Robust multi-dimensional trust computing mechanism for cloud computing

Cloud computing has become the most promising way of purchasing computing resources over the Internet.The main advantage of .cloud computing is its economic advantages over the traditional computing resource provisioning.For cloud computing to become acceptable to wider audience, it is necessary to maintain the quality of service (QoS) commitments specified in the service level agreement.In this paper, the authors propose a robust multi-level trust computing mechanism that can be used to track the performance of cloud systems using multiple QoS attributes.In addition, tests carried out show that the proposed mechanism is more robust than the ones published in the literature

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

A Fog Computing Approach for Cognitive, Reliable and Trusted Distributed Systems

In the Internet of Things era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes an impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. It worth noting that the long distance in this context refers to the physical path and time interval of when data is generated and when it get processed. To explain more, by the time the data reaches a far data centre, the importance of the data can be depreciated. Therefore, the network topologies have evolved to permit data processing and storage at the edge of the network, introducing what so-called fog Computing. The later will obviously lead to improvements in quality of service via processing and responding quickly and efficiently to varieties of data processing requests. Although fog computing is recognized as a promising computing paradigm, it suffers from challenging issues that involve: i) concrete adoption and management of fogs for decentralized data processing. ii) resources allocation in both cloud and fog layers. iii) having a sustainable performance since fog have a limited capacity in comparison with cloud. iv) having a secure and trusted networking environment for fogs to share resources and exchange data securely and efficiently. Hence, the thesis focus is on having a stable performance for fog nodes by enhancing resources management and allocation, along with safety procedures, to aid the IoT-services delivery and cloud computing in the ever growing industry of smart things. The main aspects related to the performance stability of fog computing involves the development of cognitive fog nodes that aim at provide fast and reliable services, efficient resources managements, and trusted networking, and hence ensure the best Quality of Experience, Quality of Service and Quality of Protection to end-users. Therefore the contribution of this thesis in brief is a novel Fog Resource manAgeMEnt Scheme (FRAMES) which has been proposed to crystallise fog distribution and resource management with an appropriate service's loads distribution and allocation based on the Fog-2-Fog coordination. Also, a novel COMputIng Trust manageMENT (COMITMENT) which is a software-based approach that is responsible for providing a secure and trusted environment for fog nodes to share their resources and exchange data packets. Both FRAMES and COMITMENT are encapsulated in the proposed Cognitive Fog (CF) computing which aims at making fog able to not only act on the data but also interpret the gathered data in a way that mimics the process of cognition in the human mind. Hence, FRAMES provide CF with elastic resource managements for load balancing and resolving congestion, while the COMITMENT employ trust and recommendations models to avoid malicious fog nodes in the Fog-2-Fog coordination environment. The proposed algorithms for FRAMES and COMITMENT have outperformed the competitive benchmark algorithms, namely Random Walks Offloading (RWO) and Nearest Fog Offloading (NFO) in the experiments to verify the validity and performance. The experiments were conducted on the performance (in terms of latency), load balancing among fog nodes and fogs trustworthiness along with detecting malicious events and attacks in the Fog-2-Fog environment. The performance of the proposed FRAMES's offloading algorithms has the lowest run-time (i.e., latency) against the benchmark algorithms (RWO and NFO) for processing equal-number of packets. Also, COMITMENT's algorithms were able to detect the collaboration requests whether they are secure, malicious or anonymous. The proposed work shows potential in achieving a sustainable fog networking paradigm and highlights significant benefits of fog computing in the computing ecosystem

IoT Application Provisioning Service

Constant development of software requires updating our Internet of Things (IoT) devices regularly. Some services such as transportation, health care, surveillance and electronic payments require high availability, even during a software update. IoT updates in urban scenarios require connectivity based on the Internet Protocol (IP) and long range connection with adequate speed. Normally, these requirements are provided by cellular network (i.e., using a SIM card) to connect to the Internet. This option presents several disadvantages: it is very expensive and it exposes IoT devices to security threats due to the permanent connection to the Internet. These challenges could be addressed by leveraging long-range broadcast communication (e.g., FM broadcast). IoT devices periodically listen for and receive updates through such a communication infrastructure, without actually being connected to the Internet. This thesis presents a system to provide software updates for IoT devices through long-range broadcast communication technologies. A prototype has been developed based on the concept of “seamless updates”. This allows performing software updates in the background, hence ensuring the availability of a device during the installation time of an update. This seamless update process was implemented on an embedded device (i.e., a Raspberry Pi 3) with a Linux-based operating system. Furthermore, a web-based backend has been implemented. Such a backend allows IoT developers to upload their updates targeting a specific class of devices and schedule when the update will be sent. The security goals of integrity and authentication are accomplished by signing the updates in the backend and verifying it at the IoT device. Moreover, a performance evaluation is performed for the system upgrade service with different parameters to sign the updates