Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy

Experimental Comparison of Simulation Tools for Efficient Cloud and Mobile Cloud Computing Applications

Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities

BRACELET: Hierarchical Edge-Cloud Microservice Infrastructure for Scientific Instruments’ Lifetime Connectivity

Recent advances in cyber-infrastructure have enabled digital data sharing and ubiquitous network connectivity between scientific instruments and cloud-based storage infrastructure for uploading, storing, curating, and correlating of large amounts of materials and semiconductor fabrication data and metadata. However, there is still a significant number of scientific instruments running on old operating systems that are taken offline and cannot connect to the cloud infrastructure, due to security and performance concerns. In this paper, we propose BRACELET - an edge-cloud infrastructure that augments the existing cloud-based infrastructure with edge devices and helps to tackle the unique performance and security challenges that scientific instruments face when they are connected to the cloud through public network. With BRACELET, we put a networked edge device, called cloudlet, in between the scientific instruments and the cloud as the middle tier of a three-tier hierarchy. The cloudlet will shape and protect the data traffic from scientific instruments to the cloud, and will play a foundational role in keeping the instruments connected throughout its lifetime, and continuously providing the otherwise missing performance and security features for the instrument as its operating system ages.NSF Award Number 1659293NSF Award Number 1443013Ope

Sphere: Simulator of edge infrastructures for the optimization of performance and resources energy consumption

Edge computing constitutes a key paradigm to address the new requirements of areas such as smart cars, industry 4.0, and health care, where massive amounts of heterogeneous data from continuous geographically-distributed sources have to be processed and computed near real-time. To this end, new distributed infrastructures consisting on small computing clusters close to data sources, also known as Cloudlets have emerged. In order to evaluate the performance of these solutions we present Sphere, a simulation tool that enables researchers to establish various scenarios, including: (a) topology and orchestration model of the infrastructure; (b) incoming workload patterns; (c) resource-managing models; and (d) scheduling policies. Moreover, Sphere allows researchers to apply efficiency and performance policies both at infrastructure and cluster levels. The simulator presents the following benefits: (a) Evaluation of various orchestration models; (b) Analysis of resource-efficiency and performance strategies at Edge-infrastructure and cluster (Cloudlet/Cloud) level; (c) Execution of diverse workload generation patterns; (d) Evaluation of strategies for the infrastructure communication, as well as the impact on tasks completion time (makespan); and (e) Simulation of each cluster (Cloudlet/Cloud) independently, including resource-managing, scheduling and resource-efficiency models. Finally, we performed a deep evaluation based on realistic Edge-Computing use cases. The results of the experiments confirm that it is a performant and reliable tool for the analysis of orchestration, graph-resolving, energy-efficiency, resource-managing and scheduling strategies in Edge-computing environments.Ministerio de Ciencia, Innovación y Universidades RTI2018-098062-A-I0