Scaling Virtualized Smartphone Images in the Cloud

Üks selle Bakalaureuse töö eesmärkidest oli Android-x86 nutitelefoni platvormi juurutamine pilvekeskkonda ja välja selgitamine, kas valitud instance on piisav virtualiseeritud nutitelefoni platvormi juurutamiseks ning kui palju koormust see talub. Töös kasutati Amazoni instance'i M1 Small, mis oli piisav, et juurutada Androidi virtualiseeritud platvormi, kuid jäi kesisemaks kui mobiiltelefon, millel teste läbi viidi. M1 Medium instance'i tüüp oli sobivam ja näitas paremaid tulemusi võrreldes telefoniga. Teostati koormusteste selleks vastava tööriistaga Tsung, et näha, kui palju üheaegseid kasutajaid instance talub. Testi läbiviimiseks paigaldasime Dalviku instance'ile Tomcat serveri. Pärast teste ühe eksemplariga, juurutasime külge Elastic Load Balancing ja automaatse skaleerimise Amazon Auto Scaling tööriista. Esimene neist jaotas koormust instance'ide vahel. Automaatse skaleerimise tööriista kasutasime, et rakendada horisontaalset skaleerimist meie Android-x86 instance'le. Kui CPU tõusis üle 60% kauemaks kui üks minut, siis tehti eelmisele identne instance ja koormust saadeti edaspidi sinna. Seda protseduuri vajadusel korrati maksimum kümne instance'ini. Meie teostusel olid tagasilöögid, sest Elastic Load Balancer aegus 60 sekundi pärast ning me ei saanud kõikide välja saadetud päringutele vastuseid. Serverisse saadetud faili kirjutamine ja kompileerimine olid kulukad tegevused ja seega ei lõppenud kõik 60 sekundi jooksul. Me ei saanud koos Load Balancer'iga läbiviidud testidest piisavalt andmeid, et teha järeldusi, kas virtualiseeritud nutitelefoni platvorm Android on hästi või halvasti skaleeruv.In this thesis we deployed a smartphone image in an Amazon EC2 instance and ran stress tests on them to know how much users can one instance bear and how scalable it is. We tested how much time would a method run in a physical Android device and in a cloud instance. We deployed CyanogenMod and Dalvik for a single instance. We used Tsung for stress testing. For those tests we also made a Tomcat server on Dalvik instance that would take the incoming file, the file would be compiled with java and its class file would be wrapped into dex, a Dalvik executable file, that is later executed with Dalvik. Three instances made a Tsung cluster that sent load to a Dalvik Virtual Machine instance. For scaling we used Amazon Auto Scaling tool and Elastic Load Balancer that divided incoming load between the instances

Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

The electricity industry is now at the verge of a new era. An era that promises, through the evolution of the existing electrical grids to Smart Grids, more efficient and effective power management, better reliability, reduced production costs and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the Smart Grid and the Smart Home, which we present as an integral part of the Smart Grid. Based on several scenarios we aim to present some of the most representative threats to the Smart Home / Smart Grid environment. The threats detected are categorized according to specific security goals set for the Smart Home/Smart Grid environment and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes the paper

A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE

L’Ambient Intelligence (AmI) è caratterizzata dall’uso di sistemi pervasivi per monitorare l’ambiente e modificarlo secondo le esigenze degli utenti e rispettando vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti come la scalabilità e la trasparenza per l’utente. Una tecnologia che consente di raggiungere questi obiettivi è rappresentata dalle reti di sensori wireless (WSN), caratterizzate da bassi costi e bassa intrusività. Tuttavia, sebbene in grado di effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacità di elaborazione necessarie a supportare un sistema intelligente; d’altra parte senza questa attività di pre-elaborazione la mole di dati sensoriali può facilmente sopraffare un sistema centralizzato con un’eccessiva quantità di dettagli superflui. Questo lavoro presenta un’architettura cognitiva in grado di percepire e controllare l’ambiente di cui fa parte, basata su un nuovo approccio per l’estrazione di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione. Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacità computazionali vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire ad un sistema centralizzato intelligente di effettuare ragionamenti di alto livello. L’architettura proposta è stata utilizzata per sviluppare un testbed dotato degli strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo esterno in maniera affidabile, per richiedere servizi ad agenti esterni, l’architettura è stata arricchita con un protocollo di gestione distribuita della reputazione. È stata inoltre sviluppata un’applicazione di esempio che sfrutta le caratteristiche del testbed, con l’obiettivo di controllare la temperatura in un ambiente lavorativo. Quest’applicazione rileva la presenza dell’utente attraverso un modulo per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla base delle preferenze dell’utente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive equipments for monitoring and modifying the environment according to users’ needs, and to globally defined constraints. Furthermore, such systems cannot ignore requirements about ubiquity, scalability, and transparency to the user. An enabling technology capable of accomplishing these goals is represented by Wireless Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However, although provided of in-network processing capabilities, WSNs do not exhibit processing features able to support comprehensive intelligent systems; on the other hand, without this pre-processing activities the wealth of sensory data may easily overwhelm a centralized AmI system, clogging it with superfluous details. This work proposes a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part, based on a new approach to knowledge extraction from raw data, that addresses this issue at different abstraction levels. WSNs are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts in order to carry on symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users’ desires, taking into account both implicit and explicit feedbacks from the users, while considering global system-driven goals, such as energy saving. The proposed conceptual architecture was exploited to develop a testbed providing the hardware and software tools for the development and management of AmI applications based on WSNs, whose main goal is energy saving for global sustainability. In order to make the AmI system able to communicate with the external world in a reliable way, when some services are required to external agents, the architecture was enriched with a distributed reputation management protocol. A sample application exploiting the testbed features was implemented for addressing temperature control in a work environment. Knowledge about the user’s presence is obtained through a multi-sensor data fusion module based on Bayesian networks, and this information is exploited by a multi-objective fuzzy controller that operates on actuators taking into account users’ preference and energy consumption constraints

Pervasive CSCW for smart spaces communities

Future pervasive environments will take into consideration not only individual users' interest, but also social relationships. In today's scenarios, the trend is to make use of collective intelligence, where the interpretation of context information can be harnessed as input for pervasive systems. Therefore, social CSCW applications represent new challenges and possibilities in terms of use of group context information for adaptability and personalization in pervasive computing. The objective of this paper is to present two enterprise scenarios that support collaboration and adaption capabilities through pervasive communities combined with social computing. Collaborative applications integrated with pervasive communities can increase the activity's quality of the end user in a wide variety of tasks

On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

Middleware architectures for the smart grid: A survey on the state-of-the-art, taxonomy and main open issues

The integration of small-scale renewable energy sources in the smart grid depends on several challenges that must be overcome. One of them is the presence of devices with very different characteristics present in the grid or how they can interact among them in terms of interoperability and data sharing. While this issue is usually solved by implementing a middleware layer among the available pieces of equipment in order to hide any hardware heterogeneity and offer the application layer a collection of homogenous resources to access lower levels, the variety and differences among them make the definition of what is needed in each particular case challenging. This paper offers a description of the most prominent middleware architectures for the smart grid and assesses the functionalities they have, considering the performance and features expected from them in the context of this application domain