Speculative Execution

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Nigel Linge - One of the best experts on this subject based on the ideXlab platform.

  • A Speculative Execution strategy based on node classification and hierarchy index mechanism for heterogeneous Hadoop systems
    2017 19th International Conference on Advanced Communication Technology (ICACT), 2017
    Co-Authors: Jian Shen, Zhangjie Fu, Nigel Linge
    Abstract:

    MapReduce (MR) has been widely used to process distributed large data sets. MRV2 working on Yarn, as a more advanced programing model, has gained lots of concerns. Meanwhile, Speculative Execution is known as an approach for dealing with same problems by backing up those tasks running on a low performance machine to a higher one. In this paper, we have modified some pitfalls and taken heterogeneous environment into consideration. Besides, Node classification is used and a novel hierarchy index mechanism is created. We also have implemented it in Hadoop-2.6 and the strategy above is called Speculation-NC while optimized Hadoop is called Hadoop-NC. Experiment results show that our method can correctly backup a task, improve the performance of MRV2 and decrease the Execution time and resource consumption compared with traditional strategies.

  • a survey of Speculative Execution strategy in mapreduce
    International Conference on Cloud Computing, 2016
    Co-Authors: Qi Liu, Xiaodong Liu, Dandan Jin, Nigel Linge
    Abstract:

    MapReduce is a parallel computing programming model designed to process large-scale data. Therefore, the accuracy and efficiency for computing are needed to be assured and Speculative Execution is an efficient method for calculation of fault tolerance. It reaches the goals of shortening the Execution time and increasing the cluster throughput through selecting slow tasks and Speculative copy these tasks on a fast machine to be executed. Hadoop naive Speculative Execution strategy assumes that the cluster is homogeneous, and this assumption leads to the poor performance in heterogeneous environment. Several Speculative Execution strategies which aim to improve the MapReduce Performance in the heterogeneous environments are reviewed in this paper like LATE, MCP, ex-MCP and ERUL, then the comparison between these methods are listed.

Qi Liu - One of the best experts on this subject based on the ideXlab platform.

  • An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes
    'Computers Materials and Continua (Tech Science Press)', 2020
    Co-Authors: Liu Xiaodong, Qi Liu, Jiang Yinghang, Dannah Williams, Jin Dandan, Sun Mingxu
    Abstract:

    Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present Speculative Execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of Speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task Execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e. the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of Speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data

  • An optimized Speculative Execution Strategy Based on Local Data Prediction in Heterogeneous Hadoop Environment
    Zhonghua Minguo Diannao Xuehui, 2018
    Co-Authors: Liu Xiaodong, Qi Liu, Jin Dan-dan, Linge Nigel
    Abstract:

    Hadoop is a famous parallel computing framework that is applied to process large-scale data, but there exists such a task in hadoop framework, which is called “Straggling task” and has a serious impact on Hadoop. Speculative Execution (SE) is an effective way to deal with the “Straggling task” by monitoring the real-time rate of running tasks and back up the “Straggler” on another node to increase the opportunity of completing backup task ahead of original. There are many problems in the proposed SE strategies, such as “Straggling task” misjudgment, improper selection of backup nodes, which will result in inefficient implementation of SE. In this paper, we propose an optimized SE strategy based on local data prediction, it collects task Execution information in real time and uses Local regression to predict remaining time of the current task, and selects the appropriate backup task node according to the actual requirements, at the same time, it uses the consumption and benefit model to maximizes the effectiveness of SE. Finally, the strategy is implemented in Hadoop-2.6.0, the experiment proves that the optimized strategy not only enhances the accuracy of selecting the “Straggler” task candidates, but also shows better performance in heterogeneous Hadoop environment

  • an optimized Speculative Execution strategy based on local data prediction in a heterogeneous hadoop environment
    Computational Science and Engineering, 2017
    Co-Authors: Xiaodong Liu, Qi Liu
    Abstract:

