Efficient revocable ID‐based encryption with cloud revocation server

The capability to efficiently revoke compromised/misbehaving users is important in identity‐based encryption (IBE) applications, as it is not a matter of if but of when that one or more users are compromised. Existing solutions generally require a trusted third party to update the private keys of nonrevoked users periodically, which impact on scalability and result in high computation and communication overheads at the key generation center. Li et al proposed a revocable IBE scheme, which outsources most of the computation and communication overheads to a Key Update Cloud Service Provider (KU‐CSP). However, their scheme is lack of scalability since the KU‐CSP must maintain a secret value for each user. Tseng et al proposed another revocable IBE scheme with a cloud revocation authority, seeking to provide scalability and improve both performance and security level. In this paper, we present a new revocable IBE scheme with a cloud revocation server (CRS). The CRS holds only one secret time update key for all users, which provides the capability to scale our scheme. We demonstrate that our scheme is secure against adaptive‐ID and chosen ciphertext attacks under the k‐CAA assumption and outperforms both schemes mentioned above, in terms of having lower computation and communication overheads.     

An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Summary

With the advances of cloud computing, business and scientific‐oriented jobs with certain workflows are increasingly migrated to and run on a variety of cloud environments. These jobs are often with the property of deadline constraint and have to be completed within limited time. Therefore, to schedule a job with workflow (short for workflow) with deadline constraint is increasingly becoming a crucial research issue. In this paper, we, based on previous work, propose an agent‐based workflow scheduling mechanism to schedule workflows that are with deadline constraint into federated cloud environment.

Design and Methods

We add a workflow agent into the original framework to schedule the deadline‐constraint workflow. The workflow agent can smoothly schedule workflows to the cloud system according to their required resource and automatically monitor their execution. In order to accurately predict the execution time of each task to meet deadline constraint on certain VM with given resource, we inherit the use of rough set theory to estimate execution time of task in our previous work.

Result and Discussion

A heuristic algorithm that is embedded into the workflow agent is also proposed because the problem had been shown to be NP‐complete. The mechanism also adopts dynamic job dispatching method to reduce the usage of VM and to improve the resource utilization. We also conducted experiments to evaluate the efficiency and effectiveness.

Conclusion

The experimental results show that the prediction time is very close to the real execution time and can efficiently schedule multiple scientific workflows to meet the deadline constraints simultaneously.