vhadoop: A Scalable Hadoop Virtual Cluster Platform for MapReduce-Based Parallel Machine Learning with Performance Consideration

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1 2012 IEEE International Conference on Cluster Computing Workshops vhadoop: A Scalable Hadoop Virtual Cluster Platform for MapReduce-Based Parallel Machine Learning with Performance Consideration Kejiang Ye, Xiaohong Jiang, Yanzhang He, Xiang Li, Haiming Yan, Peng Huang College of Computer Science, Zhejiang University Hangzhou , China {yekejiang,jiangxh,heyanzhang,lixiang,yanhaiming,huangpeng}@zju.edu.cn Abstract Big data processing is currently becoming increasingly important in modern era due to the continuous growth of the amount of data generated by various fields such as particle physics, human genomics, earth observation, etc. However, the efficiency of processing large-scale data on modern virtual infrastructure, especially on the virtualized cloud computing infrastructure, is not clear. This paper focuses on the performance of hadoop virtual cluster and proposes a scalable hadoop virtual cluster platform vhadoop for the large-scale MapReduce-based parallel data processing. We first describe the design and implementation of vhadoop platform. Then we perform a series of experiments to investigate both the static and dynamic performance of vhadoop platform, such as the performance characterization of cross-domain hadoop virtual cluster and live migraiton of hadoop virtual cluster. After that, we use the vhadoop platform to process 6 typical parallel clustering algorithms, such as Canopy, Dirichlet, Fuzzy k-means, k-means, MeanShift, MinHash, etc, on two typical datasets. Experimental results verify the efficiency of vhadoop platform to process the MapReduce-based parallel machine learning applications. Keywords-Hadoop; MapReduce; Virtual Cluster; Cloud Computing; Machine Learning; Big Data I. INTRODUCTION Big data [1] has recently received considerable attention due to the continuous growth of the amount of data generated by various fields such as particle physics, human genomics, earth observation, etc. How to compute, transfer, and store these huge data is a prominent challenge which will bring great impact on the traditional architectures and methods of computation, networking and storage. MapReduce [2] is an efficient parallel programming model for the dataintensive applications with the benefits such as simplicity, fault tolerance, and scalability. Hadoop [3] is the opensource implementation of MapReduce which can process hundreds of terabytes of data on at least 10,000 cores. This efficient parallel programming model also benefits the machine learning algorithms, such as clustering, classification, recommendations, to improve the processing efficiency on big data sets. Meanwhile, with rapid development of virtualization technology [4] and cloud computing technology [5], virtual machine () will be the basic computation unit in the cloud computing era to conduct computation in the future. Virtualization provides an abstraction of hardware resources enabling multiple instantiation of operating systems to run simultaneously on a single physical machine, i.e. server consolidation, to improve resource utilization [6]. Another prominent advantage of virtualization is the live migration technique which refers to the act of migrating a virtual machine from one physical machine to another even when the virtual machine continues to execute. This is an effective means to improve the dynamic manageability in the virtualized cloud computing data center [7, 8]. Although virtualization and MapReduce have been widely studied respectively for several years, there are relatively few studies on the combination of these two technologies together that running MapReduce applications on hadoop virtual cluster environment. As the cloud computing becomes more and more mature, big data processing on virtual infrastructure will become more and more common. There are several reasons for this trend: (i) Big data processing with high efficiency is a big challenge which needs to be executed on distributed platforms in parallel. MapReduce is a popular parallel computing framework for the big data processing. (ii) In the cloud era, resource virtualization is a typical feature that most of tasks will be executed on the virtual infrastructure. For example, users can simply rent a hadoop virtual cluster from Amazon EC2 cloud to run the MapReduce tasks without purchasing expensive physical servers. (iii) Virtualization holds many other benefits such as rapid startup, dynamic configuration, high scalability, etc. The hadoop virtual cluster can benefit from all the above advantages. (iv) Moving data to computing resources is more expensive than moving computing resources (such as ) to data due to the high overheads of transferring large amounts data. While virtual machine is more convenient to transfer (or migrate) from one physical machine to another with very low overheads. In this paper, we propose a scalable hadoop virtual cluster platform vhadoop for the large-scale MapReducebased parallel data processing with performance consideration. We first describe the design and implementation of vhadoop platform. Then we perform a series of experiments /12 $ IEEE DOI /ClusterW

