Investigations of an SLA Support System for Cloud Computing (SLACC)

Cloud Providers (CP) and Cloud Users (CU) need to agree on a set of parameters expressed through Service Level Agreements (SLA) for a given Cloud service. However, even with the existence of many CPs in the market, it is still impossible today to see CPs who guarantee, or at least offer, an SLA specification tailored to CU's interests: not just offering percentage of availability, but also guaranteeing, for example, specific performance parameters for a certain Cloud application. Due to (1) the huge size of CPs' IT infrastructures and (2) the high complexity with multiple inter-dependencies of resources (physical or virtual), the estimation of specific SLA parameters to compose Service Level Objectives (SLOs) with trustful Key Performance Indicators (KPIs) tends to be inaccurate. This paper investigates an SLA Support System for CC (SLACC) which aims to estimate in a formalized methodology - based on available Cloud Computing infrastructure parameters - what CPs will be able to offer/accept as SLOs or KPIs and, as a consequence, which increasing levels of SLA specificity for their customers can be reache

Competitive Sustainable Globalization General Considerations and Perspectives

Globalization has essentially empowered both newly industrialized and early industrialized countries but also caused considerable global challenges on economic, environmental, and social stability. Trends and risks of globalization and sustainability are specified by reports of global stakeholders as IMF, OECD, UN and its related organizations, WEF, WTO, and WWF. Competitive Sustainable Globalization (CSG) is introduced as a new paradigm and as a means to cope with the respective challenges. Competitive Sustainable Manufacturing (CSM) can be a fundamental enabler for CSG, proposing a global as well as a local approach for manufacturing. Potentials of value creation by manufacturing with reference to business models, education, and innovation are presented

KYoT: Self-sovereign IoT Identification with a Physically Unclonable Function

The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices’ SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards

LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers

In today's computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and the Edge computing paradigm, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. The literature has extensively explored the performance of IoT devices when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets that analyze the hardware components of each device are needed. This low-level device understanding enables new AI solutions for network, system and service management based on device performance, such as individual device identification, so it is an area worth exploring more in detail. In this paper, we present LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in different application scenarios. Finally, to demonstrate the inter-scenario capability of the created dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as examples and exploration of the published data

Synthèse CIRED 2015 Rapport sur le Congrès International des Réseaux Electriques de Distribution

International audienceUne synthèse globale de la conférence CIRED 2015 fait par des doctorants de plusieurs écoles