590 research outputs found
Implementation of the optimizer of SOA system deployment architecture
Optimization of business processes in SOA systems has been done using three separate types of methods: Resource Allocation, Service Scheduling and Service Composition. All three may influence each other, so the new method has been proposed to find an optimal combination of those three. It is based on a genetic algorithm that uses a simulator of the SOA system to evaluate solutions. The article describes a model for the optimization criteria for such solutions. Subsequently, some basic concepts used to implement the simulator and optimizer have been presented. Finally, the performance results of the optimizer have been described, including the conclusions on how they might be improved.Optimization of business processes in SOA systems has been done using three separate types of methods: Resource Allocation, Service Scheduling and Service Composition. All three may influence each other, so the new method has been proposed to find an optimal combination of those three. It is based on a genetic algorithm that uses a simulator of the SOA system to evaluate solutions. The article describes a model for the optimization criteria for such solutions. Subsequently, some basic concepts used to implement the simulator and optimizer have been presented. Finally, the performance results of the optimizer have been described, including the conclusions on how they might be improved
A cost engine system for estimating whole-life cycle cost of long-term digital preservation activities
This research paper presents a cost engine system that estimates the whole life cycle cost of long-term digital preservation (LTDP) activities using cloud-based technologies. A qualitative research methodology has been employed and the activity based costing (ABC) technique has been used to develop the cost model. The unified modelling language (UML) notation and the object oriented paradigm (OOP) are utilised to design the architecture of the software system. In addition, the service oriented architecture (SOA) style has been used to deploy the function of the cost engine as a web service in order to ensure its accessibility over the web. The cost engine is a module that is part of a larger digital preservation system and has been validated qualitatively through experts’ opinion. Its benefits are realised in the accurate and detailed estimation of cost for companies wishing to employ LTDP activities
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Survey of Autonomic Computing and Experiments on JMX-based Autonomic Features
Autonomic Computing (AC) aims at solving the problem of managing the rapidly-growing complexity of Information Technology systems, by creating self-managing systems. In this thesis, we have surveyed the progress of the AC field, and studied the requirements, models and architectures of AC. The commonly recognized AC requirements are four properties - self-configuring, self-healing, self-optimizing, and self-protecting. The recommended software architecture is the MAPE-K model containing four modules, namely - monitor, analyze, plan and execute, as well as the knowledge repository.
In the modern software marketplace, Java Management Extensions (JMX) has facilitated one function of the AC requirements - monitoring. Using JMX, we implemented a package that attempts to assist programming for AC features including socket management, logging, and recovery of distributed computation. In the experiments, we have not only realized the powerful Java capabilities that are unknown to many educators, we also illustrated the feasibility of learning AC in senior computer science courses
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
This paper presents an accurate and robust embedded motor-imagery
brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet,
matches the requirements of memory footprint and computational resources of
low-power microcontroller units (MCUs), such as the ARM Cortex-M family.
Furthermore, the paper presents a set of methods, including temporal
downsampling, channel selection, and narrowing of the classification window, to
further scale down the model to relax memory requirements with negligible
accuracy degradation. Experimental results on the Physionet EEG Motor
Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and
65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global
validation, outperforming the state-of-the-art (SoA) convolutional neural
network (CNN) by 2.05%, 5.25%, and 5.48%. Our novel method further scales down
the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory
footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The
scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and
consuming 4.28mJ per inference for operating the smallest model, and on a
Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model,
enabling a fully autonomous, wearable, and accurate low-power BCI
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
Personalized ubiquitous healthcare solutions require energy-efficient
wearable platforms that provide an accurate classification of bio-signals while
consuming low average power for long-term battery-operated use. Single lead
electrocardiogram (ECG) signals provide the ability to detect, classify, and
even predict cardiac arrhythmia. In this paper, we propose a novel temporal
convolutional network (TCN) that achieves high accuracy while still being
feasible for wearable platform use. Experimental results on the ECG5000 dataset
show that the TCN has a similar accuracy (94.2%) score as the state-of-the-art
(SoA) network while achieving an improvement of 16.5% in the balanced accuracy
score. This accurate classification is done with 27 times fewer parameters and
37 times less multiply-accumulate operations. We test our implementation on two
publicly available platforms, the STM32L475, which is based on ARM Cortex M4F,
and the GreenWaves Technologies GAP8 on the GAPuino board, based on 1+8 RISC-V
CV32E40P cores. Measurements show that the GAP8 implementation respects the
real-time constraints while consuming 0.10 mJ per inference. With 9.91
GMAC/s/W, it is 23.0 times more energy-efficient and 46.85 times faster than an
implementation on the ARM Cortex M4F (0.43 GMAC/s/W). Overall, we obtain 8.1%
higher accuracy while consuming 19.6 times less energy and being 35.1 times
faster compared to a previous SoA embedded implementation.Comment: 4 pages, 1 figure, 2 table
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