The Design and Implementation of Elastic Quotas: A System for Flexible File System Management

We introduce elastic quotas, a disk space management technique that makes disk space an elastic resource like CPU and memory. Elastic quotas allow all users to use unlimited amounts of available disk space while still providing system administrators the ability to control how the disk space is allocated among users. Elastic quotas maintain existing persistent file semantics while supporting user-controlled policies for removing files when the file system becomes too full. We have implemented an elastic quota system in Solaris and measured its performance. The system is simple to implement, requires no kernel modifications, and is compatible with existing disk space management methods. Our results show that elastic quotas are an effective, low-overhead solution for flexible file system management

SensorCloud: Towards the Interdisciplinary Development of a Trustworthy Platform for Globally Interconnected Sensors and Actuators

Although Cloud Computing promises to lower IT costs and increase users' productivity in everyday life, the unattractive aspect of this new technology is that the user no longer owns all the devices which process personal data. To lower scepticism, the project SensorCloud investigates techniques to understand and compensate these adoption barriers in a scenario consisting of cloud applications that utilize sensors and actuators placed in private places. This work provides an interdisciplinary overview of the social and technical core research challenges for the trustworthy integration of sensor and actuator devices with the Cloud Computing paradigm. Most importantly, these challenges include i) ease of development, ii) security and privacy, and iii) social dimensions of a cloud-based system which integrates into private life. When these challenges are tackled in the development of future cloud systems, the attractiveness of new use cases in a sensor-enabled world will considerably be increased for users who currently do not trust the Cloud.Comment: 14 pages, 3 figures, published as technical report of the Department of Computer Science of RWTH Aachen Universit

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoning in a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method

Applying Genetic Algorithms for Software Design and Project Planning

Today's software systems are growing in size and complexity. This means not only increased complexity in developing software systems, but also increase in the budget and completion time. This trend will lead to a situation where traditional manual software engineering practices are not sufficient to develop and evolve software systems in an economic and timely manner. Automated support can aid software engineers in reducing the time-to-market and improving the quality of the software. This thesis work explores the application of genetic algorithms for automated software architecture design and project planning.Software architecture design and project planning are non-trivial and challenging tasks. This thesis applies genetic algorithms to introduce automation into these tasks. The proposed genetic algorithm exploits reusable solutions, such as design patterns, architecture styles and application specific solutions for transforming a given initial rudimentary model into detailed design. The architectures are evaluated using multiple quality attributes, such as modifiability, efficiency and complexity. The fitness function encompasses the knowledge required for evaluating the architectures according to multiple quality attributes. The output from the genetic algorithm is an architecture proposal optimized with respect to multiple quality attributes.A genetic algorithm has also been devised for assigning work across teams located in distributed sites. The genetic algorithm takes information about the target system and the development organization as input and produces a set of work distribution and schedule plans optimized with respect to cost and duration objectives. The fitness function considers the differences in teams and barriers created by global dispersion into account in evaluating the work assignment. In addition, the genetic algorithm also takes solutions that ease or hamper distributed development into account in allocating the work. The genetic algorithm has been further extended with Pareto optimality to find a set of suitable work distribution proposals in a tradeoff between project cost and duration. In the experiments, an electronic home control system was developed by a set of different organizations structures. The results demonstrate that the proposed genetic algorithm can create reasonable work distribution proposals that conform to the general assumptions about the nature of cost and project completion time, i.e., cost of the project can be reduced at the expense of project completion time and vice-versa.In addition, variations have been made to the genetic algorithm approach to software architecture design. To accelerate the genetic algorithm towards multi-objective solutions, a quality farms approach has been developed. The approach uses the idea of cross breeding, where different individuals that are good with respect to one quality objective are combined for producing software architecture proposals that are good in multiple objectives. Also, to explore the suitability of other methods for software architecture synthesis, a constraint satisfaction approach has been developed. The approach models the software architecture design problem as a constraint satisfaction and optimization problem and solves it using constraint satisfaction techniques. This approach can provide rationale about why certain decisions are chosen in the proposed architecture proposals.Tool support for genetic algorithm-based architecture design and work planning approaches has been proposed. It facilitates an end user to give input, view and analyze the results of the developed genetic algorithm based approaches. The tool also provides support for semi-automated architecture design, where a human architect can guide the genetic algorithm towards optimal solutions. An empirical study has also been performed. It suggests that the quality of the proposals produced through semiautomated architecture design is roughly at the level of senior software engineering students. Furthermore, the project manager can interact with the tool and perform whatif analysis for choosing the suitable work distribution for the project at hand

