An adaptive trust based service quality monitoring mechanism for cloud computing

Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing. The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection

Systematic and recomputable comparison of multi-cloud management platforms

With the growth and evolution of cloud applications, more and more architectures use hybrid cloud bindings to optimally use virtual resources regarding pricing policies and performance. This process has led to the creation of multi-cloud management platforms as well as abstraction libraries. At the moment, many (multi-)cloud management platforms (CMPs) are designed to cover the functional requirements. Along with growing adoption and industrial impact of such solutions, there is a need for a comparison and test environment which automatically assesses and compares existing platforms and helps in choosing the optimal one. This paper focuses on the creation of a suitable testbed concept and an actual extensible software prototype which makes multi-cloud experiments repeatable and reusable by other researchers. The work is evaluated by an exemplary comparison of 4 CMPs bound to AWS, showcasing standardised output formats and evaluation criteria

Optimal Assignment of Augmented Reality Tasks for Edge-Based Variable Infrastructures

In the last few years, the number of devices connected to the Internet has increased considerably; so has the data interchanged between these devices and the Cloud, as well as energy consumption and the risk of network congestion. The problem can be alleviated by reducing communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby devices to where data is produced or consumed. One of the main challenges of these paradigms is to cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing the energy consumption of the overall application. The heterogeneity is represented and managed by using Feature Models, widely employed in Software Product Lines. Given the application and infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task allocation and resources assignment. The resultant deployment represents the most energy efficient configuration at load-time, without compromising the user experience. The scalability and energy saving of the approach are evaluated in the domain of augmented reality applicationsHADAS TIN2015-64841-R (co-funded by FEDER funds), TASOVA MCIU-AEI TIN2017-90644-REDT, MEDEA RTI2018-099213-B-I00 (co-funded by FEDER funds) LEIA UMA18-FEDERJA-157 (co-funded by FEDER funds) Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Revisiting the high-performance reconfigurable computing for future datacenters

Modern datacenters are reinforcing the computational power and energy efficiency by assimilating field programmable gate arrays (FPGAs). The sustainability of this large-scale integration depends on enabling multi-tenant FPGAs. This requisite amplifies the importance of communication architecture and virtualization method with the required features in order to meet the high-end objective. Consequently, in the last decade, academia and industry proposed several virtualization techniques and hardware architectures for addressing resource management, scheduling, adoptability, segregation, scalability, performance-overhead, availability, programmability, time-to-market, security, and mainly, multitenancy. This paper provides an extensive survey covering three important aspects-discussion on non-standard terms used in existing literature, network-on-chip evaluation choices as a mean to explore the communication architecture, and virtualization methods under latest classification. The purpose is to emphasize the importance of choosing appropriate communication architecture, virtualization technique and standard language to evolve the multi-tenant FPGAs in datacenters. None of the previous surveys encapsulated these aspects in one writing. Open problems are indicated for scientific community as well

Cloud Services Brokerage for Mobile Ubiquitous Computing

Recently, companies are adopting Mobile Cloud Computing (MCC) to efficiently deliver enterprise services to users (or consumers) on their personalized devices. MCC is the facilitation of mobile devices (e.g., smartphones, tablets, notebooks, and smart watches) to access virtualized services such as software applications, servers, storage, and network services over the Internet. With the advancement and diversity of the mobile landscape, there has been a growing trend in consumer attitude where a single user owns multiple mobile devices. This paradigm of supporting a single user or consumer to access multiple services from n-devices is referred to as the Ubiquitous Cloud Computing (UCC) or the Personal Cloud Computing. In the UCC era, consumers expect to have application and data consistency across their multiple devices and in real time. However, this expectation can be hindered by the intermittent loss of connectivity in wireless networks, user mobility, and peak load demands. Hence, this dissertation presents an architectural framework called, Cloud Services Brokerage for Mobile Ubiquitous Cloud Computing (CSB-UCC), which ensures soft real-time and reliable services consumption on multiple devices of users. The CSB-UCC acts as an application middleware broker that connects the n-devices of users to the multi-cloud services. The designed system determines the multi-cloud services based on the user's subscriptions and the n-devices are determined through device registration on the broker. The preliminary evaluations of the designed system shows that the following are achieved: 1) high scalability through the adoption of a distributed architecture of the brokerage service, 2) providing soft real-time application synchronization for consistent user experience through an enhanced mobile-to-cloud proximity-based access technique, 3) reliable error recovery from system failure through transactional services re-assignment to active nodes, and 4) transparent audit trail through access-level and context-centric provenance

Data Science as an Interdiscipline: Historical Parallels from Information Science

