Delay Scheduling with Reduced Workload on JobTracker in Hadoop

Abstract—Efficiently scheduling Map Reduce tasks is considered as one of the major challenges that face Map Reduce frameworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing on the data locality property for tasks scheduling. The data locality may cause less physical resources utilization in non-virtualized clusters and more power consumption. Virtualized clusters provide a viable solution to support both data locality and better cluster resources utilization. Two major factors are used to test the evaluated algorithms; the simulation time and the energy consumption. The evaluated schedulers are compared and the results showed the superiority and the preference of the MTL Scheduler over the existing schedulers. Also, we presented a comparison study between virtualized and non-virtualized clusters for Map Reduce tasks scheduling. Huge amounts of data are produced daily.

Jiayin Wang

Abstract Abstract—With the rapid increase in size and number of jobs that are being processed in the MapReduce framework, efficiently scheduling jobs under this framework is becoming increasingly important. We consider the problem of minimizing the total flowtime of a sequence of jobs in the MapReduce framework, where the jobs arrive over time and need to be processed through both Map and Reduce procedures before leaving the system.

We show that for this problem for non-preemptive tasks, no on-line algorithm can achieve a constant competitive ratio defined as the ratio between the completion time of the online algorithm to the completion time of the optimal non-causal off-line algorithm. We then construct a slightly weaker metric of performance called the efficiency ratio. Under some weak assumptions, we then show a surprising property that, for the flow-time problem, any work-conserving scheduler has a constant efficiency ratio in both preemptive and nonpreemptive scenarios.

New Hybrid Genetic Based Approach for Real-Time Scheduling of Multiprocessor Reconfigurable Embedded Systems Ibrahim Gharbi, Hamza Gharsellaoui, Sadok Bouamama.

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Adaptive and scalable comparison scheduling

Teaches imperative programming and methods for ensuring the correctness of programs. Students will learn the process and concepts needed to go from high-level descriptions of algorithms to correct imperative implementations, with specific application to basic data structures and algorithms. Much of the course will be conducted in a subset of C amenable to verification, with a transition to full C near the end.

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Our work is strongly motivated by recent real-world use cases that point to the need for a general, unified data processing framework to support analytical queries with different latency requirements. Toward this goal, we start with an analysis of existing big data systems to understand the causes of high latency. We then propose an extended architecture with mini-batches as granularity for computation and shuffling, and augment it with new model-driven resource allocation and runtime scheduling techniques to meet user latency requirements while maximizing throughput.

Results from real-world workloads show that our techniques, implemented in Incremental Hadoop, reduce its latency from tens of seconds to sub-second, with 2x-5x increase in throughput. Our system also outperforms state-of-the-art distributed stream systems, Storm and Spark Streaming, by orders of magnitude when combining latency and throughput. MapReduce is a popular programming model for processing large datasets using a cluster of machines.

However, the traditional MapReduce model is not well-suited for one-pass analytics, since it is geared towards batch processing and requires the dataset to be fully loaded into the cluster before running analytical queries. This article examines, from a systems standpoint, what architectural design changes are necessary to bring the benefits of the MapReduce model to incremental one-pass analytics. Our empirical and theoretical analyses of Hadoop-based MapReduce systems show that the widely used sort-merge implementation for partitioning and parallel processing poses a fundamental barrier to incremental one-pass analytics, despite various optimizations.

To address these limitations, we propose a new data analysis platform that employs hash techniques to enable fast in-memory processing, and a new frequent key based technique to extend such processing to workloads that require a large key-state space.

Matchmaking: A New MapReduce Scheduling Technique – UNL CSE

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Digital Science Center Publications. Reports and Papers. Geoffrey Fox, “Task Scheduling in Big Data – Review, Research: Challenges, and Prospects” Technical Report October 31 MapReduce for scientific computing Proceedings of the second international workshop on MapReduce and its applications MapReduce ’11 Pages ACM New York.

Vinod Ramachandran January 31, Condor is a well-developed system for identifying unused compute cycles and making them available to other users both within an outside an organization. Important problems considered by the Condor project include representing diverse management policies in a scheduling system and securely executing untrusted code without placing a large burden on programmers. The computing needs of a reasonably sophisticated user can vary considerably over time.

Condor addresses the problem of smoothing out discrepancies in computing needs and capabilities caused by this variation. This service allows users to rapidly acquire more computing power, while preventing waste of excess power. Without Condor, and organization would need to build specific infrastructure for its most advanced computing needs.

The Google file system

General One service that Cloudera provides for our customers is help with tuning and optimizing MapReduce jobs. There are a number of key symptoms to look for, and each set of symptoms leads to a different diagnosis and course of treatment. The first few tips are cluster-wide, and will be useful for operators and developers alike. The latter tips are for developers writing custom MapReduce jobs in Java. Please note, also, that these tips contain lots of rules of thumb based on my experience across a variety of situations.

They may not apply to your particular workload, dataset, or cluster, and you should always benchmark your jobs before and after any changes.

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It then calls the JobClient. We’ll learn more about JobConf, JobClient, Tool and other interfaces and classes a bit later in the tutorial. MapReduce – User Interfaces This section provides a reasonable amount of detail on every user-facing aspect of the MapReduce framework. This should help users implement, configure and tune their jobs in a fine-grained manner. Let us first take the Mapper and Reducer interfaces.

Applications typically implement them to provide the map and reduce methods. Finally, we will wrap up by discussing some useful features of the framework such as the DistributedCache, IsolationRunner etc. Payload Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods.

HDFS Basics – Blocks, Namenodes and Datanodes, Hadoop and mapreduce

In the last decade, efficient data analysis of data-intensive applications has become an increasingly important research issue. The popular map-reduce framework has offered an enthralling solution to this problem by means of distributing the work load across interconnected data centers. Hadoop is most widely used platform for data intensive application such as analysis of web logs, detection of global weather patterns, bioinformatics applications among others.

However, most Hadoop implementations assume that every node attached to a cluster are homogeneous in nature having same computational capacities which may reduce map-reduce performance by increasing extra over-head for run-time data communications.

Falkon integrates (1) multi-level scheduling to separate resource acquisition (via, e.g., requests to batch schedulers) from task dispatch, and (2) a streamlined dispatcher. We describe Falkon architecture and implementation, and present performance results for both microbenchmarks and applications.

This course will be broken up into 5 weekly modules covering the following topics: Gossip, Membership, Grids Week 3: P2P Systems Week 4: Snapshots, Multicast, Paxos Week 6: Leader Election, Mutual Exclusion Week 7: Concurrency Control, Replication Control Week 8:

Lecture topic-MM_slides – Matchmaking A New MapReduce…

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This thesis proposes several energy aware resource management techniques that can effectively perform matchmaking and scheduling of MapReduce jobs each of which is characterized by a Service Level Agreement (SLA) that includes a client specified earliest start time, execution time and a deadline with the objective of minimizing energy : Software Developer at .

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Rule Based Method for Entity Resolution