MASHUP FOR INTERNSHIP PLACEMENT (MlP)

Mashup, a web application that integrates data from multiple data sources or APis to provide unique services. In this project Mashup for Internship Placement (MIP), involves solving multiple problems such as extracting data from multiple sources, cleaning it, and integrate it together. In the case study presented in this paper, Mashup has been applied as one of the pattern for knowledge sharing. The problem statement for this research paper is there is difficulties for student to decide which internship offer to accept because they lack of knowledge about the company and the job description. The objective of the research are to investigate Mashup pattern for knowledge sharing system and to develop a prototype system that incorporate features that allow user to view the location and information about the company which offer the internship placement. The methodology used to create Mashup system is Prototyping based methodology and Yahoo Pipes as the platform. Data from Google Maps is extract using Fetch Feed widget in Yahoo Pipes and integrate the feeds with Yahoo Pipes to show result on interactive map rather than traditional list. MIP contains information about company that offers internship placement with the location view on the map. This Mashup also can help student to be focus on the decision making for their internship placement as they may get all information gathered in one application

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules

An adaptive service oriented architecture:Automatically solving interoperability problems

Organizations desire to be able to easily cooperate with other companies and still be flexible. The IT infrastructure used by these companies should facilitate these wishes. Service-Oriented Architecture (SOA) and Autonomic Computing (AC) were introduced in order to realize such an infrastructure, however both have their shortcomings and do not fulfil these wishes. This dissertation addresses these shortcomings and presents an approach for incorporating (self-) adaptive behavior in (Web) services. A conceptual foundation of adaptation is provided and SOA is extended to incorporate adaptive behavior, called Adaptive Service Oriented Architecture (ASOA). To demonstrate our conceptual framework, we implement it to address a crucial aspect of distributed systems, namely interoperability. In particular, we study the situation of a service orchestrator adapting itself to evolving service providers.

Composition adaptative de services pour l’Internet des objets

L'internet des objets (IoT) est une technologie émergente, qui représente l’intégration ou la fusion de l'espace d'information et de l'espace physique. Au fil du temps, l’IoT est devenu de plus en plus populaire dans plusieurs endroits. Afin de répondre à la demande compliquée des utilisateurs, la plupart des appareils IoT ne fonctionnent pas seuls, une composition de services multiples doit être effectuée et elle est définie comme la composition de services. Pour des raisons de conductivités, pannes, batterie, charge et autres, la disponibilité des services IoT est imprévisible. Cette imprévisibilité de la disponibilité et l'évolution dynamique des besoins des utilisateurs, font que la composition du service doit gérer cette dynamique et s'adapter à de nouvelles configurations non prévues à la conception. La composition adaptative des services consiste à modifier le système pour lui permettre de se comporter correctement dans différents contextes afin d'assurer la disponibilité des services offerts, afin de répondre à une situation non prévue lors de la phase de conception. De ce fait, notre objectif est de proposer une méthode de composition de services IoT adaptative et sensible au contexte afin de satisfaire les besoins des utilisateurs. Dans notre travail, nous considérons que la croissance de l'Internet des Objets (IoT) implique la disponibilité d'un très grand nombre de services qui peuvent être similaires ou identiques, la gestion de la Qualité de Service (QoS) permet de différencier un service d'un autre. La composition de services offre la possibilité d'effectuer des activités complexes en combinant les fonctionnalités de plusieurs services au sein d'un seul processus. Très peu de travaux ont présenté une solution de composition de services adaptative gérant les attributs de QoS, en plus dans le domaine de la santé, qui est l'un des plus difficiles et délicats car il concerne la précieuse vie humaine. Dans cette thèse, nous présenterons une approche de composition de services adaptative sensible aux QoS basée sur un algorithme génétique multipopulation dans un environnement Fog-IoT. Notre algorithme P-MPGA implémente une méthode de sélection intelligente qui nous permet de sélectionner le bon service. En outre, PMPGA implémente un système de surveillance qui surveille les services pour gérer le changement dynamique des environnements IoT. Les résultats expérimentaux montrent les excellents résultats du P-MPGA en termes de temps d'exécution, de valeurs de fitness moyennes et de rapport temps d'exécution / meilleure valeur de fitness malgré l'augmentation de la population. P-MPGA peut rapidement obtenir un service composite satisfaisant les besoins de QoS de l'utilisateur, ce qui le rend adapté à un environnement IoT à grande échelle

An adaptive service oriented architecture: Automatically solving interoperability problems.

