Cloud Analyst Tool Research Articles

Cloud service analysis using round-robin algorithm for quality-of-service aware task placement for internet of things services

Round-robin (RR) is a process approach to sharing resources that requires each user to get a turn using them in an agreed order in cloud computing. It is suited for time-sharing systems since it automatically reduces the problem of priority inversion, which are low-priority tasks delayed. The time quantum is limited, and only a one-time quantum process is allowed in round-robin scheduling. The objective of this research is to improve the functionality of the current RR method for scheduling actions in the cloud by lowering the average waiting, turnaround, and response time. CloudAnalyst tool was used to enhance the RR technique by changing the parameter value in optimizing the high accuracy and low cost. The result presents the achieved overall min and max response times are 36.69 and 650.30 ms for running 300 min RR. The cost for the virtual machines (VMs) is identified from $0.5 to $3. The longer the time used, the higher the cost of the data transfer. This research is significant in improving communication and the quality of relationships within groups.

A load balancing strategy for reducing data loss risk on cloud using remodified throttled algorithm

<p><span>Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.</span></p>

Towards Investigation of Various Load Balancing Techniques in Cloud Computing

Cloud computing is an on-demand service where customers may access any time common IT resources, information, software and other equipment. It is a web-based development that offers virtual resources over the Internet as a service. The higher the cloud use, the higher the charge. The allocation of loads to components processing is a challenging phenomenon.In a multi-node system, there is a very good possibility that some nodes will be idle while others will be overloaded. The load balancing algorithms' objective is to keep the load on each processing element constant. A technique for Random Load Balancing in Cloud Computing is devised, in which the amount of user requests is instantly assigned to the resources. This method employs a random strategy to decrease the request's waiting time. This proposed load balancing technique is simulated in Cloud Analyst tool. The performance comparison study between proposed random method and other available load balancing methods is conducted. It is found that the performance of proposed random method is better in terms of response time.Keywords: Cloud Computing, Load Balancing, Response Time, Virtual Machine, Sorting

ITA: The Improved Throttled Algorithm of Load Balancing on Cloud Computing

Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.

Simulation of load balancing algorithms using cloud analyst for distinct regions IT resources

Cloud computing is currently on the rise as new technology and as a new business model, according to experts. Data archiving, servers (both real-world and virtual), telecommunications, machine learning tools for data analysis, and a cloud services provider's (CSP) data center are all examples of cloud computing resources that are available on demand over the internet. Cloud computing allows client devices to access data from faraway servers over the internet, which is referred to as "cloud computing." Rather than purchasing, operating, and maintaining physical data centers and servers, we can take advantage of technology services provided by third parties. A pay-per-use pricing structure is in place. It is spreading over the world simply because of its simple and service-oriented business approach. It delivers the greatest computing service to the user and makes the execution of large-scale activities much easier than before. Through the use of a cloud analyst tool, this study provides an overview of the evaluation of the response time and processing time of several existing algorithms that are used in cloud computing.

A proposed framework for cloud-aware multimodal multimedia big data analysis toward optimal resource allocation

Abstract The main goal of this paper is to demonstrate the structural design of Multimodal Multimedia Services in Cloud Platform (MMSCP). Thus, our proposed MMSCP architecture is built of three levels such as, Service Platform, Execution Platform and Structural platform. The functionality of service platform is to gather different forms of video files generated by the media creators and to store these files on the local platform. The second execution platform integrates both the Hadoop and Mapreduce functionalities. Finally, QoS based cloud computing functionalities (i.e. load balancing, security, resource allocation and network traffic management) is employed at the third structural platform. Likely, we introduced the Crow Search Algorithm (CSA) in structural platform for optimal allocation of resources. We adapt a Hadoop cluster to perform the experiment. Also, to conduct the resource allocation experiment we used some of the conventional optimization algorithms such as, ABC, GA and PSO for comparison with our proposed CSA algorithm imposed on the structural platform. However, to evaluate the performance of the algorithms we configured the CloudAnalyst tool. The simulation results illustrate that the proposed algorithm can allocate the virtual machine (VM) optimally to attain a minimal response time.

Applicability of MMRR load balancing algorithm in cloud computing

One of cloud computing’s fundamental problems is the balancing of loads, which is essential for evenly distributing the workload across all nodes. This study proposes a new load balancing algorithm, which combines maximum-minimum and round-robin (MMRR) algorithm so that tasks with long execution time are allocated using maximum-minimum and tasks with lowest execution task will be assigned using round-robin. Cloud analyst tool was used to introduce the new load balancing techniques, and a comparative analysis with the existing algorithm was conducted to optimize cloud services to clients. The study findings indicate that ’MMRR has brought significant changes to cloud services. MMRR performed better from the algorithms tested based on the whole response time and cost-effectiveness (89%). The study suggested that MMRR should be implemented for enhancing user satisfaction in the cloud service.

RETRACTED ARTICLE: Performance analysis of nature inspired load balancing algorithm in cloud environment

Cloud computing has emerged as a new technology which allows the users to store their data and retrieve it over internet on demand instead of using their own hardware. Cloud works with different data centers (DCs) (server) and user bases (UBs) (clients). One of the main challenges which requires focus in the cloud computing is task scheduling. In task scheduling the cloud should have the capability of managing the incoming load to achieve the better performance by allocating suitable resources as per the user request. The performance of the cloud can be further increased by selecting suitable DC which is closer to the UB. This article measures the performance analysis of various nature inspired load balancing algorithms to identify total response time (TRT) and data center processing time (DCPT) in cloud environment. The simulation is carried out using cloud analyst tool which is an extension of cloudsim and the results obtained which indicates water wave algorithm performs better in terms of TRT and particle swarm optimization scores well in the aspect of DCPT for varying different number of DCs and UBs.

