Efficient multimedia data storage in cloud environment

With the rapid adoption of social media, people are more habituated to utilize the images and video for expressing themselves. Future communication will replace the conventional means of social interaction with the video or images. This, in turn, requires huge data storage and processing power. This paper reports a compression/decompression module for image and video sequences for the cloud computing environment. The reported mechanism acts as a submodule of IaaS layer of the cloud. The compression of the images is achieved using redundancy removal using block matching algorithm. The proposed module had been evaluated with three different video compression algorithms and variable macroblock size. The experimentations has been carried out on a cloud host environment by using VMWarework station platform. Apart from being simple in execution, the proposed module does not incur an additional monetary burden, hardware or manpower to achieve the desired compression of the image data. Experimental analysis has shown a considerable reduction in data storage requirement as well as the processing time.Web of Science39444243

Security and service assurance issues in Cloud environment

Cloud security and service assurance is a wide research area with an unrestrained amount of apprehensions, ensuring equipment and stage innovations, to secure information and asset access. In spite of the colossal advantages of Cloud computing paradigm, the security and service concerns have consistently been the center of various Cloud clients and obstruction to its extensive acceptance. The paper reports a meticulous review in the field of Cloud computing with a focus on the security risk assessment and service assurance. This effort will serve as a ready reckoner to the research aspirants to encompass a general thought of the risk factors in security and the service assurance in a Cloud environment.Web of Science9120719

Performance Evaluation of ParalleX Execution model on Arm-based Platforms

The HPC community shows a keen interest in creating diversity in the CPU ecosystem. The advent of Arm-based processors provides an alternative to the existing HPC ecosystem, which is primarily dominated by x86 processors. In this paper, we port an Asynchronous Many-Task runtime system based on the ParalleX model, i.e., High Performance ParalleX (HPX), and evaluate it on the Arm ecosystem with a suite of benchmarks. We wrote these benchmarks with an emphasis on vectorization and distributed scaling. We present the performance results on a variety of Arm processors and compare it with their x86 brethren from Intel. We show that the results obtained are equally good or better than their x86 brethren. Finally, we also discuss a few drawbacks of the present Arm ecosystem