Cloud Analyst

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Cloud Analyst is a research field focused on the evaluation and optimization of cloud computing environments. It involves analyzing performance metrics, resource allocation, and cost-effectiveness of cloud services to enhance decision-making for cloud infrastructure deployment and management.

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Key research themes

1. How can cloud simulation tools support performance evaluation and resource management in cloud computing environments?

This research theme focuses on the development, comparison, and application of cloud simulation frameworks and tools that enable modeling, simulation, and performance evaluation of cloud infrastructure, applications, resource allocation, and scheduling policies. These tools are crucial for overcoming the high costs and complexities of evaluating cloud systems in real environments, allowing researchers and developers to test configurations, analyze load balancing, and optimize resource usage in controlled, repeatable settings.

Comparison of Various Cloud Simulation tools available in Cloud Computing

by Mayank Shekhar

2015, International Journal of Advanced Research in Computer and Communication Engineering

Key finding: This paper provides a detailed comparison of over a dozen cloud simulation tools including CloudSim, CloudAnalyst, SPECI, Green-Cloud, and GroudSim, highlighting their architectures, capabilities in modeling data centers,... Read more

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A COMPREHENSIVE STUDY OF CLOUDANALYST TOOL AND ITS VARIOUS ALGORITHMS

by IJESMR Journal

2016

Key finding: This study focuses on CloudAnalyst, a GUI-based extension of CloudSim, designed to facilitate simulation of large-scale cloud applications with geographic distributions and varying workloads without programming. It... Read more

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Cloudanalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications

by Sunitha Arumugapandian

2012, … and Applications (AINA …

Key finding: This work introduces CloudAnalyst, built on CloudSim, to simulate large-scale cloud applications with geographic user distributions and data centers, providing metrics such as response and processing time. It enables... Read more

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Energy Aware Of Cloud Computing Development Using Cloud Simulators

by Ionel Gordin

2023, The European Proceedings of Social and Behavioural Sciences

Key finding: The paper emphasizes the advantage of cloud simulators for research by providing virtual cloud environments that obviate the high cost of physical infrastructure. Through evaluating features of multiple cloud simulators like... Read more

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Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim

by International Journal of Electrical and Computer Engineering (IJECE)

2020, International Journal of Electrical and Computer Engineering (IJECE)

Key finding: This research uses CloudAnalyst simulation tool to model cloud server topologies and analyze virtual machine allocation and load balancing policies. It presents empirical results comparing response times and internet... Read more

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2. What machine learning and predictive modeling techniques improve resource management and load balancing in cloud environments?

This line of research investigates integrating machine learning models, such as linear regression and simulated annealing, to predict resource utilization and optimize task scheduling, virtual machine migration, and load balancing in cloud infrastructures. Accurate prediction of resource demand and intelligent load redistribution help in ensuring quality of service and minimizing SLA violations. These studies explore frameworks that combine big data analytics with cloud computing to enhance resource provisioning efficiency and system reliability.

A Linear Regression-Based Resource Utilization Prediction Policy for Live Migration in Cloud Computing

by Santanu Dam and

2020, Springer, Singapore

Key finding: The paper proposes a linear regression model to predict short-term future resource usage based on historical data, integrating this prediction into VM migration policies to transfer loads from overloaded to underloaded hosts.... Read more

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Detailed Cloud Linear Regression Services in Cloud Computing Environment

by Ban N . Dhannoon

2025

Key finding: This work introduces Cloud Linear Regression (CLR), a novel cloud-based ML framework employing linear regression to enhance task scheduling, resource provisioning, and allocation. CLR demonstrated improvements in predictive... Read more

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Cloud-Based Ml Framework Working with Analytic Tools

by International Journal of Scientific Research in Science and Technology IJSRST

2021, International Journal of Scientific Research in Science and Technology

Key finding: This paper presents a prototype cloud-based machine learning framework combining big data analytics and cloud computing for aerospace applications, focusing on condition-based maintenance. It outlines the integration of cloud... Read more

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Load Balancing Strategy in Cloud Computing Using Simulated Annealing

by Paramartha Dutta

2024, Communications in Computer and Information Science

Key finding: The study proposes a simulated annealing-based load balancing algorithm to dynamically identify underloaded nodes and redistribute excess load from overloaded nodes, treating load balancing as an optimization problem.... Read more

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Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

by International Journal of Electrical and Computer Engineering (IJECE)

2022, International Journal of Electrical and Computer Engineering (IJECE)

Key finding: This research proposes a two-listed throttled load balancer (TLT-LB) algorithm modifying conventional throttled load balancing to classify VMs into available and busy lists, optimizing task assignment. Simulation results show... Read more

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3. How do cloud service broker policies and data center selection algorithms affect response times and load distribution in cloud platforms?

This research area explores optimization of service broker policies that direct user requests to geographically distributed data centers. By considering factors such as network latency, bandwidth, job size, and current data center loads, these policies aim to achieve minimal response and processing times and balanced utilization across data centers. Simulation studies illuminate how heuristic routing strategies can reduce overload and improve end-user experience in multi-data center cloud environments.

