MOON: MapReduce On Opportunistic eNvironments

Abstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments

Predicting Scheduling Failures in the Cloud

Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim to reduce the turnaround time of tasks and improve resource utilization. Several task scheduling algorithms have been proposed in the literature for cloud computing systems, the majority relying on the computational complexity of tasks and the distribution of resources. However, several tasks scheduled following these algorithms still fail because of unforeseen changes in the cloud environments. In this paper, using tasks execution and resource utilization data extracted from the execution traces of real world applications at Google, we explore the possibility of predicting the scheduling outcome of a task using statistical models. If we can successfully predict tasks failures, we may be able to reduce the execution time of jobs by rescheduling failed tasks earlier (i.e., before their actual failing time). Our results show that statistical models can predict task failures with a precision up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of such predictions using the tool kit GloudSim and found that they can improve the number of finished tasks by up to 40%. We also perform a case study using the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene expression correlations analysis study from breast cancer research. We find that when extending the scheduler of Hadoop with our predictive models, the percentage of failed jobs can be reduced by up to 45%, with an overhead of less than 5 minutes

On the Usability of Shortest Remaining Time First Policy in Shared Hadoop Clusters

International audienceHadoop has been recently used to process a diverse variety of applications, sharing the same execution infrastructure. A practical problem facing the Hadoop community is how to reduce job makespans by reducing job waiting times and ex- ecution times. Previous Hadoop schedulers have focused on improving job execution times, by improving data locality but not considering job waiting times. Even worse, enforcing data locality according to the job input sizes can be ineffi- cient: it can lead to long waiting times for small yet short jobs when sharing the cluster with jobs with smaller input sizes but higher execution complexity. This paper presents hSRTF, an adaption of the well-known Shortest Remaining Time First scheduler (i.e., SRTF) in shared Hadoop clus- ters. hSRTF embraces a simple model to estimate the re- maining time of a job and a preemption primitive (i.e., kill) to free the resources when needed. We have implemented hSRTF and performed extensive evaluations with Hadoop on the Grid’5000 testbed. The results show that hSRTF can significantly reduce the waiting times of small jobs and therefore improves their makespans, but at the cost of a rel- atively small increase in the makespans of large jobs. For instance, a time-based proportional share mode of hSRTF (i.e., hSRTF-Pr) speeds up small jobs by (on average) 45% and 26% while introducing a performance degradation for large jobs by (on average) 10% and 0.2% compared to Fifo and Fair schedulers, respectively

Real-Time MapReduce Scheduling

In this paper, we explore the feasibility of enabling the scheduling of mixed hard and soft real-time MapReduce applications. We first present an experimental evaluation of the popular Hadoop MapReduce middleware on the Amazon EC2 cloud. Our evaluation reveals tradeoffs between overall system throughput and execution time predictability, as well as highlights a number of factors affecting real-time scheduling, such as data placement, concurrent users, and master scheduling overhead. Based on our evaluation study, we present a formal model for capturing real-time MapReduce applications and the Hadoop platform. Using this model, we formulate the offline scheduling of real-time MapReduce jobs on a heterogeneous distributed Hadoop architecture as a constraint satisfaction problem (CSP) and introduce various search strategies for the formulation. We propose an enhancement of MapReduce’s execution model and a range of heuristic techniques for the online scheduling. We further outline some of our future directions that apply state-of-the-art techniques in the real-time scheduling literature

A Survey on Job and Task Scheduling in Big Data

Bigdata handles the datasets which exceeds the ability of commonly used software tools for storing, sharing and processing the data. Classification of workload is a major issue to the Big Data community namely job type evolution and job size evolution. On the basis of job type, job size and disk performance, clusters are been formed with data node, name node and secondary name node. To classify the workload and to perform the job scheduling, mapreduce algorithm is going to be applied. Based on the performance of individual machine, workload has been allocated. Mapreduce has two phases for processing the data: map and reduce phases. In map phase, the input dataset taken is splitted into keyvalue pairs and an intermediate output is obtained and in reduce phase that key value pair undergoes shuffle and sort operation. Intermediate files are created from map tasks are written to local disk and output files are written to distributed file system of Hadoop. Scheduling of different jobs to different disks are identified after completing mapreduce tasks. Johnson algorithm is used to schedule the jobs and used to find out the optimal solution of different jobs. It schedules the jobs into different pools and performs the scheduling. The main task to be carried out is to minimize the computation time for entire jobs and analyze the performance using response time factors in hadoop distributed file system. Based on the dataset size and number of nodes which is formed in hadoop cluster, the performance of individual jobs are identified\ud Keywords — \ud hadoop; mapreduce; johnson algorith

