A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

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.

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.

Bandwidth load balancing is a critical technique for improving the quality of service (QoS) in local area networks (LANs). Insufficient load balancing and QoS utilization can lead to network congestion, reduced performance, latency, packet loss, and an overall degradation of network performance. This paper presents an overview of load balancing techniques, load balancing algorithms, QoS parameters, load balancing strategies, and considerations for calculating bandwidth. The challenges of implementing load balancing algorithms and preferences for their implementation are discussed. Accurate network measurements and monitoring, coordination between network devices, security and privacy considerations, and careful evaluation of load balancing algorithms are among the challenges faced. The paper also discusses optimization and monitoring of bandwidth use, including options such as increasing bandwidth, bandwidth throttling, and data transfer throttling. The findings presented in this paper provide insights into the importance of load balancing and QoS in LANs and offer guidance for addressing related challenges.

Objectives: To enlighten the issues and challenges of Load Balancing Algorithm (LBA) for cloud computing environment. In this study the authors proposed a LBA for cloud computing server. Methods/Statistical Analysis: The numerous LBAs are compared with objective, merit, demerit and challenges of the LBA in terms of parameters such as performance, response time, scalability, overhead and many more. This study facilitates identification of efficient LBA for optimizes resource use, minimum response time, maximum throughput and avoidance of overload. Findings: The load balancer is permitted in most instances to provide support continuity as well as dealing with extra traffic. Consequently the useful load balancing algorithms required to find inexpensive resource usage by provisioning of resources to cloud users. The use of proposed algorithm is to improve the overhead and load imbalance factor of the system. Application: The Load Balancing (LB) is a vital feature of cloud computing environment. The economical LB algorithm assures cost effective resource usage by provisioning of resources to cloud users on demand schedule in pay-as-you-say-manner. The LB may actually provide assistance prioritizing users by using suitable scheduling requirements and also it is crucial part in cloud computing for maintaining the work load across various systems over the network and gives minimum processing time, minimum response time and ignores the workload.

When provided as a service, Cloud Computing (CC) makes it possible to access dynamically scalable and regularly virtualized resources through the Internet. It is hoped that this research will contribute to the disciplines of load balancing and task scheduling by understanding the concept of load balancing in cloud computing through the use of a simulator known as "CloudSim." In this research, it was found that, one of the most significant challenges in cloud computing is load balancing. This prevents scenarios in which some nodes are heavily loaded while others are idle or performing limited work. Load balancing (LB) spreads the dynamic local workload evenly among all the nodes in the cloud. The most important decision to make is the sort of program to run on the available system in order to maximise resource utilisation. Scheduling is the process of allocating a job to the resources that can complete it in the Cloud Computing environment.A heuristic is a strategy that may be used to solve issues rapidly. It is vital to have efficient and effective scheduling procedures in place in order to make full use of the large array of cloud computing possibilities. As a consequence of this investigation, a novel scheduling algorithm is introduced that is derived from two old approaches, Min-min and Max-min, and is designed to capitalise on their advantages while eliminating their disadvantages. The parameters are then evaluated by the CloudSim simulator. There are high-level techniques that function as the driving force behind a problem-specific heuristic known as Meta-Heuristics. A New Meta-Heuristic methodology has been developed by investigating and analysing previously developed processes. The primary purpose is to avoid the drawbacks of iterative improvement, particularly descents, by allowing the local search to escape local optima while doing local searches. One technique to accomplish this is to provide more "intelligent" possibilities to the local search engine rather than random initial responses as a way to assist it. The proposed meta-heuristic model is evaluated using CloudSim and the advantages of two existing techniques, Particle Swarm Optimisation and Genetic Algorithm with the results of several simulations.