    Hadoop is a famous distributed computing framework that is applied to process large-scale data. "Straggling tasks" have a serious impact on Hadoop performance due to imbalance of slow tasks distribution. Speculative Execution (SE) presents a way to deal with Straggling tasks by monitoring the real-time progress of running tasks and replicating potential "Stragglers" on another node to increase the opportunity of completing backup tasks ahead of original. Current proposed SE strategies meet their challenges such as misjudgment of "Straggling tasks", improper selection of backup nodes, etc., which result in inefficient performance of the SE and its Hadoop system. In this paper, we propose an optimized SE strategy based on local data prediction, which collects task Execution information in real time and uses Locally Weighted Regression (LWR) to predict remaining time of each running tasks, and selects an appropriate backup task node according to the actual requirements. It also combines a cost-benefit model to maximize the effectiveness of SE. According to the results, the proposed SE strategy implemented in Hadoop-2.6.0 enhances the accuracy of selecting potential Straggler task candidates, and shows better performance in various situations in a heterogeneous Hadoop environment.

  • a survey of Speculative Execution strategy in mapreduce
    International Conference on Cloud Computing, 2016
    Co-Authors: Qi Liu, Xiaodong Liu, Dandan Jin, Nigel Linge
    Abstract:

    MapReduce is a parallel computing programming model designed to process large-scale data. Therefore, the accuracy and efficiency for computing are needed to be assured and Speculative Execution is an efficient method for calculation of fault tolerance. It reaches the goals of shortening the Execution time and increasing the cluster throughput through selecting slow tasks and Speculative copy these tasks on a fast machine to be executed. Hadoop naive Speculative Execution strategy assumes that the cluster is homogeneous, and this assumption leads to the poor performance in heterogeneous environment. Several Speculative Execution strategies which aim to improve the MapReduce Performance in the heterogeneous environments are reviewed in this paper like LATE, MCP, ex-MCP and ERUL, then the comparison between these methods are listed.

Bramas Bérenger - One of the best experts on this subject based on the ideXlab platform.

  • SPETABARU: A Task-based Runtime System with Speculative Execution Capability
    HAL CCSD, 2019
    Co-Authors: Bramas Bérenger
    Abstract:

    International audienceWhile task-based programming models allow expressing the parallelism of algorithms finely, the traditional data accesses used in the sequential task-flow model (STF) can restrict the parallelism and hide useful information. In this presentation, we describe how more precise data accesses can be used to get better performance, and how uncertain modifications of the data by the tasks open the possibility for Speculative Execution. We detail different Speculative Execution models when this uncertainty exists. We also introduce our Speculative runtime system, SPETABARU, and provide examples with the parallelization of the Monte Carlo and replica exchange Monte Carlo simulations

  • Increasing the Degree of Parallelism Using Speculative Execution in Task-based Runtime Systems
    'PeerJ', 2019
    Co-Authors: Bramas Bérenger
    Abstract:

    Task-based programming models have demonstrated their efficiency in the development of scientific applications on modern high-performance platforms. They allow delegation of the management of parallelization to the runtime system (RS), which is in charge of the data coherency, the scheduling, and the assignment of the work to the computational units. However, some applications have a limited degree of parallelism such that no matter how efficient the RS implementation, they may not scale on modern multicore CPUs. In this paper, we propose using speculation to unleash the parallelism when it is uncertain if some tasks will modify data, and we formalize a new methodology to enable Speculative Execution in a graph of tasks. This description is partially implemented in our new C++ RS called SPETABARU, which is capable of executing tasks in advance if some others are not certain to modify the data. We study the behavior of our approach to compute Monte Carlo and replica exchange Monte Carlo simulations.Comment: 24 pages, https://peerj.com/articles/cs-183

Craig A Knoblock - One of the best experts on this subject based on the ideXlab platform.

  • Speculative plan Execution for information gathering
    Artificial Intelligence, 2008
    Co-Authors: Greg Barish, Craig A Knoblock
    Abstract:

    The Execution performance of an information gathering plan can suffer significantly due to remote I/O latencies. A streaming dataflow model of Execution addresses the problem to some extent, exploiting all natural opportunities for parallel Execution, as allowed by the data dependencies in a plan. Unfortunately, plans that integrate information from multiple sources often use the results of one operation as the basis for forming queries to a subsequent operation. Such cases require sequential Execution, an inefficiency that can erase prior gains made through techniques like streaming dataflow. To address this problem, we present a technique called Speculative plan Execution, an out-of-order method that capitalizes on knowledge gained from prior Executions as a means for overcoming remaining data dependencies between plan operators. Our approach inserts additional plan operators that generate and confirm Speculative results, while preserving the safety and fairness of overall Execution. To increase the utility of Speculative Execution, we propose a method of value prediction that combines caching with the more effective and space-efficient techniques of classification and transduction. We present experimental results that demonstrate how the performance of information gathering plans can benefit from Speculative Execution and how its overall utility can be increased through our hybrid method of value prediction.

