Benchmarking Dependability of MapReduce Systems

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2016

Applications such as web search and social networking have been moving from centralized to decentralized cloud architectures to improve their scalability. MapReduce, a programming framework for processing large amounts of data using thousands of machines in a single cloud, also needs to be scaled out to multiple clouds to adapt to this evolution. The challenge of building a multi-cloud distributed architecture is substantial. Notwithstanding, the ability to deal with the new types of faults introduced by such setting, such as the outage of a whole datacenter or an arbitrary fault caused by a malicious cloud insider, increases the endeavor considerably. In this paper we propose Medusa, a platform that allows MapReduce computations to scale out to multiple clouds and tolerate several types of faults. Our solution fulfills four objectives. First, it is transparent to the user, who writes her typical MapReduce application without modification. Second, it does not require any modification to the widely used Hadoop framework. Third, the proposed system goes well beyond the fault-tolerance offered by MapReduce to tolerate arbitrary faults, cloud outages, and even malicious faults caused by corrupt cloud insiders. Fourth, it achieves this increased level of fault tolerance at reasonable cost. We performed an extensive experimental evaluation in the ExoGENI testbed, demonstrating that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience.

2012

MapReduce is a promising programming model for distributed data processing. Extensive research has been conducted on the scalability of MapReduce, and several systems have been proposed in the literature, ranging from job scheduling to data placement and replication. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce techniques have been evaluated using microbenchmarks in an overly simplified setting, which may not be representative of real-world applications. This paper presents MRBS, a comprehensive benchmark suite for evaluating the performance of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to characterize application workload and dataload, and produces extensive high-level and low-level performance statistics. We illustrate the use of MRBS with Hadoop clusters running on Amazon EC2.

Sensors

Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages. This paper analyses the response time under two main conditions: fail-stop and fail-slow, when they manifest with node, service, and the task at runtime. In addition, we focus on the relationship between the time for detecting and recovering faults. The experimental analysis is conducted on a real Hadoop cluster comprising MapReduce, YARN and HDFS frameworks. Our analysis shows that the recovery of a single fault leads to an average of 67.6% re...

2011

The MapReduce framework is typically deployed on very large computing clusters where task and node failures are no longer an exception but the rule. Thus, fault-tolerance is an important aspect for the efficient operation of MapReduce jobs. However, currently MapReduce implementations fully recompute failed tasks (subparts of a job) from the beginning. This can significantly decrease the runtime performance of

IEEE Cloud Computing

MapReduce is a popular distributed data-processing system for analyzing big data in cloud environments. This platform is often used for critical data processing, e.g., in the context of scientific or financial simulation. Unfortunately, there is accumulating evidence of severe problems-including arbitrary faults and cloud outages-affecting the services that run atop cloud services. Faced with this challenge, we have recently explored multicloud solutions to increase the resilience and availability of MapReduce. Based on this experience, we present system design guidelines that allow to scale out MapReduce computation to multiple clouds in order to tolerate arbitrary and malicious faults, as well as cloud outages. Crucially, the techniques we introduce have reasonable cost and do not require changes to MapReduce or to the users' code, enabling immediate deployment.

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2017

MapReduce is a framework for processing large data sets much used in the context of cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but not arbitrary faults. Unfortunately, there is evidence that arbitrary faults do occur and can affect the correctness of MapReduce job executions. Furthermore, many outages of major cloud offerings have been reported, raising concerns about the dependence on a single cloud. In this paper we propose a novel execution system that allows to scale out MapReduce computations to a cloud-of-clouds and tolerate arbitrary faults, malicious faults, and cloud outages. Our system, Chrysaor, is based on a fine-grained replication scheme that tolerates faults at the task level. Our solution has three important properties: it tolerates the above-mentioned classes of faults at reasonable cost; it requires minimal modifications to the users' applications; and it does not involve changes to the Hadoop source code. We performed an extensive evaluation of our system in Amazon EC2, showing that our fine-grained solution is efficient in terms of computation by recovering only faulty tasks. This is achieved without incurring a significant penalty for the baseline case (i.e., without faults) in most workloads.

Proceedings of the 2008 14th IEEE International Conference …

MapReduce is Google's programming model for easy development of scalable parallel applications which process huge quantity of data on many clusters. Due to its conveniency and efficiency, MapReduce is used in various applications (e.g., web search services and online analytical processing.) However, there are only few good benchmarks to evaluate MapReduce implementations by realistic testsets. In this paper, we present MRBench that is a benchmark for evaluating MapReduce systems. MRBench focuses on processing business oriented queries and concurrent data modifications. To this end, we build MR-Bench to deal with large volumes of relational data and execute highly complex queries. By MRBench, users can evaluate the performance of MapReduce systems while varying environmental parameters such as data size and the number of (Map/Reduce) tasks. Our extensive experimental results show that MRBench is a useful tool to benchmark the capability of answering critical business questions.

MapReduce (MR) based systems are the most popular solution for large-scale data processing, where developers can easily use a large number of machines to implement large-scale data intensive applications. These systems are often deployed on large-cluster of commodity machines, where failures happen constantly due to bugs, hardware problems, outages, etc. In order to ensure their trustworthy, MR based systems must be tested extensively under abnormal conditions. Moreover, some features should be considered when testing these systems, such as: system evaluation during execution, distributed testing control, real-world condition (e.g. volatility control and fault injections), and MR components control and monitoring. In this article we present a framework for testing MR based systems. This framework is based on the individual control and monitoring of MR components and combines fault injection with functional tests. We used this framework to test two applications deployed with Hadoop, the Apache open source MapReduce implementation.

2011

Abstract MapReduce is often used to run critical jobs such as scientific data analysis. However, evidence in the literature shows that arbitrary faults do occur and can probably corrupt the results of MapReduce jobs. MapReduce runtimes like Hadoop tolerate crash faults, but not arbitrary or Byzantine faults. We present a MapReduce algorithm and prototype that tolerate these faults.

2010 10th IEEE/ACM …, 2010

MapReduce is a programming paradigm for parallel processing that is increasingly being used for data-intensive applications in cloud computing environments. An understanding of the characteristics of workloads running in MapReduce environments benefits both the service providers in the cloud and users: the service provider can use this knowledge to make better scheduling decisions, while the user can learn what aspects of their jobs impact performance. This paper analyzes 10-months of MapReduce logs from the M45 supercomputing cluster which Yahoo! made freely available to select universities for systems research. We characterized resource utilization patterns, job patterns, and sources of failures. We use an instance-based learning technique that exploits temporal locality to predict job completion times from historical data and identify potential performance problems in our dataset.