2 nmon Monitor Machine Learning Algorithm Library MapReduce Tuner Folk Physical Machine A Assign Maps Master Assign Reduces Input Data Output Data Physical Machine B Map Phase Reduce Phase Figure 1. vhadoop Platform for the Parallel Machine Learning with Performance Consideration. to investigate both the static and dynamic performance of vhadoop, such as the performance characterization of cross-domain virtual cluster and virtual cluster migration. After that, we use the vhadoop platform to process several typical parallel clustering tasks, including Canopy, Dirichlet, Fuzzy k-means, k-means, MeanShift, MinHash, ontwo typical datasets. Experimental results verify the efficiency of vhadoop platform to process the MapReduce-based parallel machine learning applications. The rest of the paper is structured as follows. In Section II, we design and implement a platform vhadoop for the parallel machine learning on hadoop virtual cluster. In Section III, we study both the static and dynamic performance of vhadoop. In Section IV, we use the real parallel machine learning applications to verify the efficiency of vhadoop platform. Section V presents the related work. Finally we give our conclusion and future work in Section VI. II. VHADOOP PLATFORM In this section, we propose a platform vhadoop for the large-scale parallel machine learning on hadoop virtual cluster. A. System Architecture & Flow Figure 1 illustrates the vhadoop architecture for the parallel machine learning. It consists of five main modules: Virtualization Module, Hadoop Module, Machine Learning Algorithm Library, nmon Monitor, MapReduce Tuner. All the five modules corporate with each other to provide a scalable hadoop virtual cluster platform for parallel machine learning. The vhadoop execution flow is shown as follows: 1) Machine Learning Algorithm Library triggers and sends a hadoop virtual cluster request. 2) The Virtualization Module calls and starts a hadoop virtual cluster. 3) The Hadoop Module configures the hadoop parameters, such as master and worker virtual machines. 4) The input data is prepared by uploading to the Hadoop Distributed File System (HDFS). 5) The master virtual machine assigns maps and reduces to the worker virtual machines. 6) Perform the mapping operation. 7) Perform the reducing operation. 8) Collect and analyze the output data. When the whole process begins, both the master virtual machine and worker virtual machines are monitored by the nmon Monitor. 9) The vhadoop performance can be adjusted by the MapReduce Tuner based on the monitoring data. B. Platform Design & Implementation Virtualization Module: is the basic module to implement the resource virtualization. By using the virtualization technology, one physical machine can be shared by several virtual machines. We currently use Xen [4] as infrastructure virtualization layer. Xen supports live migration of virtual machines which is often used to achieve the goal of load balancing, energy saving, and online maintains. Hadoop Module: is responsible for the initial configuration of hadoop virtual cluster. The parameters include: the name of master node and work nodes, dfs.replication, dfs.block.size, map.tasks.maximum, reduce.tasks.maximum, etc. We currently configure Hadoop in images of vhadoop. Machine Learning Algorithm Library: is the library for MapReduce-based parallel machine learning algorithms, including clustering, classification, recommendations. There 153

3 are various algorithms being categorized into the above three categories. For example, Canopy, Dirichlet, Fuzzy k-means, k-means, MeanShift, MinHash, used in this paper, can be categorized to the clustering algorithms. We construct the algorithm library based on the Mahout 1 library which is an open-source machine learning library on hadoop. nmon Monitor: is responsible for monitoring the resource status of both the master virtual machine and worker virtual machines. The utilization of CPU, memory, disk, and network are all monitored. Performance bottleneck can be found by analyzing the monitored data. nmon 2 is an opensource performance monitor for the traditional Linux system. It monitors the comprehensive performance of the Linux system. We extend it to our distributed vhadoop platform to monitor the node performance in parallel. nmon analyser is another tool to generate graphics by using the nmon output files. MapReduce Tuner: is responsible for tuning the configuration parameters of hadoop virtual cluster. The adjustment can be done according to the results generated by the nmon Monitor. It can be implemented by re-configuring the parameters of vhadoop platform or using the live migration technique to dynamically adjust the vhadoop configurations. III. PERFORMANCE ANALYSIS OF HADOOP VIRTUAL CLUSTER In this section, we study both the static and dynamic performance of hadoop virtual cluster. In the static performance analysis, we mainly study the performance of crossdomain hadoop virtual cluster and the scalability of hadoop virtual cluster. While in the dynamic performance analysis, we investigate the live migration performance of hadoop virtual cluster. A. Experimental Configuration 1) Hadoop Virtual Cluster Configuration: All the experiments are performed on Dell T710 servers, with 2 Quad-core 64-bit Xeon processors E5620 at 2.40GHz and 32GB DRAM. We use CentOS 5.6 with kernel version e15xen in Domain 0, and Xen as the virtualization hypervisor. Each virtual machine is installed with Ubuntu 8.10 as the guest OS with the configuration of 1VCPU and 1024MB vmemory. The Hadoop version is , the Mahout version is 0.6. All the virtual machine images are stored on a separate NFS server. 