A Variable Service Broker Routing Policy for data center selection in cloud analyst

by Tariq Alsmadi

2022, Journal of King Saud University - Computer and Information Sciences

Key finding: The paper proposes VSBRP, a heuristic service broker routing policy that routes requests to data centers by jointly considering communication bandwidth, latency, and job size. Simulation results demonstrate that VSBRP... Read more

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Resource Management Services for a Grid Analysis Environment

by MUHAMMAD Owais Zafar

2024, 2005 International Conference on Parallel Processing Workshops (ICPPW'05)

Key finding: Though developed for grid computing, this paper's interactive resource management services including a steering service and estimator service provide users greater control over job execution by monitoring resource... Read more

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Cloudbus Toolkit for Market-Oriented Cloud Computing

by Christian Vecchiola

2015

Key finding: The work presents Cloudbus, a market-oriented toolkit enabling negotiation-based, SLA-aware resource management and dynamic cloud federation. It outlines architectures and resource management strategies incorporating... Read more

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All papers in Cloud Analyst

Task Scheduling in Cloud Computing

by Snehal Deore

2025, International Journal of Advance Research and Innovative Ideas in Education

Now a days, cloud computing is a center of attraction of many researchers, scientist. To provide services online and with pay as you go makes it a strong. Still cloud computing has not reached to its best because of its some issues. Out... more

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Hybrid algorithm for optimized clustering and load balancing using deep Q reccurent neural networks in cloud computing

by beei iaes and

2025, Bulletin of Electrical Engineering and Informatics

Cloud services are among the technologies that are developing the fastest. Additionally, it is acknowledged that load balancing poses a major obstacle to reaching energy efficiency. Distributing the load among several resources in order... more

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Comparative Study of Load Balancing Algorithms through Virtual Machine Scheduling in Cloud Computing Environment

by Bhawna Mallick

2024, International Journal of Computer Applications

Cloud Computing is a new trend emerging in IT environment with huge requirements of infrastructure and resources. Load Balancing is an important aspect of cloud computing environment. Efficient load balancing scheme ensures efficient... more

1.2.3. Cloud Developer Table 1: Stakeholders of Cloud

Table 2: Comparison between Resource Allocation and Task Scheduling Case 2: Hosts and VMs, both are provisioned to VMs and tasks respectively in time sharing manner. must be mapped onto appropriate VM based upon its configuration and availability.

Table 3: Comparison Table of Load Balancing Algorithms in Cloud Computing Environment 3.3. Load Balancing Based on Task Dependencies balancing in cloud computing environment. This is a self organizing algorithm and uses queue data structure for its implementation. Biased random sampling [18] is another distributed load balancing technique which uses virtual graph as the knowledge base.

Table 4: Comparison of different Types of Load Balancing Scenarios in Cloud Computing Environment

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Load Balancing Strategy in Cloud Computing Using Simulated Annealing

by Paramartha Dutta

2024, Communications in Computer and Information Science

Cloud computing is an idea of diminution-based distributional model which depends on appropriate protocol over the internet. To meet the end user requirement here the resources like CPU, bandwidth and memory are provisioned and... more

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Efficient criticality oriented service brokering policy in cloud datacenters

by International Journal of Electrical and Computer Engineering (IJECE)

2024, International Journal of Electrical and Computer Engineering (IJECE)

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of... more

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A Systematical Review on Round Robin as Task Scheduling Algorithms in Cloud Computing

by Muhammad Fakhrul Safitra

2024, 2023 6th International Conference on Information and Communications Technology (ICOIACT)

The utilization of cell phones and desktop computers on a global scale has experienced a significant growth in recent years. This tendency has heightened global competition and demanded firms' regional development for their continued... more

“ABLE L. RESEARCH QUESTIONS This paper's primary contribution is the proposal of a review paper that addresses the shortcomings of the classic RR algorithm and each of its advantages and disadvantages. The paper's section is structured as follows. Section II presents a literature review of CC and RR algorithm. In section II, presents discussions and the result of the findings. Section IV summarizes the work with contributions and future research directions. Several algorithms in CC task scheduling: (1) traditional ones that are widely used, including RR, FCFS, LJF, SJF; (2) Heuristic algorithms, such as Max-Min and Min-Min algorithms; (3) Meta-heuristic algorithms, such as ant colony optimization algorithms. [8-11]. Table I presents the study's research questions. This research utilizes prominent journal database search terms to answer the research question.

BLE Il. SCHEDULING ALGORITHM PARAMETERS [20, 21] In this study, "parameters" refer to the focus of attention carried out in previous research studies. These parameters provide additional information or context needed to improve optimization in task scheduling. An assignment is a short piece of work that must be completed within a certain period. The information service manager will provide data/information to the cloud task scheduler regarding the condition of available resources so that these resources can be assigned to certain jobs so that the task can be carried out. In CC, there are several task scheduling that can be implemented. [16-20]. When it comes to scheduling algorithms, several key parameters need to be considered. Table II lists and provides a brief description of each parameter.

