359 research outputs found
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
Performance Evaluation of Job Scheduling and Resource Allocation in Apache Spark
Advancements in data acquisition techniques and devices are revolutionizing the way image data are collected, managed and processed. Devices such as time-lapse cameras and multispectral cameras generate large amount of image data daily. Therefore, there is a clear need for many organizations and researchers to deal with large volume of image data efficiently. On the other hand, Big Data processing on distributed systems such as Apache Spark are gaining popularity in recent years. Apache Spark is a widely used in-memory framework for distributed processing of large datasets on a cluster of inexpensive computers. This thesis proposes using Spark for distributed processing of large amount of image data in a time efficient manner. However, to share cluster resources efficiently, multiple image processing applications submitted to the cluster must be appropriately scheduled by Spark cluster managers to take advantage of all the compute power and storage capacity of the cluster. Spark can run on three cluster managers including Standalone, Mesos and YARN, and provides several configuration parameters that control how resources are allocated and scheduled. Using default settings for these multiple parameters is not enough to efficiently share cluster resources between multiple applications running concurrently. This leads to performance issues and resource underutilization because cluster administrators and users do not know which Spark cluster manager is the right fit for their applications and how the scheduling behaviour and parameter settings of these cluster managers affect the performance of their applications in terms of resource utilization and response times.
This thesis parallelized a set of heterogeneous image processing applications including Image Registration, Flower Counter and Image Clustering, and presents extensive comparisons and analyses of running these applications on a large server and a Spark cluster using three different cluster managers for resource allocation, including Standalone, Apache Mesos and Hodoop YARN. In addition, the thesis examined the two different job scheduling and resource allocations modes available in Spark: static and dynamic allocation. Furthermore, the thesis explored the various configurations available on both modes that control speculative execution of tasks, resource size and the number of parallel tasks per job, and explained their impact on image processing applications. The thesis aims to show that using optimal values for these parameters reduces jobs makespan, maximizes cluster utilization, and ensures each application is allocated a fair share of cluster resources in a timely manner
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments
This chapter presents software architectures of the big data processing
platforms. It will provide an in-depth knowledge on resource management
techniques involved while deploying big data processing systems on cloud
environment. It starts from the very basics and gradually introduce the core
components of resource management which we have divided in multiple layers. It
covers the state-of-art practices and researches done in SLA-based resource
management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
- …