Considerable debate exists today on almost every facet of what data science entails. Almost all commentators agree, however, that data science must be characterized as having an interdisciplinary or metadisciplinary nature. There is interest from many stakeholders in formalizing the emerging discipline of data science by defining boundaries and core concepts for the field. This paper presents a comparison between the data science of today and the development and evolution of information science over the past century. Data science and information science present a number of similarities: diverse participants and institutions, contested disciplinary boundaries, and diffuse core concepts. This comparison is used to discuss three questions about data science going forward: (1) What will be the focal points around which data science and its stakeholders coalesce? (2) Can data science stakeholders use the lack of disciplinary clarity as a strength? (3) Can data science feed into an “empowering profession”? The historical comparison to information science suggests that the boundaries of data science will be a source of contestation and debate for the foreseeable future. Stakeholders face many questions as data science evolves with the inevitable societal and technological changes of the next few decades

Distributed EaaS simulation using TEEs: A case study in the implementation and practical application of an embedded computer cluster

Internet of Things (IoT) devices with limited resources struggle to generate the high-quality entropy required for high-quality randomness. This results in weak cryptographic keys. As keys are a single point of failure in modern cryptography, IoT devices performing cryptographic operations may be susceptible to a variety of attacks. To address this issue, we develop an Entropy as a Service (EaaS) simulation. The purpose of EaaS is to provide IoT devices with high-quality entropy as a service so that they can use it to generate strong keys. Additionally, we utilise Trusted Execution Environments (TEEs) in the simulation. TEE is a secure processor component that provides data protection, integrity, and confidentiality for select applications running on the processor by isolating them from other system processes (including the OS). TEE thereby enhances system security. The EaaS simulation is performed on a computer cluster known as the Magi cluster. Magi cluster is a private computer cluster that has been designed, built, configured, and tested as part of this thesis to meet the requirements of Tampere University's Network and Information Security Group (NISEC). In this thesis, we explain how the Magi cluster is implemented and how it is utilised to conduct a distributed EaaS simulation utilising TEEs.Esineiden internetin (Internet of Things, IoT) laitteilla on tyypillisesti rajallisten resurssien vuoksi haasteita tuottaa tarpeeksi korkealaatuista entropiaa vahvan satunnaisuuden luomiseen. Tämä johtaa heikkoihin salausavaimiin. Koska salausavaimet ovat modernin kryptografian heikoin lenkki, IoT-laitteilla tehtävät kryptografiset operaatiot saattavat olla haavoittuvaisia useita erilaisia hyökkäyksiä vastaan. Ratkaistaksemme tämän ongelman kehitämme simulaation, joka tarjoaa IoT-laitteille vahvaa entropiaa palveluna (Entropy as a Service, EaaS). EaaS-simulaation ideana on jakaa korkealaatuista entropiaa palveluna IoT-laitteille, jotta ne pystyvät luomaan vahvoja salausavaimia. Hyödynnämme simulaatiossa lisäksi luotettuja suoritusympäristöjä (Trusted Execution Environment, TEE). TEE on prosessorilla oleva erillinen komponentti, joka tarjoaa eristetyn ja turvallisen ajoympäristön valituille ohjelmille. TEE:tä hyödyntämällä ajonaikaiselle ohjelmalle voidaan taata datan suojaus, luottamuksellisuus sekä eheys eristämällä se muista järjestelmällä ajetuista ohjelmista (mukaan lukien käyttöjärjestelmä). Näin ollen TEE parantaa järjestelmän tietoturvallisuutta. EaaS-simulaatio toteutetaan Magi-nimisellä tietokoneklusterilla. Magi on Tampereen Yliopiston Network and Information Security Group (NISEC) -tutkimusryhmän oma yksityinen klusteri, joka on suunniteltu, rakennettu, määritelty ja testattu osana tätä diplomityötä. Tässä diplomityössä käymme läpi, kuinka Magi-klusteri on toteutettu ja kuinka sillä toteutetaan hajautettu EaaS-simulaatio hyödyntäen TEE:itä

Cyber-storms come from clouds:Security of cloud computing in the IoT era

The Internet of Things (IoT) is rapidly changing our society to a world where every “thing” is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity and data collection relies more and more on the Cloud, where data analytics and intelligence actually reside. Cloud computing has indeed revolutionized the way computational resources and services can be used and accessed, implementing the concept of utility computing whose advantages are undeniable for every business. However, despite the benefits in terms of flexibility, economic savings, and support of new services, its widespread adoption is hindered by the security issues arising with its usage. From a security perspective, the technological revolution introduced by IoT and Cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. While the literature mostly focuses on the security of IoT and Cloud computing as separate entities, in this article we provide an up-to-date and well-structured survey of the security issues of cloud computing in the IoT era. We give a clear picture of where security issues occur and what their potential impact is. As a result, we claim that it is not enough to secure IoT devices, as cyber-storms come from Clouds