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Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing

by Pooja Samal

2024

Cloud computing is the emerging internet based technology which emphasizes commercial computing. Cloud is a platform providing dynamic pool resources and virtualization. Based on a pay-as-you-go model, it enables hosting of pervasive... more

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A Simulated Performance Analysis of Load Balancing Algorithms in Cloud Computing

by Yogesh Banyal

2024

Cloud computing is a generic term used for the delivery of hosted services over the Internet. It provides an infrastructure for resource sharing, software hosting and service delivering in a pay as you go model which makes it easy and... more

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Development and analysis of a three phase cloudlet allocation algorithm

by Utpal Biswas

2024, Journal of King Saud University - Computer and Information Sciences

Cloud computing is one of the most popular and pragmatic topics of research nowadays. The allocation of cloudlet(s) to suitable VM(s) is one of the most challenging areas of research in the domain of cloud computing. This paper highlights... more

Figure 5 Cloudlets in CSNA phase. It is the last phase of RB2B. After the first two phases if the bal- ancing factors decide to allocate the arriving cloudlet to a VM, ADCB will move on to the next cloudlet. But if the arriving

Figure 6 Before increment of “‘Balance” value. In Fig. 6, at first assume the initial value of Balance is set to 2. After allocating cloudlets CO to C5 to the VMs the number of cloudlets allocated to each VM becomes equal to the current Balance value but still new cloudlet C6 arrives. Then ADCB

Figure 7 After increment of “Balance” value.

Figure 8 Variation of local queue length of VMs.

This section deals with analyzing the improvement of the results of the RB2B compared to RASA, Max-—min, Min— min, RRA and CA. A reference stream of 1000 cloudlets with cloudlet length varying from 1000 MI to 100,000 MI and 10 VMs with processing speed varying from 1000 MIPS to 10,000 MIPS is used for the comparison. Due to space con- straints, only this experimental scenario is being considered, but in experiments with larger number of cloudlets and VMs identical results have been found. The simulated results are evaluated and analyzed in several aspects. The performance was measured by setting up simulation environments (36) in Figure 12 Average make span comparison.

CloudSim 3.0.3. The improvement of completion time and makespan is explained with an interpretation of the graphs in tabular form. Due to space constraint, the details of the reference cloudlet string and the VMs could not be given here. The improvement in the result of the proposed work indicates the efficiency of RB2B. Five parameters are taken into account to compare and analyze the performance of these algorithms. They are average waiting time (AWT), average turnaround

It allocates the cloudlet to first available VM. For example, con- sider there are four cloudlets (Co, C1, Co, C3, and C4) and three

Table 2. A comparative study of existing policies. Several Grid simulators (George Amalarethinam and Muthulakshmi (2011)) such as GridSim, SimGrid, and Gang- Sim are used to simulate the Grid application in a distributed environment, but don’t support the infrastructure and application-level requirements to simulate Cloud computing paradigm (Khanli and Analoui, 2008). A Cloud computing environment simulator must support for real-time trading of services between customers and providers. The CloudSim framework (Belalem et al., 2010) which is an open source cloud computing environment simulator is developed on GridSim toolkit (Bhatia et al., 2010). The CloudSim supports resource management and application scheduling simulation and imple- mentation of new policies. The CloudSim provides a series of extended classes and methods. It also helps to analyze new cloudlet allocation policies and scheduling criteria at different levels. The CloudSim supports modification of in built classes, module deployment techniques and performance analysis by Min-—min algorithm lacks uniform resource utilization in a way that it chooses smaller cloudlets first which makes use of VMs with higher processing speed. Therefore, the scheduling is not optimal when the number of smaller cloudlets is greater than the large ones. To overcome this drawback (Armbrust M et al., 2010), Max—min algorithm schedules larger cloudlets at first. But sometimes, due to the prior execution of larger cloudlets the makespan may increase. Max—min also increases the waiting time of smaller cloudlets. RASA has disadvantages of both Min—min and Max-—min, although it approaches to reduce them. In RRA, large cloudlets are often assigned to the VMs with low MIPS and hence take a longer time to exe- cute as well as increasing the waiting time and the response

Table 3. Cloudlet length acceptability ranges for each VM. and Cmax are the minimum and maximum cloudlet length (In MI). MIPS; is the processing speed of VM;. So, the total of the processing speeds of the VMs is

Table 8 Comparison result for performance evaluation.

Table 10 A comparative study of existing algorithms & proposed RB2B algorithm.

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A Simulated Performance Analysis of Load Balancing Algorithms in Cloud Computing

by Jawahar Thakur

2024

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Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing

by ABDULLAH MUHAMMED

2023

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Figure 1. The relationship between virtual machines in the RR algorithm [1]. This is one of the simpler schedule techniques which uses the time-slicing concept. ere, the duration is split into several segments, and each node is assigned a different ime-slicing or time range, i.e., it uses the timing schedule rule. Every node is assigned an action and a quantum [18]. Time quantum is the main consideration for LB algorithms [33]. The RR algorithm is the same as the first-come-first-served algorithm. Furthermore, when the time quantum is insufficient, the RR algorithm is referred to as a processor-sharing algorithm [17]. The service providers provide services to the requested customer on a time- slicing plan [18]. The procedure of virtual machines running in an RR model is depicted in Figure 1 [1].

Figure 2. The process of the ESCE algorithm [1].

the SBP helps to determine which data center will be offering the service for potential demand [12]. Figure 4 displays the data centers which are spread geographically [13].

Figure 5. The categorization of service broker policies [14] “ all The newest version of cloud analyst incorporates three various kinds of service brokers, each with its unique routing strategy [14]. Figure 5 indicates the policies for the service brokers [24]. There are clear pros and cons of these service broker policies that are shown in Table 3.

