Modified Honey Bee Algorithm with Random Selection of Virtual Machines for Dynamic Load Balancing

Objectives: In light of the growing number of open-source simulation tools for load balancing in cloud computing, the objective of this research is to assess some of the most significant tools before selecting the best one depending on the requirements and goals of the research and also implementing the load balancing approach in the CloudAnalyst tool for dynamic workload (HTTP request) allocation and virtual machine selection in the Infrastructure as a Service (IaaS) heterogeneous cloud environment, with the aim of minimizing the searching time, improving the response time and datacenter processing time and also adds a new performance metrics such as makespan. Methods: We conducted a comparative examination of a few cloud simulators using a variety of criteria, including their core components, working models, scalability, performance metrics, simulation technique, and structural design, it incorporated and experimented with the load balancing approach with significant performance metrics such as searching time, response time, datacenter processing time and makespan in CloudAnalyst simulation tool. Findings: This research finding shows that, after reviewing, examining, and comparing some of the significant cloud simulation tools and their features in the Infrastructure as a Service cloud computing environment, the CloudAnalyst tool was best suited for implementing and experimenting with the load balancing strategies in a cloud environment with respect to significant performance metrics such as response time, datacenter processing time, cost, searching time and, throughput. Moreover, it evaluates the performance of the load balancing approach by implementing it in the CloudAnalyst tool and proves that there is an improvement in average response time of 270.91milliseconds, Datacenter processing time by 212.74 milliseconds and there is a reduction in searching time of 127.21 milliseconds and makespan value of 192.89 compared to existing approaches in a heterogeneous IaaS cloud environment. Novelty: The novelty of this research provides recommendations for researchers that the CloudAnalyst simulation tool is the ideal tool for the implementation of the load balancing approach and also incorporates the new metrics termed makespan in a dynamic IaaS cloud environment. Keywords: Cloud Computing, Load Balancing, Simulation Platforms, Virtual Machine, Infrastructure as a Service, Datacenter

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.

Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power; ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS).

Cloud Computing is computing technology in which the word cloud is defined as delivery of demand services and resources from user's application to cloud data center through internet connection as pay on demand. Cloud computing has face lots of challenges like security, data migration, work load management, data integrity, confidentiality and many more. In cloud computing, data is t on cloud data center and cloud service provider s provides data to the customer as per their requirement. Customers also access data from anywhere any place ubiquitously. In cloud computing load balancing technology is used to distribute workload for balancing load between two or more cloud server. Load balancing main aim is to optimize resource utilization, maintain cost of data center and virtual machine, maximize throughput, reduce response time and avoid overloading of load. The main objective of this research paper is to reduce data center transfer cost, total virtual machine cost, data center processing time and reduce response time using throttled load balancing policy with optimize response time service based policy. In this research paper researcher used cloud analyst simulator of cloudsim for simulation and modeling of data. This study has evaluated throttled load balancing and their optimization criteria like data center processing time and reduces cost. One more thing studied is this experiment has done for hybrid cloud by adding user bases from private and public cloud.

”Cloud computing” hosts software and other services in remote data centers that customers can access worldwide. The user may access all the services and applications online. The IT industry has benefited greatly from the proliferation of cloud computing. On the flip side, organizations moved their operations to the cloud as a result of industrial automation. A surge in demand for cloud computing was directly correlated to the quick migration of businesses. Businesses looking to minimize expenses without sacrificing service quality will find this approach to be ideal. Considering the meteoric rise of cloud computing, service providers are delighted. Contrarily, distributing resources is a challenging task. Cloud computing overcomes some of its most fundamental obstacles, one of which is the load-balancing approach employed by load-balancers to economically optimize costs while minimizing time expenditures. Quick services for cloud customers and minimal cost for cloud providers are the goals of the optimal resource allocation method. This research suggests a novel approach to increase task processing time, which can aid in increasing cloud computing’s load balancing capabilities. The proposed method Enhanced Throttled Load Balancing Algorithm (ETLBA) is an upgrade to the original Throttled Algorithm, which efficiently performs resource allocation and load balancing. The proposed ETLBA is contrasted with the existing algorithms, Round Robin, Active Monitoring Load Balancing Algorithm (AMLBA) and Throttled Load Balancing Algorithm (TLBA) to display the efficacy. Cloud Analyst tool simulates the proposed and existing methods. According on the results of the simulations, the proposed algorithm ETLBA achieves better outcomes than the popular existing algorithms in terms of processing time, request processing time, and datacenter cost. It shows 18% reduction in response time, 7% reduction in data center processing time, 16% reduction in data center request processing time and 4% less data center cost compared to the existing solutions. ETLBA performs better by selecting virtual machines using a prioritized indextable and consumption index. It limits idling resources, improves response as well as reduces processing times, and cloud costs compared to conventional solutions.

With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual machine cost, the data center processing time and the response time. The proposed method is realized using Java and CloudAnalyst Simulator. Besides, we have compared the proposed algorithm with other task scheduling approaches such as Round Robin algorithm, Throttled algorithm, Equally Spread Current Execution Load algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). As a result, the proposed algorithm performs better in terms of service rate and response time.