2) MapReduce-based Benchmarks: We choose four typical MapReduce-based benchmarks to test the MapReduce and HDFS performance of hadoop virtual cluster. Table I describes the four benchmarks. The Wordcount benchmark reads text files and counts how often words occur. Each mapper takes a line as input and breaks it into words. It then emits a key/value pair of the Table I MAPREDUCE-BASED PARALLEL BENCHMARKS Name Category Description Wordcount MapReduce Reads text files and counts how often words occur MRBench MapReduce Checks whether small job runs are responsive and running efficiently on the cluster TeraSort MapReduce Sorts the data as fast as possible, & HDFS combining testing the HDFS and MapReduce layers DFSIOTest HDFS Is a read and write test for HDFS Figure 2. Performance Comparison of Wordcount Benchmark between Normal and Cross-Domain Hadoop Virtual Cluster. word and 1. Each reducer sums the counts for each word and emits a single key/value with the word and sum. The MRBench benchmark [9] checks whether small jobs are responsive and running efficiently on the cluster. It focuses on the MapReduce layer since its impact on the HDFS layer is very limited. The TeraSort benchmark is to sort 1TB of data (or any other amount of data you want) as fast as possible. It is a benchmark that combines testing the HDFS and MapReduce layers of an hadoop cluster. A full TeraSort benchmark run consists of the following three steps: (i) Generating the input data via TeraGen. (ii) Running the actual TeraSort on the input data. (iii) Validating the sorted output data via TeraValidate. The TestDFSIO benchmark is a read and write test for HDFS. It is helpful for tasks such as stress testing HDFS, to discover performance bottlenecks in the network. 3) Live Migration Benchmark: To measure the migration performance and overheads of hadoop virtual cluster, we extend our formal Virt-LM Benchmark [10] from single virtual machine migration to multiple virtual machines (virtual cluster) migration which can record the migration time and downtime of each virtual machine and the whole virtual cluster. 154

4 (a) Map Scales (b) Reduce Scales Figure 3. Performance Comparison of MRBench Benchmark between Normal and Cross-Domain Hadoop Virtual Cluster. (a) TeraSort Test (b) DFSIO Test Figure 4. Performance Comparison of TeraSort and DFSIO Benchmarks between Normal and Cross-Domain Hadoop Virtual Cluster. 4) Experimental Precision: In order to ensure the data precision, each of the showed experimental results were obtained via running benchmarks three times with the same configuration and average the three values. B. Static Performance Analysis Due to the large size of virtual cluster and the limited resources in physical machine, a virtual cluster may cross multiple domains (physical machines). We create 16-node hadoop virtual cluster (1 namenode and 15 datanode) to compare the performance of cross-domain hadoop virtual cluster with normal hadoop virtual cluster. In the crossdomain case, 16 virtual machines are distributed equally to the two physical machines, while in the normal case, all the 16 virtual machines are distributed to only one physical machine. Figure 2 shows the Wordcount performance when running on normal and cross-domain hadoop virtual cluster with 16 nodes. The input data is the chosen from the TOEFL (The Test of English as a Foreign Language) reading materials. From the figure, it is obviously that the running time increases as the size of input data scales. Further, the crossdomain hadoop virtual cluster acquires poor performance compared to the normal case which means the MapReduce performance can be obviously affected by the cross-domain configuration due to the increase of network I/O delay. Figure 3 shows the MRBench performance. In Figure 3(a), we set the reduce=1 and scale the number of maps from 1 to 6, while in Figure 3(b), we set the map=15, and scale the number of reduces from 1 to 6. From the figure, we find that as the number of maps and reduces scales, the running time increases quickly. It is because the concurrent running 155

5 (a) Migration Time. (b) Downtime. Figure 5. The Migration Overheads of Idle and Wordcount Hadoop Virtual Cluster with Different DRAM Configurations. will cause the network congestion, thus leading the longer execution time. The performance of cross-domain hadoop virtual cluster is worse than the normal case which is similar to the phenomenon of Figure 2. Figure 4(a) shows both the data generation time and the sort time of TeraSort benchmark. From the figure, we find that when the data size is small, both the data generation time and sort time is relatively small. However, when the data size exceeds 400MB, the running time increases quickly. The performance of cross-domain hadoop virtual cluster is relatively worse. Figure 4(b) shows the DFS performance with DFSIO benchmark. From the figure, we can find that read throughput is better than write throughput. The performance of cross-domain hadoop virtual cluster is worse than the normal case. Discussion