Figure 6. The cloud SBP services in three roles [11]. A cloud broker typically provides services in three categories [11]. Figure 6 demon- strates services under these three roles.

The dataset of the simula four LB algorithms (PSO, RR, t broker policies (CDC, ORT, and simulated environment opera five virtual machines in Data Center 1 and Data Center 2, and fifty vir ted application is represented by the = reconfigure dynamically with load) ted by taking six user bases and three configuration values for inputs. These values are based on six user bases. Figure 7 depicts the setting values for six user bases (location—peak—hours-—user number). The findings s hrottled, and ESCE) performs with each of the three service how how each of the The cloud analyst tool was used to analyze the various LB algorithms. To implement the various algorithms, the data centers, having tual machines in Data Center 3. Each simulation was performed for 60 minutes. They considered average peak users as 500, 1500, 1000, 35,000, and 5000, and average off-peak users o be 50, 100, 150, 350, and 500 in user bases. The SBP used for simulation was CDC, which optimizes response time, and reconfigures dynamically with the load. The deployment setup is shown in Figures 8-10. Figure 7. Six user bases with different regions and durations.

Figure 9. Application deployment configuration of Data Centers 1 to 3 based on ORT SBP.

Figure 11. The analysis of the performance parameters of PSO based on 3 service broker policies.

Figure 12. The analysis of the performance parameters of RR based on 3 service broker policies

Figure 13. The analysis of the performance parameters of the ESCE algorithm on 3 service broker policies.

Figure 14. The analysis of the performance parameters of the throttled algorithm on 3 service broker policies.

The observation in this CDC SBP is that the two LB algorithms RR and ESCE perform almost the same for overall response time, DCPT, and cost parameters, but throttled and PSO algorithms performed higher than RR and ESCE LB algorithms. However, the values of DCPT and cost parameters are almost the same for PSO, RR, ESCE, and throttled. This is because all requests are passed to the closest data center SBP. Figure 15 shows the output of this observation.

PSO algorithms have lower output values than them. This is because the ORT SBP delivers the majority of requests to the closest data center SBP and selects the data center with the shortest response time for the remaining requests. Figure 16 shows the output of this observation.

The observations show with regard to this reconfigure dynamically with load SBP that the four LB algorithms PSO, RR, ESCE, and throttled performed almost the same for overall response time, DCPT, and cost parameters but the throttled algorithm has higher output values than them. The overhead of scaling the virtual machines up or down depending on the peak load is added to the reconfiguring dynamically with load SBP. As a result, the numbers obtained in this scenario for overall response time and DCPT are greater than the cost parameter. Figure 17 shows the output of this observation.

Table 1. A summary of the literature on load-balancing algorithms in cloud computing environments

There are no free hosts for the next data center.

Ahmed I. El Karadawy et al. [1] discussed a comprehensive analysis of the cloud analyst simulator on various algorithms of LB with different service broker policies. Specifically, they analyzed three distinct LB algorithms: RR, throttled, and ESCE. Sunny Nandwani et al. [2] discussed the numerous and current service broker policies and LB algorithms. These LB algorithms were also compared with various service broker policies and simulations on cloud analysts to check the execution of existing algorithms; using hese can compare the performance based on different metrics. S. Suguna and R. Barani [3] suggested that two LB algorithms, the ESCE algorithm and throttled algorithms, are used by cloud analysts to evaluate the accuracy of the algorithms. Simar Preet Singh et al. [4] suggested the cloud analyst simulator and the importance of cloud architecture infras- tructure services. Divyani et al. [5] carried out simulation studies for the performance of different variations of LB and service broker policies, and also developed a new dynamic LB algorithm to integrate the key qualities of other policies. Ritesh Patel and Sandip Patel [6] this research paper proposed the problem of data centers and services that could meet demand and decided on a few issues, such as higher prices and under-use of assets. Furthermore, they proposed an effective performance data center algorithm that determines the data center and resource to host ratios and has unlimited available resources, which is dependent on virtual machines in a similar direction. Ahmed M. Manasrah et al. [7] proposed a variable service broker routing

Table 2. Pros and cons of the load-balancing algorithm. The LB algorithms add to a cloud analyst simulator. The PSO is provided that solves the problem of loading balance of virtual machines to effectively create corresponding relationships between tasks and virtual machines. Aiming at finding an almost optimized scheduling solution not only makes the task execution time the shortest but can make the resource utilization of virtual machines the maximum possible. The PSO algorithm involves numerous metrics such as simulation duration, number of regions user bases, data center, number of virtual machines, user grouping factor in user bases, data center request grouping factor, executable instruction length per request, etc. [46].

3.6.1. SBP Proximity-Based Routing [14] Table 3. Pros and cons of service broker policies in cloud computing environments.

