Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many e-business or social network servers have been constructed on cloud. On such open environments, private data of massive users have to be protected by encrypting, such as using Advanced Encryption Standard AES, and furthermore, this process must be finished in a short time for users' better experience. This gives huge pressure on cloud servers, especially common servers, such as b servers.  urgently need an inexpensive and highly efficient method to relieve cloud servers' pressure. Fortunately, many cores of a graphics processing unit GPU can undertake this hard mission because of stronger computing por and lor price. The GPU environments can be virtualized on demand by cloud through the vCUDA technology. Of course, for those clouds not equipped with a GPU, a central processing unit CPU can still work as multithreads in parallel. Thus, in a cloud, AES can be parallelized using many cores of a GPU or multicores of a CPU with high efficiency and low cost. For typical cloud applications, such as b services, there are massive users and each one has short plaintext. If  simply parallelize AES in such an application,  cannot obtain better performance because of the GPU's extra data transferring cost. Thus,  coalesce the massive users' data and cut these data into same-length slices for improving the performance of parallel AES as much as possible. So  design six parallel AES algorithms using GPU parallelism or CPU parallelism, which differ in parallel scope and whether data are coalesced or cut to slices. Specifically, they are coalescent and sliced GPU GCS, coalescent and unsliced GPU, uncoalescent GPU, coalescent and sliced CPU, coalescent and unsliced CPU, and uncoalescent CPU. Moreover,  implement them on two representative platforms and evaluate their performance. Through comparing their performance, GCS has the best performance among these algorithms. In a cloud with Nvidia GPUs, GCS is a more porful algorithm for massive users' data encrypting, relatively. Copyright © 2015 John Wiley & Sons, Ltd.
['Xiongwei Fei', 'Kenli Li', 'Wangdong Yang', 'Keqin Li']
I am writing a research paper on a idea Practical parallel AES algorithms on cloud for massive users and their performance evaluation
[{"from": "human", "value": "I am writing a research paper on a idea Practical parallel AES algorithms on cloud for massive users and their performance evaluation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many e-business or social network servers have been constructed on cloud. On such open environments, private data of massive users have to be protected by encrypting, such as using Advanced Encryption Standard AES, and furthermore, this process must be finished in a short time for users' better experience. This gives huge pressure on cloud servers, especially common servers, such as b servers.  urgently need an inexpensive and highly efficient method to relieve cloud servers' pressure. Fortunately, many cores of a graphics processing unit GPU can undertake this hard mission because of stronger computing por and lor price. The GPU environments can be virtualized on demand by cloud through the vCUDA technology. Of course, for those clouds not equipped with a GPU, a central processing unit CPU can still work as multithreads in parallel. Thus, in a cloud, AES can be parallelized using many cores of a GPU or multicores of a CPU with high efficiency and low cost. For typical cloud applications, such as b services, there are massive users and each one has short plaintext. If  simply parallelize AES in such an application,  cannot obtain better performance because of the GPU's extra data transferring cost. Thus,  coalesce the massive users' data and cut these data into same-length slices for improving the performance of parallel AES as much as possible. So  design six parallel AES algorithms using GPU parallelism or CPU parallelism, which differ in parallel scope and whether data are coalesced or cut to slices. Specifically, they are coalescent and sliced GPU GCS, coalescent and unsliced GPU, uncoalescent GPU, coalescent and sliced CPU, coalescent and unsliced CPU, and uncoalescent CPU. Moreover,  implement them on two representative platforms and evaluate their performance. Through comparing their performance, GCS has the best performance among these algorithms. In a cloud with Nvidia GPUs, GCS is a more porful algorithm for massive users' data encrypting, relatively. Copyright \u00a9 2015 John Wiley & Sons, Ltd."}]