The Effect of Knowledge Sharing on IS Outsourcing Success

IS outsourcing is one of the major issues facing organizations in today’s rapidly changing business environment. It is often presented as an attractive business proposition to improve productivity, reduce costs and increase competitiveness. This study examined the relationship between knowledge sharing and IS outsourcing success based on the social capital theory. It shows the knowledge sharing is critical factor for outsourcing partners to face potential changes and challenges over time to lead to IS outsourcing success. The research findings are depicted as following: Trust and shared vision are significant variables to knowledge sharing. The social interaction benefits in building trust and reaching shared vision. Knowledge sharing has a positive effect on IS outsourcing success and organizational learning capability moderates the relationship between knowledge sharing and IS outsourcing success especially for the lower learning capability company

Genetic Algorithms in Software Architecture Synthesis

Ohjelmistoarkkitehtuurien suunnittelu on kriittinen vaihe ohjelmistokehitystä, sillä arkkitehtuuri määrittelee ohjelmiston rungon: miten ohjelma jaetaan eri komponentteihin, ja miten komponentit ovat yhteydessä toisiinsa. Ohjelmisto voidaan yleensä toteuttaa toimivasti monella eri tavalla, mutta toimiva toteutus ei aina takaa, että ohjelmisto on myös toteutettu laadukkaasti. Laadun takeena onkin huolella ja taidolla suunniteltu arkkitehtuuri. Ohjelmistoarkkitehtuurin suunnittelu on haastavaa. Suunnitelmaa tehdessä tulee ottaa huomioon monen eri sidosryhmän (esim. käyttäjä, toteuttaja, markkinoija) vaatimukset ja miettiä, miten mahdollisimman suuri osa vaatimuksista voidaan toteuttaa arkkitehtuurissa. Arkkitehtuurisuunnittelu vaatiikin kokeneen ohjelmistoarkkitehdin, joka on hankkinut tietotaitonsa vuosien ajalta eri ohjelmistoprojekteista. Kokemukseen perustuvan tiedon lisäksi ohjelmistoarkkitehtuurisuunnittelun käytäntöjä on koottu eräänlaisiksi katalogeiksi, joissa esitellään hyväksi havaittuja ratkaisuja, ns. suunnittelutyylejä ja -malleja, yleisiin arkkitehtuurisuunnitteluongelmiin. Voidaankin ajatella, että arkkitehtuuri tuotetaan etsimällä (kokemukseen nojaten) paras mahdollinen kombinaatio suunnittelumalleja ja -tyylejä. Arkkitehtuurin suunnittelu onkin siis eräänlainen optimointiongelma. Ohjelmistoista tulee jatkuvasti yhä monimutkaisempia. Sovelluksien monimutkaistuessa myös arkkitehtuurisuunnittelu muuttuu entistä vaikeammaksi ja vie yhä enemmän aikaa. Suunnittelun perustuminen hiljaiseen tietoon ja arkkitehtien kokemukseen tekee prosessista yhä hitaamman ja läpinäkymättömämmän. Arkkitehtuurisuunnittelun automatisointi toisikin suuria säästöjä. Henkilöstövaihdosten yhteydessä ei myöskään tarvitsisi pelätä tietotaidon katoamista, kun arkkitehtuurisuunnittelu olisi helposti toistettavissa aina alusta lähtien. Tässä väitöskirjassa on tutkittu, miten parhaan mahdollisen ratkaisun etsintäprosessin (eli suunnittelumallien ja -tyylien soveltamisen) voisi automatisoida. Monimutkaisissa optimointiongelmissa käytetään etsintäalgoritmeja, jotka haravoivat hakuavaruutta jollain satunnaistetulla menetelmällä. Yksi suosituimmista etsintäalgoritmeista on geneettinen algoritmi. Geneettiset algoritmit tarkastelevat aina pientä ratkaisujoukkoa kerrallaan ja etsivät parasta ratkaisua yhdistelemällä osia löydetyistä ratkaisuista sekä muuntelemalla ratkaisuja. Jokaiselle ratkaisulle lasketaan laatuarvo, ja luonnonvalintaa jäljitellen jatketaan parhaiden vaihtoehtojen tarkastelua sekä kehittelyä ja hylätään huonoimmat ratkaisut. Etsintäalgoritmien käyttämistä ohjelmistokehityksen ongelmiin, esim. ohjelmistosuunnitteluun, testaukseen ja projektinhallintaan, kutsutaan etsintäperustaiseksi ohjelmistokehitykseksi. Väitöskirja kuuluu etsintäperustaisen ohjelmistosuunnittelun alaan, ja siinä tutkitaan ns. ohjelmistoarkkitehtuurisynteesiä geneettisten algoritmien avulla. Ohjelmistoarkkitehtuurisynteesi lähtee ns. nolla-arkkitehtuurista , joka toteuttaa järjestelmän toiminnalliset vaatimukset, mutta ei ota kantaa laatuvaatimuksiin. Laatua pyritään parantamaan lisäämällä lähtöarkkitehtuuriin suunnittelutyylejä ja -malleja. Väitöskirjassa laatuarviointiin on käytetty muunneltavuutta, tehokkuutta ja ymmärrettävyyttä. Lopputuloksena saadaan ehdotus arkkitehtuurista, joka toteuttaa toiminnalliset vaatimukset ja on myös laadukas. Geneettisiä algoritmeja ei ole aiemmin sovellettu vastaavantasoisiin suunnitteluongelmiin, joten toteutuksessa on kehitetty uusi tapa mallintaa arkkitehtuuri geneettiselle algoritmille sekä laskukaava arkkitehtuurin laadulle. Perustoteutuksen