We analyze the performance of the Amazon EC2 platform using micro-benchmarks, kernels, and ⦠From this point on, the increase in the vCPU number, from 4 to 8, raised the execution time by, approximately, 13%. Performance analysis of many connected applications and their sub-elements were being thought about in the concert of the helpful and principally used analysis space within the recent years. For this reason, the ReMM reduced the number of vCPUs by 50% (from 8 to 4), plus the cost by 4% (Fig 12d), to ensure compliance with the SLA (Fig 11). Its behavior is shown in Fig 10. Cloud Computing model, based on the Amazon Elastic Compute Cloud (EC2) architecture that implements a Gang Scheduling scheme. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, The art of computer systems performance analysis. In this paper, we provide an overall perception on cloud computing and draw attention to its services. This is not an easy undertaking because it requires taking account of some key parameters such as budgeted resources, time constraints, and/or the desired quality of service. Due to popularity and progress of cloud in different organizations, cloud performance evaluation is of special importance and this evaluation can help users make right decisions. the display of certain parts of an article in other eReaders. The above mentioned providers do not change the VMs configurations on-the-fly. In the ReMM, the change in the cost when vertical scalability is used is proportional to the change in the number of vCPUs, while in the case of horizontal scalability, the scalability type that most providers use, the change in the cost is proportional to the number of instances. that can be effective on performance from the usersâ view. However, this advanced technology has immediately highlighted many serious security troubles. Experts believe cloud computing is currently reshaping information technology and the IT marketplace. use standard best practice ICT frameworks such as eTOM, ITIL, etc. This change is necessary as the current configuration, in this example, is using more resources than needed. Load balancer is a key element-in resource provisioning for high available cloud solutions, and yet its performance depends on the traffic-offered load. On the basis of this analysis, the prototype adaptively allocates resources to the back-ends to meet the clients’ requests. The primary purpose of Dryad is to distribute the execution of parallel applications on the cloud. As the weight for each parameter acquired by conducting expertsâ opinions or user preferences in above works, as a result, certain recommended cloud services are not always the best to meet usersâ requir e-ments. rated capacity, this will reduce performance of services. previous section but some of the factors will be important in special criteria: with this criterion is network bandwidth ,availability and scalability. In this way, it is possible to quantify the influence of configurations of a different number of VMs and virtual cores (vCPUs), disk size, network type and memory RAM capacity on the performance of the system. Handbook of Cloud Computing includes contributions from world experts in the field of cloud computing from academia, research laboratories and private industry. As a result, the proposed module seeks to ensure the requested execution time is met by respecting the SLA Margin and changing the resources configuration, if necessary, with a corresponding change in price. KPIs for measuring cloud adoption success. Different applications were executed by different instances in environments with and without ReMM. In [10], for instance, the authors carry out a study about the allocation of resources among multiple HPC (High Performance Computing) systems such as cluster, grid and cloud. Due to popularity and progress of cloud in different organizations, cloud performance evaluation is of special importance and this evaluation can help users make right decisions. This paper tries to bring awareness among managers and computing professionals to use Cloud computing as an alternative to large in-house data centers. Although this execution mean time in a Common environment was approximately 47% lower, the SLA was not complied with. The results showed that ReMM effectively changed the available resources during the execution time, ensured compliance with the SLA and the efficient use of resources at a fair price. Two benchmarks were used in the experiments: Apache—This System-Bound benchmark uses the number of served requests per second as a response variable [20]. This allowed the percentages of the respective influences to be obtained. Cloud Computing makes the dream of computing real as a tool and in the form of service. Disk capacity, can also have a negative or positive impact on performance in cloud[17]. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price. Another limitation of this prototype is the lack of scaling down capability as the number of requests made by active clients declines. The analysis of the waiting lines may be analyzed merely with the assistance of queuing theory models. The purpose of this was to determine if the same behavior is maintained with different workloads. The performance study includes evaluating the chance of immediate serving or-rejecting incoming tasks. By conducting this analysis, it is possible to show the described behavior, and that there is an increase in the vCPUs number, from 8 to 16, 16 to 32 and 32 to 64, leading to an increase of approximately 97%, 129% and 100%, respectively, in the response variable. The remainder of this paper is structured as follows: the Section on Related Work conducts a brief analysis of the works available in the literature; Section ReMM—Resource Management Module outlines the proposed module that is deployed with the aid of CloudSim simulator [9]; the experiments that show the benefits of using ReMM were carried out and analysed in the Section called Performance Evaluation; finally, there is the Conclusion and Future Work which summarizes the main results and make recommendations for future work, respectively. after some additional data center and costs is too high. The next stages will entail analysing and formulating policies and methodologies for the admission control (where different priorities of clients can be considered); workload prediction, load balancing and optimization metrics. In Figs Figs1111 and 12d, the amount of available resources in m3.2xlarge instances for the execution of Medium applications was sufficient in all the environments. However, in the shown results, there is only a kind of virtual machine with fixed configurations that is assigned to meet any demands. For example, if an application needs significantly more CPU power, but has no additional memory, and the next VM class provides an average increase in both areas (CPU and memory), the client can change from a VM type to another one with more resources. For this reason, this paper outlines a module for resource management in a cloud environment that examines how to handle the available resources on-the-fly and the effect of this manipulation on both the performance of the system and the business model. Proposal and evaluation of a dynamic resource allocation method based on the load of VMs on IaaS. An experimental evaluation in Amazon Elastic Compute Cloud (EC2) verified our approach. Fig 5 illustrates this behavior. Furthermore, there are unpredictable situations that can impair the efficiency of the provisioning services and delivery during the execution time, such as demand estimation with expenditure measurement errors, dynamic workload and unpredictable behavior of the system [8]. A client’s request will be answered by a provider, which uses three layers and can provide different services: application layer (Software as a Service—SaaS), platform layer (Platform as a Service—PaaS) and infrastructure layer (Infrastructure as a Service—IaaS). As an example, increasing power and speed of the data center is not always efficient. Most word cloud generators have features that allow users to change colors, font, and exclude Cloud computing is changing the way application s are deployed, monitored, and used by the end â users. The resources might be changed, depending on the results of the analysis. The changes applied by the vertical and horizontal scalability in m3.large instances yielded similar results when both were compared with the Common results. This insight into the essential characteristics of cloud and its enabling technologies provides a good foundation for understanding and a hint on how to leverage desirable strengths of these technologies in the cloud by way of extension and or inheritance. However, the simulated environment does not change the capabilities of the VMs. It helps in providing services and resources with the help of internet. The same behavior occurred in the experiment with 2 VMs with 1 vCPU that obtained a similar result to the experiment with 2 VMs with 2 vCPUs. In the results, changes in the memory capacity, disk size and network type did not have a significant impact on the response variable. When the application is not the root of a performance issue, and we've confirmed increased latency at the virtualization layer, then we know that a ⦠This result is shown in Figure 12. section, we have considered 18 users and 3 data centers, which are placed in three cases. As more and more IT systems are externalised, making sure you pick the right cloud providers has become critical to long-term success.However, the available market is vast, with a myriad of providers offering an even larger number of services. Resources are always provided by the Cloud Service Provider. In Fig 7, combination of the number of VMs and vCPUs factors had the greatest influence on the results with 53.8% (Fig 7a), 43.3% (Fig 7b) and 46.4% (Fig 7c). This meant that, no changes were made in the number of vCPUs and instances, resulting in the same values for the execution mean times and costs. In this paper, we review current state of cloud computing research and technology with emphasis in the adoption of cloud computing in the healthcare information system, and also discuss a foundation for establishing a framework for cloud-based e-prescription system using cloud computing infrastructure. an evaluation of the usefulness of the current cloud computing services for scientiï¬c computing. These factors give rise to problems in workload behavior estimation and related requirements for