Table 4. Comparative analysis of cloud service broker policy. 3.9. Deployment Configuration of Service Broker Policy

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Load Balancing Techniques – A Review

by Jai Bhagwan

2023, International Journal for Research in Applied Science and Engineering Technology

In recent years, cloud has been recognized as one of the emerging worldwide technologies in which shared resources are provided on the user's request at any time. The whole work of cloud is based on three service models (IaaS, SaaS and... more

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Dynamic Job Scheduling Algorithms Based on Round Robin for Cloud Environment

by Bossy Mohamed

2023, Research Journal of Applied Sciences, Engineering and Technology

This study attempts to solve the problem of the static scheduling algorithms by developing a dynamic version of Round Robin scheduling algorithm; Dynamic Priority Round Robin and Enhanced Dynamic Priority Round Robin. The proposed... more

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Dynamic Job Scheduling Algorithms Based on Round Robin for Cloud Environment

by Bossy Mohamed

2023, Research Journal of Applied Sciences, Engineering and Technology

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Load balancing with preemptive and non-preemptive task scheduling in cloud computing

by Dr. Mohammad Riyaz Belgaum

2023

In Cloud computing environment the resources are managed dynamically based on the need and demand for resources for a particular task. With a lot of challenges to be addressed our concern is Load balancing where load balancing is done for... more

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Fuzzy based Efficient Service Broker Policy for Cloud

by Sajjad WAHEED

2023, International Journal of Computer Applications

Cloud computing is a type of internet based computing that provides shared computer processing resources, storage and data to computers on user demand. Today's time, it becomes very popular due to new facilities and technologies. It deals... more

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Comparative Study of Load Balancing Algorithms through Virtual Machine Scheduling in Cloud Computing Environment

by Atul Mishra

2023, International Journal of Computer Applications

This is age of internet. There is hardly any domain where internet do not play significant role. Cloud Computing relates to internet computing that has revolutionized the whole world. Cloud Computing has undeniably benefited the users in... more

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Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey

by Dr. Manish Shrimali

2023, Journal of Ambient Intelligence and Humanized Computing

Cloud computing is the big boom technology in IT industry infrastructure. Many people are moving to cloud computing because of dynamic allocation of resources and reduction in cost. Cloud computing delivers infrastructure, software, and... more

Many types of research have been done in the field of CC. Some of the general challenges includes load balanc- ing, resource scheduling, service broker policies, resource

Fig. 2 Types of services model using different clouds

This LB algorithm (Chen et al. 2017) is the most advance algorithm for load balancing. It will select only lowest number of server for transaction. This algorithm is also a dynamic algorithms, the main function of this algorithm is that it can able to count the number of connections for each server dynamically and estimate the load condition. It is used to evaluate the workloads condition. The LC method takes the present workload servers into process. The server

The Weighted Least-Connection (WLC) (Diaz et al. 2016) gorithm is a superset of the least-connection algorithm, n which you can assign a performance weight to each real erver. The servers with a higher weight value will receive i larger percentage of active connections at any one time. The default server weight is one, and the IP Virtual Server IPVS) administrator or monitoring program can assign any

4.3.1.3. Max-Min LB algorithm The performance of Max— Min (Ghumman and Kaur 2015) is nearly similar to the pre- vious algorithm, the only difference is that extreme values are selected after finding the finishing time of the jobs. Then the node’s waiting time is updated after having mapped the

Fig. 10 Flow diagram for Genetic Algorithm

Fig. 11 Flow structure for Honey Bee Foraging Technique

where 7, represent the pheromone trail of VM, 7; 1s a heu- ristic value, and, a and f determines the effect of the phero- mone track. The function of n' is based on the distance between physical node and VM is shown in Ea. (3). between physical node and VM is shown in Eq. (3).

Planning of virtual machines with balancing of work load in the CC is used to assign the cloud servers and sched- ule the resources to each servers based on priority order. When planning in a CC system, it is essential to consider the performance of the various processor parts. Because the Scheduling Algorithm (SA) is a long-lasting task and the scheduler can perform parallel job planning on a core. In a cloud network with high computational requirements, it is possible to balance the workload and evenly distribute the load on all nodes, using planning algorithms appropriately (Smanchat and Viriyapant 2015). This way of planning the load will create problems because during the allocation of jobs a node can be heavily loaded and one can be slightly loaded regardless of their capacity. This resource manage- ment nodes are responsible for planning the process and assigning all resources owned by the data center. In addi- tion, resource management nodes manage and monitor all resources, at the time they can implement the LB of the cloud datacenter. This type of scheduling used to reduce the

energy feeding and resource costs in the data center. The scheduling algorithm for heterogeneous VM in CC environ- ments used to manage all workloads before a deadline (Zuo et al. 2018). Scheduling model for the CC environment, are as shown in Fig. 12. of resources among different users based on certain rules and regulating the use of resources in a specified cloud environment. Examine algorithms to increase performance, involving dynamic planning based on threshold, Genetic Algorithm (GA) optimized with dual suitability and Ant Colony Optimization Algorithm (ACA) optimized for plan- ning (Madni et al. 2016; Bansal et al. 2016).

Fig. 14 Classification of CSBP The cloud service brokering contain types of service bro- kering policies, which is used to provide the service to user request based on their demand. The service broker routing policy has some basic mechanisms that are responsible for the proper allocation of datacenters to meet different user requests. Unfortunately, standard SPBR are established on the cost, location and processing time of the datacenters in use. Analyzing these problems, a pricing routing policy for cloud providers to CSB are presented. Cloud brokering

Fig. 16 Pie chart of parameter comparison

weight to real server. In the WLC method, a low number of server is connected to a new network. Here, large number of weighted value is connected to each and every server, this makes the performance of algorithm very fast. Figure 8 shows the server 2 is selected by the load bal- ancer which has a fewest number of transaction with low assigned weight. Here the algorithm connect the request according to the Least Connection provided in the balancer according to the weight.

