creates replicas to release the excess load of each server beyond the derived upper bound. Its workload consolidation algorithm tries to maximally reduce servers while still satisfying the SLO to maximize the resource utilization. Its data placement optimization algorithm re-schedules the data placement to minimize the transmission cost of data replication. We further propose three enhancement methods to further improve the performance of DGCloud . A dynamic load balancing method allows an overloaded server to quickly offload its excess workload. A data request queue improvement method sets different priorities to the data responses in a server’s queue so that more requests can satisfy the SLO requirement. A wakeup server selection method selects a sleeping server that stores more popular data to wake up, which allows it to handle more data requests. Our trace-driven experiments in simulation and Amazon EC2 show the superior performance of DGCloud compared with previous methods in terms of deadline guarantees and system resource utilization, and the effectiveness of its individual algorithms.

LES of a turbulent swirl flame using a mesh adaptive level-set method with dynamic load balancing

A turbulent lean premixed swirl flame is numerically investigated by solving the Navier–Stokes equations with a finite-volume large-eddy simulation (LES) method. A combined G-equation progress variable approach is applied to model the combustion process using a solution adaptive level-set solver. The finite-volume and the level-set solver are parallelized and coupled on a joint hierarchical Cartesian mesh, where each solver can individually use and adapt a subset of the mesh cells. The level-set solver adapts the mesh locally in a band close to the flame front location to automatically satisfy the high accuracy requirements of the level-set solution. The numerical results for the turbulent swirl flame are in excellent agreement with experimental findings. Since the flame shape varies during the computation, the number of cells associated with the flame shifts between the individual subdomains which changes the workload distribution on the parallel computing processes. To achieve a high parallel efficiency, a dynamic load balancing method is applied which determines a new partitioning of the grid using estimated cell weights based on measured computing times to redistribute the cells among all subdomains accordingly. The efficiency of the dynamic load balancing scheme to reduce load imbalances is demonstrated for a large-scale simulation of the investigated swirl flame on 170,000 compute cores. That is, the dynamic load balancing scheme reduces the computing time of this simulation by approximately 30%.

Research on dynamic load balancing of data flow under big data platform

In the big data platform, because of the large amount of data, the problem of load imbalance is prominent. Most of the current load balancing methods have problems such as high data flow loss rate and long response time; therefore, more effective load balancing method is urgently needed. Taking HBase as the research subject, the study analyzed the dynamic load balancing method of data flow. First, the HBase platform was introduced briefly, and then the dynamic load-balancing algorithm was designed. The data flow was divided into blocks, and then the load of nodes was predicted based on the grey prediction GM(1,1) model. Finally, the load was migrated through the dynamic adjustable method to achieve load balancing. The experimental results showed that the accuracy of the method for load prediction was high, the average error percentage was 0.93%, and the average response time was short; under 3000 tasks, the response time of the method designed in this study was 14.17% shorter than that of the method combining TV white space (TVWS) and long-term evolution (LTE); the average flow of nodes with the largest load was also smaller, and the data flow loss rate was basically 0%. The experimental results show the effectiveness of the proposed method, which can be further promoted and applied in practice.

MQHD: Dynamic Load Balancing Method for Energy-Aware Storage Systems

Aiming at the problem that some disks of energy-aware storage systems are easily overloaded, a dynamic load balancing method based on multiqueue and heat degree (MQHD) is proposed. According to data popularity, MQHD divides data into multiple least recently used (LRU) queues by data access frequency and access temporal locality and uses heat degree to measure the load pressure brought by each data unit to a disk. When a disk is overloaded, MQHD calculates the load pressure ratio (LPR) according to the disk overload degree and then selects some appropriate data to migrate according to the LPR. The experimental results show that, compared with the popular data concentration method (PDC), the workload-adaptive management method (WAM), and the energy model-based file migration strategy (EM-FMS), MQHD is the most effective. Under given experimental conditions, the request change ratio (RCR) value of MQHD is 0.371, EM-FMS is 0.2872, and WAM is 0.0114. Compared with EM-FMS, MQHD has better rapidity and less overhead.