DualTable: A Hybrid Storage Model for Update Optimization in Hive

Hive is the most mature and prevalent data warehouse tool providing SQL-like interface in the Hadoop ecosystem. It is successfully used in many Internet companies and shows its value for big data processing in traditional industries. However, enterprise big data processing systems as in Smart Grid applications usually require complicated business logics and involve many data manipulation operations like updates and deletes. Hive cannot offer sufficient support for these while preserving high query performance. Hive using the Hadoop Distributed File System (HDFS) for storage cannot implement data manipulation efficiently and Hive on HBase suffers from poor query performance even though it can support faster data manipulation.There is a project based on Hive issue Hive-5317 to support update operations, but it has not been finished in Hive's latest version. Since this ACID compliant extension adopts same data storage format on HDFS, the update performance problem is not solved. In this paper, we propose a hybrid storage model called DualTable, which combines the efficient streaming reads of HDFS and the random write capability of HBase. Hive on DualTable provides better data manipulation support and preserves query performance at the same time. Experiments on a TPC-H data set and on a real smart grid data set show that Hive on DualTable is up to 10 times faster than Hive when executing update and delete operations.Comment: accepted by industry session of ICDE201

Mapreduce and Heterogeneity: Power-Aware Bag-of-Tasks, Framework Parameter Sensitivity, and Dynamic Cluster Aware Framework Configuration

This dissertation presents the techniques for adaptation of MapReduce frameworks to incorporate heterogeneity-aware scheduling algorithms, an inspection of cluster configurations and how they impact these scheduling algorithms, an analysis regarding how the cluster configuration and the heterogeneity-aware scheduling can work together to minimize turnaround time and/or power consumption of the cluster when executing MapReduce applications, and how these lessons can be applied more broadly to Big Data infrastructure outside of MapReduce that supports multiple Big Data frameworks simultaneously. Heterogeneity exists in various capacities in any given cluster, from static (Physical and Platform) heterogeneity to dynamic heterogeneity (Transient Data, Transient Applications, and Irregular Hardware Behavior). Within the cluster there are historically several types of mitigation strategies for each of these types of heterogeneity, and each has their pros and cons. We discuss these mitigation strategies and the types of heterogeneity each of these strategies is able to address, and the history of the related work in the field. After this, we consider taking host-level metrics and using them to schedule tasks in real time, with a desire to address cluster-wide energy usage. To do this, we consider estimators for power consumption that are available on-chip, namely temperature. We establish a correlation between CPU temperature and power consumption, then derive a scheduling algorithm that eliminates nodes that are consuming too much power from the pool of schedule-able resources. In order to do this we focus on the ability of MapReduce frameworks, constructed as we have constructed the frameworks described in this thesis, to delay binding of tasks to specific workers. We analyze the impacts this has on turnaround time of a MapReduce application, with analysis around setting this threshold properly to reduce impact on turnaround time while shifting power consumption around in the cluster, away from nodes that are over-consuming. We also address concerns with respect to upgrading a cluster in stages, introducing more Physical Heterogeneity at various levels and the types of adjustments that need to be made to MapReduce configurations in order to combat the increased Heterogeneity. In particular, we look at the concerns for MapReduce platform mis-configuration and its impacts on turnaround time, analyzing the ways in which these types of errors can be mitigated between incremental platform upgrades. In an effort to address this, we introduce a Dynamic Heterogeneity Awareness (DHA) module to our MapReduce framework in order to address these upgrades, and allow better spreading of tasks by the framework, in order to further improve turnaround time and resource utilization. Finally we consider the implications for framework and application co-tenancy, and we describe the state of art in these areas. We focus on describing what co-tenancy is, why it\u27s important, and how the state of the art can be expanded to in order to leverage findings from this thesis to make these co-tenant clusters increase application and framework performance as well as improving these clusters with considerations for energy efficiency