resources. As discussed in [4], resource matching and issues about making recommendations have often been neglected, such as the use of attribute weights and the collaborative application of empirical data, marginal utility, and QoS constraints. This report provides an independent assessment of the breadth, scope, and impact of health services and primary care research funded by the U.S. Department of Health and Human Services and U.S. Department of Veterans Affairs from FYs 2012 to 2018. According to NIST (National Institute of Standards and Technology), “Cloud computing is a model that allows ubiquity, convenience and on-demand access to a shared pool of configurable resources and can be quickly delivered with minimum managerial effort on the part of the clients” [1]. We are experimenting with display styles that make it easier to read articles in PMC. Performance feedback also fulfills a basic human need to be recognized and valued, which leads to higher self-esteem and motivation for optimal performance. In this service model, the platforms, operating systems, applications (SaaS), and software are all available in the server. Evaluate workloads or the group of applications that the customer wants to move to cloud. Another limitation is that the prototype is not dynamic, because it is not possible to add or remove instances or even change the VM capabilities during the execution time. If a request is accepted, virtual resources are allocated in the physical resources in accordance with the SLA specifications. As can be seen in figure 5 too, increasing 5 Data. also increase. The module proposed in this paper, called ReMM, concerns the way the available resources are handled during the execution time with regard to the QoS metrics defined in the SLA. However, even with different configurations, the experiments showed almost the same behavior. 2. A company that focuses on service-oriented architectures (SOA) needs to know what configuration would provide the proper levels for individual services if they are deployed in the cloud. Services requests will be analysed by the Admission Control after a negotiation between the client and the provider. On the basis of the results, only changes in the number of virtual cores were considered in the ReMM implementation. The initial configuration of the VM varies in accordance with the experimental design. This means that, the execution mean times and the costs (Fig 12c) were the same. You may notice problems with Cloud Computing provides infrastructure for computing and processing of all types of data resources and adopted to deal with the large amounts of data. Performed the experiments: BGB. Opt to use plain language over more technical language. In this way, analyses considering the factors Disk size, Network type, Memory (RAM) capacity, VMs number and vCPUs number were performed. performance is one of the cloud advantages which must be satisfactory for each service[1-5]. Calheiros, Ranjan and Buyya [8] propose an adaptive approach to investigate the automated task management and resource scalability to ensure the QoS contracted by clients. The infrastructure layer carries out the initiation and removal of VMs with specific resource configurations for the client in a transparent way. This book presents the systems, tools, and services of the leading providers of cloud computing; including Google, Yahoo, Amazon, IBM, and Microsoft. In this way, though the combination shown in Fig 7b, the idleness of processing resources during the experiments was found to be lower than in Fig 7a, while the competition for these resources was also lower than in Fig 7c. The vertical scalability applied an increase in the number of vCPUs of 100% for running Low, 300% for Medium and 700% for Heavy applications (columns Resources—Common and Vert. It is just for an organization's infrastructure. In this methodology, the planning and analysis of experiments include both factors and levels, where the factors correspond to environmental characteristics and the levels are the possible environmental variations. Social Worker Performance Evaluation Lower Kuskokwim School District P.O. Accessed: 2015-09-03. A full factorial experimental planning was used for both sets of experiments. However, there are no changes in the VM capabilities during the request execution time. Cloud computing evaluation checklist item 2: There should be transparent communication when it comes to the business continuity and disaster recovery plan. The number of I / O commands per second(IOPS)or unit time, Average waiting time per unit time [6,18], Workload(requests) to be serviced per second(Mbps) or a unit of time [6], The average time of processing(exe / sec), The number of requests executed per unit time, The number of requests per unit time buffer, The number of rejected requests per unit time, Minimum, maximum and average overall response time, Minimum, maximum and average processing time in the overall data center, Minimum, maximum and average response time per user, Minimum, maximum and average time per data center, Evaluation Metric for Cloud applicationsâ, International, Miguel G. Xavier & Marcelo V. Neves & Fabio D. Rossi, (2012), âPerformance Evaluation of. 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