Architecting Enterprise Applications for the Cloud: The Unicorn Universe Cloud Framework

© Springer International Publishing AG, part of Springer Nature 2018. Recent IT advances that include extensive use of mobile and IoT devices and wide adoption of cloud computing are creating a situation where existing architectures and software development frameworks no longer fully support the requirements of modern enterprise application. Furthermore, the separation of software development and operations is no longer practicable in this environment characterized by fast delivery and automated release and deployment of applications. This rapidly evolving situation requires new frameworks that support the DevOps approach and facilitate continuous delivery of cloud-based applications using micro-services and container-based technologies allowing rapid incremental deployment of application components. It is also becoming clear that the management of large-scale container-based environments has its own challenges. In this paper, we first discuss the challenges that developers of enterprise applications face today and then describe the Unicorn cloud framework (uuCloud) designed to support the development and deployment of cloud-based applications that incorporate mobile and IoT devices. We use a doctor surgery reservation application “Lekar” case study to illustrate how uuCloud is used to implement a large-scale cloud-based application

Exploring intelligent service migration in vehicular networks

Mobile edge clouds have great potential to address the challenges in vehicular networks by transferring storage and computing functions to the cloud. This brings many advantages of the cloud closer to the mobile user, by installing small cloud infrastructures at the network edge. However, it is still a challenge to efficiently utilize heterogeneous communication and edge computing architectures. In this paper, we investigate the impact of live service migration within a Vehicular Ad-hoc Network environment by making use of the results collected from a real experimental test-bed. A new proactive service migration model which considers both the mobility of the user and the service migration time for different services is introduced. Results collected from a real experimental test-bed of connected vehicles show that there is a need to explore proactive service migration based on the mobility of users. This can result in better resource usage and better Quality of Service for the mobile user. Additionally, a study on the performance of the transport protocol and its impact in the context of live service migration for highly mobile environments is presented with results in terms of latency, bandwidth, and burst and their potential effect on the time it takes to migrate services

Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach

Cloud Computing enables the construction and the provisioning of virtualized service-based applications in a simple and cost effective outsourcing to dynamic service environments. Cloud Federations envisage a distributed, heterogeneous environment consisting of various cloud infrastructures by aggregating different IaaS provider capabilities coming from both the commercial and the academic area. In this paper, we introduce a federated cloud management solution that operates the federation through utilizing cloud-brokers for various IaaS providers. In order to enable an enhanced provider selection and inter-cloud service executions, an integrated monitoring approach is proposed which is capable of measuring the availability and reliability of the provisioned services in different providers. To this end, a minimal metric monitoring service has been designed and used together with a service monitoring solution to measure cloud performance. The transparent and cost effective operation on commercial clouds and the capability to simultaneously monitor both private and public clouds were the major design goals of this integrated cloud monitoring approach. Finally, the evaluation of our proposed solution is presented on different private IaaS systems participating in federations. © 2013 Springer Science+Business Media Dordrecht

Using social and behavioural science to support COVID-19 pandemic response

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behavior with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic, and also highlight important gaps researchers should move quickly to fill in the coming weeks and months

Edge-Assisted CNN Inference Over Encrypted Data for Internet of Things

Supporting the inference tasks of convolutional neural network (CNN) on resource-constrained Internet of Things (IoT) devices in a timely manner has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, one prevalent solution is to offload the CNN inference tasks to the public cloud. However, this “offloading-to-cloud” solution may cause privacy breach since the offloaded data can contain sensitive information. For privacy protection, the research community has resorted to advanced cryptographic primitives to support CNN inference over encrypted data. Nevertheless, these attempts are limited by the real-time performance due to the heavy IoT computational overhead brought by cryptographic primitives. In this paper, we propose an edge-computing-assisted scheme to boost the efficiency of CNN inference tasks on IoT devices, which also protects the privacy of IoT data to be offloaded. In our scheme, the most time-consuming convolutional and fully-connected layers are offloaded to edge computing devices and the IoT device only performs efficient encryption and decryption on the fly. As a result, our scheme enables IoT devices to securely offload over 99% CNN operations, and edge devices to execute CNN inference over encrypted data as efficiently as on plaintext. Experiments on AlexNet show that our scheme can speed up CNN inference for more than 35× with a 95.56% energy saving for IoT devices

Understanding Users' Preferences for Privacy and Security Features – A Conjoint Analysis of Cloud Storage Services

Digital transformation has produced different applications and services for personal use. In an interconnected world, privacy and security concerns become main adoption barriers of new technologies. IT companies face an urgent need to address users’ concerns when delivering convenient designs. Applying conjoint analysis (CA) from consumer research, we explore users’ preferences and willingness-to-pay for privacy preserving features in personal cloud storage. Our contributions are two-fold: For research, we demonstrate the use of CA in understanding privacy tradeoffs for the design of personal ICTs. For practice, our findings can inform service designers about preferred privacy and security options for such services