From the above analysis, we find that when the data size and concurrent number are small, the performance of cross-domain and normal case are very similar. The gap will become increasingly evident as the data size or concurrent number scales. The reason is that, when the data size and concurrent number scales, the network communication overheads become the main bottleneck itself. The distribution of virtual machines across multiple domains will further affect the network communication performance, thereby affecting the performance of the MapReduce applications. C. Dynamic Performance Analysis Live migration is a key ingredient behind the management activities of cloud computing system to achieve the goals of load balancing, energy saving, failure recovery, and system maintenance. Figure 5 shows the migration time and downtime of each node in the 16-node hadoop virtual cluster which migrates Table II OVERALL MIGRATION TIME AND DOWNTIME OF 16-NODE HADOOP VIRTUAL CLUSTER Overall Migration Overall Time (s) Downtime (ms) idle.1024mb idle.512mb wordcount.1024mb wordcount.512mb from one physical machine to the other. From the figure, we can get the following observations: (i) The larger the memory incurs the longer the migration time will be, while the downtime doesn t has the causal relationship with the size of memory. (ii) Compared with the idle hadoop virtual cluster, the migration time of hadoop virtual cluster running Wordcount benchmark is slightly longer than that of idle hadoop virtual cluster. However, the downtime of hadoop virtual cluster running Wordcount benchmark is much longer than that of idle hadoop virtual cluster. (iii) The downtime of each node in the hadoop virtual cluster running Wordcount benchmark varies widely because of the imbalance of each node in the hadoop virtual cluster. Table II shows the overall migration time and downtime of the whole hadoop virtual cluster. The migration time of hadoop virtual cluster running Wordcount benchmark is about three times of that of idle hadoop virtual cluster. While the downtime of hadoop virtual cluster running Wordcount benchmark is about 13 times of that of idle hadoop virtual cluster. Discussion From the above analysis, we find that live migration of hadoop virtual cluster incurs some overheads, especially the downtime. Fortunately, it is tolerable for the hadoop virtual cluster due to efficient fault tolerant mechanism in hadoop itself. The unavailable service during 156

6 Figure 6. Parallel Clustering on Synthetic Control Data Set with Different Hadoop Virtual Cluster Scales. the period of downtime can be restored by re-sending the requests or obtaining from other available data block copies. Despite a long downtime, the MapReduce workloads can be successfully finished. IV. PARALLEL MACHINE LEARNING ON HADOOP VIRTUAL CLUSTER In this section, we run several typical parallel clustering algorithms on two data sets to illustrate the efficiency of running parallel machine learning on the vhadoop platform. A. MapReduce-based Clustering Algorithms Canopy Clustering is a very simple, fast and accurate method for grouping objects into clusters. All objects are represented as a point in a multidimensional feature space. Canopy Clustering is often used as an initial step in more rigorous clustering techniques, such as K-Means Clustering. k-means Clustering is a rather simple but well known algorithm for grouping objects. All objects need to be represented as a set of numerical features. In addition, the user has to specify the number of groups (referred to as k) he/she wishes to identify. Fuzzy k-means Clustering is an extension of K-Means, the popular simple clustering technique. While K-Means discovers hard clusters (a point belong to only one cluster), Fuzzy K-Means is a more statistically formalized method and discovers soft clusters where a particular point can belong to more than one cluster with certain probability. Mean Shift Clustering produces arbitrarily-shaped clusters depending upon the topology of the data without a priori knowledge of the number of clusters (as required in K- Means). Dirichlet Process Clustering performs Bayesian mixture modeling. Minhash Clustering performs probabilistic dimension reduction of high dimensional data. The essence of the technique is to hash each item using multiple independent hash functions such that the probability of collision of similar items is higher. Multiple such hash tables can then be constructed to answer near neighbor types of queries efficiently. B. Clustering on Synthetic Control Chart Time Series Data Set The Synthetic Control Chart Time Series Data Set 1 contains 600 examples of control charts synthetically generated by the process in Alcock and Manolopoulos in There are six different classes of control charts: (i) Normal, (ii) Cyclic, (iii) Increasing trend, (iv) Decreasing trend, (v) Upward shift, and (vi) Downward shift. We use this real data set to perform the MapReduce-based parallel machine learning on the vhadoop platform. Figure 6 shows the parallel clustering results on the synthetic control chart time series data set with different hadoop virtual cluster sizes. From the figure, we find that the running time of all the three clustering algorithms - canopy, dirichlet, meanshift - increase as the hadoop virtual cluster scales from 2 nodes (1 namenode and 1 datanode) to 16 nodes (1 namenode and 15 datanode). Because the size of data set is fixed, the larger virtual cluster size incurs more data communication between each node in the hadoop virtual cluster. C. Visualizing Sample Clustering We use the DisplayClustering to generates 1000 samples from three symmetric distributions. The data set can be used by the other clustering programs. It displays the points on