  • Speculative plan Execution for information agents
    2003
    Co-Authors: Craig A Knoblock, Greg Barish
    Abstract:

    While information agents make it possible to gather, combine, and process data on networks like the Internet, Execution performance often suffers due to remote source latencies. Agents do not control remote sources and must wait an undetermined amount of time for a query to be answered. The problem becomes worse when an agent plan requires that the answers provided by one source be used as a basis for querying another source. In this dissertation, I make three related contributions that address these problems and significantly improve information agent performance. The first is an expressive agent plan language and a streaming dataflow Execution system. The combination of both allows agent plans to be described and efficiently executed, realizing the maximum parallelism allowable by the data dependencies in the plan. My experimental results confirm that Execution is efficient and that the plan language is expressive enough to support tasks beyond those supported by traditional network query engines, such as recursive information gathering and monitoring. A second contribution is a strategy for Speculative plan Execution within a streaming dataflow architecture. Under Speculative Execution, certain operators are issued ahead of schedule, using data predicted from experience. Through this technique, remaining costly data dependencies between I/O-bound operators can be broken, leading to parallelism beyond the normal dataflow limit. My experimental results demonstrate that Speculative Execution can lead to significant speedups in both Web agent plans as well as certain types of queries for distributed database systems. A third contribution is a technique for learning how to predict data for Speculative plan Execution. This approach combines caching with classification and transduction as a means for predicting future values from prior hints. Classification and transduction are more space efficient than caching and can improve the accuracy of prediction because each is capable of responding to new hints. The resulting improved accuracy increases the utility of Speculative Execution and leads to greater average plan speedups. My experimental results for a set of Web agent plans confirm these space-efficiency and accuracy claims.

Xiaodong Liu - One of the best experts on this subject based on the ideXlab platform.

  • an optimized Speculative Execution strategy based on local data prediction in a heterogeneous hadoop environment
    Computational Science and Engineering, 2017
    Co-Authors: Xiaodong Liu, Qi Liu
    Abstract:

    Hadoop is a famous distributed computing framework that is applied to process large-scale data. "Straggling tasks" have a serious impact on Hadoop performance due to imbalance of slow tasks distribution. Speculative Execution (SE) presents a way to deal with Straggling tasks by monitoring the real-time progress of running tasks and replicating potential "Stragglers" on another node to increase the opportunity of completing backup tasks ahead of original. Current proposed SE strategies meet their challenges such as misjudgment of "Straggling tasks", improper selection of backup nodes, etc., which result in inefficient performance of the SE and its Hadoop system. In this paper, we propose an optimized SE strategy based on local data prediction, which collects task Execution information in real time and uses Locally Weighted Regression (LWR) to predict remaining time of each running tasks, and selects an appropriate backup task node according to the actual requirements. It also combines a cost-benefit model to maximize the effectiveness of SE. According to the results, the proposed SE strategy implemented in Hadoop-2.6.0 enhances the accuracy of selecting potential Straggler task candidates, and shows better performance in various situations in a heterogeneous Hadoop environment.

  • a survey of Speculative Execution strategy in mapreduce
    International Conference on Cloud Computing, 2016
    Co-Authors: Qi Liu, Xiaodong Liu, Dandan Jin, Nigel Linge
    Abstract:

    MapReduce is a parallel computing programming model designed to process large-scale data. Therefore, the accuracy and efficiency for computing are needed to be assured and Speculative Execution is an efficient method for calculation of fault tolerance. It reaches the goals of shortening the Execution time and increasing the cluster throughput through selecting slow tasks and Speculative copy these tasks on a fast machine to be executed. Hadoop naive Speculative Execution strategy assumes that the cluster is homogeneous, and this assumption leads to the poor performance in heterogeneous environment. Several Speculative Execution strategies which aim to improve the MapReduce Performance in the heterogeneous environments are reviewed in this paper like LATE, MCP, ex-MCP and ERUL, then the comparison between these methods are listed.