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The need of mechanisms to automate and regulate the interaction amongst the parties involved in the offered cloud services is exacerbated by the increasing number of providers and solutions that enable the cloud paradigm. This regulation needs to be defined through a contract, the so-called Service Level Agreement (SLA). We argue that the current solutions for SLA specification cannot cope with the distinctive characteristics of clouds. Therefore, in this paper we define a language, named SLAC, devised for specifying SLA for the cloud computing domain. The main differences with respect to the existing specification languages are: SLAC is domain specific, its semantics are formally defined in order to avoid ambiguity, it supports the main cloud deployment models, and it enables the specification of multi-party agreements. Moreover, SLAC supports the business aspects of the domain, such as pricing schemes, business actions and metrics. Furthermore, SLAC comes with an open-source software framework which enables the specification, evaluation and enforcement of SLAs for clouds. We illustrate potentialities and effectiveness of the SLAC language and its management framework by experimenting with an Open Nebula cloud system

Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.

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