9 research outputs found
SDSN@RT: a middleware environment for single-instance multi-tenant cloud applications
With the Single-Instance Multi-Tenancy (SIMT) model for composite
Software-as-a-Service (SaaS) applications, a single composite application
instance can host multiple tenants, yielding the benefits of better service and
resource utilization, and reduced operational cost for the SaaS provider. An
SIMT application needs to share services and their aggregation (the
application) among its tenants while supporting variations in the functional
and performance requirements of the tenants. The SaaS provider requires a
middleware environment that can deploy, enact and manage a designed SIMT
application, to achieve the varied requirements of the different tenants in a
controlled manner. This paper presents the SDSN@RT (Software-Defined Service
Networks @ RunTime) middleware environment that can meet the aforementioned
requirements. SDSN@RT represents an SIMT composite cloud application as a
multi-tenant service network, where the same service network simultaneously
hosts a set of virtual service networks (VSNs), one for each tenant. A service
network connects a set of services, and coordinates the interactions between
them. A VSN realizes the requirements for a specific tenant and can be
deployed, configured, and logically isolated in the service network at runtime.
SDSN@RT also supports the monitoring and runtime changes of the deployed
multi-tenant service networks. We show the feasibility of SDSN@RT with a
prototype implementation, and demonstrate its capabilities to host SIMT
applications and support their changes with a case study. The performance study
of the prototype implementation shows that the runtime capabilities of our
middleware incur little overhead
The impact of Cloud Computing adoption on IT Service Accounting approaches – A Customer Perspective on IaaS Pricing Models
Cloud computing has been recently a trending topic beyond the technological field, due
to its implementation and expansion thanks to the internet revolution. Although it has
reached end-users’ hands during the past two years, the technology has been used for
a longer period in the business world. In a scenario where cost-cutting strategies and
start-up companies seem to have an increasing importance in global economy, cloud
computing has been one of the pillars of many business’ success in recent times.
Companies like Netflix, Instagram or Spotify are recent examples of how an enterprise
can grow spectacularly quick and become a market-leader basing its business activity
on the cloud technology.
This master thesis tries to explain how companies should behave when acquiring a
cloud service. Due to the wideness of the cloud market, the specific focus of the work
is infrastructure as a service, and pay-as-you-go model was chosen for the study due
to the novelty it introduces in the information technology market. Apart from technical
details, the economic point of view of cloud computing has also been researched, as
not only providers care about how their service have to be priced, but also companies
want to predict the expenditure to make in their brand new information service.
Through the pages of the work, the predecessors of cloud computing are presented as
well as the theories appeared to explain its costs and accounting aspects, to finally
explain how cloud computing changed the role. After a brief introduction to cloud
computing and its different service models, a market analysis of different providers is
performed, to extract the patterns and peculiarities of the actual situation of cloudmarket.
Transferring the knowledge obtained in the market analysis, an accounting
model is developed, based on costs categories and factors and a metering framework.
Finally, a case study is performed applying the model to the market situation extracted
from the market analysis.Outgoin
Transitioning power distribution grid into nanostructured ecosystem : prosumer-centric sovereignty
PhD ThesisGrowing acceptance for in-house Distributed Energy Resource (DER) installations at lowvoltage
level have gained much significance in recent years due to electricity market liberalisations
and opportunities in reduced energy billings through personalised utilisation
management for targeted business model. In consequence, modelling of passive customers’
electric power system are progressively transitioned into Prosumer-based settings where presidency
for Transactive Energy (TE) system framework is favoured. It amplifies Prosumers’
commitments into annexing TE values during market participations and optimised energy
management to earn larger rebates and incentives from TE programs. However, when dealing
with mass Behind-The-Meter DER administrations, Utility foresee managerial challenges
when dealing with distribution network analysis, planning, protection, and power quality
security based on Prosumers’ flexibility in optimising their energy needs.