Heterogeneous CPU+GPU-Accelerated FDTD for Scattering Problems With Dynamic Load Balancing

To fully exploit hardware resources and maximize the throughput of the heterogeneous systems, a graphics processing unit (GPU)+CPU collaborative computing method for the finite-difference time-domain (FDTD) method scattering simulation is proposed in this article. For the workload imbalance caused by different computational performances and device-module affinity of different processors in the heterogeneous systems, a dynamic load balancing method is proposed, which can distribute parallel tasks intelligently according to the captured runtime performance index of processors. The method proposed herein can be applied to different heterogeneous hardware environments adaptively, owing to its online adjustment of workload distribution. Simulation results show that the proposed dynamic load balancing-based heterogeneous parallel FDTD method does not introduce additional errors in solving the scattering problems and has a great improvement in efficiency compared to a parallel implementation using a single kind of processor. It shows up to $56.76\times $ and $5.85\times $ speedups over the multicore CPU and multi-GPU parallel execution, respectively, which is quite an attractive improvement for practical applications.

A dynamic load balancing method for the evaluation of chemical reaction rates in parallel combustion simulations

The development and assessment of an efficient parallelization method for the evaluation of reaction rates in combustion simulations is presented. Combustion simulations where the finite-rate chemistry model is employed are computationally expensive. In such simulations, a transport equation for each species in the chemical reaction mechanism has to be solved, and the resulting system of equations is typically stiff. As a result, advanced implicit methods must be applied to obtain accurate solutions using reasonable time-steps at expenses of higher computational resources than explicit or classical implicit methods. In the present work, a new algorithm aimed to enhance the numerical performance of the time integration of stiff systems of equations in parallel combustion simulations is presented. The algorithm is based on a runtime load balancing mechanism, increasing noteworthy the computational performance of the simulations, and consequently, reducing significantly the computer time required to perform the numerical combustion studies.

A Lagrangian Inertial Centroidal Voronoi Particle method for dynamic load balancing in particle-based simulations

In this paper we develop a Lagrangian Inertial Centroidal Voronoi Particle (LICVP) method to extend the original CVP method (Fu et al., 2017) to dynamic load balancing in particle-based simulations. Two new concepts are proposed to address the additional problems encountered in repartitioning the system. First, a background velocity is introduced to transport Voronoi particles according to the local fluid field, which facilitates data reuse and lower data redistribution cost during rebalancing. Second, in order to handle problems with skew-aligned computational load and large void space, we develop an inertial-based partitioning strategy, where the inertial matrix is utilized to characterize the load distribution, and to confine the motion of Voronoi particles dynamically adapting to the physical simulation. Intensive numerical tests in fluid dynamics simulations reveal that the underlying LICVP method improves the incremental property remarkably without sacrifices on other objectives, i.e. the inter-processor communication is optimized simultaneously, and the repartitioning procedure is highly efficient.

Hybrid-Grained Dynamic Load Balanced GEMM on NUMA Architectures

The Basic Linear Algebra Subprograms (BLAS) is a fundamental numerical software and GEneral Matrix Multiply (GEMM) is the most important computational kernel routine in the BLAS library. On multi-core and many-core processors, the whole workload of GEMM is partitioned and scheduled to multiple threads to exploit the parallel hardware. Generally, the workload is equally partitioned among threads and all threads are expected to accomplish their work in roughly the same time. However, this is not the case on Non-Uniform Memory Access (NUMA) architectures. The NUMA effect may cause threads to run at different speeds, and the overall executing times of GEMM is determined by the slowest thread. In this paper, we propose a hybrid-grained dynamic load-balancing method to reduce the harm of the NUMA effect by allowing fast threads to steal work from slow ones. We evaluate the proposed method on Phytium 2000+, an emerging 64-core high-performance processor based on Arm’s AArch64 architecture. Results show that our method reduces the synchronization overhead by 51.5% and achieves an improvement of GEMM performance by 1.9%.

Chunked Bounding Volume Hierarchies for Fast Digital Prototyping Using Volumetric Meshes.

