The prevalence of Internet of Things (IoT) in contemporary settings has induced systems composed of heterogeneous devices, computing infrastructures, and cloud services. New paradigms have emerged where computational resources are managed closer to IoT end-devices, within a general theme of decoupling from the cloud. This is because meeting application demands must occur at runtime, in the face of uncertainty and in a decentralized manner. Taking advantage of available resources closer to devices calls for novel resource allocation techniques that comply with latency, privacy and decentralization demands of IoT applications. To this end, we propose a novel decentralized resource management technique and accompanying technical framework for the deployment of latency-sensitive IoT applications on edge devices. Our technique is inspired from the functionality of an auction house and has two objectives; (i) find a deployment mapping for an arbitrary application, compliant with its individual resource requirements and latency constraints, (ii) facilitate privacy, as each device participates at their own will, based on its own availability and privacy preferences. Our approach ensures seamless deployment at runtime, assuming no designtime knowledge of device resources or network topology.
Novel pervasive systems integrate technologies and paradigms, such as mobile and cloud computing and Internet of Things, where systems are composed of heterogeneous infrastructures and services. Privacy emerges as a first-class design goal throughout such systems' development lifecycle, and suggests its management to occur architecturally at the network edge, closer to end-users as the privacy stakeholders.We discuss concerns emerging from privacy requirements and how they pertain to contemporary pervasive systems, and we distill architectural considerations highlighting privacy protection mechanisms and tactics for edge computing.& "AND ABOUT WHATEVER I may see or hear in treatment, in the life of human beings-things that should not ever be blurted out outside-I will remain silent, holding such things to be unutterable;" Article 8 of the Hippocratic Oath provides a strong metaphor for engineering privacyaware-by design and by default-systems. 1 Hippocrates talk about treatment of possibly sensitive medical information by a healthcare provider. Current more than ever, Hippocrates provides us the foundation of privacy-aware data management: a system may collect, use for some intended purpose, but not misuse or disclose private information. Such an ancient principle is particularly relevant in the increasingly integrated and pervasive computing environments of today, where mobile, cloud, and Internet of Things (IoT) converge inducing systems that collect, process, and disseminate information, which may be sensitive. Traditionally, organizations have been viewed as trusted custodians of information; however, data breaches, misuse, or malicious use of the sensitive information can harm privacy of the individuals.Especially relevant in today's integrated world, comprehensive privacy mechanisms are essential
Computational resources distributed at the edge of the network are the fundamental infrastructural component of edge computing. The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. Many application scenarios require edge computing resources to provide reliable response times while operating in dynamic and resource-constrained environments. In this paper, we present a novel architecture for energyaware, cluster-based edge computers that are designed to be portable and usable in fieldwork scenarios. We use compact general-purpose commodity hardware to build a high-density cluster prototype, and implement a power-management runtime to enable real-time energy-awareness. Furthermore, we present an experimental analysis of the energy and resource-consumption characteristics of our prototype in the context of a data analytics application. The results show the feasibility of our prototype for the presented scenarios, but also reveal the intricacies of power-management approaches already built into modern CPUs. We show that different load balancing policies and cluster configurations have a significant impact on energy consumption and system responsiveness. Our insights lay the groundwork for future research on energy-consumption optimization approaches for cluster-based edge computers.
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