This article discusses research challenges related to devising a new IoT programming paradigm for orchestrating IoT application composition and data processing across heterogeneous computing infrastructure (Cloud, Edge, and Things).

The significant growth in cloud computing has led to increasing number of cloud providers, each offering their service under different conditions - one might be more secure whilst another might be less expensive or more reliable. At the same time user applications have become more and more complex. Often, they consist of a diverse collection of software components, and need to handle variable workloads, which poses different requirements on the infrastructure. Therefore, many organisations are considering using a combination of different clouds to satisfy these needs. It raises, however, a non-trivial issue of how to select the best combination of clouds to meet the application requirements. This paper presents a novel algorithm to deploy workflow applications on federated clouds. First, we introduce an entropy-based method to quantify the most reliable workflow deployments. Second, we apply an extension of the Bell-LaPadula Multi-Level security model to address application security requirements. Finally, we optimise deployment in terms of its entropy and also its monetary cost, taking into account the cost of computing power, data storage and inter-cloud communication. We implemented our new approach and compared it against two existing scheduling algorithms: Extended Dynamic Constraint Algorithm (EDCA) and Extended Biobjective dynamic level scheduling (EBDLS). We show that our algorithm can find deployments that are of equivalent reliability but are less expensive and meet security requirements. We have validated our solution through a set of realistic scientific workflows, using well-known cloud simulation tools (WorkflowSim and DynamicCloudSim) and a realistic cloud based data analysis system (e-Science Central).

This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction.

Cloud computing has the potential to provide low-cost, scalable computing, but cloud security is a major area of concern. Many organizations are therefore considering using a combination of a secure internal cloud, along with (what they perceive to be) less secure public clouds. However, this raises the issue of how to partition applications across a set of clouds, while meeting security requirements. Currently, this is usually done on an ad-hoc basis, which is potentially error-prone, or for simplicity the whole application is deployed on a single cloud, so removing the possible performance and availability benefits of exploiting multiple clouds within a single application. This paper describes an alternative to ad-hoc approaches-a method that determines all ways in which applications structured as workflows can be partitioned over the set of available clouds such that security requirements are met. The approach is based on a Multi-Level Security model that extends Bell-LaPadula to encompass cloud computing. This includes introducing workflow transformations that are needed where data is communicated between clouds. In specific cases these transformations can result in security breaches, but the paper describes how these can be detected. Once a set of valid options has been generated, a cost model is used to rank them. The method has been implemented in a tool, which is described in the paper.