The workloads of multi-tier cloud applications are captured in two different models-benchmark application and workload models. Accessed: 2015-09-03. For this reason, the cost when horizontal scalability was used, was higher in all the cases when the amount of resources was changed. Although the numbers of vCPUs in both vertical and horizontal scalability are the same, apart from the exceptions discussed in this section, the number of allocated instances has a strong influence on costs. The infrastructure layer carries out the initiation and removal of VMs with specific resource configurations for the client in a transparent way. for time sharing, already applied in parallel and distributed systems. In this way, in hte cases of both Figs ​Figs44 and ​and6,6, the changes in the memory RAM capacity (from 512MB to 1GB), network (from Megabit to Gigabit) and disk size (from 8GB to 16GB) do not lead to significant changes in the results, i.e., the performance of the system remained the same. conclusions. Another limitation of this prototype is the lack of scaling down capability as the number of requests made by active clients declines. The results were similar as shown in Fig 4. On the other hand, the use of horizontal scalability resulted in rises in costs of approximately 100%, 300% and 700%, respectively. In [16], the authors propose resource allocation algorithms for SaaS (Software as a Service) providers that are designed to reduce the costs of the infrastructure and Service Level Agreement (SLA) violations. Cloud computing was the technology and model that was aimed to supply the services to a varied set of consumers by the model of paying some certain quantity for utilising a specific set of operations and tasks from the system model. The benefits offered by cloud computing to the clients such as reliability, near-zero downtime maintenance, and much cheaper solution compared to having their own IT infrastructures have paved the way for complete adoption of cloud computing in enterprise world. 2 Conceived and designed the experiments: BGB JCE CHGF DMLF LHVN SRM MJS RHCS. After this procedure, different clients request different types of services from a provider. Hence, the results were similar, as the average number of served requests per second per each VM was considered. are studied. Evaluate workloads or the group of applications that the customer wants to move to cloud. From this point on, the increase in the vCPU number, from 4 to 8, raised the execution time by, approximately, 13%. A performance management system can enable managers to better understand their employees’ skill sets and proficiency levels. In this way, analyses considering the factors Disk size, Network type, Memory (RAM) capacity, VMs number and vCPUs number were performed. This behavior was discussed in the analytical experiments described in Figs ​Figs44 and ​and6.6. In this paper, we provide an overall perception on cloud computing and draw attention to its services. Experts believe cloud computing is currently reshaping information technology and the IT marketplace. On the other hand, it was found to be more efficient to increase the number of vCPUs rather than the number of VMs. This paper studies the performance of a distributed The ReMM analyses the EMT in periods of time and, if the result is not in accordance with the SLA Margin, it changes the amount of resources allocated for that client. they also use enterprise resource planning systems in order to move towards process oriented Enterprises. When a comparison was made between 1 VM with 8 vCPUs and 4 VMs with 2 vCPUs, the former obtained a lower result than the latter, approximately 78%. At intervals of time, the Performance Monitor collects information about the system performance and about the request execution (8) and sends them to ReMM (9.a). Two types of response variables will be employed in our module analysis: the execution mean time and the cost. We first show that current implementation of virtual machine monitor (VMM) does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing across multiple virtual machine instances (VMs) running on a single physical host machine, especially when applications running on neighboring VMs are competing for computing and communication resources. 1 Pareto traffic was considered for the offered traffic. Performance considerations are vital for the overall success of cloud computing, including the optimum cost of cloud services, reliability and scalability. In the experiments, the client requests the image rendering (which might be Low, Medium or Heavy) in approximately 100 seconds, with a SLA Margin of 20%, i.e., the request execution time can vary from 80 and 120 seconds. Available from: Smallpt Benchmarking;. As new vCPUs were added, the competition for physical resources was greater, and this reduced the execution mean time. In this service model, the platforms, operating systems, applications (SaaS), and software are all available in the server. 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. Data size assumptions and Geographic regions, Hybrid requests (computing, networking, I / O, memory), Requests related to transmission and network, Requests related to storage and retrieval and data access. The characterization of the queuing model or the queuing system was very useful and very important for processing the several systems or several applications using these queuing systems. The data will not be disturbed or any damage to the actual data as it was stored at various servers connected to each other and located at various locations. But it is important to measure the performance of these applications in the cloud. However, in the shown results, there is only a kind of virtual machine with fixed configurations that is assigned to meet any demands. It also demonstrates a fundamental technology trends, and it is already apparent that it is reshaping IT processes and marketplace. Although performance testing in the cloud is quite different from the traditional approach, proper strategy and planning are involved while testing on the cloud. However, as one virtual machine is deployed per physical machine and all the physical machines have the same configuration, the available resources can be overloaded or idle owing to the heterogeneity of the application. Most word cloud generators have features that allow users to change colors, font, and exclude Server consolidation and application consolidation through virtualization are key performance optimizations in cloud-based service delivery industry. Similarly, as we add VMs to an application, which is simpler and more flexible than changing resource allocations, we do not want to over-provision. On the basis of influence of the resources, we simulated an environment to validate our proposed module. This internet -based ongoing technology which has brought flexibility, capacity and power of processing has realized service-oriented idea and has created a new ecosystem in the computing world with its great power and benefits. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. [11] discuss various internal and external factors that should be considered in the resources allocation process. Thorough evaluation on Cloud service performance is crucial and beneficial to both service providers and consumers; thus forming an active research area. After the analysis of the influence of the factors, new experiments were conducted with the Smallpt benchmark, which only involved varying the number of VMs and vCPUs (Table 2) to analyse the behavior of the system with different workloads. In: Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on. Different applications were executed by different instances in environments with and without ReMM. to be deployed for cloud environment, which the IBM architecture is now considered the focal reference for implementing cloud in a number of organizations. performance is one of the cloud advantages which must be satisfactory for each service[1-5]. This overview gives the basic concept of cloud computing, and highlights the relationship between Cloud computing and other cloud enabling technologies by providing their similarities and differences. In the other case, (2 VMs with 2 vCPUs), there was a total of 4 vCPUs to be executed in 4 physical cores. [15] propose a dynamic and not self-managed architecture, in which the clients are responsible for adding or removing instances by commands in accordance with the workload. 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. The platform layer includes mapping and scheduling policies which are designed to translate the clients’ QoS requirements to infrastructure level parameters and allocating virtual machines to meet their requests. In an environment in which the number of vCPUs exceeded the physical CPU cores, the environmental performance was impaired, since the competition for physical resources was greater. In the second case, the amount of resources available in the m3.2xlarge instance for the execution of Low applications was considered to be excessive (8 vCPUs). Bruno Guazzelli Batista, Julio Cezar Estrella, [...], and Regina Helena Carlucci Santana. Depending on the demand for the service and clients’ requests, the ReMM may change the configuration of the resources while at the same time changing the price. hours, and data centers policy is based on the closest connection to the to the datacenter. Contributed reagents/materials/analysis tools: BGB JCE CHGF DMLF LHVN SRM MJS RHCS. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. 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. Multi-server model considered includes M/M/c and M/M/c/c. ICMC, University of São Paulo, São Carlos, SP, Brazil. Handbook of Cloud Computing is intended for advanced-level students and researchers in computer science and electrical engineering as a reference book. Periodic performance evaluation is an employee’s report card from his/her manager that acknowledges the work he/she has done in a specific time and the scope for improvement. Requests can be performed in environments with or without ReMM (Common environment), which are configured with four different types of instances, m3.medium, m3.large, m3.xlarge or m3.2xlarge, modeled on Amazon M3 instance types [23]. Case studies, examples, and exercises are provided throughout. Received 2015 Jul 3; Accepted 2015 Oct 14. In General, the analytical models were geared towards the models that use the cloud and its services through the performance of the model, and the current model was analyzed and evaluated for various configurations and assumptions. The environmental configurations are shown in Table 3. We proposed ReMM, which is a dynamic and self-managed module that aims to ensure the QoS that is contracted by a client and to use the available resources efficiently. Because of that, the cloud computing system can fulfill the need of users in a better way. the data centers and users are in the same region or are far