6,528 research outputs found
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Observations on Factors Affecting Performance of MapReduce based Apriori on Hadoop Cluster
Designing fast and scalable algorithm for mining frequent itemsets is always
being a most eminent and promising problem of data mining. Apriori is one of
the most broadly used and popular algorithm of frequent itemset mining.
Designing efficient algorithms on MapReduce framework to process and analyze
big datasets is contemporary research nowadays. In this paper, we have focused
on the performance of MapReduce based Apriori on homogeneous as well as on
heterogeneous Hadoop cluster. We have investigated a number of factors that
significantly affects the execution time of MapReduce based Apriori running on
homogeneous and heterogeneous Hadoop Cluster. Factors are specific to both
algorithmic and non-algorithmic improvements. Considered factors specific to
algorithmic improvements are filtered transactions and data structures.
Experimental results show that how an appropriate data structure and filtered
transactions technique drastically reduce the execution time. The
non-algorithmic factors include speculative execution, nodes with poor
performance, data locality & distribution of data blocks, and parallelism
control with input split size. We have applied strategies against these factors
and fine tuned the relevant parameters in our particular application.
Experimental results show that if cluster specific parameters are taken care of
then there is a significant reduction in execution time. Also we have discussed
the issues regarding MapReduce implementation of Apriori which may
significantly influence the performance.Comment: 8 pages, 8 figures, International Conference on Computing,
Communication and Automation (ICCCA2016
Efficient data reliability management of cloud storage systems for big data applications
Cloud service providers are consistently striving to provide efficient and reliable service, to their client's Big Data storage need. Replication is a simple and flexible method to ensure reliability and availability of data. However, it is not an efficient solution for Big Data since it always scales in terabytes and petabytes. Hence erasure coding is gaining traction despite its shortcomings. Deploying erasure coding in cloud storage confronts several challenges like encoding/decoding complexity, load balancing, exponential resource consumption due to data repair and read latency. This thesis has addressed many challenges among them. Even though data durability and availability should not be compromised for any reason, client's requirements on read performance (access latency) may vary with the nature of data and its access pattern behaviour. Access latency is one of the important metrics and latency acceptance range can be recorded in the client's SLA. Several proactive recovery methods, for erasure codes are proposed in this research, to reduce resource consumption due to recovery. Also, a novel cache based solution is proposed to mitigate the access latency issue of erasure coding
Universal Workload-based Graph Partitioning and Storage Adaption for Distributed RDF Stores
The publication of machine-readable information has been significantly increasing both in the magnitude and complexity of the embedded relations. The Resource Description Framework(RDF) plays a big role in modeling and linking web data and their relations. In line with that important role, dedicated systems were designed to store and query the RDF data using a special queering language called SPARQL similar to the classic SQL. However, due to the high size of the data, several federated working nodes were used to host a distributed RDF store. The data needs to be partitioned, assigned, and stored in each working node. After partitioning, some of the data needs to be replicated in order to avoid the communication cost, and balance the loads for better system throughput. Since replications require more storage space, the important two questions are: what data to replicate? And how much? The answer to the second question is related to other storage-space requirements at each working node like indexes and cache. In order to efficiently answer SPARQL queries, each working node needs to put its share of data into multiple indexes. Those indexes have a data-wide size and consume a considerable amount of storage space. In this context, the same two questions about replications are also raised about indexes. The third storage-consuming structure is the join cache. It is a special index where the frequent join results are cached and save a considerable amount of running time on the cost of high storage space consumption. Again, the same two questions of replication and indexes are applicable to the join-cache.
In this thesis, we present a universal adaption approach to the storage of a distributed RDF store. The system aims to find optimal data assignments to the different indexes, replications, and join cache within the limited storage space. To achieve this, we present a cost model based on the workload that often contains frequent patterns. The workload is dynamically analyzed to evaluate predefined rules. Those rules tell the system about the benefits and costs of assigning which data to what structure. The objective is to have better query execution time.
Besides the storage adaption, the system adapts its processing resources with the queries' arrival rate. The aim of this adaption is to have better parallelization per query while still provides high system throughput
Scalable Reliable SD Erlang Design
This technical report presents the design of Scalable Distributed (SD) Erlang: a set of language-level changes that aims to enable Distributed Erlang to scale for server applications on commodity hardware with at most 100,000 cores. We cover a number of aspects, specifically anticipated architecture, anticipated failures, scalable data structures, and scalable computation. Other two components that guided us in the design of SD Erlang are design principles and typical Erlang applications. The design principles summarise the type of modifications we aim to allow Erlang scalability. Erlang exemplars help us to identify the main Erlang scalability issues and hypothetically validate the SD Erlang design
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