Dynamic Monitoring Interval to Economize SLA Evaluation in Cloud Computing Nor Shahida Mohd Jamail, Rodziah Atan, Rusli Abdullah, Mar Yah Said

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1 Dynamic Monitoring to Economize SLA Evaluation in Cloud Computing Nor Shahida Mohd Jamail, Rodziah Atan, Rusli Abdullah, Mar Yah Said Abstract Service level agreement (SLA) is a contract between service provider and user about the quality of service (QoS) in cloud computing. The SLA monitoring tools is unavoidable to evaluate the agreed SLAs at the run time and detect any probable SLA violations. The cost value and benefit value of SLA monitoring systems is a concerned issue in cloud computing. The interval value of monitoring system has a direct impact to the cost and benefit values of monitoring system. This study proposes the dynamic monitoring interval (DMI) to make a balance between cost value and benefit value of SLA monitoring system in different environmental situation (ES). The DMI adapts interval value based on proposed formula by measuring the cost value and benefit value of monitoring system at run time. The cost value and benefit value of monitoring tools are measured at run time to determine an appropriate monitoring interval value periodically. Finally, the cost-benefit analysis of results demonstrated that the proposed DMI adapted the interval value successfully to have a reasonable profit in all predefined ESs. Index Terms Cloud computing, Cost, Dynamic monitoring interval, Monitoring tools, Service level agreement I. INTRODUCTION Cloud computing rapidly is gaining momentum as an alternative to traditional IT infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements (SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs [1]. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. Several services are executed in cloud environment at the same time for numerous users [2]. Each service should be monitored at the run-time to evaluate related SLA. Accordingly, SLA monitoring systems face several monitoring targets with different SLAs [3]. Adaptability of SLA monitoring systems is a challenging issue in cloud computing to manage the monitoring threads effectively based on services status. Current monitoring systems have often constant interval value and individual monitoring process for all services and SLAs which it decrease the adaptability and increase the cost of monitoring systems [4]. The long interval value leads a few numbers of SLA evaluation repetitions; on the other hand, the short interval value causes a lot of repetitions for SLA evaluations. Although the short interval value makes the monitoring loop faster and can detect more number of probable SLA violations, it increases the cost value of SLA monitoring tools. The high interval value has a low overhead cost but it can detect a few numbers of probable SLA violations. Therefore the determination of appropriate interval value is a challenge issue and the constant monitoring interval cannot make a balance between cost value and benefit value of SLA monitoring systems [5-6]. This paper proposes the SLA evaluation model to and dynamic monitoring interval (DMI) to increase the adaptability of SLA monitoring systems and subsequently detect the most probable SLA violations with a reasonable overhead cost. The proposed DMI aims to adapt the SLA monitoring interval at run time to make a balance between cost values and benefit value of SLA monitoring system. Furthermore, The DMI adjusts the monitoring interval based on faced environmental situations concerning the number of SLA violations to achieve the reasonable costs and benefits. The life cycle of SLA in cloud computing consist of SLA negotiation, SLA evaluation, and SLA expiration. The SLA is contracted after SLA negotiation between service provider and consumer. Agreed service is deployed and the related SLA is evaluated periodically [7-8]. If any SLA violation is detected, the reaction can be applied to revive the service [9]. SLA report shows the quality of service and the number of SLA violations. Finally, agreed service is terminated when the SLA duration is expired. This study is concentrated on SLA evaluation phase [10]. The costs and benefits of SLA monitoring system is the main focus of this study. value of SLA monitoring system is the central concentrated issue in this area because it has a high impact on cost value and benefit value of monitoring system. An existing cloud service is 249