Cloud computing is a new era technology, which is entirely dependent on the internet to maintain large applications, where data is shared over one platform to provide better services to clients belonging to different organizations. It ensures maximum utilization of computational resources by making availability of data, software and infrastructure with at a lower cost in a secure, reliable and flexible manner. Fog computing is emerging as a powerful and popular computing paradigm, which extends cloud computing paradigm to enable the service execution in the edge network. The mobile and IoT (Internet of Things) applications could choose the computing nodes in both fog and cloud for resource provisioning. Generally, load balancing is one of the key factors to achieve resource efficiency and avoid bottleneck, overloaded and low-load resource usage. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of Service (QoS) parameters such as resource utilization, cost, response time, performance, and energy consumption can be improved with load balancing. Recently, several studies have been conducted on load balancing in fog computing. This paper introduces an Artificial Immune System (AIS)-based load balancer for efficient resource allocation in fog computing, utilizing a multi-metric fitness function incorporating CPU and RAM availability, energy efficiency, and proximity. A mutation mechanism enhances adaptability and prevents allocation bias. the results reveal that the AIS achieves 41.67% better resource allocation efficiency compared to Random Load Balancer and up to 30.77% improvement over Least connection and Round Robin strategies. The AIS model effectively balances CPU utilization, latency, and energy consumption, providing a robust and scalable solution for dynamic fog networks.

Cloud computing delivers the on-demand virtualized resources to its consumer for servicing their request on a metered basis. During the high demand of cloud resources the load on system increases that may unbalance the system which affects the quality of service parameters (QoS) adversely that leads to violations of service level agreement (SLA). Role of load balancing is significant in such an environment as it enhances the distribution of workload across multiple devices for example across network links, a cluster of servers, disk drives, etc. The present research work introduced a multi scheduler for balancing the load across the system that aims to optimize the QoS parameters such as response time, resource utilization, and the average waiting time by exploiting these virtual resources in the cloud environment. The performance of the proposed approach analyzed and tested in CloudSim that to optimize these parameters for the current approach. The authors found that our QoS enabled JMLQ approach achieved better results in comparison to our previous JMLQ approach and other variants.

Fog computing idea is presented to reduce the burden on cloud and deliver similar facilities as cloud. However, fog encompasses small area relatively to cloud by saving the data for shorter amount of time and sending it to cloud for permanent storage. In this paper, a joint cloud and fog centered environment for efficient energy supervision of buildings is proposed. It caters for the data of groups of buildings at buyers’ end. 12 fogs are utilized for 6 different regions in the world which are based on 6 continents. Additionally, each fog is linked to a group of buildings and two fogs are linked to two groups. Each group comprises of multiple smart buildings and these buildings has at least 100 apartments. To manage the energy requirement of consumers, micro grids (MGs) are available near the buildings and accessible by the fogs. Energy is managed for the apartments and fog helps the consumers to fulfill their load demands through nearby MGs and cloud servers’ communication. So, the load on cloud and fog should be balanced and load balancing algorithms are used to manage the load using VMs. These algorithms are round robin (RR) and throttled and Priority Based load balancing and these algorithms are compared for a single service broker policy. Service broker policy considered in this paper is; dynamically reconfigure with load. Priority based load balancing is proposed for balancing the load on fog and results of proposed balancing algorithm are compared with other algorithms. While considering the proposed algorithm, results are compared with two load balancing algorithms and from this, the proposed algorithm gives better results than RR algorithm rather than throttled.

Cloud Computing is a major area of research. Cost and Load balancing has become an important QoS parameter. Load Balancing directly affects the Reliability, Response Time, Throughput and Energy Efficiency of a Server. A good Load Balanced architecture implies minimized overall time, less server failure, minimized response time, increased throughput and less wastage of energy. Such architecture also implies a better scheduling of jobs and achieving a better Quality of Service (QoS). The paper states that Load Balanced Resource Scheduling also leads to a decreased overall cost. A new modified cost effective algorithm is proposed which minimizes the overall resource cost. Various Simulations demonstrated in the paper prove the above fact. Also as the approach deals with the energy efficiency concept, it is also a move towards Green IT.