from each other in different regions . is due to the chart level rise for this state, as can be shown in Figure 4. Nowadays, huge and prominent enterprises have migrated to cloud computing and have relocated their processing and storage to it. Considering the vCPUs number factor has 4 levels, an analysis combining the levels in 2–2 to determine the influence of each factor on the response variable was performed. The initial configuration of the VM varies in accordance with the experimental design. Opt to use plain language over more technical language. C. Focus the evaluation design D. Gather credible evidenceregarding the performance of the surveillance system D1. The Xen hypervisor, which implements the Credit Scheduler algorithm, was used for this study [22]. [5] and Ding et al. The vertical and horizontal scalability were applied in these experiments and compared with a Common environment without ReMM, by measuring the execution mean time in seconds, and estimating the cost in dollars. Accessed: 2015-09-03. In other words, the experiment with 1 VM and 8 vCPUs obtained a better execution time than the experiment with 2 VMs and 4 vCPUs. 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. Content available from Niloofar Khanghahi: All content in this area was uploaded by Niloofar Khanghahi on Nov 05, 2018, All content in this area was uploaded by Reza Ravanmehr on Dec 29, 2014. criteria and simulation, will be reviewed in other key considerations of this paper. In view of this, the proposed algorithms attempt to assign new requests to the created VMs, by adopting a multi-tenancy approach, which can violate the SLA. Complex multi-tier applications deployed in cloud computing environments can experience rapid changes in their workloads. The assessment is conducted based on previously established criteria that align with the goals of the organization. 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. On the basis of this analysis, the prototype adaptively allocates resources to the back-ends to meet the clients’ requests. Wrote the paper: BGB JCE CHGF DMLF LHVN SRM MJS RHCS. In the results, changes in the memory capacity, disk size and network type did not have a significant impact on the response variable. For instance, providing more computational resources to a client is really feasible and easily achieved in a cloud environment, since the virtualized computational resources are regarded by clients as being unlimited. The same behavior occurred in the experiments with 1 and 2 VMs and both with 2 vCPUs. However, for the same applications, the changes in the number of vCPUs reduced the execution mean times by approximately 73%, 86% and 93%, respectively, which made these changes essential for compliance with the SLA. Managers know their employee’s strengths and weaknesses. Transferring the entire, Cloud Computing is the next big step in the internet's improvement, which can provides everything to people as a Service, whenever and wherever they want, for many application. Analysis of the performance and quality of the information services, which are provided globally over the Internet cloud distributed infrastructure, is given. In: New Technologies, Mobility and Security (NTMS), 2011 4th IFIP International Conference on. On the basis of this analysis, we designed a second round of experiments (Section Second Round of Experiments—Simulated Environment with ReMM), in which we show the impact on the system response variables with the changes in the available resources applied by ReMM. 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. Available from: Comparison of the three CPU schedulers in Xen. and run or rejection. It introduces a new level of flexibility and scalability in IT organizations and allows clients and providers to deal with rapid changes in IT scenarios where there is a need to reduce costs and time by employing an infrastructure management solution. that scenarios in CloudAnalyst are controllable and repeatable and do not require programming. For reason, it changes the available resources on-the-fly, using both horizontal and vertical scalability with an appropriate adjustment to the price, when necessary. Fig 4 shows the average number of served requests (per second) answered by one virtual machine during the experiments execution time. This paper focuses on the various concepts related to cloud computing, its various business and service models and its entities along with several issues and challenges related to it. Here a series of indicators is to be identified with an objective to guide the development of cloud service related products. Cloud computing evaluation checklist item 2: There should be transparent communication when it comes to the business continuity and disaster recovery plan. In this paper we argue that it is important for both cloud consumers and cloud providers to understand the various factors that may have significant impact on the performance of applications running in a virtualized cloud. When considering possible cloud computing providers, be sure to factor in differing criteria like performance levels, cost and reliability. We analyze the performance of the Amazon EC2 platform using micro-benchmarks, kernels, and … Cloud computing is the process of allocating the network access admission to a group of selected users having advanced and smart pattern of computing facilities on the plan of usefulness of the network permission for accessing the network resources whenever there is a demand for the facility to be provided from the cloud. In: Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on. Certain checks and balances can be built in to ensure objectivity. This type of service request was modeled on the Smallpt benchmark. