Research for the Data Transmission Model in Cloud Resource Monitoring Zheng Zhi yun, Song Cai hua, Li Dun, Zhang Xing -jin, Lu Li-ping

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1 Research for the Data Transmission Model in Cloud Resource Monitoring 1 Zheng Zhi-yun, Song Cai-hua, 3 Li Dun, 4 Zhang Xing-jin, 5 Lu Li-ping 1,,3,4 School of Information Engineering, Zhengzhou University, 5 Department of Information and Engineering, Henan College of Finance and Taxation Abstract Focusing on the problem that frequent data transmission in cloud resource monitoring system will cause dramatic communication cost, a model for data transmission based on resource monitoring is proposed. The proposed model fully combined the advantages of both pull and push models; it can classify the changes of resource load and then transfer those critical changes under push model. And it also predicts changes for resource load by time sequence model to adjust data transferring frequency dynamically. Besides, the model can inquire about the load of monitored resource timely under pull model to keep data consistency. Finally, the experimental results show that the model can reduce communication overhead and improve data consistency efficiently compared with the traditional monitoring model. Keywords: cloud computing, cloud resource monitoring, data transmission model 1. Introduction Cloud Computing is a new model of business computing. By distributing the computing tasks to the virtualized resource pool which is composed of a large number of computers, various application systems have access to require computing power, storage space and a variety of software services [1]. With the expansion of cloud computing scale, both the overload of server and unstable performance of network may affect the performance of servers and virtual machines, and lead to quality reduction of cloud service. M. Armbrust pointed out that the availability of service is one of the major technical challenges to cloud computing []. Resource monitoring, as one of the important points in service availability protections, has become a hot research topic. Cloud resource monitoring system, which is responsible for collecting the resource load information, is the premise of network analysis, job scheduling, load balance, event prediction, fault detection and etc. Unless performance guarantees at the level of hardware resources like CPU, Memory and I/O Devices are given, there is no way that an application s performance can be guaranteed [3]. Cloud resource monitoring system should reflect the load changes in the node timely, and the obtained information should be consistent with the actual condition of the monitored resources. But the existing data transmission models based on resource monitoring increase the data consistence between monitoring components and monitored resources through frequent data exchange which will cause dramatic communication cost and quality reduction of system performance. Basing on the above problem, many scholars have done a lot of researches. The offset sensitive mechanisms (OSM), time-sensitive mechanism (TSM) and mixed mechanism (ACTC), etc, are proposed to solve the problem. The data transmission models above can improve data consistency or reduce the communication overhead, but there is a certain lack (see Section for details). Through the study of strengths and weaknesses of existing data transmission models, a Push-Pull Hybrid Model (PPHM) is proposed to achieve effective resource load monitoring with the least communication cost. The proposed model takes advantages of the push and pull model. In the monitor terminal, the proposed model predicts changes for resource load by time sequence model to adjust data transferring frequency dynamically. At the same time, it classifies the changes of resource loading and then transfers those critical changes under push model to reduce communication cost and insure the higher consistency. In the monitor terminal, if the monitoring components haven t receive the information of resource loading from the monitored node within a predetermined time, the model will use the pull model to inquire about the load of monitored resource timely to keep uninterrupted communication between monitoring components and monitored resources. The experimental results show that the model can effectively improve data consistency and reduce communication overhead compared with the push and pull models. Section describes the data transmission model and the related research. Section 3 is the description and analysis to PPHM model. Section 4 is the experimental verification. The last is the summary and outlook. 83 Research Notes in Information Science (RNIS) Volume14,June 013 doi: /rnis.vol14.49

