Efficient Load Balancing Technique Research Articles

An efficient load balancing technique using CAViaR-HHO enabled VM migration and replica management in cloud computing

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.

An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

Background: In the cloud environment, satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumer and service provider. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objective: The research aim is to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, authors have proposed a new load-balancing algorithm named eB-GAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant-Colony algorithm. Based on the availability of resources and load on a virtual machine, a fitness value is assigned to all virtual machines. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment.

An efficient load balancing technique for task scheduling in heterogeneous cloud environment

An efficient load balancing technique for task scheduling in heterogeneous cloud environment

SLLB-DEVS: An Approach for DEVS Based Modeling of Semiconductor Lithography Load Balance

In industrial applications, software related to computational lithography using a DP system method, which refers to how efficiently hardware resources are used, has a significant impact on performance. Because the amount of data to be processed per unit of time is comparatively large in the current semiconductor industry, the efficiency of hardware should be increased through job 12 scheduling by using the most efficient load balancing techniques possible. For efficient scheduling of the load balancer, these are necessary to predict the end time of a given job; this is calculated based on the performance of computing resources and the development of effective traffic distribution algorithms. Due to the high integration of semiconductor chips, the volume of mask exposure data has increased exponentially, the number of slave nodes is increasing, and most EDA tools require one license per DP node to perform a simulation. In this paper, in order to improve efficiency and reduce cost through more efficient load balancing scheduling, a new type of DEVS load balancing method was studied based on the existing industrial E-beam cluster model. The designed DEVS model showed up to four times the throughput of the existing legacy model for medium and large clusters when the BSF policy was applied.

A Two-Phase Load Balancing Algorithm for Cloud Environment

Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.

Comparative Analysis of Load Balancing Algorithms for Cloud Computing in IoT

Background: Cloud computing is a widely adopted computing paradigm and its importance has increased multi-folds in the recent past due to the inception of Internet of Things (IoT). Objectives: Efficient load balancing techniques are required to optimize the use of the cloud resources although load balancing in cloud is known to be a NP-hard problem. Methods: This work focuses on multiple load balancing algorithms whose performance has been analysed and compared under varying load conditions. Results: Comparative analysis of 5 algorithms is given, among which max-min algorithm is found to be the best performing algorithm with approximately 28% better job finish time and 23% higher throughput than the worst performing algorithm (FCFS). Conclusion: Simulations have been performed in CloudSim under varying input loads and the performance has been analysed under multiple scenarios. All the simulations have pointed towards the superiority of Max-min algorithm over other algorithms. This work will prompt the researchers to further investigate into load balancing algorithms so that better results are achieved.

Load Balancing in Software-Defined Networks Using Spider Monkey Optimization Algorithm for the Internet of Things

In the Internet of Things (IoT), the number of devices connected to the internet, and they can collect and exchange information at any time. IoT is helpful for the progress of a smart city and different applications. Software-Defined Network (SDN) offers programmability and flexibility in the IoT network. Nevertheless, the adoption of the number of gadgets will increase the transmission delay and this will lead the network to heavy loaded. To overcome this issue, an efficient load balancing technique has to be presented in the SDN network. By considering this solution as an aim, spider monkey optimization algorithm based load balancing (LB-SMOA) is presented in this paper. Using this technique, the controller with minimum load is selected and this selected controller balances the load of the heavily loaded controller. Simulation results show that the performance of the proposed LB-SMOA outperforms the existing load balancing techniques in terms of average response time, packet loss rate, and throughput.

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q- learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

Path based load balancing for data center networks using SDN

Due to the increase in the number of users on the internet and the number of applications that is available in the cloud makes Data Center Networking (DCN) has the backbone for computing. These data centre requires high operational cost and also experience the link failures and congestions often. Hence the solution is to use Software Defined Networking (SDN) based load balancer which improves the efficiency of the network by distributing the traffic across multiple paths to optimize the efficiency of the network. Traditional load balancers are very expensive and inflexible. These SDN load balancers do not require costly hardware and can be programmed, which it makes it easier to implement user-defined algorithms and load balancing strategies. In this paper, we have proposed an efficient load balancing technique by considering different parameters to maintain the load efficiently using Open FlowSwitches connected to ONOS controller.

Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach

Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users’ requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.

Cloud Computing based on the Load Balancing Algorithm

Emerging cloud computing technology is a big step in virtual computing. Cloud computing provides services to clients through the internet. Cloud computing enables easy access to resources distributed all over the world. Increase in the number of the population has further increased the challenge. The main challenge of cloud computing technology is to achieve efficient load balancing. Load balancing is a process of assigning load to available resources in such a way that it avoids overloading of resources. If load balancing is performed efficiently, it improves QoS metric including cost, throughput, response time, resource utilization and performance. Efficient load balancing techniques also provide better user satisfaction. Various load balancing algorithms are used in different scenarios for ensuring the same. In the current research, we will study different algorithms for load balancing and benefits and limitations caused to the system due to the algorithms. In this paper, we will compare static and dynamic load balancing algorithms for various measures of efficiency. These will be useful for future research in the concerned field.

Green Computing: Efficient Energy Load Balancing Technique in Cloud Computing

Green computing comes under many aspects of emerging technologies to improve the product's life time. In this paper the aim of green computing is maximizing the energy efficiency and load balancing on data centres and green server. In this paper mathematical model proved that improve efficiency of servers and effective transmission of data, we described effective load balancing that provide the energy consumption on data servers in cloud computing. The effective results are carried out by the energy efficient load balancing technique.

Energy-efficient load balancing scheme for two-tier communication in wireless sensor networks

Wireless sensor network (WSN) has gained an enormous attention of researchers with its dynamic applications. Energy is considered as a scarce and the most vital resource in WSNs. In clustering-based approach, there is huge energy consumption while communicating data from cluster to base station (BS) and from sensor nodes to cluster head within a cluster. The repetitive use of same nodes and paths can result in network hole problem and service unavailability. There are number of research areas which have addressed the issues of energy efficiency and service availability. Load balancing is considered as one of the key techniques which are used to balance the trade-off between energy efficiency and service availability. In this research work, a novel real-time energy efficient load balancing technique for two-tier communication is proposed in which, initially in Tier 1, the energy consumption is reduced for communication between cluster to BS by applying space-time block coding over M-ary quadrature amplitude modulation and binary phase-shift keying modulations. In Tier 2, within a cluster, the energy consumption of communication among sensor nodes and CHs is reduced by utilizing the concepts of feedback control system, in which there is no need of the knowledge of static traffic demands. The performance of the proposed technique has been compared with the existing techniques in terms of energy utilization variation with varying order of transmit diversity and varying cluster BS distances, optimization of constellation sizes, energy utilization variation with cluster–BS distances for varying maximum link utilization and data rates along with varying traffic distribution and topology.

Group-based Load Balancing Approach for Cloud Data Centre

Nowadays most of the IT organisations are using cloud to fulfil computation and storage requirements. Cloud computing provides various benefits such as high availability, fault tolerance, flexibility, reduce overheads and cost for users, scalability and on-demand services. Many of these depend upon proper load balancing. An efficient load balancing technique effectively utilises the resource and avoids uneven distribution among various jobs. In existing load balancing approaches, virtual machines are assigned to Virtual Machines (VM) without considering the requirements of the other jobs. This work introduces a new scheduling policy of VM allocation in cloud system which considers requirements of multiple cloudlets and selects the cloudlets for maximising the CPU utilisation. Furthermore, on comparing our results with other approaches, significant increase in performance has been observed and our technique outplays other techniques in all testing scenarios.

A Framework to Optimize Load Balancing to Improve the Performance of Distributed Systems

Load balancing in distributed systems is the technique to handle maximum requests in minimum time. Load balancing aims to optimize resource use, maximum throughput, minimize response time, increase reliability and scalability. Trying to achieve this is however not an easy task. Various techniques and approaches have been used to achieve this goal. The proposed work is inspired by the Ant Colony Optimization (ACO) technique. The algorithm is designed on a Semi-Distributed system where clusters are formed and each cluster consists of number nodes. Ant is created on these clusters and acts accordingly the needs of the environment. The objective of paper is to develop an efficient load balancing technique that can improve the performance of system.

An Analysis of Swarm Intelligence based Load Balancing Algorithms in a Cloud Computing Environment

The current advances in cloud computing layout well established research in Distributed computing, virtualization, web services, utility computing, have provided many advantages in scalability, cost and efficiency for cloud service users. These advantages are further expected to satisfy the demands of cloud users for cloud services in efficient and effective manner. This brings the issue of fault tolerance, scalability, efficiency, high availability. Central to these problems require the dynamic and efficient load balancing techniques. In this paper, we survey a special group of Swarm intelligence based load balancing algorithm and discuss the advantages and issues of these algorithms for cloud computing environment.

Experimental Evaluation of the Performance of Processing Stealing Technique: A Scalable Load Balancing Technique for a Dynamic Multiprocessor System

paper reports preliminary experimental evaluation of a Processing Elements Stealing (PE-S) technique which was targeted as efficient and scalable load balancing technique for dynamically structured multiprocessor systems. The multiprocessor system is imagined as a dynamic cluster based multiprocessor. Each cluster of the multiprocessor system is a node in symmetric multiprocessor architecture and the number of Processing Element (PE) in each cluster is dynamically determined at runtime. The PE-S technique dynamically computes the configuration ratio using the number of threads in the dynamically assigned tasks to generate the new number of PE for each cluster. This new configuration ratio is thereafter used to balance the additional computational work generated by runtime instantiation of current workloads for each cluster. In this work, the efficiency of the PE-S was evaluated using memory traces of some tightly parallel applications where the amount of parallelism is parameterized. These traces were used as workloads on two different simulation setups; the first is a dynamic multiprocessor with PE-S while the other was also a dynamic multiprocessor but without PE-S. This is to evaluate the performance of the PE-S load balancing technique on the targeted multiprocessor. Also the efficiency of PE-S reconfigurations was compared with other possible reconfiguration ratios. The experimental results showed that the load balancing algorithm is efficient and scalable for balancing at least 100,000 instructions tasks and PE-S generated ratios are averagely better than any other reconfiguration ratios.

An Optimization-Based Parallel Particle Filter for Multitarget Tracking

Particle filters are used in state estimation applications because of their capability to solve nonlinear and non-Gaussian problems effectively. However, they have high computational requirements, especially in the case of multitarget tracking, where data association is the bottleneck. In order to perform data association and estimation together, an augmented state vector, whose dimensions depend on the number of targets, is typically used in particle filters. With data association, the computational load increases exponentially as the number of targets increases. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In the work presented here, an optimization-based scheduling algorithm, that is suitable for parallel implementation of particle filter, is presented. This proposed scheduling algorithm minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. Further, this scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication-intensive parallel implementation of the particle filter without compromising tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization.

Parallel processing for stepwise generalisation method on multi-core PC cluster

An approximate query, which is an approximate pattern matching in sequence databases, is one of the most important techniques for many different areas, such as computational biology, text mining, web intelligence and pattern recognition; it returns many similar sub-sequences. In this paper, we refer to a set of such similar sub-sequences as a mismatch cluster. To support users who execute an approximate query on a sequence database to find the regularities of approximate patterns that similar to the query pattern, we have developed the stepwise generalisation method that extracts a reduced expression, called a minimum generalised set, from a mismatch cluster. This paper proposes a novel parallelisation model with a hierarchical task pool for the parallel processing of the stepwise generalisation method on a multi-core PC cluster. To manage tasks efficiently on multi-core CPUs, the proposed model uses the hierarchical task pool and an efficient hierarchical dynamic load balancing technique. We evaluate the proposed method using real protein sequences on an actual multi-core PC cluster. Experimental results confirm that the proposed method performs well on multi-core CPUs and on a multi-core PC cluster.