lisäksi myös geneettisen algoritmin eri ominaisuuksia, ns. risteytysoperaatiota ja laatufunktiota on tutkittu tarkemmin, ja niille on kehitetty vaihtoehtoisia toteutuksia. Tapaustarkasteluista saadut tulokset osoittavat, että tällä hetkellä geneettisiin algoritmeihin perustuvaa arkkitehtuurisynteesi tuottaa suunnilleen samantasoisia ratkaisuja kuin kolmannen vuosikurssin ohjelmistotekniikan opiskelija.This thesis presents an approach for synthesizing software architectures with genetic algorithms. Previously in the literature, genetic algorithms have been mostly used to improve existing architectures. The method presented here, however, focuses on upstream design. The chosen genetic construction of software architectures is based on a model which contains information on functional requirements only. Architecture styles and design patterns are used to transform the initial high-level model to a more detailed design. Quality attributes, here modifiability, efficiency and complexity, are encoded in the algorithm s fitness function for evaluating the produced solutions. The final solution is given as a UML class diagram. While the main contribution is introducing the method for architecture synthesis, basic tool support for the implementation is also presented. Two case studies are used for evaluation. One case study uses the sketch for an electronic home control system, which is a typical embedded system. The other case study is based on a robot war game simulator, which is a typical framework system. Evaluation is mostly based on fitness graphs and (subjective) evaluation of produced class diagrams. In addition to the basic approach, variations and extensions regarding crossover and fitness function have been made. While the standard algorithm uses a random crossover, asexual reproduction and complementary crossover are also studied. Asexual crossover corresponds to real-life design situations, where two architectures are rarely combined. Complementary crossover, in turn, attempts to purposefully combine good parts of two architectures. The fitness function is extended with the option to include modifiability scenarios, which enables more targeted design decisions as critical parts of the architecture can be evaluated individually. In order to achieve a wider range of solutions that answer to competing quality demands, a multi-objective approach using Pareto optimality is given as an alternative for the single weighted fitness function. The multi-objective approach evaluates modifiability and efficiency, and gives as output the class diagrams of the whole Pareto front of the last generation. Thus, extremes for both quality attributes as well as solutions in the middle ground can be compared. An experimental study is also conducted where independent experts evaluate produced solutions for the electronic home control. Results show that genetic software architecture synthesis is indeed feasible, and the quality of solutions at this stage is roughly at the level of third year software engineering students

A Systematic Process for Implementing Gateways for Test Tools

Test automation is facing a new challenge because tools, as well as having to provide conventional test functionalities, must be capable to interact with ever more heterogeneous complex systems under test (SUT). The number of existing software interfaces to access these systems is also a growing number. The problem cannot be analyzed only from a technical or engineering perspective; the economic perspective is as important. This paper presents a process to systematically implement gateways which support the communication between test tools and SUTs with a reduced cost. The proposed solution does not preclude any interface protocol at the SUT side. This process is supported using a generic architecture of a gateway defined on top of OSGi. Any test tool can communicate with the gateway through a unique defined interface. To communicate the gateway and the SUT, basically, the driver corresponding to the SUT software interface has to be loaded

Implementing BPMN 2.0 Scenarios for AAL@Home Solution

Aging tendency of European population and live longer and independently desire requires AAL solution for particular elders (chronic diseases, disabilities, aso). NITICS project aim is to develop advanced ITC solutions including monitoring and navigational support for indoor to support elderly in their daily activities. This paper offers a BPMN implementation for indoor assistance based on IoT (sensor monitoring) and Activity workflow implementation. Our solution offers an intelligent Care Center solution for caregivers monitoring and elders support