Mobile Cloud-Privacy and Data Security in Healthcare Environment Using Cloudsim Simulator

The advancement of telecommunication in the medical field makes the diagnosis and treatment of patients in a faster manner because the patient information are stored and maintained in an electronic storage device. But there are some issues related to physical data storage, privacy by accessing user data and security etc. With the help of the cloud computing these issues are reduced now. The mobile health care system can improve the quality of patient care and reduce a medical cost for both patients and hospitals. The simulation tool CloudSim is used to generate the data center details, provider’s details and processing time. For the analysis, the cloud analyst tool can simulate the data center and others. The patient information in the Healthcare Information System (HIS) and Electronic Medical Records (EMRs) is stored in a data center or cloud platform in a secure manner.

Different Types of Review Approaches in Load Balancing Techniques in Cloud Computing Environment

Cloud computing is the most emerging and powerful technology in the IT world. The interesting features with enhancement facilities grab the attention of the researchers into its field. Its special kind is pay-as-you-go model for the consumers, by this way the user may this service as they want like as infrastructure, platform and software according to their needs. Cloud computing is an effective management system which built a great relationship between the consumer and the cloud owner’s. However there is a positive aspect still some negatives makes the cloud computing still research field. Some of the major drawbacks still to be discussed were fault tolerance, load balancing and security. The main concept need to be discussed is load balancing; it is mainly depend on requesting the resource services. Some of the key points in load balancing is optimization in resources usage, minimum response time, boost turnout and enabling free from needless burden. Another critical issue is increased user list and cloud application types. In this exploration we focused for Load balancing, how to balance and constructed a present day strategy in a distributed computing condition. Before construct an apparatus how about we do a point by point overview look into in different existing load balancing procedures and cloud analyst tool by various authors.

Analysis of Load Balancing Algorithms using Cloud Analyst

This paper discusses the Cloudanalyst tool. Cloudanalyst tool is used to determine the better load balancing algorithm from various scheduling and load balancing techniques e.g. round robin algorithm. This learning will help valued understanding to design infrastructure services of the Cloud. Different areas like coordination between one data center and other data center, algorithms of load balancing as well as other value-added services are also kept in mind, that are possible like service broker policies, which synchronize efficiently among data centers and enhance cost and throughput which is given to the owners.

CELBT: An Algorithm for Efficient Cost based Load Balancing in Cloud Environment

Cloud computing is interesting era of research where the multiple component, heavy hardware and architecture take part to execute the data and user processes. To maintain different virtual machines, data centers at different locations are required. Sometimes may be over burden on any datacenter and failure or downtime of the datacenter occurred. Thus in order to provide solution a load balancing technique is used which directs the request to an appropriate server for fast computation. Various traditional techniques such as Throttle, Round Robin were introduced but having some drawback with computation time and communication cost which makes them less efficient. In this paper we present CELBT (Cost Effective Load Balancing Technique) which compute the same data transfer in less computation time and cost. In this paper an evaluation is performed on CloudAnalyst tool to compute the parameters like time, cost and found the algorithm proven its effectiveness while compared with existing algorithms.

An Energy Efficient Data Centre Selection Framework for Virtualized Cloud Computing Environment

Background: Data centers are the major sources for implementing critical issues in a cloud computing environment like networking, task scheduling, storage and the distribution of data. The objective is to design a data centre selection framework for submission of tasks to a data centre with minimal energy consumption. Methods: A framework is proposed to select a data centre for tasks submission with minimum total energy consumed by the servers, computer room air conditioning units and other IT equipments like routers, switches. The service provider has to register all the data centers in a data centre registry and the data centre selection framework decides upon task allocation to the appropriate data centre with minimal energy consumption. The results are simulated using Google Cloud Trace logs and cloud analyst tool. The proposed work is compared with shortest distance first and round robin methods. Findings: The performance analysis indicate that the proposed framework is an efficient way for tasks submission to a data centre as it concentrates on minimizing the energy consumption which in turn reduces operational expenditures of the cloud environment. The comparisons of the work with other algorithms indicate that the proposed framework consumes far less energy. Improvements: The framework can be implemented by tools like IBM Bluemix which uses Internet of Things (IoT) technology for reading the energy using sensors.

Comparative Study of Load Balancing Algorithms in Cloud Environment using Cloud Analyst

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 many ways. Load Balancing is emerging as the hottest issue in cloud environment at present. Load due to client requests must be balanced by resources of service provider. In this study we proposed a theoretical framework which is combination of load balancing policy and service broker policy for cloud environment. This paper includes the comparison of the results of proposed algorithm with existing algorithm .This study accomplish that there is extreme reduction in response time at client part and reduction in datacenter request servicing time and resources of resource providers are managed efficiently and effectively by using the proposed algorithm. Several parameters are also identified and these are utilized to compare the traditional round robin load balancing policy with proposed load balancing strategy by using cloud analyst tool as simulator.