We present a novel approach to using Bounding Volume Hierarchies (BVHs) for collision detection of volumetric meshes for digital prototyping based on accurate simulation. In general, volumetric meshes contain more primitives than surface meshes, which in turn means larger BVHs. To manage these larger BVHs, we propose an algorithm for splitting meshes into smaller chunks with a limited-size BVH each. Limited-height BVHs make guided, all-pairs testing of two chunked meshes well-suited for GPU implementation. This is because the dynamically generated work during BVH traversal becomes bounded. Chunking is simple to implement compared to dynamic load balancing methods and can result in an overall two orders of magnitude speedup on GPUs. This indicates that dynamic load balancing may not be a well suited scheme for the GPU. The overall application timings showed that data transfers were not the bottleneck. Instead, the conversion to and from OpenCL friendly data structures was causing serious performance impediments. Still, a simple OpenMP acceleration of the conversion allowed the GPU solution to beat the CPU solution by 20 percent. We demonstrate our results using rigid and deformable body scenes of varying complexities on a variety of GPUs.

Towards Long-View Computing Load Balancing in Cluster Storage Systems

In large-scale computing clusters, when the server storing a task's requested data does not have sufficient computing capacity for the task, current job schedulers either schedule the task to the closest server and transmit to it the requested data, or let the task wait until the server has sufficient computing capacity. The former solution generates network load while the latter solution increases task delay. To handle this problem, load balancing methods are needed to reduce the number of overloaded servers due to computing workloads. However, current load balancing methods do not aim to balance the computing load for the long term. Through trace analysis, we demonstrate the diversity of computing workloads of different tasks and the necessity of balancing the computing workloads among servers. Then, we propose a cost-efficient Computing load Aware and Long-View load balancing approach ( CALV ). CALV is novel in that it achieves long-term computing load balance by migrating out an overloaded server data blocks contributing more computing workloads when the server is more overloaded and contribute less computing workloads when the server is more underloaded at different epochs during a time period. Based upon the task schedules, we further propose a task reassignment algorithm that reassigns tasks from an overloaded server to other data servers of the tasks to make it non-overloaded before CALV is conducted. The above methods are for the tasks whose submission times and execution latencies can be predicted. To handle unexpected tasks or insufficiently accurate predictions, we propose a dynamic load balancing method, in which an overloaded server dynamically redirects tasks to other data servers of the tasks, or replicates the tasks’ requested data to other servers and redirects the tasks to those servers in order to become non-overloaded. Finally, we propose a proximity-aware tree based distributed load balancing method to reduce the reallocation cost and improve the scalability of CALV . Trace-driven experiments in simulation and a real computing cluster show that CALV outperforms other methods in terms of balancing the computing workloads and cost efficiency.

Highly scalable SFC-based dynamic load balancing and its application to atmospheric modeling

Load balance is one of the major challenges for efficient supercomputing, especially for applications that exhibit workload variations. Various dynamic load balancing and workload partitioning methods have been developed to handle this issue by migrating workload between nodes periodically during the runtime. However, on today’s top HPC systems–and even more so on future exascale systems–runtime performance and scalability of these methods becomes a concern, due to the costs exceeding the benefits of dynamic load balancing. In this work, we focus on methods based on space-filling curves (SFC), a well-established and comparably fast approach for workload partitioning. SFCs reduce the partitioning problem from n dimensions to one dimension. The remaining task, the so-called 1D partitioning problem or chains-on-chains partitioning problem, is to decompose a 1D workload array into consecutive, balanced partitions. While published parallel heuristics for this problem cannot reliably deliver the required workload balance, especially at large scale, exact algorithms are infeasible due to their sequential nature. We therefore propose a hierarchical method that combines a heuristic and an exact algorithm and allows to trade-off between these two approaches. We compare load balance, execution time, application communication, and task migration of the algorithms using real-life workload data from two different applications on two different HPC systems. The hierarchical method provides a significant speed-up compared to exact algorithms and yet achieves nearly the optimal load balance. On a Blue Gene/Q system, it is able to partition 2.6 million tasks for 524288 processes with over 99% of the optimal balance in 23.4 ms only, while a published fast exact algorithm requires 6.4 s. We also provide a comparison to parallel load balancing methods implemented in the Zoltan library and present results from applying our methods to COSMO-SPECS+FD4, a detailed atmospheric simulation model that requires frequent dynamic load balancing to run efficiently at large scale.