Cloud Load Balancing - Honey Bees Inspired Effective Request Balancing Strategy

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 load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional cloud load balancing algorithms.

Cloud computing is an increasing field in studies and industry today, which includes virtualization, distributed computing, net, software program and web services. A cloud consists of several factors consisting of customers, records centers and disbursed servers, net and it includes fault tolerance, excessive availability, effectiveness, scalability, flexibility, reduced overhead for users, decreased fee of ownership, on demand offerings and etc. The offerings of cloud computing are becoming ubiquitous, and function the primary source of computing power for special packages like corporations and personal computing programs. In this paper we delivered the novel load balancing algorithm the use of fuzzy technique in cloud computing, in which load balancing is a middle and hard problem in Cloud Computing. The processor pace and assigned load of Virtual Machine (VM) are used to stability the burden in cloud computing thru fuzzy logic.

In cloud computing, load balancing is an important issue. We propose an improvement on random sampling algorithm for load balancing in cloud based systems. We have redesigned the Biased Random Sampling algorithm by making use of queue length and processing time as parameters for arriving at load balancing among competing nodes. Our experimental results show that the proposed algorithms perform better than the current Biased Random Sampling algorithm.

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.

Cloud computing has become biggest buzz in the computer era these days. It runs entire operating systems on the cloud and do everything on cloud to store data off-site. Cloud computing is primarily based on grid computing, but it’s a new computational model. Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services. The primary substance of cloud computing is to deal the computing power, storage, different sort of stages and services which assigned to the external users on demand through the internet. Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing. In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example, when cloud clients try to access and send request to the same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers. Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded. A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time. The proposed hybrid algorithm has been experimented by using CloudSim simulator. The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm. Â

Objectives: One of the essential requirements for improving Quality of Service of Cloud is to design load balancing algorithms that equally spread the load among the VMs such that neither of them is overloaded at any given point of time while ensuring that each of the VM is optimally utilised. The objective of this paper is to propose an efficient VM load balancing policy called Timestamp based Stateful Throttled VM load balancing algorithm. Methods/Statistical Analysis: The proposed algorithm deals with the space performance constraints of the existing Stateful Throttled VM Load balancing algorithm. It is based on the principle of locality of reference and deletes the old state information to reclaim space. The authors have carried out an experimental analysis to compare the proposed algorithm with the existing algorithms. Findings: The comparative analysis shows that the algorithm behaves in the same manner as Stateful Throttled Algorithm while taking into consideration its space performance limitations. The authors have observed better performance in terms of response time and data center processing time for Timestamp based Stateful Throttled VM load balancing algorithm than Throttled VM load balancing algorithm in case of spatially distributed VMs. Application/Improvements: The salient features of the proposed algorithm are better response time and data center processing time in case of spatially distributed data centers in cloud.

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.

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.

The rapid adoption of cloud-native architectures, driven by containerization and orchestration platforms such as Kubernetes, has transformed the scalability and flexibility of modern applications. Yet, achieving true high availability (HA) in such dynamic environments remains challenging, as traditional load balancing approaches—such as Round Robin or Least Connections—are inherently reactive and fail to capture the transient health conditions of containerized workloads. This paper presents an integrated approach to cloud load balancing and container orchestration for high availability through the Predictive and Health-Aware Load Balancing (PHAL) algorithm. PHAL combines two synergistic components: a Predictive Forecasting Module (PFM), which leverages Long Short-Term Memory (LSTM) networks to anticipate workload surges and proactively scale resources, and a Health-Aware Routing Module (HRM), which dynamically distributes traffic across pods based on a composite health score derived from latency, CPU utilization, active connections, and predicted headroom. Through experimental evaluation on a Google Kubernetes Engine cluster running a multi-service e-commerce application, PHAL demonstrates significant improvements over conventional load balancing strategies, including reduced tail latency, enhanced throughput, and resilience against node and pod failures. The study highlights that integrating intelligent cloud load balancing with orchestration frameworks is essential for ensuring robust, fault-tolerant, and highly available cloud-native systems.

Load balancing algorithms are designed to achieve efficient resource utilisation in large-scale distributed systems. The primary goal of designing load balancing algorithms is to balance the overall workload among all the nodes in distributed systems to improve the performance. This paper presents an in-depth study and detailed comparative analysis of different load monitoring and balancing algorithms employing fuzzy logic and mobile agents. A detailed qualitative as well as quantitative analysis of algorithmic performances is included. The analytical survey presented in this paper would enable understanding of applications of different algorithms under different computing environments. Furthermore, this paper proposes a hybrid architecture combining fuzzy logic and mobile agents for load balancing and monitoring in distributed systems.

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.

Infrastructure as a service (IaaS) is a type of cloud computing in which a third-party provider hosts virtualized computing resources over the Internet for executing tasks in the cloud computing. Whenever some VMs are overloaded and some VMs are under loaded, this situation may cause to SLA violation and leads to the reduction of customer satisfaction level and further affects the cloud provider leading to penalty. However, in this study, we propose a Multiple Agent-based Load Balancing Algorithm (MA) in which shift the load in the IaaS to achieve well dynamic load balancing across virtual machines for maximizing the utilization. The proposed algorithm with regard to changing environment and characteristics of the VMs, perform both of sender-initiated and receiver-initiated approach to balances the load of an IaaS in such a way that the amount of waiting time of the tasks in the queue is minimal and at the same time the SLA is guaranteed.We have compared the proposed algorithm with existing load balancing and scheduling algorithms via simulation. The simulation results show that the proposed algorithm is more effective and there is a good improvement in the load-balance, response time and makespan.

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.