2 . Related work In the traditional distributed resource monitoring systems, there are two basic communication models between the monitoring components and monitored nodes according to the way to get the information of resource loading, push and pull data transmission models [4]. In the push model, the monitoring components collect the information of the resource load by receiving the unsolicited information from each monitored node passively. On the contrary, in the pull model, the monitoring components query the monitored nodes periodically for resource load. Data consistency and communication overhead are the most used evaluation indexes to evaluate monitor performance of the different data transmission models [5]. Data consistency means the degree of consistency between information obtained by the monitoring system and actual resource status based on the model. Communication overhead is the data transmissions to complete the monitoring information interaction. In the push model, when the load of monitored resource changes, monitor nodes send the resource load information. Push model has a higher data consistency, but leads to larger system communication overhead because of the frequent data transmission caused by every change of the resource load. Pull model usually periodically queries monitored nodes for the resource load and has less communication overhead, but its data consistency is poor as it will omit changes of resource load during the inquire cycle. OSM TSM and ACTC are all based on push model [6]. They focus on decreasing the transmission of useless updated information. In the OSM, the threshold d_threshold tries to capture the average amount of changes for the resource load. Only when the load variation of monitored nodes is greater than d_threshold would the model push the change information. OSM ignores useless updates, reduces the frequency of data transmission and guarantees the data consistency. But if the d_threshold is particularly large, most of the change information will not be pushed and lead to data consistency degradation. In the TSM, the time interval d_timeinterval is based on the average of time intervals between resource load changes, and it tries to describe the frequency of changes of resource load. When the inquiring interval is greater than d_timeinterval, the monitor nodes push the information of resource load. TSM reduces push times to a certain extent. In an inquiring interval, even if there is no change in the resource load, the model still pushes the data to increase the unnecessary communication overhead. ACTC balances the average amount and frequency of changes of the resource load. It combines the advantages of OSM and TSM, can reduce communication overhead appropriately under the premise of ensuring the data consistency. But the aforementioned d_threshold and d_timeinterva are both the average during a period of time in the above strategies, which is not suitable for the actual situation that the resource load changes dynamically. The Slacker polling strategy, adaptive polling strategy are all based on pull model, they aim to maximize the data consistency between monitoring components and monitored resource [7][8]. Based on adaptive polling strategy, the query interval is adjusted dynamically according to the polling results, and the time of the next query is adjusted in accordance with the damping coefficient d (a constant), it is simple, but the data consistency is not significantly improved. In the Slacker polling strategy, monitoring components make use of time sequence model to predict changes of monitored resource load. The pull operation is triggered according to the load change trends, and the monitoring period is dynamically adjusted. The strategies above can reduce the frequency of data transmission to reduce communication overhead, but disadvantage of the pull model itself is not completely overcome, such as missing the changes of monitored resources load which degrades the data consistency. P&P, a Combined Push-Pull Model, introduce the user tolerance degree UTD (given by the system administrator) which describes how tolerant a user is to the status inaccuracy [9]. Depending on UTD, either Push or Pull operations are triggered irregularly. The P&P model can guarantee the high data consistency, but it can t ignore the most useless updates, just reduce the frequency of data transmission to a certain extent. 3. Push-pull hybrid model In order to ensure the data consistency of monitoring system and deduction in communication overhead, through the study of existing data transmission models, and combined with the advantages of the push model and pull model, a hybrid push-pull model (PPHM) is proposed. The model classifies the changes of resource load as critical changes and non-critical changes, and then it transfers the critical changes which affect the system data consistency under the push model while ignoring the 84