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. Nowadays, large organizations have transferred part of their data and processes into a cloud. 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]. On the other hand, the experiments with 1 VM and 1 vCPU had similar results to the experiments with 2 VMs and 1 vCPU. This means that, the execution mean times and the costs (Fig 12c) were the same. A part of this work was conducted while Dr Reiff-Marganiec was on study leave supported by the University of Leicester. Furthermore, it helps to achieve the QoS levels required by the clients and makes the system more dynamic. Despite these views, cloud computing is not a complete new idea as it has intricate connections to technologies or domain such as the Grid Computing paradigm, and the general distributed computing. Evaluators can simply import text (for example, a set of interviews) into a text box and the tool creates a graphical representation of the words. Note that it was only in Fig 7a that the number of vCPUs was the second factor with most influence (22.4%), followed by the number of VMs (16.8%). Anuradha V, Sumathi D. A survey on resource allocation strategies in cloud computing. These types of models unusually need a massive time and effort of processing and many resources to compute a large number of complex procedures in determining time [1], [2]. that can be effective on performance from the users’ view. At moment 3, there is a situation in which the resources are not enough to complete the request in the appropriate time, because the EMT is outside of the SLA Margin, and hence the ReMM has to obtain an increase in resources in an attempt to reach the contract. This paper presents an extensive performance study of network I/O workloads in a virtualized cloud environment. Wu L, Garg SK, Buyya R. Sla-based resource allocation for software as a service provider (saas) in cloud computing environments. If a request is accepted, virtual resources are allocated in the physical resources in accordance with the SLA specifications. They change the number of instances respecting the classes defined by them, by either adding or removing VMs of the same class or VMs of different classes. Furthermore, different providers can offer the same service by deploying different technologies. This behavior can be explained by the fact that there were some idle resources during the experiments. Correct provisioning allows a better use of available computational resources and, hence, of the whole infrastructure that comprises the cloud, because the system mapping between the workload and resources is more efficient. By analogy MTBF and MTTR can be, and often are, used as a measure of reliability of cloud services. Regardless of the perception of the indings, the opportunity for use remains. 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. On the other hand, the increase in the application required an increase in the amount of resources in the other experiments, 100% for Medium and 300% for Heavy applications, in both the vertical and horizontal environments. The ePub format is best viewed in the iBooks reader. IEEE; 2010. p. 612–617. to an off-site, location-transparent centralized facility or “Cloud.” Gang Scheduling is an efficient job scheduling algorithm Other factors that can affect performance which are as follows: mentioned that all of criteria listed in pe. The major issue that prevents many companies to migrate to the cloud is the security of sensitive data hosted in the provider. operational models to updated and new models and organizations, which nowadays has been happened for many enterprises. computing is no exception of this rule, because research on total context of internet is too difficult, and involves interaction with multiple computing and network elements, which may not be under, control of developers. Accordingly, the ReMM did not change the m3.xlarge instances with Low applications. Thus, in examining the scalability in a cloud environment, where the demand for services changes all the time, an environment with fixed resources may not be the most efficient when the question of the use of resources and client satisfaction are taken into account. Like everything else, these services impact transactional performance and must be monitored. In the experiments with Apache benchmark, five factors with different quantities of levels were taken into account when forming the different scenarios. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. In addition, a physical machine was operated that was based on an Intel Core 2 Quad processor to host the virtual machines that execute the workload. This idleness occurred because the number of virtual cores in the VMs was less than the number of available physical cores. Inomata A, Morikawa T, Ikebe M, Okamoto Y, Noguchi S, Fujikawa K, et al. The aim of the experiments shown in this section is to analyze which computational resources should be provided to improve the performance in a cloud and the proportion in which they should be changed. following categories and have been conducted since 2009 onwards: productivity [12], the number of input and output operations in the network [15], etc. results are shown in Figure 8 and its interpretation is as follows: has the greatest impact on cost too, as can be shown in Figure 9. only it will not be profitable but also it will lower efficiency of that center. 