2 executed in developed test bed and the predefined SLA attributes are evaluated by developed monitoring tools. II. THE PROPOSED DYNAMIC MONITORING TOOLS The dynamic monitoring interval (DMI) is proposed to economize the SLA monitoring system. DMI aims to reduce the overhead of monitoring system when the agreed service is working normally. Moreover, The SLA violations should be detected when the quality of agreed service exceed the defined quality in SLA. The number of missed violation detection should be minimized. The SLA monitoring tools is developed based on proposed DMI to monitor the agreed SLAs. Figure 1 shows the developed SLA monitoring tools at the run time. It is monitoring the processor, memory and storage usage value of employed VMs. Each resource usage is presented in forms of total consumption and VMs consumption. Detected SLA violations and the violated attribute value are shown in a column. The cost and benefit of monitoring tools are also measured at the run time and they are used in monitoring interval adaptation process. The changes of interval value are presented in Text Box. Fig 1. Snapshot of developed SLA monitoring tools Figure 1 presents the monitoring results of agreed SLA that the CPU, Memory and Storage usage should be more than 90%, 146 MB, and MB respectively. The resources of 4 VMs are monitored periodically and the total CPU, Memory and Storage usage are also presented. The overhead and benefit of monitoring tools are measured every 5 iteration of SLA evaluation. The monitoring interval value is adopted based on measured cost value, benefit value and proposed DMI formula. The benefit of monitoring tools is 4 $ versus 0.84 $ cost in first assessment; and the monitoring interval is changing from 3000 MS to 1500 MS. III. EXPERIMENT AND DESIGN Error! Reference source not found.figure 2 shows the experiment design featuring the experiment environment and experiment variables. Distributed image rendering service is uploaded in a specified cloud infrastructure. V-Ray Host manages the distributed image rendering process on 4 employed VMs. A 3dsMax scene file is sent to the V-Ray Host as an input for image rendering. V-Ray Host distributes the task among VMs and waits for the VMs responding. V-Ray Slaves execute the tasks and send the output to the V-Ray Host for results combination. 250

3 Fig 2. Experiment and design According to the predefined SLA by Emeakaroha et al. [5-6] total amounts of employed CPU, Memory, and Storage resources by VMs are the targeted attributes. 90% of CPU resources should be employed at the execution time by VMs for image rendering. The Memory and Storage usages should be also more than 1.43 GB and 99GB respectively. The installed monitoring engine collects data from VMs based on agreed SLA attributes. The monitoring engine sends the collected data to the developed monitoring tools periodically. The monitoring tools evaluate the agreed SLA by comparing the predefined SLO and actual QoS. The image rendering service is executed in different situations based on the state of SLA. The experiment is repeated in certain situations portraying different scenarios:without any SLA violation, with always SLA violation, with managed SLA violation, and in normal situation. In each situation, the execution of monitoring system is repeated for employing certain values of monitoring interval consist of 500 MS, 2500 MS, 5000 MS, 7500 MS, MS, and proposed DMIvalues. IV. RESULTS AND DISCUSSION The proposed dynamic interval and four constant intervals are applied on developed SLA monitoring system to be executed in a cloud environment. The SLA monitoring system is executed in different situations of cloud service consists of without any SLA violation, with always SLA violation, with managed SLA violation, and actual situation. A. Results for Sit1: Without any SLA Violation The CPU, memory, and storage consumption of SLA monitoring system is measured when the monitoring system was set with certain intervals consist of 500, 2500, 5000, 7500, milliseconds. Monitoring system is also executed with proposed dynamic interval whereas the monitored service is run without any SLA violations. The aim of this experiment is to measure the cost of SLA monitoring system with certain interval value when the cloud service is always run successfully and there is not any SLA violation. Figure 3 presents the SLA monitoring results for CPU consumption of image rendering service with predefined SLA. 251

4 Fig 3. CPU consumption in Sit1 Fig 4. Memory consumption in Sit1 Fig 5. Storage consumption in Sit1 SLA monitoring system with certain intervals executed in a same situation without any SLA violation. The CPU consumption is always more than 90% and the monitoring system did not detect any SLA violations as presented in Figure 3. Obviously, the monitoring system with 500 ms interval has collected more number of instances compared to the 10000ms interval. However, both of them have not detected any SLA violation. Figure 4 shows the monitoring results for memory consumption which should be more than 1455 MB based on predefined SLA. In this scenario, the SLA violation is not detected. The number of collected data is increased when the interval value is increased from 500 ms to ms. The monitoring result is presented in Figure 5 when there is not any SLA violation. Although monitoring system with different interval values are same in undetected SLA violations, the cost of monitoring system with different interval value are dissimilar. B. Results for Sit2: With always SLA Violation SLA monitoring system is executed in cloud test bed with certain interval values whereas agreed service always faced SLA violations during run-time period. Monitoring system has monitored CPU, memory and storage attributes employed by image rendering service. Figure 6, Figure 7 and Figure 8 present the results of SLA monitoring system. Monitoring system with different interval values detects the SLA violations; however, the number of detected violations is different in different intervals. Figure 6 illustrates the CPU consumption of image rendering service which always faced SLA violation. The CPU consumption is recorded less than 90% during the run time; however the number of detected violation is different in varied intervals as stated in Table

