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teaching:mfe:is [2018/04/23 09:57] svsummer [Master Thesis in Collaboration with Euranova] |
teaching:mfe:is [2018/04/30 11:10] svsummer [Dynamic Query Processing on GPU Accelerators] |
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| - | ====== MFE 2017-2018 : Web and Information Systems ====== | + | ====== MFE 2018-2019 : Web and Information Systems ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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| Since our current development is done in the Scala programming language, prospective students should either know Scala, or being willing to learn it within the context of the master thesis. | Since our current development is done in the Scala programming language, prospective students should either know Scala, or being willing to learn it within the context of the master thesis. | ||
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| **Validation of the approach** Validation of master thesis' work should be done on two levels: | **Validation of the approach** Validation of master thesis' work should be done on two levels: | ||
| * a theoretical level; by proposing and discussing alternative ways to do incremental computation on GPU architectures, and comparing these from a theoretical complexity viewpoint | * a theoretical level; by proposing and discussing alternative ways to do incremental computation on GPU architectures, and comparing these from a theoretical complexity viewpoint | ||
| * an experimental level; by proposing a benchmark collection of CEP queries that can be used to test the obtained versions of the interpreter/compiler, and report on the experimentally observed performance on this benchmark. | * an experimental level; by proposing a benchmark collection of CEP queries that can be used to test the obtained versions of the interpreter/compiler, and report on the experimentally observed performance on this benchmark. | ||
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| **Deliverables** of the master thesis project | **Deliverables** of the master thesis project | ||
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| * The implementaiton of the evaluation algorithm(s) (as an interpreter/compiler) | * The implementaiton of the evaluation algorithm(s) (as an interpreter/compiler) | ||
| * A benchmark set of queries and associated data sets for the experimental validation | * A benchmark set of queries and associated data sets for the experimental validation | ||
| + | * An experimental validation of the compiler, and analysis of the results. | ||
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| + | **Interested?** Contact : [[svsummer@ulb.ac.be|Stijn Vansummeren]] | ||
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| + | **Status**: available | ||
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| + | ===== Multi-query Optimization in Spark ===== | ||
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| + | Distributed computing platforms such as Hadoop and Spark focus on addressing the following challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously running applications. However, it is up to the developers to decide on what to share. | ||
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| + | The objective of this master thesis is to optimize various applications running on a Spark platform, optimize their execution plans by autonomously finding sharing opportunities, namely finding the RDDs that can be shared among these applications, and computing these shared plans once instead of multiple times for each query. | ||
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| + | **Deliverables** of the master thesis project | ||
| + | * An overview of the Apache Spark architecture. | ||
| + | * Develop a performance model for queries executed by Spark. | ||
| + | * An implementation that optimizes queries executed by Spark and identify sharing opportunities. | ||
| + | * An experimental validation of the developed system. | ||
| + | |||
| + | **Interested?** | ||
| + | * Contact : //Iman Elghandour// or //Stijn Vansummeren// | ||
| + | |||
| + | **Status**: available | ||
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| + | ===== Accelerated Distributed Platform for Spatial Queries ===== | ||
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| + | It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark using accelerators such as GPUs and FPGAs. | ||
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| + | The objective of this master thesis is to build a framework that efficiently executes spatial queries on a Spark version that is enabled to run its tasks on GPUs. | ||
| + | |||
| + | **Deliverables** of the master thesis project | ||
| + | * An overview of Spatial queries and frameworks for processing big spatial data. | ||
| + | * A study of best approaches to represent spatial data while it is queried by Spark and GPUs. | ||
| + | * An implementation of common spatial operations and computational geometry algorithm on GPUs and Spark. | ||
| + | * An experimental validation of the developed system. | ||
| + | |||
| + | **Interested?** | ||
| + | * Contact : //Iman Elghandour// or //Stijn Vansummeren// | ||
| + | |||
| + | **Status**: available | ||
| + | |||
| + | ===== Co-locating Big Spatial Data Stored in HDFS ===== | ||
| + | |||
| + | Spatial databases employ spatial indexes to speedup the access of spatial data. New frameworks are introduced to build such indexes for Hadoop and Spark. However, there are not fully integrated on the file system level. | ||
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| + | The objective of this master thesis is to build these indexes within the layer of HDFS and use this implementation to co-locate files that are typically accessed together by the spatial queries. | ||
| + | |||
| + | **Deliverables** of the master thesis project | ||
| + | * An overview of spatial queries and frameworks for processing big spatial data. | ||
| + | * A study of different types of indexes how they can be built in HDFS, and how we can use the replicas of HDFS to store multiple types of indexes | ||
| + | * An implementation of spatial indexes in HDFS. | ||
| + | * An experimental validation of the developed system. | ||
| + | |||
| + | **Interested?** | ||
| + | * Contact : //Iman Elghandour// or //Stijn Vansummeren// | ||
| + | |||
| + | **Status**: available | ||
| + | |||
| + | ===== Complex Event Processing in Apache Spark and Apache Storm ===== | ||
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| + | The master thesis is put forward in the context of the SPICES "Scalable Processing and mIning of Complex Events for Security-analytics" research project, funded by Innoviris. | ||
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| + | Within this project, our lab is developping a declarative language for Complex Event Processing (CEP for short). The goal in Complex Event Processing is to derive pre-defined patterns in a stream of raw events. Raw events are typically sensor readings (such as "password incorrect for user X trying to log in on machine Y" or "file transfer from machine X to machine Y"). The goal of CEP is then to correlate these events into complex events. For example, repeated failed login attempts by X to Y should trigger a complex event "password cracking warning" that refers to all failed login attempts. | ||
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| + | The objective of this master thesis is to build an interpreter/compiler for this declarative CEP language that targets the distributed computing frameworks Apache Spark and/or Apache Storm as backends. Getting aquaintend with these technologies is part of the master thesis objective. | ||
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| + | **Validation of the approach** Validation of the proposed interpreter/compiler should be done on two levels: | ||
| + | * a theoretical level; by comparing the generated Spark/Storm processors to a processor based on "Incremental computation" that is being developped at the lab | ||
| + | * an experimental level; by proposing a benchmark collection of CEP queries that can be used to test the obtained interpreter/compiler, and report on the experimentally observed performance on this benchmark. | ||
| + | |||
| + | **Deliverables** of the master thesis project | ||
| + | * An overview of the processing models of Spark and Storm | ||
| + | * A definition of the declarative CEP language under consideration | ||
| + | * A description of the interpretation/compilation algorithm | ||
| + | * A theoretical comparison of this algorithm wrt an incremental evaluation algorithm. | ||
| + | * The interpreter/compiler itself (software artifact) | ||
| + | * A benchmark set of CEP queries and associated data sets for the experimental validation | ||
| * An experimental validation of the compiler, and analysis of the results. | * An experimental validation of the compiler, and analysis of the results. | ||