13,112 research outputs found
Reliable and randomized data distribution strategies for large scale storage systems
The ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in
such a dynamic environment. This paper presents and evaluates the Random Slicing strategy, which incorporates lessons learned
from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy
that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations, drastically reducing the required amount of randomness while delivering a perfect load distribution.Peer ReviewedPostprint (author’s final draft
Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments
Data centres that use consumer-grade disks drives and distributed
peer-to-peer systems are unreliable environments to archive data without enough
redundancy. Most redundancy schemes are not completely effective for providing
high availability, durability and integrity in the long-term. We propose alpha
entanglement codes, a mechanism that creates a virtual layer of highly
interconnected storage devices to propagate redundant information across a
large scale storage system. Our motivation is to design flexible and practical
erasure codes with high fault-tolerance to improve data durability and
availability even in catastrophic scenarios. By flexible and practical, we mean
code settings that can be adapted to future requirements and practical
implementations with reasonable trade-offs between security, resource usage and
performance. The codes have three parameters. Alpha increases storage overhead
linearly but increases the possible paths to recover data exponentially. Two
other parameters increase fault-tolerance even further without the need of
additional storage. As a result, an entangled storage system can provide high
availability, durability and offer additional integrity: it is more difficult
to modify data undetectably. We evaluate how several redundancy schemes perform
in unreliable environments and show that alpha entanglement codes are flexible
and practical codes. Remarkably, they excel at code locality, hence, they
reduce repair costs and become less dependent on storage locations with poor
availability. Our solution outperforms Reed-Solomon codes in many disaster
recovery scenarios.Comment: The publication has 12 pages and 13 figures. This work was partially
supported by Swiss National Science Foundation SNSF Doc.Mobility 162014, 2018
48th Annual IEEE/IFIP International Conference on Dependable Systems and
Networks (DSN
Distributed and Load-Adaptive Self Configuration in Sensor Networks
Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead
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
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
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