Abstract-Hybrid cloud is a type of the general cloud computing platform, that is composed of both public and private cloud. Scheduling plays a key role in the efficient use of hybrid cloud resources. In this paper, focus is on a scheduling algorithm for hybrid cloud that tries to optimize both execution time and cost. Execution time and cost are conflicting objectives, i.e. when one is made better, the other becomes worse off. Multiobjective evolutionary algorithm is used to find the optimal schedule. The widely used scheduling implementations seen in hybrid cloud try to optimize either execution time or cost, but not both simultaneously. The proposed algorithm is compared with the more widely used scheduling optimization techniques and seen to have much better performance.

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Cyber-crimes in the present world are rising in a manifold manner. So are the challenges faced by cyber forensic investigators to collect evidence and prove the crime. Current techniques rely heavily on live analysis, but is possible only when the machine is on. Use of any online cyber-crime footprints obtained from the non-volatile part of the digital equipment (from the chain of custody) will make the process of proving an online cyber-crime highly efficient. Here we propose a new log based scheme, which uses details from web browser and stores the information securely in slack spaces of the hard disk. These files, even in enciphered form, can be recovered for further investigations and proving the online criminal activity. Since the log files are stored in non-volatile memory, the retrieval and analysis of log files can be done without any loss of data, in linear time. Thus it overcomes the shortcomings of existing methodologies.