3 non-critical changes to reduce the frequency of data transmission. When the monitoring components can t receive the information of resource load sent by monitored nodes in a predetermined time, then it will query for the resource load actively under the pull model to keep uninterrupted communication between monitoring components and monitored resources Critical changes The PPHM model set parameters: threshold, min-threshold, interval, timer, C, and use those parameters above to identify critical changes and non-critical changes. Among them, C is the absolute value of the difference between the real load of monitored resource and monitoring value saved by monitoring components. This paper defines critical changes as following: (1) C is greater than threshold; () in order to keep the communication between the monitoring nodes and the monitored resource, after an inquiry interval, C is greater than min-threshold. The changes do not meet the conditions above is non-critical changes. Assuming that the CPU utilization is the monitored resource load, threshold values 0.1, min-threshold values 0.0, and interval values 4 seconds, critical change of CPU part-time utilization is shown in Table 1 as examples. Table 1. Examples of critical changes and non-critical changes time CPU utilization of Monitored node (%) C timer CPU utilization in Monitoring components (% ) is critical change 14:00: no 14:00: yes 14:00: no 14:00: yes 14:00: no 14:00: no 14:00: no 14:00: yes 14:00: no 14:00: no 14:00: no 14:00: no 14:00: no According to Table 1, C at 14:00:0 and 14:00:04 is greater than threshold and corresponds to condition (1); at 14:00:08, timing a cycle and C is greater than min-threshold that corresponds to condition (). So, the above three CPU utilization changes are critical changes that will trigger data transmission, and the data of monitoring components will update; other changes are non-critical changes that will not trigger data transmission, so the data of monitoring components won t update. 3.. Exponentially weighted moving average method In 1997 and 1998, Dinda tracks and samples a resource pool which is consist of 38 computers that including cluster servers, computing servers and desktop hosts two times, getting a lot of load pattern [10]. Statistical analysis of those drawing sum up the resource load characteristics: (1) load is lower and has random variation, but volatility is strong; () the change strongly correlating with time and owns a high degree of self-similarity. Therefore, the changes of a large resource pool load are time sequences, and Exponentially Weighted Moving Average (EWMA) can be used to predict the random load. EWMA is a commonly used sequence data process method. It obtains a formula of smooth forecasting from the mean of historical data. Due to the historical data on different time points have different influence degrees on current predicted points, weighted influence of value shows exponential decline with time. The selection of weight is directly related to the sequence of historical data, which reflects a trend presented by measured value sequences. To archive the dynamic adjustment of the data transmission frequency, this paper uses the EWMA to forecast threshold_e, the value between two critical changes in successive, and make it as threshold. At the same time, EWMA is used to predict interval_e, the load change interval, make it as interval. Prediction formula is defined by Eq. 1 and Eq.. threshold _ E t threshold (1 )threshold _ E (1) 85

4 At any time t, threshold_e t refers to the predictive value of the critical change. Threshold t-1 refers to actual observations of last critical changes closest to time t. α defines the weight between the most recently observation and last predicted value threshold_e t-1, range of α is [0, 1]. int erval_ E t int erval (1 )int erval_ E () At any time t, interval_e t are predicted interval between the resource load changes, interval t-1 is the last measured time interval between the resource load changes closest to time t. Predictive validity depends on the values of α. When the load changes are steady, cycle is long, then α will be turned up appropriately. But if conditions are conversely, α will be turned down appropriately. So an appropriate α can be selected according to the load variation PPHM model algorithm description The PPHM model contains two core algorithms: push algorithm and pull algorithm, runs on monitored nodes and monitoring components respectively. Push algorithm is designed to identify critical changes and non-critical changes, and then it only transmits critical changes to reduce the communication overhead. And it calculates the threshold and interval as Eq. 1 and Eq. to adjust data transferring frequency dynamically. The Pull algorithm actively queries load information to keep abreast of the system performance Push algorithm: When the monitored resource load changes(c is greater than 0), the interval as monitoring cycle is calculated as Eq. 1 to forecast the next time when resource load will change; If the C is greater than the threshold, it will be regarded as a critical change, whether the timing timer has expired, load information and the current interval are pushed, and threshold is calculated as Eq. to forecast the next critical change C, timing restarted. Otherwise, the change is ignored to reduce the number of data transmission. If timer expires, restart timing; at the same time, if C is greater than min_threshold, it will be regarded as a critical change. Then load information and the current interval are pushed, the calculation of threshold is repeated, and timing restarted. The algorithm is following: while(true) if status change calculate the interval; if C > threshold push the resource load and interval; calculate