7 a screen and superimposes the model parameters that were used to generate the points. Figure 7 shows the visualization sample clustering results on the vhadoop platform with different cluster sizes. Compared to Figure 6, the visualizing sample clustering performs relatively smooth as the size of hadoop virtual cluster scales from 2 to 16. It is because, the workload of visualizing sample clustering is relatively light and can be finished quickly, thereby didn t cause too much pressure on the network. Figure 8(a)-(f) show the screenshot of sample points and clustering results with different clustering algorithms. They display the sample points and then superimpose all of the clusters from each iteration. The last iteration s clustering results are in bold red and the previous several results are colored (orange, yellow, green, blue, magenta) in order after which all earlier clusters are in light grey. This helps to visualize how the clusters converge upon a solution over multiple iterations. V. RELATED WORK Virtualization technology is currently becoming increasingly popular as a core technology to implement the cloud computing paradigm. Many efforts have been made to study the performance characterization of virtualization, including performance evaluation [11, 12], performance modeling [13 15], and performance optimization [16, 17]. Server consolidation [6] is one of the most important application scenario of virtualization to improve the resource utilization. While the live migration technique [18 21] is often used to achieve the goal of load balancing, energy saving [22], online maintenance, etc, in the cloud computing environments. MapReduce technology is an efficient technique to process huge amount of data in parallel. Kambatla et al. [23] optimized the hadoop provisioning in the cloud to reduce the cost and improve the performance. Ibrahimet et al. compared the performance of hadoop cluster on virtual machines and physical machines and found that running MapReduce application on virtual machines incurs additional performance degradation compared to the case that running on physical machines [24]. They also discussed the issues of implementing MapReduce on virtual machines by decoupling the storage unit from the computation unit to reduce the disk I/O overheads [25, 26]. Zaharia et al. pointed out the virtual machine interference, especially the network I/O interference, is the main reason causing the performance degradation of MapReduce system [27]. However, they only focus on the static performance analysis and have not referred to the dynamic performance, i.e. the live migration performance of hadoop virtual cluster. Further, they don t refer to the problem of parallel machine learning on the hadoop virtual cluster which is becoming increasing important in the big data processing on virtualized cloud computing infrastructures. VI. CONCLUSION In this paper, we study the performance and efficiency of running MapReduce-based parallel machine learning applications on hadoop virtual cluster. We first propose a scalable hadoop virtual cluster platform vhadoop for the parallel machine learning with performance consideration through binding the nmon performance monitor, mahout machine learning library, and MapReduce tuner on Xen virtualization platform. Then we perform a series of experiments to investigate both the static and dynamic performance of hadoop virtual cluster, such as the performance characterization of cross-domain virtual cluster and virtual cluster migration, which is helpful to improve the performance of real hadoop virtual cluster. After that, we verify the performance and efficiency of running MapReduce-based parallel machine learning applications, such as Canopy, Dirichlet, Fuzzy k- Means, k-means, MeanShift, MinHash, on our vhadoop platform. Experimental results show that: (i) The network I/O and NFS disk I/O are two main bottlenecks of vhadoop platform due to the shared resource contention and interference. The poor I/O performance in virtualization system and the heavy network communication operations in hadoop system make the network as the main performance bottleneck. (ii) There is a performance degradation when the data size or cluster scale increases. The cross-domain distribution of hadoop virtual cluster will also affect the communication performance of vhadoop. (iii) The vhadoop can perform the live migration of hadoop virtual cluster successfully. 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8 (a) Canopy (b) Dirichlet (c) Fuzzy k-means (d) Kmeans (e) MeanShift (f) MinHash Figure 7. Parallel Visualizing Sample Clustering with Different Hadoop Virtual Cluster Scales. (a) Sample Data (b) Canopy (c) Dirichlet (d) Fuzzy k-means (e) k-means (f) Means Shift Figure 8. The Screenshot of Clustering Results with Different Clustering Algorithms. 159

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