Table 3 Comparative analysis of most predominant techniques in LB

Table 4 Survey of static and dynamic LB algorithms Table 5 gives some basic ideas about the classification of the LB algorithm, and represents what types of metrics are present for each LB algorithm. In Table 6, the complexity and statistical analysis among various load balancing algo- rithms are presented.

Table 5 Comparison of LB algorithms based on various metrics Table 6 Comparison of complexity and statistical analysis of various scheduling LB algorithms

Ground sim, and Green cloud. The comparison of some tools is given in Table 7.

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Data Distribution Handling on Cloud for Deployment of Big Data

by Akshay Mote

2023, International Journal on Cloud Computing: Services and Architecture

Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data.... more

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Data Distribution Handling on Cloud for Deployment of Big Data

by vaibhav kale

2023, International Journal on Cloud Computing: Services and Architecture

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Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing

by Pooja Samal

2023

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An Efficient Load Balancing Approach in a Cloud Computing Platform

by Karan Vishwakarma

2023

Technology has always played an important role changing human lives and helping to perform faster and easier job. In recent years cloud computing has replaced the traditional method of grid computing, cluster computing and introduced... more

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Developing Wisely Randomized Weighted Throttled Load-Balancing Algorithm for Cloud-Computing Environment

by EYOB TEFERA

2023

From other challenges of cloud computing, Load balancing is one of the main issues in the environment. Due to current vast demand on the technology, the load-balancing algorithms must have the ability to control the efficiency in... more

management effort or service provider communication [10]. network, servers, storage, software can be quickly provisioned and released with a least

accountable to assigns the virtual machine to the user request based on a defined loac

characteristics. By picking two unique virtual machines and by separating the available anc busy list table during searching WRWT algorithm fills the problem on searching the next

Fig. 6. Sample simulation output window experiment part, it facilitates the comparison and analysis mechanism by its table and chart-

efficiency of simulation [28]. Fig. 8. User base configuration and application deployment configuration

Table 3.VM-STATE-LISTS of Proposed algorithms (a) Available List (b) Busy List In the proposed load balancing algorithm, generally, two hash-maps are created. The first allocation. this helps the load-balancer to search within only available unoccupied VM

Fig. 11. Flow Chart of WRWTA LBA during assignation of next VM

Fig. 12. User base configuration in experiment |

Fig. 14. Advanced tab configuration in experiment 1 Fig. 13. Datacenter configuration in experiment 1|

Fig. 20. Average Data Center Processing Time in experiment |

Fig. 21. UB configuration and application deployment configuration for experiment 2

Fig. 22. Datacenter configuration for experiment 2

Fig. 28. Average Data Center Processing Time in experiment 2

Fig. 29. VM allocation count in the case of 5 VM in experiment 3

Fig. 30. Virtual machine allocation count in experiment 4 5.2.3. Experiment 4: Test WRWTA resource utilization for larger user-request per hour

Table 2. VM-STATE-LIST of Throttled, Modified Throttled, and RWT others’ VM-STATE-LIST hash-maps is shown below with an example data.

5.2 Experimentations Table 4. Distribution of Facebook user in each user base

Table 5. Virtual machine allocation count for experiment 3

Discussion Table 6. VM allocation counts in experiment 4 with 5 virtual machines

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Improved Strategies for Enhanced Business Performance in Cloud based IT Industries

by Vaidehi Nedu

2023

Emergence of sophisticated technologies in IT industries has posed several challenges such as production of products using advanced technical process for instance Result Orientation Approach, Deployment, Assessment and Refinement (RADAR)... more

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Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE

by Amlan Datta

2023, Open Computer Science

This Paper focuses on multi-criteria decision making techniques (MCDMs), especially analytical networking process (ANP) algorithm to design a model in order to minimize the task scheduling cost during implementation using a queuing model... more

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Cloud forecaster: Gizmo for Evaluation of Bulky Cloud Computing Surroundings to Propagate ICT based Education

by Dr. Abhay Saxena

2023, International Journal of Engineering and Technology

In recent year cloud computing become one of the rapid advancement field in computer science, which offers to access remote server for storing and retrieving our data. In this paper, we have used Cloudanalyst tool to determine load balancing factor, response time and cost of our data center for developing ICT based Education through Mobile Application. For analyzing our result, we have used 3 and 7 Data Centers and used Round Robin algorithm. For algorithm simulation, we have used five user bases named as UB1, UB2, UB3, UB4, UB5 and region R2. In our work, we basically concentrated on calculated Response Time and Data Center processing time for three and seven Data Centers. In our analysis, for three Data Centers we found that minimum Total Response Time is 42.23ms, maximum is 60.25ms and minimum data center processing time is 0.02 ms, maximum is 1.35 ms. For seven data centers we found that minimum Total Response Time is 42.23ms, maximum is 59.93ms and minimum Data Center Processing time is 0.04 ms, maximum is 1.40 ms. Keywords-Cloud analyst, Data center (DC), Virtual machines (VM), User bases (UB), Region, load balancing algorithms, service broker policy. I. INTRODUCTION Over the last few years, cloud computing services have become increasingly popular due to the evolving data centers and parallel computing paradigms [1]. A cloudis a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumers [2]. Virtualization is the principle aspect of cloud system. Using virtualization technique, cloud computing virtualized a single system into number of virtual systems. Basically a virtual machine is a software implementation of physical resource [3]. Fig. 1, shows that cloud computing is a combination of three important factors characteristics, deployment model and service model.