Cloud computing is one of the most important fields in the area of high performance computing. It enables the users to access the resources any time anywhere over the internet. As the number of users of cloud computing services are growing tremendously the need of load balancing becomes important. Load balancing is a mechanism, which mainly focuses on dividing the workload among the available resources equally so that we can achieve more throughput, less response time and reduce the overload on individual systems. Even though there are many existing algorithms to resolve the load balancing issues in cloud computing environment most of them lack significant amount of output while considering the response time and data center processing time. Maintenance of data in cloud infrastructure will be an easier task compared to traditional database management system. So in our work we minimize the response time, data center processing time of virtual machines and balance the load of the virtual machines with the help of Enhanced Honeybee Inspired load balancing algorithm, which will assign the task to virtual machines based on their priority, resource requirement and by evaluating the computing power of the virtual machines.

A dynamic low earth orbit multi satellite hopping beam resource allocation method based on time slicing is proposed to address the issue of changing coverage and beam interference caused by satellite mobility in multi satellite and multi beam communication scenarios. The method involves six processes: (1) Establishing a low earth orbit satellite system with three identical configuration parameters and motion speed parameters; (2)Assign a value to the time slicing weight coefficient and use the principle of load balancing to minimize the difference in demand for dual star services as the optimization objective; (3) Obtain initial power resource allocation with maximum system capacity as the optimization objective; (4) Considering the mobility of low earth orbit satellites, updating weight coefficients and dynamically allocating power resources, a time resource dynamic allocation matrix and the optimal service cell set are obtained; (5) Update the independent coverage cell set of the third low earth orbit satellite, use the Lagrange equation of the weighted fair objective function to obtain the time resource allocation optimization matrix, and use one-way search to determine the optimal service cell set; (6) With the goal of maximizing system capacity, the optimal solution of the power resource allocation matrix for the third low earth orbit satellite is obtained. The system throughput is introduced to intuitively measure system performance, and adjustments are made based on the overlap of Samsung coverage. The resource allocation method is based on a fairness model, considering load balancing and dynamic coverage range changes. On the basis of improving the overall system throughput, it ensures the fairness of resource allocation and allocates time, frequency, and power resources to multiple satellites with overlapping coverage, which helps to improve the throughput and resource utilization of satellite systems.

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 helps to provide quality of service to the end users within required time frame. Serving user requests using distributed network of virtualized data centers is a challenging task as response time increases significantly without a proper load balancing strategy. There are many algorithms proposed in the literature to support load balancing in cloud environment. All of them have their merits and demerits. The inherent structure of load balancing is rather imprecise. In this research, we model the imprecise requirements of memory, bandwidth and disk space through the use of fuzzy logic. Then we design and evaluate an efficient dynamic fuzzy load balancing algorithm which could efficiently predict the virtual machine where the next job will be scheduled. We implement a cloud model in simulation environment and compare the result of our novel approach with existing techniques. Simulation results demonstrate that our fuzzy algorithm outperforms other scheduling algorithms with respect to response time, data center processing time, etc.

ORT Broker Policy: Reduce Cost and Response Time Using Throttled Load Balancing Algorithm

Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

Now a day’s cloud computing breaks almost all the barriers of large scale computing and widens the scope of massive computational possibilities. Cloud computing provides various benefits to the whole computing societies such as on demand flexi pay access to techno business services and wide range of computing resources requires an exponential growth in its technology put forth serious challenges including VM load balancing especially in cloud data centers. It dynamically distributes the workload across multiple servers in the cloud data center so that not even a single server involved is underutilized or overutilized. If load balancing is not done properly in the cloud then it leads to the inefficiency in processor utilization that in turn risks the provider by creating a significant problem of increase in overall energy consumption and the world by increasing the carbon emissions. Lots of different techniques like Round Robin, Throttled and Equally Spread Current Execution are claimed to provide efficient mechanisms to resolve this problem. This paper compares and summaries the existing load balancing techniques which are used to solve the issues in cloud environment by considering the data center processing time and response time and propose an improved load balancing strategy believed to be a efficient solution for the cloud load balancing issues.

A cloud computing platform provides access to shared resources along with diverse services including computation and storage to its users. The ubiquitous access to resources requires the service providers to ensure an efficient, reliable, and a fault-tolerant infrastructure. In this context, the cloud brokering mechanisms enable the cloud computing platforms to manage cloud resources through mediation between cloud service providers and cloud users. Corresponding to user requests, the cloud service brokering and load balancing aim at efficient real-time provision of services with minimal monetary cost through selection of appropriate data centers and virtual machines. This paper proposes a normalization-based hybrid service brokering approach integrated with throttled round-robin load balancing to improve resource management through cost- and performance-aware provision of cloud services. The proposed approach incorporates a hybrid (static and dynamic) evaluation criteria using normalization for determining the impact of cost and performance-oriented parameters in a multi-cloud environment. The subsequent selection of the most appropriate service provider along with throttled round-robin load balancing optimizes cloud resource management. The experiments performed with diverse number of user bases and data centers show that the proposed cloud service brokering approach outperforms other well-known approaches by improving response time, data center processing time, and monetary cost up to 17.39%, 31.35%, and 7.06%, respectively.

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.