Abstract: In-memory database systems are becoming popular
due to the availability and affordability of sufficiently large RAM and
processors in modern high-end servers with the capacity to manage
large in-memory database transactions. While fast and reliable inmemory
systems are still being developed to overcome cache misses,
CPU/IO bottlenecks and distributed transaction costs, disk-based data
stores still serve as the primary persistence. In addition, with the
recent growth in multi-tenancy cloud applications and associated
security concerns, many organisations consider the trade-offs and
continue to require fast and reliable transaction processing of diskbased
database systems as an available choice. For these
organizations, the only way of increasing throughput is by improving
the performance of disk-based concurrency control. This warrants a
hybrid database system with the ability to selectively apply an
enhanced disk-based data management within the context of inmemory
systems that would help improve overall throughput.
The general view is that in-memory systems substantially
outperform disk-based systems. We question this assumption and
examine how a modified variation of access invariance that we call
enhanced memory access, (EMA) can be used to allow very high
levels of concurrency in the pre-fetching of data in disk-based
systems. We demonstrate how this prefetching in disk-based systems
can yield close to in-memory performance, which paves the way for
improved hybrid database systems. This paper proposes a novel EMA
technique and presents a comparative study between disk-based EMA
systems and in-memory systems running on hardware configurations
of equivalent power in terms of the number of processors and their
speeds. The results of the experiments conducted clearly substantiate
that when used in conjunction with all concurrency control
mechanisms, EMA can increase the throughput of disk-based systems
to levels quite close to those achieved by in-memory system. The
promising results of this work show that enhanced disk-based
systems facilitate in improving hybrid data management within the
broader context of in-memory systems.
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: The ever increasing use of World Wide Web in the
existing network, results in poor performance. Several techniques
have been developed for reducing web traffic by compressing the size
of the file, saving the web pages at the client side, changing the burst
nature of traffic into constant rate etc. No single method was
adequate enough to access the document instantly through the
Internet. In this paper, adaptive hybrid algorithms are developed for
reducing web traffic. Intelligent agents are used for monitoring the
web traffic. Depending upon the bandwidth usage, user-s preferences,
server and browser capabilities, intelligent agents use the best
techniques to achieve maximum traffic reduction. Web caching,
compression, filtering, optimization of HTML tags, and traffic
dispersion are incorporated into this adaptive selection. Using this
new hybrid technique, latency is reduced to 20 – 60 % and cache hit
ratio is increased 40 – 82 %.