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Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

by Anisah Hamimi Zamri

2023, International Journal of Advanced Computer Science and Applications

Cloud computing is one of the significant technologies that is used to provide seamless internet surfing for large-scale applications and data storing purposes. The cloud is described as a large platform that enables users to access data... more

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Load Balancing in Cloud Computing by Hybrid Approach Using Static and Dynamic Load Management Algorithm

by Dr. Vipul Vekariya

2023

Cloud Computing is further generation technology for IT enterprise. Cloud processing has turned out to be basic popular expression in the Information Technology and is a next stage in the advancement of Internet, The Load adjusting issue... more

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Performance Evaluation of Novel Random Biased-Genetic Algorithm (NRB-GA): A Hybrid Load Balancing Algorithm in a Cloud Computing Environment

by Karpaga Selvi Subramanian Karpaga Selvi Subramanian

2023, Scientific Programming

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time... more

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Prioritized scheduling technique for healthcare tasks in cloud computing

by eman shaaban

2023, The Journal of Supercomputing

The Internet-of-things (IoT) plays a significant role in healthcare monitoring, where the IoT Cloud integration introduces many new opportunities for real-time remote monitoring of the patient. Task scheduling is one of the major... more

Fig. 2. Effect of a varying number of tasks on a Makespan, b Delay, c Guarantee Ratio, d Failure Rate, and e Cost of Processing

Prioritized scheduling technique for healthcare tasks in... Fig. 3 Effect of a varying number of VMs on a Makespan, b Delay, e Guarantee Ratio, d Failure Rate, and e Cost of Processing

Fig. 4 Effect of varying the number of tasks on a Missed Critical Tasks and b Delay

Fig. 5 Effect of varying the number of VMs on a Missed Critical Tasks and b Delay

Table 1 Task scheduling with IoT cloud architecture comparison from related work

Table 2 Simulation parameters for Tasks [40]

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DCSim: A Data Centre Simulator

by Hanan Lutfiyya

2023

Developing algorithms to dynamically manage resources in a virtualized, multi-tenant data centre is challenging. Experimenting with such algorithms on the data centre scale is impractical due to size and complexity. Thus, there is a need... more

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Data Distribution Handling on Cloud for Deployment of Big Data

by Suvarna Kadam

2023, International Journal on Cloud Computing: Services and Architecture

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A Comparative Analysis of Cloud Simulators

by Pericherla S Suryateja

2023, International Journal of Modern Education and Computer Science

Cloud Computing is a new paradigm which allows individuals and organizations to purchase resources like compute, storage, networking etc as a utility with minimum human intervention. According to recent surveys more than half of the large... more

Fig.1. Architecture of CloudSim Architecture of CloudSim [24] is as shown in ollowing figure Fig. 1:

CloudAnalyst provides an easy to use GUI to configure any geographically distributed system [11], such as description of application workloads, including information of geographic location of users generating traffic and location of data centers, number of users per data center and number of resources in each data center. CloudAnalyst can generate output in the form of chart or table that summarizes the huge amount of users and system statistics during the simulation time. Fig. 2.Architecture of CloudAnalyst

Fig.3. Architecture of GreenCloud GreenCloud. This simulator is only suitable for work related to calculating energy consumption in data centers (cloud).

Fig.4. Architecture of iCanCloud to help to take a decision for paying a corresponding budget of machines.

Fig.5. Architecture of NetworkCloudSim Architecture of NetworkCloudSim [28] is as shown in following figure Fig. 5:

Fig.7. Overview of GroudSim Core GroudSim [18][30] (Gr-Grid and oud-Cloud Simulator) is a discrete event simulation platform for both cloud and grid computing environments. It is specifically developed for simulating scientific applications in grid and cloud environments. Base programming language for GroudSim is Java. GroudSim can be extended very easily by adopting probability distribution packages. One unique feature is that, GroundEntity in the GroudSim has its own definition for error behaviors, user can change this configuration during each error occurrence. Core components of GroudSim [18] are as shown in following figure Fig. 7:

MR-CloudSim EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model.

SimIC SmartSim [21] is uniquely built for simulating applications for mobile cloud computing. SmartSim is the first ever simulator built that supports mobile cloud applications. Its main feature is to model mobile cloud application running in mobile devices. SmartSim supports both the system and behavior modeling of Smart Mobile Device (SMD) components such as application processor, memory, resources provision, computing resources utilization evaluation, dynamic processing management policies and computational intensive mobile application modeling for SMD. Architecture of SmartSim [21] is as shown in following figure Fig. 9:

Fig.10. Architecture of SmartSim CloudSimSDN § [37][38] based on CloudSim is a lightweight and scalable simulation environment to analyse the network allocation capacity policies like measuring the network performance and host capacity allocation approaches simultaneously within a data center. Architecture [38] of CloudSimSDN is as shown in following figure Fig. 10:

Base Platform: Most of the simulators have been developed based on existing simulation framework(s). All the features of the base platform/framework are incorporated in to the new simulation application.