5 Fig 6. CPU consumption in Sit2 Table 1 presents the number of detected SLA violation by monitoring system with different interval values. The number of detected violations is decreased when the interval is reduced. The number of detected violations by proposed dynamic interval is close to the amount of detected violations by 500 ms interval. Table -1 Number of detected violations for CPU consumption in Sit Dynamic 303 Figure 7 illustrates the recorded memory consumption by SLA monitoring system. Monitoring system with certain interval values frequently detects SLA violations during run-time execution; nevertheless, the number of detected violations is different in dissimilar intervals as presented in Table 1. Fig 7. Memory consumption in Sit2 253

6 The storage consumption is another monitored attribute stated in SLA. The recorded values of storage consumption and the number of detected violations are presented in Figure 8 and Table 2 respectively. SLA monitoring system detected the most and least number of violations with and 500 ms interval values respectively. Fig 8. Storage consumption in Sit2 Table -2 Number of detected violations for storage consumption in Sit Dynamic 303 C. Results for Sit3: With managed SLA Violation periods SLA monitoring system is executed when certain periods of SLA violations are released. The recorded values for CPU, memory and storage consumptions are illustrated in Figure 9, Figure 10 and Figure 11 respectively. The SLA violations are detected by monitoring system with different interval values; however, the number of detected violations is different in dissimilar intervals. Figure 9 illustrates the CPU consumption of image rendering service when certain periods of SLA violations are performed. The reduction periods of CPU consumption value are recorded by SLA monitoring system with different intervals. Table 3 presents the number of detected violations per each interval value. SLA monitoring system with 500 ms interval detected the most number of SLA violations while ms interval value detected the least number of SLA violation. 254

7 Fig 9.CPU consumption in Sit3 Table -3 Number of detected violations for CPU consumption in Sit Dynamic 79 The recorded value by SLA monitoring system about memory consumption of image rendering service is illustrated in Figure 10. Managed violation periods have created a fluctuation trend in recorded value of memory consumptions. Table 4 presents the number of detected violations by employing different interval values. The monitoring system with 500 ms interval has detected 109 SLA violations while 24 SLA violations are detected with ms interval value for a same period of violations. Proposed dynamic interval recorded 77 number of SLA violations. Table -4 Number of detected violations for memory consumption in Sit Dynamic

8 Fig 10. Memory consumption in Sit3 The value of storage consumption is also recorded by SLA monitoring system as illustrated in Figure 11. The CPU and memory consumptions of image rendering service are decreased in managed violation periods; but, the generated periods of SLA violation did not affect the storage consumption attribute. However, if the value of one SLA attribute exceeds from SLO, the SLA is violated. Fig 11. Storage consumption in Sit3 256

9 D. Results for Sit4: In the Normal Environment Developed SLA monitoring system is executed to monitor the image rendering service in an actual environment. There is no any managed SLA violations in this scenario. Predefined SLA consists of CPU, memory and storage consumption attributes. Results of SLA monitoring system with certain SLA interval values are illustrated in Figure 12, Figure 13 and Figure 14. Figure 12 presents the recorded CPU consumption value by monitoring system with different intervals. The CPU consumption value should be more than 90% as stated in SLA. The number of detected violations by employing certain intervals is presented in Table 5. Fig 12. CPU consumption in Sit4 SLA monitoring system detected 11 number of SLA violations when the interval value is set by 500 ms. The number of detected violations by other interval values is presented in Table 5. SLA monitoring system with 10000ms interval detected 4 number of SLA violations whereas the proposed dynamic interval recorded 13 SLA violations. Recorded values of memory consumption are presented in Figure 13. SLA monitoring system shows quite similar trend of memory consumption in different intervals; however, the numbers of detected SLA violations are different as presented in Table 6. Table -5 Number of detected violations for CPU consumption in Sit Dynamic

10 Fig 13. Memory consumption in Sit4 Table 6 presents the number of detected violations by setting different interval values. 500 ms and ms intervals have the most and least numbers of violation detection respectively as usual. The proposed dynamic interval detected the lesser number of SLA violations compared to the 500 ms interval. Table -6 Number of detected violations for Memory consumption in Sit Dynamic 113 Fig 14. Storage consumption in Sit4 258

11 Figure 14 presents the recorded value of storage consumption by SLA monitoring system. The value of storage consumption should be more than MB as stated in SLA. Image rendering service needs storage resources for extracting and recording the original file and rendered image. The storage consumption is increased when generated parts of image are retrieved from VMs. The number of detected violations for storage attribute is presented in Table 7. Dynamic interval detected the same number of SLA violations with 500 ms interval by 41 SLA violations. Table -7 Number of detected violations for Memory consumption in Sit Dynamic 41 IV.CONCLUSION The SLA monitoring model and DMI are proposed to evaluate agreed SLAs and detect any probable SLA violations at run time. The interval value is also adapted at run time by proposed DM Ito have a reasonable cost and benefit value from SLA monitoring execution. The SLA monitoring tools is developed based on proposed DMI to monitor a predefined SLA in experimental environment. The DMI is evaluated based on cost-benefit criteria in certain environmental situations. The collected data are mapped to the agreed attributes and the measured QoS values are compared to the SLO values for SLA evaluation. The cost is measured based on amount of consumed resources by monitoring system. The benefit of SLA monitoring is assessed based on the number of detected SLA violations. If the total cost value is more than benefit value, the interval value is increased to reduce the cost. If the total benefit value is more than the cost value, the interval value is decreased to increase the benefit. The experiment results and detected violations by SLA monitoring tools are presented for different repetitions of experiment. Each settled interval value was suitable for specific environmental situation. For instance, The SLA monitoring tools had a highest benefit value in always violation situation by employing 500 MS interval but this interval value was not reasonable for without SLA violation situation. The same trend is observed in experiment for other constant monitoring intervals. The SLA monitoring tools had acceptable cost and benefit values by employing the proposed DMI in all environmental situations. Therefore, the interval value is adapted successfully for different situations by proposed DMI. REFERENCES [1] P. Patel, A.H. Ranabahu, and A.P. Sheth, Service level agreement in cloud computing. OOPSLA Cloud Computing workshop, pp. 1-10, [2] M. Alhamad, T. Dillon, and E. Chang, Conceptual SLA framework for cloud computing. 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), pp , [3] M. Maurer, I. Brandic, and R. Sakellariou, Adaptive resource configuration for Cloud infrastructure management. Future Generation Computer Systems, Vol.29, No.2, pp , [4] M. Boniface, S.C. Phillips, A. Sanchez-Macian, and M. Surridge, Dynamic service provisioning using GRIA SLAs. Service-Oriented Computing-ICSOC 2007 Workshops, pp , [5] V.C. Emeakaroha, M.A. Netto, R.N. Calheiros, I. Brandic, R. Buyya, and C.A. De Rose, "Towards autonomic detection of sla violations in cloud infrastructures." Future Generation Computer Systems, Vol. 28, No.7, pp , [6] V.C. Emeakaroha, T.C. Ferreto, M.A.S. Netto, I. Brandic, and C.A. De Rose, "Casvid: Application level monitoring for sla violation detection in clouds." 2012 IEEE 36th Annual Computer Software and Applications Conference (COMPSAC),. pp , [7] R. Buyya, S.K. Garg, and R.N. Calheiros, "SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions." 2011 International Conference on Cloud and Service Computing (CSC), pp. 1-10, [8] A.C. Barbosa, J. Sauvé, W. Cirne, and M. Carelli, Evaluating architectures for independently auditing service level agreements. Future Generation Computer Systems, Vol.22. No.7. pp ,

12 [9] S. Anithakumari, and K.C. Sekaran, Autonomic SLA Management in Cloud Computing Services. Recent Trends in Computer Networks and Distributed Systems Security. pp , [10] V.C. Emeakaroha, I. Brandic, M. Maurer, and S. Dustdar, "Low level metrics to high level SLAs-LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments." 2010 International Conference on High Performance Computing and Simulation (HPCS),. pp , AUTHOR BIOGRAPHY Nor Shahida Bt Mohd Jamail is a PhD student in Faculty of Computer Science and Information Technology at Universiti Putra Malaysia. She received Master of Science major in Software Engineering from University Putra Malaysia in She currently continuing her PhD focusing on Software Engineering at University PutraMalaysia. Her research interests are focusing on Cloud Computing, Service Level Agreement, Quality in Software Engineering and Verification and Validation process. Author s formal photo Rodziah binti Atan is an Associate Professor at Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM). She can be contacted at rodziah@upm.edu.my Author s formal photo Author s fo rmal photo Rusli bin Abdullah is a Professor in Computer Science at the Department of Software Engineering Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia (UPM) and prior to that he was a Deputy Dean (academic and Student Affairs). He received Master of Science major in Computer Science from University Putra Malaysia in In 2005 he received PHD in Computer Science from University Technology Malaysia. His main research interests are in the fields of Software Engineering and Knowledge Management. He can be contacted at rusli@upm.edu.my. Mar Yah Bt Said is a Lecturer at Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM). She can be contacted at maryah@upm.edu.my. Author s formal photo 260