DEEP LEARNING BASED LOAD BALANCING USING MULTIDIMENSIONAL QUEUING LOAD OPTIMIZATION ALGORITHM FOR CLOUD ENVIRONMENT.

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.

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR - Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.
 Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.
 Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.
 Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.
 Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.
 Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

Background: With the further development of the electric Internet of Things (eIoT), IoT devices in the distributed network generate data with different frequencies and types. Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time. Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through the K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm. Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate, and energy consumption, which provides a better guarantee for eIoT. Conclusion: Resource scheduling is an important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. A new method of resource scheduling and load balancing is proposed. The evaluation was performed, and it proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

The concept of virtual machines is critical to manage the scalable utilization of the resources in cloud computing. Hence the scheduling and balancing the assigned job loads to virtual machines is essential and exclusive need of the cloud computing environment, which is a critical research objective since, improving the service provision in cloud computing is not only through scheduling the individual resources to the target jobs, also the scheduling the virtual machines and balancing the load at virtual machines. In the context of this argument, a method called, Hierarchical Tabu Search with Fuzzy Reasoning (HTSFR) is proposed to escalate the optimality of resource scheduling and load balancing that deals the virtual machines as resources. The performance analysis was done on simulated environment established by using Cloud SIM. The results obtained from proposal were compared to other contemporary models, which evinced the significant performance advantage of the proposal on other contemporary models compared.

HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment

Load balancing is the core of virtual resource management and scheduling in cloud computing. For network applications, the cost of user would be greatly saved if load balancer could dynamically adjust cluster resources in accordance with the current applied load. The current load balancing products of cloud, such as Amazon's ELB, can be used to manage virtual machines in the cloud. The main drawbacks are still only supporting template-based deployment of new virtual machines, not supporting the trend prediction, failing to gain resources dynamically, and not sufficiently providing the elastic management of resources. Since the virtual machine for load balancing management in cloud computing can be dynamically applied and released, an algorithm of prediction-based elastic load balancing resource management (TeraScaler ELB) is presented to overcome the drawbacks. Experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraScaler ELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. Besides it could make full use of the 'use on demand' feature of cloud computing, TeraScaler ELB leads to a better application of prediction based load balancing in cloud computing. It concludes that compared with the traditional elastic resource management algorithm, TeraScaler ELB is more reasonable for providing scalability and high availability.

ABSTRACTTwo of the most essential elements of cloud resource management systems are resource management as well as scheduling. Due to the heterogeneity of resources, their interdependencies, and the unpredictable nature of load in a cloud environment, cloud Resource Scheduling (RS) is the most important issue to handle effectively. One of the most challenging tasks in cloud computing is RS, where resources must be assigned to the necessary tasks or jobs in accordance with the necessary Quality of Service (QoS) of the cloud applications. This study suggests a unique method for scheduling cloud resources based on a virtual data center and machine learning model. The genetic reinforcement kernel swarm optimization methodology and cloud data centers are deployed here. The suggested network analysis aims to balance energy usage and SLA. The suggested framework is assessed by doing experiments on the Google Cluster dataset, Planet Lab, and Bitbrains VM traces, as well as three real‐world workload datasets. Execution cost, Execution time, Makespan, Energy consumption, Resource utilization, and Scalability are factors that were examined here. The proposed framework is superior to several performance indicators compared to state‐of‐the‐art techniques. The project's methodology addresses the issues of load imbalance and excessive migration expenses.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Cloud computing provides us various services over the Internet and services have various problems includes security issues, energy efficiency, big data problem, load balancing etc. Proper Load balancing is used to accomplish proper resource utilization, optimal throughput achievement, reduce response time. Various research papers focused on various technique used to optimize response time, effectively lessens the system load, low computation and other overhead. Resources scheduling in virtual machine in cloud computing environment have also scheduling strategy constructed on genetic algorithm to reduce or avoids dynamic migration. The review paper specially focused on comparison of two algorithms PSO and SJF-MMBF on parameters like total flow time, completion time variance, RPD makespan, RPD total flow time etc.

At present, in researches of Iaas cloud resource scheduling strategies, it is focused that SLA violation or overloaded physical machine can trigger the migration of virtual machines, which will reduce the performance of the system and cause extra energy cost. In this paper, we model the resource of IaaS cloud based on Hidden Markov process to predict the status and the time that the physical machine is overloading, which will serve as a guideline for the resource scheduling in the IaaS cloud. Specifically, the resource status of physical machine will be chosen as the hidden status, meanwhile, the operations of virtual machine will be an observation set of the visible status, which are a modelling process. And then, we present the optimal path of the status transition probability as the core method of the physical machine status prediction. Finally, through real experimental scenarios, we verify the effectiveness of physical machine status prediction in the IaaS cloud environment.

Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing. We describe Hadoop MapReduce and its schedulers, and present recent research efforts in this area including alternative schedulers and enhancements to existing schedulers. The second scheduling problem is the provisioning of virtual machines to resources in the cloud. We present a survey of the different approaches to solve this resource allocation problem. We also include recent research and standards for inter-connecting clouds and discuss the suitability of running scientific applications in the cloud.

Online teaching platforms have been popularized and promoted as a result of the development of network information technology, and colleges and universities encourage teachers to use a variety of network teaching platforms to innovate teaching models and improve teaching effectiveness. Using an ideological and political online course as an example, it analyzes the teaching design concepts, instructional effects, and existing problems on the online learning platform, and extracts recommendations for online course construction that have a specific reference for online course teaching. Additionally, aiming at the multiobjective cloud resource scheduling problem, this article aims to optimize the total completion time and total execution cost of the task. It does so by utilizing fuzzy mathematics, establishing a fuzzy cloud resource scheduling model, and proposing a hybrid intelligent optimization algorithm CO. The CO algorithm is validated by randomly generating cloud-computing resource scheduling data using the CloudSim simulation platform. The experimental results indicate that the CO algorithm outperforms traditional cloud resource scheduling algorithms in terms of optimization and load balancing performance.

Load Balancing is an important factor handling resource during running and execution time in real time applications. Virtual machines are used for dynamically access and share the resources. As per current scenario cloud computing is played major for storage, resource accessing, resource pooling and internet based service offering. Usage of cloud computing services is dynamically increased such as online shopping, education, ticketing, etc. Many users can use the cloud resources and load balancing is used for adjusting the virtual machine and balance the node. Our proposed virtualized genetic algorithms are to provide balanced virtual machine services in Hybrid cloud. The proposed algorithm and experiments are implemented by using Cloud simulator. In this paper the experiments are done with cloud computing models, Virtual Machine allocation, load balancing and simulations. Also compare the results using response time, throughput and turnaround time using cloud sim. The accuracy can be compared with existing load balancing techniques.

Cloud computing(CC) delivers multiple services to users by processing complex tasks through internet connections to serve as a dynamic, dependable, and flexible computing solution. With the increased Internet speed, the user demands to perform many tasks through computing provided by cloud virtual machines (VMs) created in heterogeneous servers. The complexity of cloud computing(CC) increases through the diversity of servers and the increased demand for resources to perform more tasks, which makes the cloud data center(CDC) unbalanced in load. Therefore, future cloud systems will require more effective resource management(RM) methods to balance load and improve Quality of Service (QoS). This paper proposes KMPPVM-DRM, a dynamic clustering and scheduling approach for VM resource allocation(RA) based on the KMeans++ algorithm. VMs are clustered into three groups (high, medium, low) according to their real-time normalized CPU and RAM weights, using weighting parameters α = 0.6 and β = 0.4. Tasks are categorized into three levels based on length and mapped to appropriate VM clusters. Within each cluster, a Utilization-Level Comparator (ULC) dynamically selects the optimal VM for task execution. The experimental results were conducted using CloudSim, with 5 and 10 VMs and task counts ranging from 1000 to 10000. The proposed model was compared with PSO, ACO, and DBSCAN algorithms using execution time, average start time, and average finish time. Results show that with 10 VMs, the proposed model achieved a harmonic mean execution time of 886.72 ms, compared to 4102.99 ms (PSO), 4672.25 ms (ACO), and 3071.51 ms (DBSCAN). It also attained the lowest harmonic mean start and finish times of 166.41 ms and 167.19 ms, respectively. Relative reduction in execution time against DBSCAN ranged from 69.90% to 71.52%, with start and finish time improvements between 52.65% and 81.51%.To ensure statistical reliability, a paired t-test confirmed that the performance improvements were statistically significant (p < 0.05) across all task sizes. The findings confirm that KMPPVM-DRM enhances resource allocation(RA) efficiency and scheduling effectiveness, maintaining balanced loads even with limited resources (e.g., only 5 VMs) and outperforming PSO, ACO, and DBSCAN in all tested scenarios.