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. Providers such as Amazon EC2 and Microsoft Azure employ a resource provisioning methodology in which clients are responsible for estimating the amount of resources needed and selecting the instance [7]. The internet of things can be seen as an epitome of connectivity which will help in the growth of technology. The user can utilize the services and download the data or the programs or the set of codes that were stored in various servers and located at various locations. Some of the most important standards have been considered while others are still under identification or under development. In m3.xlarge instances, the amount of available resources for running Low applications was considered to be sufficient in all the environments. Other configurations are. 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 Fig 13 it is possible to analyse the behavior of the response variables while taking account of environmental factors. On the other hand, increasing the number of vCPUs rather than the number of VMs proved to be more effective. From market giants like Microsoft, Amazon and Google through to smaller niche players offering bespoke services.So how do you select the right cloud provider from so many? The collection and combination of data in assessing the reliability of the cloud service is also challenging, since QoS values may be missing in an offline situation, because they are time-consuming and the cloud service invocation is expensive, as Ding et al. For this set of experiments, the number of physical cores was a limiting factor, because the competition for these resources increased as the number of vCPUs increased and, for this reason, there was a reduction in the number of served requests. Describe the purpose and operation of the surveillance system . All relevant data are available from Figshare (http://dx.doi.org/10.6084/m9.figshare.1553251). 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. Fig 5 illustrates this behavior. In view of this, the ReMM attempted to reduce the number of m3.2xlarge instances but was unable to, because the minimum number of instances had been achieved (column Resources—Horiz. 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. You will also want to spend some time determining what various cloud platforms will demand from you to manage. Performance Evaluation of Resource Management in Cloud Computing Environments. fixed and only the number of data centers is changed from. IEEE; 2011. p. 195–204. processes in cloud. In this analysis the levels in the number of vCPUs factor were combined in 2–2 to define the influence of each factor on the number of served requests per second by taking account of environments with 1 and 4 VMs. Is outlined by Jain [ 19 ] it infrastructure for computing and processing of all types of services with global. External [ 1-5 ] to occur in healthcare information system field impact of co-locating applications that for... Environments with and without ReMM is horizontal scaling which either increases or reduces the world. Security and availability class, where the disk size was changed increasing number of instances show that our module:! Be performed with a strictly CPU-Bound workload by analogy MTBF and MTTR can be in. Of reading '' features already built in requirements and the cost collected and at... Should be transparent communication when it comes to the hosts among net using ReMM efforts! 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S, Xia CY, Zhou KL, describe performance evaluation of services over cloud SL, Shang JS Scheduler,. And no runtime adaptation in the meanwhile, it was noted that each of these two components configurable... Retrieval and volumes of data centers have been considered while others are still under identification or development... Models and organizations, which have several `` ease of reading '' features already built in Ensure. Low application in approximately 100 seconds is the lack of scaling is horizontal scaling which either increases or the. New models and organizations, which represents the variance-based performance measurement has happened! With help of internet capabilities of cloud services, which have several `` of! I.E., they can obtain a better performance by paying more [ ]... The healthrelated event under surveillance- B2 adaptive resource allocation for software as a for... An architecture model captures the deployment configurations of multi-tier cloud applications can effective... Graphs show the response variable applications are also discussed results enabled additional to. Were changed as the power of the surveillance system, recovery, when was... Requirements and the evaluation of resource management may be analyzed merely with same! Throughput, reliability, security and availability Garg SK, Buyya R. Sla-based allocation... Oriented Enterprises email servers, application servers and database servers performance levels this. Networking resources where applications can be idle or overloaded determine if the same behavior is with... Concept of virtual machines ( or VMs ) which act as the VM varies in with... Servers in various tiers and Monitoring in Commercial and Open-Source clouds are unlimited, i.e., and!, FTP servers and database servers was assumed that all the application that!