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Load Balancing Strategy in Cloud Computing Using Simulated Annealing

by Kousik Dasgupta

2023, Communications in Computer and Information Science

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Special issue on software solutions for contemporary applications

by Kousik Dasgupta

2023, Innovations in Systems and Software Engineering

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Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach

by Kousik Dasgupta

2023, Procedia Technology

Cloud Computing, a new concept is a pool of virtualized computer resources. An Internet-based development where dynamically scalable and often virtualized resources are provided as a service over the Internet has become a significant... more

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Performance Evaluation of Novel Random Biased-Genetic Algorithm (NRB-GA): A Hybrid Load Balancing Algorithm in a Cloud Computing Environment

by Gemmachis Teshite

2023, Scientific Programming

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Analytic Study Of Load Balancing Techniques Using Tool Cloud Analyst

by Tanveer Ahmed

2023, ijera.com

Cloud computing, as of today has boomed the use of internet services and has enhanced the use of network services where the capability of one node can be utilized by other nodes. Central to the architecture is SoA and IS which raises a... more

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Energy Aware Of Cloud Computing Development Using Cloud Simulators

by Ionel Gordin

2023, The European Proceedings of Social and Behavioural Sciences

This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 Unported License, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work... more

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Performance Evaluation of Adaptive Virtual Machine Load Balancing Algorithm

by pankaj sharma

2022, International Journal of Advanced Computer Science and Applications

The conception of Cloud computing has not only reshaped the field of distributed systems but also extend businesses potential. Load balancing is a core and challenging issue in Cloud Computing. How to use Cloud computing resources... more

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Load balancing with preemptive and non-preemptive task scheduling in cloud computing

by Zainab Alansari

2022, 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS)

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Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing

by PRANATI MISHRA

2022

The scheduling process based on TSPBR experimentation is represented in Fig.3. Fig.3. Pseudo code for Task scheduling process based on TSPBRR Let ‘TQi’ is the time quantum in round i. The number of rounds i varies from / to n, where value of i increments by 1 after every round till ready queue is not equal to NULL.

Fig.6. Response time vs. Avg peak value for 25 VMs 2DC Fig.5. Response time vs. Avg peak value for 50 VMs 1DC

Fig.4. Response time vs. Avg peak value for 25 VMs 1DC

Fig.7. Response time vs. Avg peak value for 50 VMs 2DC

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Distributed Service Broker Policy Algorithm for Logistics over Cloud

by Dr. Shivani Dubey

2022, Global journal of computer science and technology

Logistics information system focuses on flow of information with storage and services of goods supply from the origin point to consumption point of organization. Logistics information system makes this flow more efficient with the help of... more

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Experimental Model of Distributed Service Broker Policy Algorithm in Cloud based Centralized and Distributed Data Center

by Dr. Shivani Dubey

2022, Journal of Engineering and Applied Sciences

In recent years cloud computing is a very advanced technique to distribute workload among all the data centers and also balance those data center very smoothly. To improve the performance of data centers, load balancing is used to... more

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Cloud Computing and Sustainability: Energy Efficiency Aspects

by Ahmad Khalifeh

2022

We would like to thank Halmstad University that gave us the opportunity to study this fascinating program. We are grateful to our supervisors Olga Torstensson and Prof Tony Larsson in directing us towards a mature work. We are honoured to... more

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Mathematical Modelling of Joint Routing and Scheduling for an Effective Load Balancing in Cloud

by Suriya begum

2022, International Journal of Computer Applications

Due to the increasing adoption of the cloud in majority of the business, the level of traffic intensity is increasing leading to a challenging situation for traffic management in cloud. There were various algorithms in past that has... more

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Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE

by Amlan Datta

2022, Open Computer Science

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Performance Evaluation of Adaptive Virtual Machine Load Balancing Algorithm

by pankaj sharma

2022, International Journal of Advanced Computer Science and Applications

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Lake-Level Changes and Their Paleo-Climatic Implications at the MIS12 Lower Paleolithic (Middle Pleistocene) Site Marathousa 1, Greece

by Eleni Panagopoulou

2022, Frontiers in Earth Science

Lithics and cut-marked mammal bones, excavated from the paleo-lake Marathousa 1 (MAR-1) sediments in the Megalopolis Basin, southern Greece, indicate traces of hominin activity occurring along a paleo-shoreline ca. 444,000 years (444 ka)... more

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Improved scheduling algorithm in cloud environment

by Nipun Madan

2022

Writing of thesis is a journey through gravel road, but you can make it with the help of the people and resources you get in touch through out your journey. I am thankful to all of them for their hands to frame this thesis. First of all,... more

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Cloud Analyst

Key research themes

1. How can cloud simulation tools support performance evaluation and resource management in cloud computing environments?

2. What machine learning and predictive modeling techniques improve resource management and load balancing in cloud environments?

3. How do cloud service broker policies and data center selection algorithms affect response times and load distribution in cloud platforms?

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