
Cloud governance, and in particular data governance in the cloud, relies on different technical and organizational practices and procedures, such as policy enforcement, risk management, incident management and remediation. The concept of accountability encompasses such practices, and is essential for enhancing security and trustworthiness in the cloud. Besides this, proper measurement of cloud services, both at a technical and governance level, is a distinctive aspect of the cloud computing model. Hence, a natural problem that arises is how to measure the impact on accountability of the procedures held in practice by organizations that participate in the cloud ecosystem. In this paper, we describe a metamodel for addressing the problem of measuring accountability properties for cloud computing, as discussed and defined by the Cloud Accountability Project (A4Cloud). The goal of this metamodel is to act as a language for describing: (i) accountability properties in terms of actions between entities, and (ii) metrics for measuring the fulfillment of such properties. It also allows the recursive decomposition of properties and metrics, from a high-level and abstract world to a tangible and measurable one. Finally, we illustrate our proposal of the metamodel by modelling the transparency property, and define some metrics for it.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
