NDLI: AJIRA: A Lightweight Distributed Middleware for MapReduce and Stream Processing

Content Provider	IEEE Xplore Digital Library
Author	Urbani, J. Margara, A. Jacobs, C. Voulgaris, S. Bal, H.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. .Sci., Vrije Univ. Amsterdam, Amsterdam, Netherlands (Urbani, J.; Margara, A.; Jacobs, C.; Voulgaris, S.; Bal, H.)
Abstract	Currently, MapReduce is the most popular programming model for large-scale data processing and this motivated the research community to improve its efficiency either with new extensions, algorithmic optimizations, or hardware. In this paper we address two main limitations of MapReduce: one relates to the model's limited expressiveness, which prevents the implementation of complex programs that require multiple steps or iterations. The other relates to the efficiency of its most popular implementations (e.g., Hadoop), which provide good resource utilization only for massive volumes of input, operating sub optimally for smaller or rapidly changing input. To address these limitations, we present AJIRA, a new middleware designed for efficient and generic data processing. At a conceptual level, AJIRA replaces the traditional map/reduce primitives by generic operators that can be dynamically allocated, allowing the execution of more complex batch and stream processing jobs. At a more technical level, AJIRA adopts a distributed, multi-threaded architecture that strives at minimizing overhead for non-critical functionality. These characteristics allow AJIRA to be used as a single programming model for both batch and stream processing. To this end, we evaluated its performance against Hadoop, Spark, Esper, and Storm, which are state of the art systems for both batch and stream processing. Our evaluation shows that AJIRA is competitive in a wide range of scenarios both in terms of processing time and scalability, making it an ideal choice where flexibility, extensibility, and the processing of both large and dynamic data with a single programming model are either desirable or even mandatory requirements.
Starting Page	545
Ending Page	554
File Size	339596
Page Count	10
File Format	PDF
ISSN	10636927
e-ISBN	9781479951697
DOI	10.1109/ICDCS.2014.62
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-30
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer architecture Scalability Computational modeling Fault tolerance Fault tolerant systems Data models Programming
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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