This dissertation contributes prepositions into modelling Distributed Energy Resources
Management System (DERMS) as an aggregator designed for Prosumer-centered cooperation,
interoperating TE control and coordination as key parameters to market for both
optimised energy trading and ancillary services in a Community setting. However, Prosumers
are primarily driven to create a profitable business model when modelling their
DERMS aggregator. Greedy-optimisation exploitations are negative concerns when decisions
made resulted in detrimental-uncoordinated outcomes on Demand-Side Response (DSR)
and capacity market engagements. This calls for policy decision makers to contract safe (i.e.
cooperative yet competitive tendency) business models for Prosumers to maximise TE values
while enhancing network’s power quality metrics and reliability performances.
Firstly, digitalisation and nanostructuring of distribution network is suggested to identify
Prosumer as a sole energy citizen while extending bilateral trading between Prosumer-to-
Prosumer (PtP) with the involvements of other grid operators−TE system. Modelling of
Nanogrid environment for DER integrations and establishment of local area network infrastructure
for IoT security (i.e. personal computing solutions and data protection) are committed
for communal engagements in a decentralise setting. Secondly, a multi-layered Distributed
Control Framework (DCF) is proposed using Microsoft Azure cloud-edge platform that cascades energy actors into respective layers of TE control and coordination. Furthermore,
modelling of flexi-edge computing architecture is proposed, comprising of Contract-Oriented
Sensor-based Application Platform (COSAP) employing Multi-Agent System (MAS) to
enhance data-sharing privacy and contract coalition agreements during PtP engagements.
Lastly, the Agents of MAS are programmed with cooperative yet competitive intelligences
attributed to Reinforcement Learning (RL) and Neural Networks (NN) algorithms to solve
multimodal socio-economical and uncertainty problems that corresponded to Prosumers’
dynamic energy priorities within the TE framework. To verify the DERMS aggregator
operations, three business models were proposed (i.e. greedy-profit margin, collegial-peak
demand, reserved-standalone) to analyse comparative technical/physical and economic/social
dimensions. Results showed that the proposed TE-valued DERMS aggregator provides
participation versatility in the electricity market that enables competitive edginess when utilising
Behind-The-Meter DERs in view of Prosumer’s asset scheduling, bidding strategy, and
corroborative ancillary services. Performance metrics were evaluated on both domestic and
industrial NG environments against IEEE Standard 2030.7-2017 & 2030.8-2018 compliances
to ensure deployment practicability.
Subsequently, proposed in-house protection system for DER installation serves as an
add-on monitoring service which can be incorporated into existing Advance Distribution
Management System (ADMS) for Distribution Service Operator (DSO) and field engineers
use, ADMS aggregator. It provides early fault detections and isolation processes from allowing
fault current to propagate upstream causing cascading power quality issues across
the feeder line. In addition, ADMS aggregator also serves as islanding indicator that distinguishes
Nanogrid’s islanding state from unintentional or intentional operations. Therefore, a
Overcurrent Current Relay (OCR) is proposed using Fuzzy Logic (FL) algorithm to detect,
profile, and provide decisional isolation processes using specified OCRs. Moreover, the
proposed expert knowledge in FL is programmed to detect fault crises despite insufficient
fault current level contributed by DER (i.e. solar PV system) which conventional OCR fails
to trigger
Bioinspired metaheuristic algorithms for global optimization
This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions
Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter
In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF
Factors Influencing Customer Satisfaction towards E-shopping in Malaysia
Online shopping or e-shopping has changed the world of business and quite a few people have
decided to work with these features. What their primary concerns precisely and the responses from
the globalisation are the competency of incorporation while doing their businesses. E-shopping has
also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce
industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction
while operating in the e-retailing environment. It is very important that customers are satisfied with
the website, or else, they would not return. Therefore, a crucial fact to look into is that companies
must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s
point of view. With is in mind, this study aimed at investigating customer satisfaction
towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students
randomly selected from various public and private universities located within Klang valley area.
Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for
further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer
satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust,
design of the website, online security and e-service quality. Finally, recommendations and future
study direction is provided.
Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia