DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments

Multi-tenancy in resource-constrained environments is a key challenge in Edge computing. In this paper, we develop 'DYVERSE: DYnamic VERtical Scaling in Edge' environments, which is the first light-weight and dynamic vertical scaling mechanism for managing resources allocated to applications for facilitating multi-tenancy in Edge environments. To enable dynamic vertical scaling, one static and three dynamic priority management approaches that are workload-aware, community-aware and system-aware, respectively are proposed. This research advocates that dynamic vertical scaling and priority management approaches reduce Service Level Objective (SLO) violation rates. An online-game and a face detection workload in a Cloud-Edge test-bed are used to validate the research. The merits of DYVERSE is that there is only a sub-second overhead per Edge server when 32 Edge servers are deployed on a single Edge node. When compared to executing applications on the Edge servers without dynamic vertical scaling, static priorities and dynamic priorities reduce SLO violation rates of requests by up to 4% and 12% for the online game, respectively, and in both cases 6% for the face detection workload. Moreover, for both workloads, the system-aware dynamic vertical scaling method effectively reduces the latency of non-violated requests, when compared to other methods

Expert perspectives on GDPR compliance in the context of smart homes and vulnerable persons

This article introduces information gathered through 21 semi-structured interviews conducted with UK, EU and international professionals in the field of General Data Protection Regulation (GDPR) compliance and technology design, with a focus on the smart home context and vulnerable people using smart products. Those discussions gave various insights and perspectives into how the two communities (lawyers and technologists) view intricate practical data protection challenges in this specific setting. The variety of interviewees allowed to compare different approaches to data protection compliance topics. Answers to the following questions were provided: when organisations develop and/or deploy smart devices that use personal data, do they take into consideration the needs of vulnerable groups of people to comply with the GDPR? What are the underlying issues linked to the practical data protection law challenges faced by organisations working on smart devices used by vulnerable persons? How do experts perceive data protection law-related problems in this context?

A Unified Theory of Data

How does the proliferation of data in our modern economy affect our legal system? Scholars that have addressed the question have nearly universally agreed that the dramatic increases in the amount of data available to companies, as well as the new uses to which that data is being put, raise fundamental problems for our regulatory structures. But just what those problems might be remains an area of deep disagreement. Some argue that the problem with data is that current uses lead to discriminatory results that harm minority groups. Some argue that the problem with data is that it impinges on the privacy interests of citizens. Still others argue that the problem with data is that its remarkable efficacy as a tool will lead to disruptions in labor markets. This Article will argue that the disagreements about data and its harms in modern society are the result of overly compartmentalized analyses of the nature of data itself. Data, after all, is a strikingly broad concept, one that spans everything from where you ate breakfast today to the genetic markers in your DNA to the returns on your 401(k) last year. By focusing narrowly on specific segments of the data industry, both scholars and policymakers have crafted a set of conflicting rules and recommendations that fail to address the core problem of data it-self. This Article aims to correct this gap. First, it provides a taxonomy of the core features of the data economy today and the various behaviors, both positive and negative, that these features make possible. Second, the Article categorizes the types of arguments made about costs and benefits of wider data usage. Finally, the Article argues that the only way to reconcile the varied and overlapping approaches to da-ta in our current regulatory system is to create a more unified law of data. This unified law of data would set forth harmonized and consistent rules for the gathering, storage, and use of data, and it would establish rules to incentivize beneficial data practices and sanction harmful ones. Ultimately, the Article concludes, governing data will require a more comprehensive approach than the limited and piece-meal efforts that have ruled to dat