Threshold-based load balancing algorithm for Big Data on a Cloud environment

The concept oft Cloud computing has significantly changed the field of parallel and distributed computing systems today. Cloud computing enables a wide range of users to access distributed, scalable, virtualized hardware and/or software infrastructure over the Internet. Load balancing is a methodology to distribute workload across multiple computers, or other resources over the network links to achieve optimal resource utilization, maximize throughput, minimum response time, and avoid overload. With recent advent of technology, resource control or load balancing in cloud computing is main challenging issue. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper presents a review of a few load balancing algorithms or technique in cloud computing. The objective of this paper is to identify qualitative components for simulation in cloud environment and then based on these components, execution analysis of load balancing algorithms are also presented.

Security of cloud computing is one of the challenges to be addressed before the novel pas-as-you-go business model is widely applied, and dependability is one of the most important means to improve security of current heterogeneous cloud platforms. Previous research on dependability in computing systems only uses qualitative approaches and there are few systematic works on dependability in cloud systems. In this paper, the definition of dependability in cloud systems is given and a series of quantitative indicators are presented to evaluate the dependability. A novel cloud dependability model CDSV is established to enhance the security of heterogeneous cloud environments. System-level virtualization techniques are used to enhance the dependability of cloud environments. Systematic analysis shows that this model can enhance the system dependability and security. Experimental results show that the dependability model CDSV can efficiently and safely construct dependability relationship in heterogeneous cloud environments.

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

An emerging method of digital computing known as "cloud computing" has recently gained immense popularity. While there are many benefits to using the cloud over the internet, there are also serious challenges that must be addressed in order to boost the efficiency of this method. Challenges to cloud computing via the internet include load balancing, work scheduling, fault tolerance, and several security concerns. The effectiveness of the cloud may be enhanced by fixing a number of problems, one of the most pressing being load balancing. To prevent any one node from being too overburdened or underused, a system employs a technique known as load balancing. In order to increase utilization and minimize overall task execution time, load balancing algorithms are developed to distribute work fairly across available resources. In this article, a review and comparison of different load-balancing algorithms is provided. This paper provides a basis of the application of different load-balancing techniques, which utilize the approach of soft computing within the cloud computing environment.

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes.

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure. User requests are submitted through an interface to these servers that provide service to them in an on-demand basis. The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category. Task scheduling in cloud poses numerous challenges impacting the cloud performance. If not handled properly, user satisfaction becomes questionable. More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment. The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution. An improvised seagull optimization algorithm which combines the features of the Cuckoo search (CS) and seagull optimization algorithm (SOA) had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment. The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment. Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization (MO-ACO), ACO and Min-Min algorithms. The proposed SOA-CS technique had produced an improvement of 1.06%, 4.2%, and 2.4% for makespan and had reduced the overall cost to the extent of 1.74%, 3.93% and 2.77% when compared with PSO, ACO, IDEA algorithms respectively when 300 vms are considered. The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

With heavy demand for cloud technology, it is important to balance the cloud load to deliver seamless Quality of Services to the different cloud users. To address such issues, a new hybridized technique Artificial Neural Network based Load Balancing (ANN-LB) is introduced to calculate an optimized Virtual Machine (VM) load in cloud systems. The Particle Swarm Optimization (PSO) technique is used to perform task scheduling. The performance of the proposed ANN-LB approach has been analyzed with the existing CM-eFCFS, Round Robin, MaxMin, and MinMin algorithms based on MakeSpan, Average Resource Utilization, and Transmission Time. Calculated values and plotted graphs illustrate that the presented work is efficient and effective for load balancing. Hybridization of ANN and iK-mean methods obtains a proper load balancing among VMs and results have been remarkable.

In the past few years, cloud computing has emerged as a vigorous technology in the era of internet. Its economic model fecilitate the client to use software, infrastructure, applications as a service by just paying according to the usage without possessing them. The development of cloud computing needs to balance the load among different replicated services offered by cloud environment. In terms of responsiveness and availability, load balancing is one major aspect to deal with. Load balancing ensures that the load is equally spread over the cloud servers. Efficient load balancing algorithm leads to decrease in maintenance costs and increase in resource availability. There are various factors that affect the efficiency of load balancing algorithm, two of such factors are addressed in the proposed algorithm. The download of large data files is handled by dual direction technique. This technique make use of replicated servers and download the partitions of a file simultaneously from multiple replica servers. Another factor that is taken into consideration is handling of high priority tasks. There are various parameters to define the effectiveness of a load balancing algorithm such as response time, data center processing time etc. In this present work a load balancing algorithm, based on honey bee optimization algorithm is proposed which has shown better response time. Experimental studies on standard data sets showed that the proposed request balanced honey bee algorithm compared to the original honey bee algorithm, has performed 5.2% to 9.7% better with respect to the response time and 13.8% to 68.2% better with respect to the processing time, which is a significant improvement.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

Load balancing is the foremost confront in a cloud environment. Load balancing is assisted to disseminate dynamic workload across many nodes to guarantee that not a single node gets overloaded. In the existing work Iterative Proximal Algorithm is introduced for the load balancing. This current work concentrates on request migration criteria between multiple servers for load balancing. It varies from common load balancing crisis, assume that it is under a disseminated, competitive environment, and is non-cooperative. For every server, its projected response time is taken to be a dis-utility function and its value is reduced. But load balancing with dependent and independent servers is a confronting errand. In order to resolve this challenge, Meta-heuristic scheme is carried out based on firefly algorithm to balance load on multiple servers. The main contribution of this research work is to perform the load balancing to improve the computational efficiency of the task submitted by the users. The anticipated multi-server load balancing is carried out based on dependent and independent tasks. The jobs comprise of various interdependent errands in which independent tasks might be processed in multiple cores of the VM or multiple VMs. The errands come through the server’s run-time in arbitrary time intervals for different loads. This crisis is resolved by using variation inequality (VI) theory and confirming that there prevails Nash equilibrium resolution set for the devised game. After this, a Nash equilibrium resolution for multi-server load balancing is calculated by using an Iterative Proximal algorithm (IP) is anticipated with Meta heuristic Firefly Optimization Algorithm. Convergence of IPA algorithm is analyzed so that it gets converged to Nash equilibrium. At last, many numerical computations are performed to confirm theoretical analysis.

Cloud computing(CC), which utilizes massively virtualized data centers to deliver quick and affordable computing solutions, has developed ‎into an established industrial standard that is growing quickly. To handle such a massive amount of data effectively, cloud computing mostly ‎relies on automation and dynamic resource management. In cloud computing, load balancing (LB) is a vital technique for maximizing ‎resource utilization and making sure that no resource is used up. Without the requirement for physical infrastructure, cloud ‎LB allows online platforms to adjust their resources in response to traffic demands. In a cloud environment, workload and resource allocation entail determining the best way to divide up work among several servers. For increasingly severe uncertainty problems, traditional LB ‎approaches are simple but ineffective; for this reason, meta-heuristic methods are employed. This algorithm is heuristic and is independent ‎of the complexity of the challenges. Meta-heuristics approaches based on Artificial Intelligence (AI) are employed to analyze real-time data ‎and intelligently distribute workload among servers. This ensures efficient operations by preventing bottlenecks and enabling proactive LB ‎decisions. The review offers a thorough analysis of meta-heuristics techniques based on artificial intelligence (AI) for static and dynamic LB ‎in both homogeneous and heterogeneous cloud systems.

Cloud computing is the means of accessing a shared pool of configurable computing resources (including hardware, software, networks, servers, storage applications and services) that can be rapidly provided, used and released with minimal effort on the part of users or service providers. But it has some of the main concerns like load management and fault tolerance. In this paper we are discussing load balancing approach in cloud computing. Load balancing is helped to distribute the workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improves the performance of the system This paper focuses on the load balancing algorithm which distributes the incoming jobs among VMs optimally in cloud data centers. In this paper, we have reviewed several existing load balancing mechanisms and we have tried to address the problems associated with them.

In Wavelength Division Multiplexing (WDM) networks, providing survivability during routing along with maintaining load balancing is a daunting task. In this paper, we propose an Ant based survivable routing and load balancing technique in WDM networks which discovers survivable and load balanced feasible paths. Ant agents are launched in the network to gather essential information such as available bandwidth, route length and number of free wavelengths. A combined feasibility value is generated from these factors for each path. While transmitting data to the destination, the source selects the path with high feasible value and low load as primary path, and other paths as backup paths. During congestion and failures, data transmission is redirected through the backup paths. Thus, our technique provides survivability and load balancing at hand efficiently. Simulation results show that our technique reduces packet drops efficiently by detecting and controlling the congestion.

Scientific applications need to handle large volume of data-intensive computations and such computational load varies over time. We propose a new load balancing strategy for cloud environments handling those applications. Our strategy exploits the advantages of both push and pull approach to achieve load balancing. Gossip protocol is used to collect the resource statistics from a small set of cluster nodes. This paper considers a dynamic, distributed system for load balancing, which uses only the neighbourhood information to achieve the same.