Organizations desire to be able to easily cooperate with other companies and still be flexible. The IT infrastructure used by these companies should facilitate these wishes. Service-Oriented Architecture (SOA) and Autonomic Computing (AC) were introduced in order to realize such an infrastructure, however both have their shortcomings and do not fulfil these wishes. This dissertation addresses these shortcomings and presents an approach for incorporating (self-) adaptive behavior in (Web) services. A conceptual foundation of adaptation is provided and SOA is extended to incorporate adaptive behavior, called Adaptive Service Oriented Architecture (ASOA). To demonstrate our conceptual framework, we implement it to address a crucial aspect of distributed systems, namely interoperability. In particular, we study the situation of a service orchestrator adapting itself to evolving service providers.

Optically amplified free-space optical communication systems

This thesis investigates terrestrial atmospheric FSO communication systems operating under the influence of turbulence-induced scintillation, beam spreading, optical interchannel crosstalk, amplified spontaneous emission noise and pointing errors. On-off keying-non–return-to-zero (OOK-NRZ) and digital pulse position (DPPM) are the modulation schemes used for the calculations. The possibility of using sophisticated performance evaluation techniques such as moment generating function (MGF)-based Chernoff bound (CB), modified Chernoff bound (MCB) and saddlepoint approximation (SPA) for terrestrial DPPM and OOK-NRZ–based FSO communication systems employing optical amplification are investigated and compared with the conventional Gaussian approximation (GA) method. Relative to the other techniques, the MCB can be considered a safe estimation method for practical systems since it provides an upper bound upon the BER. The turbulent optically preamplified DPPM FSO receiver employing integration over a time slot and comparing the results to choose the largest slot, is seen to give better advantage (about 7 - 9 dB) compared to an equivalent employing OOK-NRZ signalling. The atmospheric turbulence-induced spreading of the beam, ASE noise, and pointing error are seen to combine in a problematic way resulting in high BERs, depending on the size of the receiver and the beam’s jitter standard deviation. Using FSO communication for the distribution links of a passive optical network-like wavelength division multiplexing access network is investigated in the presence of atmospheric turbulence, ASE noise and interchannel crosstalk. The results show that, for clear atmosphere, FSO distribution link length up to 2000 m can be reliably used (depending on turbulence strength) to achieve human eye safety and high capacity access networks. Also, error floors occur due to turbulence accentuated crosstalk effect for the cases of (i) signal turbulent, but crosstalk not and (ii) crosstalk turbulent, but signal not

MASHUP FOR INTERNSHIP PLACEMENT (MlP)

Mashup, a web application that integrates data from multiple data sources or APis to provide unique services. In this project Mashup for Internship Placement (MIP), involves solving multiple problems such as extracting data from multiple sources, cleaning it, and integrate it together. In the case study presented in this paper, Mashup has been applied as one of the pattern for knowledge sharing. The problem statement for this research paper is there is difficulties for student to decide which internship offer to accept because they lack of knowledge about the company and the job description. The objective of the research are to investigate Mashup pattern for knowledge sharing system and to develop a prototype system that incorporate features that allow user to view the location and information about the company which offer the internship placement. The methodology used to create Mashup system is Prototyping based methodology and Yahoo Pipes as the platform. Data from Google Maps is extract using Fetch Feed widget in Yahoo Pipes and integrate the feeds with Yahoo Pipes to show result on interactive map rather than traditional list. MIP contains information about company that offers internship placement with the location view on the map. This Mashup also can help student to be focus on the decision making for their internship placement as they may get all information gathered in one application