the threshold; reset timer to interval; if timer is expires if C > min_threshold push the resource load and interval; calculate threshold; reset timer to interval; end while The characteristics of the algorithm: get critical change from threshold and monitoring cycle; non-critical changes are ignored; ensure effective data transmission in a certain frequency; maintain the communication between monitoring component and monitored resources; dynamically adjust the threshold and interval based on the changes of resource load Pull algorithm: In order to clearly describe the pull algorithm, the following parameters: the delay time delay_time and query cycle query_interval, are introduced. The query_interval equals to the sum of interval and delay_time. Monitoring components receive resource load and the interval from monitored node, reset the timer to query_interval. If the monitoring components don t receive the information within query_interval, the pull operation is performed to query monitored nodes for resource load actively, and find the failure of some nodes in time. The algorithm is following: while(true) if receive the load information and interval 86

5 reset timer to query_interval; if timer is expires pull; end while The characteristics of the algorithm: increase the waiting time to prevent the unresponsive transmission of information caused by network delay; reduce communication overhead; and monitoring components is able to realize the system performance at the first time. 4. Experiments and results analysis 4.1. Experimental environment Four Lenovo servers configured as the same are being used as experimental subjects, in order to evaluate the performance of the PPHM, one server is used as a monitoring node, and the others are used as monitored nodes. The configuration of the server used in the experiment is shown in Table.In this paper, the CPU utilization is used as the resource monitored load. In order to evaluate the performance of different data transmission model in the same environment, each of the monitored nodes will conduct a CPU utilization sample every second; each sample can be sustained for 4 hours. It will sample a total of 6 times, and the sample resulted as the actual load curve of the CPU. Table. Experimental server configuration Type CPU Memory Operating System Network Qitian M435E Intel i GB Windows7 100M Ethernet 4.. Performance Indicators In the monitoring system, there are a lot of data transmissions between monitoring components and monitored nodes. It is intended to trigger the data update and ensure that the monitoring components can reflect the monitor resource status. Consideration must be given to both the high consistency and low communication in a good data transfer model. Therefore, the update rate and consistency are used as the metrics to evaluate the performance of the data transmission model. Refresh rate (r) refers to the data update rate of the monitoring components, and it may reflect the size of the communication overhead. Assuming in the period [t1, t], the number of updates in monitoring components is n u, r is calculated as shown in Eq. 3 below: r n t t u (3) 1 Consistency (coh) refers to the consistency between monitoring results curve obtained by the monitoring components and the actual changes in resource load. A smaller value of coh implies a higher consistency. Suppose in [t1, t] period, the CPU utilization of the actual changes in the function of the curve is referred to as c r (t), the obtained results of the monitoring components function is referred to as c m (t),then coh are calculated as shown in Eq. 4 below: coh t ( cr( t ) cm( t )) d( t ) t1 (4) t t 1 Monitoring results curve and the curve of actual CPU utilization are valued separately, referred to as c r (i) and c m (i), with c instead coh, Eq. 4 simplifies to: c n i 1 ( c ( i ) c r n m ( i )) (n is the number of value points during [t1,t]) (5) 4.3. Experimental results and analysis 87

6 In order to verify the effectiveness of the proposed algorithm, the monitoring program can be written by java language, and the comparison of the refresh rate and consistency with PPHM model and ACTC model, the single push model and pull model, is described. Six groups sampled values respectively use the above model through two sets of experiments, to simulate monitor experimental data, and calculate the average of the experimental data. Figure 1 compares the monitoring curve with ACTC model and PPHM model. Figure, Figure 3 and Table 3 shows the comparison of the refresh rate and consistency with different models. PPHM, α = 0.1 and PPHM, α = 0.9 refers that α in EWMA algorithm of PPHM model respectively is 0.1 and 0.9. Figure 1. Changing curve for CPU part-time utilization Figure 1 shows the comparison of the monitoring curve with ACTC model and the PPHM model during the continuous period of time, in the 5th, 15th, 19th, 4th, 8th second, there are significant deviations between the monitoring value of ACTC model and sampled value, while monitoring values of the PPHM model and sampled values are substantially fitting. Obviously, consistency of PPHM model is better than the ACTC model. Figure