The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job

End-to-end slices to orchestrate resources and services in the cloud-to-edge continuum

Fog computing, combined with traditional cloud computing, offers an inherently distributed infrastructure – referred to as the cloud-to-edge continuum – that can be used for the execution of low-latency and location-aware IoT services. The management of such an infrastructure is complex: resources in multiple domains need to be accessed by several tenants, while an adequate level of isolation and performance has to be guaranteed. This paper proposes the dynamic allocation of end-to-end slices to perform the orchestration of resources and services in such a scenario. These end-to-end slices require a unified resource management approach that encompasses both data centre and network resources. Currently, fog orchestration is mainly focussed on the management of compute resources, likewise, the slicing domain is specifically centred solely on the creation of isolated network partitions. A unified resource orchestration strategy, able to integrate the selection, configuration and management of compute and network resources, as part of a single abstracted object, is missing. This work aims to minimise the silo-effect, and proposes end-to-end slices as the foundation for the comprehensive orchestration of compute resources, network resources, and services in the cloud-to-edge continuum, as well acting as the basis for a system implementation. The concept of the end-to-end slice is formally described via a graph-based model that allows for dynamic resource discovery, selection and mapping via different algorithms and optimisation goals; and a working system is presented as the way to build slices across multiple domains dynamically, based on that model. These are independently accessible objects that abstract resources of various providers – traded via a Marketplace – with compute slices, allocated using the bare-metal cloud approach, being interconnected to each other via the connectivity of network slices. Experiments, carried out on a real testbed, demonstrate three features of the end-to-end slices: resources can be selected, allocated and controlled in a softwarised fashion; tenants can instantiate distributed IoT services on those resources transparently; the performance of a service is absolutely not affected by the status of other slices that share the same resource infrastructure

Microservices and serverless functions – lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration / deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings

Homomorphic Routing: Private Data Forwarding in the Internet

We propose a new private routing and packet forwarding scheme for the Internet---Homomorphic Routing (HR)---that enables endpoints to communicate with one another without divulging source or destination addresses to the routers or service providers along the path. This is achieved via homomorphic encryption, whereby domains can match encrypted address ranges with encrypted destinations of packets without the need of decryption. Compared to approaches such as source or onion routing, HR is a hop-by-hop solution that allows current BGP-like decisions and traffic engineering techniques to remain largely unchanged, while per-flow state need not be maintained by routers. Preliminary performance evaluation shows that HR implies a tolerable computational overhead compared to plain text operations. Through aggregation we can compress inter-domain routing rules to around 5% of those required for current IPv6 and we can organize encrypted forwarding rules so that matching can be achieved in logarithmic time

Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems has been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, and heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.Comment: Journal of Cloud Computing Pages: 2

Orchestration in the Cloud-to-Things compute continuum: taxonomy, survey and future directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems have been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements (i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis

Analysis of end-to-end multi-domain management and orchestration frameworks for software defined infrastructures: an architectural survey

Over the last couple of years, industry operators' associations issued requirements towards an end-to-end management and orchestration plane for 5G networks. Consequently, standard organisations started their activities in this domain. This article provides an analysis and an architectural survey of these initiatives and of the main requirements, proposes descriptions for the key concepts of domain, resource and service slicing, end-to-end orchestration and a reference architecture for the end-to-end orchestration plane. Then, a set of currently available or under development domain orchestration frameworks are mapped to this reference architecture. These frameworks, meant to provide coordination and automated management of cloud and networking resources, network functions and services, fulfil multi-domain (i.e. multi-technology and multi-operator) orchestration requirements, thus enabling the realisation of an end-to-end orchestration plane. Finally, based on the analysis of existing single-domain and multi-domain orchestration components and requirements, this paper presents a functional architecture for the end-to-end management and orchestration plane, paving the way to its full realisation.This work was partially supported by the ICT14 5GExchange (5GEx) innovation project (grant agreement no.671636) co-funded by the European Union under the Horizon 2020 EU Framework Programme.Publicad

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries