Cloud computing: Performance analysis of load balancing algorithms in cloud heterogeneous environment

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.

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

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 provides higher level services and shared pools of configurable resources with minimal management effort over the internet. Cloud Computing achieves coherence and economic of scale by means of shared resources. The growth of cloud computing depends on the high availability of low cost computers, storage devices and networks as well as hardware virtualization and utility computing. Service Level Agreement (SLA) is a service commitment tool between a cloud service provider and a cloud customer. Using SLA in cloud computing, reduce the chances of disappointing the cloud customer and manage the expectation between the service provider and the customer. The challenge is, it is very difficult to handle all request by the cloud providers at a time during peak hours and to keep up SLA. So when there is an uneven request arrival pattern, the cloud resources may either be underutilized or over-utilized. In order to balance the load, the load balancer plays the major role in cloud computing. The load balancing algorithm equalizes the workload and computing resources in a cloud computing environment. It allows an organization to manage their workload demands by allocating resources among multiple servers and thus it minimizes the response time, minimize the waiting time, maximize the throughput time, maximize the resource utilization and minimize the communication delays of the server by meeting the SLA. The major goal of this study is to review both the existing static and dynamic load balancing algorithms proposed till now and to design and implement a Load balancer that uses a Meta Heuristics approach – Ant Colony Optimization technique to perform balancing the load so that the SLA is met evenly without any issues. Each of the load balancing algorithms is compared with other algorithms theoretically and experimentally one of it is tested with the proposed system using AWS Cloud PaaS (Platform as a Service).

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

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.

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naïve bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.

Cloud Computing Environment (CCE) is a newly rudimentary knowledge in the IT industry. Central to these problems lies in the establishment of an active LB algorithm. The network load can be CPU load, memory limit, postponement, or system load. In the proposed algorithm, Load Balancing (LB) done by considering priority policy. An Efficient and Dynamic Load Balancing Approach in Cloud Computing (EDBA-CC) proposed, and the related algorithm executed on CloudSim (CS). A resource allocation approach that takes into account Resource Utilization (RU) would lead to better energy efficiency. The outcomes indicate the effectiveness of the proposed algorithm. The outcomes exhibited that the EDBA-CC algorithm reduced Response Time (RT). The decreasing in RT is about 2.23 ms when the instruction length was 275 Byte, and the decreasing became more obviously with 10.12 ms for RT and 5.54 ms for RT when the instruction length was 2000 Byte.

Cloud computing and big data: Technologies and applications

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.

All the services offered by cloud computing are bundled into one service know as IT as a Service (ITaaS). The user’s processes are executed using these services. The scheduling techniques used in the cloud computing environment execute the tasks at different datacenters considering the needs of the consumers. As the requirements vary from one to one, and so the priorities also change. The jobs are executed either in a preemptive or non-preemptive way. The tasks in cloud computing also migrate from one datacenter to another considering load balancing. This research mainly focused on the study of how the Round Robin (RR) and Throttled (TR) scheduling techniques function subject to different tasks given for processing. An analysis is carried out to measure the performance based on the metrics like response time and service time at different userbases and data centers. The consumers have the option to select the server broker policy as they are the ultimate users and payers.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Abstract: Cloud computing has transformed user services by introducing a new model that allows for pay-per-use access to information technology services at any time and from any location. Numerous businesses are moving their operations to the cloud as a result of the flexibility offered by cloud services, and additional data centers are being set up by service providers to provide users with services. However, it is essential to ensure efficient resource utilization and cost-effective task execution. Based on load balancing, task scheduling, resource management, quality of service, and workload management, several methods to improve performance and resource use have been reported in the literature. Data centers can use load balancing in the cloud to prevent virtual machines from being overloaded or under loaded, which is a problem in and of itself in cloud computing. As a result, developers and researchers must develop and implement a suitable load balancer for distributed and parallel cloud environments. In order for researchers to develop algorithms that are more effective, this survey provides a cutting-edge review of issues and obstacles associated with existing load-balancing methods.