and Figure 3 compares the refresh rate and consistency of the dada with push model, pull model, ACTC model and PPHM model.the results show that the push model has the largest refresh rate (communications overhead is the largest) and the highest consistency. The Pull model has a low consistency with a lower refresh rate. In the ACTC model, there is a certain tradeoff between consistency and the refresh rate. The consistency and refresh rate are improved in a way compared with the pure push and pull model. PPHM model has the lowest update rate, and the higher consistency. Figure. Comparison of refresh rate under different models Figure 3. Comparison of consistency under different models Table 3 shows a comprehensive comparison of the refresh rate and consistency with the push model, the pull model, ACTC model and PPHM model. It shows that, compare with the push model, the consistency of PPHM model is relatively poorer, but refresh rate is 53% less than push model, so the PPHM can reduce the communication overhead significantly. Compared with pull model, PPHM model refresh rate is lower, and its consistency is improved by 43%. The update rate and consistency of PPHM has improved significantly compared to ACTC model. 88

7 Table 3. Comprehensive comparison of update rate and consistency under different models Data transmission model Refresh rate (%) Consistency (*1000) push pull ACTC PPHM,α= PPHM,α= Since the data transmission model in monitoring system requires higher consistency and lower communication overhead, it is need to reduce its interference to cloud computing system. PPHM model can take into account both the consistency and communication overhead. Therefore, it is more suitable for cloud resource monitoring system, and its performance is superior to traditional data transmission models. 5. Conclusions and future work Basing on the problem that frequent data transmission in cloud resource monitoring system will cause dramatic communication cost, a Push-Pull Hybrid Model (PPHM) is proposed. By taking advantages of push model and pull model and transferring the critical changes, the PPHM can improve data consistency, reduce the communication overhead, decrease the interference caused by the monitoring system on cloud platform, and guarantee service availability. In the comprehensive comparison experiments of the refresh rate and consistency with the push model, the pull model, ACTC model and PPHM model, we get the results that PPHM model has a significant improvement in consistency and communication overhead. Its communication overhead is 53% less than push model; and compare with pull model, its consistency is improved by 43%. Vmmaster is a cloud computing service model in IaaS which is developed by Zhengzhou University and Chinese Academy of Sciences. It is used in teaching and research. The PPHM has been applied in the Vmmaster. It can effectively improve the monitoring performance. The current cloud monitoring can only reflect the present performance of system, but cannot give an early warning before performance bottlenecks or failure. It makes troubleshooting with a certain lag. The future research is to predict cloud computing system performance in the next period to get a warning in advance, to avoid the impact on service under normal using in an emergency and to ensure security and stability of cloud platform. 6. References: [1] Chen Kang, Zheng Wei-nin, Cloud Computing: System Instances and Current Research, Journal of Software, vol. 0, no. 5, pp , 009. [] Armbrust M, Fox A, Griffith R, et al, Above the Clouds: A Berkeley View of Cloud Computing, EECS Department, University of California, Berkeley, UCB/EECS-009-8, 009. [3] Emeakaroha C, Netto M, Calheiros R, et al, Towards Autonomic Detection of SLA Violations in Cloud Infrastructures, Future Generation Computer Systems, vol. 8, no. 7, pp , 01. [4] Zanikolas S, Sakellariou R, A Taxonomy of Grid Monitoring Systems, Future Generation Computer Systems, vol. 1, no. 1, pp , 005. [5] Yang Gang, Sui Yu-le, Adaptive Approach to Monitor Resource for Cloud Computing Platform, Computer Engineering and Applications, vol. 45, no. 9, pp.14-17, 009. [6] Chung Wu-chun and Chang Ruay-shiung, A New Mechanism for Resource Monitoring in Grid Computing, Future Generation Computer Systems, vol. 5, no. 1, pp.1 7, 009. [7] Sundaresan R, Kurc T, Lauria M, Parthasarathy S, et al, A Slacker Coherence Protocol for Pull-based Monitoring of On-line Data Sources, In Proceeding(s) of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, pp.50 57, 003. [8] Sundaresan R, Kurc T, Lauria M, Parthasarathy S, et al, Adaptive Polling of Grid Resource Monitors using A Slacker Coherence Model, In Proceeding(s) of the 1th IEEE International Symposium on High Performance Distributed Computing, pp.60 69, 003. [9] Huang He, Wang Li-qiang, P&P: A Combined Push-Pull Model for Resource Monitoring in Cloud Computing Environment, In Proceeding(s) of the 3rd IEEE International Conference on Cloud Computing, pp.60 66, 010. [10] Dinda P, The Statistical Properties of Host Load, Scientific Programming, vol. 7, no. 4, pp.11-9,

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