The concept of cloud computing continues to spread widely, as it has been accepted recently. Cloud computing has many unique advantages which can be utilized to facilitate workflow execution. Instance-intensive cost-constrained cloud workflows are workflows with a large number of workflow instances (i.e. instance intensive) bounded by a certain budget for execution (i.e. cost constrained) on a cloud computing platform (i.e. cloud workflows). However, there are, so far, no dedicated scheduling algorithms for instance-intensive cost-constrained cloud workflows. This paper presents a novel compromised-time-cost scheduling algorithm which considers the characteristics of cloud computing to accommodate instance-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly. The simulation performed demonstrates that the algorithm can cut down the mean execution cost by over 15% whilst meeting the user-designated deadline or shorten the mean execution time by over 20% within the user-designated execution cost.

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.

Nowadays, smart environments (e.g., smart home, smart city) are built heavily relying on Cloud computing for the coordination and collaboration among smart objects. Cloud is typically centralized but smart objects are ubiquitously distributed, thus, data transmission latency (i.e., end-to-end delay or response time) between Cloud and smart objects is a critical issue especially to the applications that have strict delay requirements. To address the concern, a new Fog computing paradigm is recently proposed by the industry, while the detailed Fog platform is yet to be developed. The key idea is to bring the computing power from the remote Cloud closer to the users, which further enables real-time interaction and location-based services. In particular, the local processing capability of Fog computing significantly scales down the data volume towards the Cloud, and it in turn has great impacts on the entire Internet. In this paper, a data-centered Fog platform is developed to support smart living together with dataflow analysis. Case studies are also conducted to validate and evaluate the proposed platform.