Computational grids provide a massive source of processing power, providing the means to support processor intensive applications. The strong burstiness and unpredictability of the available resources raise the need to make applications robust against the dynamics of grid environment. The two main techniques that are most suitable to cope with the dynamic nature of the grid are load balancing and job replication. In this work, we develop a load-balancing algorithm by juxtaposes the strong points of neighbor-based and cluster-based load-balancing methods. We then integrate the proposed load-balancing approach with fault-tolerant scheduling namely MinRC and develop a performance-driven fault-tolerant load-balancing algorithm or PD_MinRC for independent jobs. In order to improve system flexibility, reliability, and save system resource, PD_MinRC employs passive replication scheme. Our main objective is to arrive at job assignments that could achieve minimum response time, maximum resource utilization, and a well-balanced load across all the resources involved in a grid. Experiments were conducted to show the applicability of PD_MinRC. One advantage of our approach is the relatively low overhead and robust performance against resource failures and inaccuracies in performance prediction information.

Cloud computing is growing rapidly over the years and it faces challenges especially in resource management. Resource management in cloud computing is necessary due to its distributed nature with different user demands. Quality of Service (QoS), load balancing and throughput are identified as some of the benefits of proper resource management. This research focuses on job scheduling and resource load balancing in cloud environment. We proposed an efficient algorithm based on multi-criteria strategy. The algorithm consists of two main phases. In the first phase the shortest job completion time is measured based on the completion time of three techniques i.e. min-min, max-min and suffrage. Meanwhile in the second phase genetic algorithm is implemented for resource load balancing. Cloud Sim simulator is used to measure the performance and efficiency of the proposed algorithm. The proposed algorithm enhances jobs scheduling and resource load balancing by ensuring an efficient utilization of the available resources. Keywords: Cloud Computing, Genetic Algorithm, Job Scheduling, Load Balancing, Virtual Machine

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.

Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms.

The increasing complexity of web applications and the growing number of concurrent users require load-balancing mechanisms that can maintain system performance and service reliability. This study aims to evaluate and compare the performance of NGINX and Caddy as load balancers for WordPress applications deployed in a Podman-based containerized environment. A quantitative experimental approach is employed by measuring key Quality of Service (QoS) metrics, including request rate, response time, and error rate, across multiple load scenarios and load-balancing algorithms, namely round-robin and least connection. The experimental results indicate that under low to medium load conditions, both NGINX and Caddy exhibit comparable performance. However, under high-load conditions, performance differences become significant. Caddy, particularly when using the least-connection algorithm, can maintain substantially higher throughput, achieving an aggregate request rate of approximately 2349.55 req/s, compared to NGINX, which records approximately 426.02 req/s under comparable configurations. In terms of response time, the P90 response time for Caddy is approximately 289.79 ms, whereas NGINX exceeds 4 seconds. Furthermore, Caddy demonstrates superior reliability with an error rate of 0% across all testing scenarios, while NGINX experiences extremely high request failure rates under extreme load conditions. These findings suggest that Caddy, particularly when combined with the least-connection algorithm, provides a more adaptive and stable load-balancing solution for dynamic, containerized web applications with fluctuating traffic patterns. Nevertheless, this study is conducted in a controlled environment using baseline configurations without advanced optimizations; therefore, the results represent a relative performance comparison rather than an absolute benchmark for all production environments. Future research is recommended to investigate advanced configurations, different web application types, and more diverse workload scenarios to achieve a more comprehensive understanding of load balancer performance in containerized environments.

A multiprocessor system is a computer with two or more central processing units (CPUs) with each one sharing the common main memory as well as the peripherals. Multiprocessor system is either homogeneous or heterogeneous system. A homogeneous system is a cluster of processors joined to a high speed network for accomplishing the required task; also it is defined as parallel computing system. Homogeneous is a technique of parallel computing system. A heterogeneous system can be defined as the interconnection of a number of processors, having dissimilar computational speed. Load balance is a method of distributing work between the processors fairly in order to get optimal response time, resource utilization, and throughput. Load balancing is either static or dynamic. In static load balancing, work is distributed among all processors before the execution of the algorithm. In dynamic load balancing, work is distributed among all processors during execution of the algorithm. So problems arise when it cannot statistically divide the tasks among the processors. To use multiprocessor systems efficiently, several load balancing algorithms have been adopted widely. This paper proposes an efficient load balance algorithm which addresses common overheads that may decrease the efficiency of a multiprocessor system. Such overheads are synchronization, data communication, response time, and throughput.