One of the Internet’s fastest-growing technologies is cloud computing, which needs each user to share various resources and virtual computers to access the services they demand. The difficulty is in allocating the user’s tasks among the many resources at hand while maintaining a balanced workload. In this work, we present the design of a new scheduling approach for cloud virtual machines in order to process events (related to resource overuse or underuse). This reduces the amount of effort consumed while maintaining the required performance levels in the cloud data center. To evaluate the effectiveness of the approach with real load paths, we conducted an experiment using the open-source cloud computing platform OpenNebula.

The objective of this article is to present the efficiency of image compression in the Wireless Multimedia Sensor Network (WMSN), the method used in this work is based on the lifting scheme coupled with the SPIHT coding to wavelet biorthogonal CDF 9/7. The effectiveness of this technique results in combining two advantages. First it allows saving energy to prolong the life of the network; second, it improves the Quality of Service (QoS) in terms of average throughput, the number of dropped packets, and End-to-End average delay. The authors examined two scenarios of the same network model in NS2 simulator, according to the above critical points of the energy economy and quality of service, the first scenario transmits an original image, and the second sends the compressed image with the used method. The simulation results presented show that the proposed system allows to extend the life of the network and minimizes the consumption of energy; and it can transmit the image in comfortable conditions for QoS of network, reduce the End-to-End average delay, no dropped packet, best average throughput and satisfied for bandwidth.

Image compression is crucial for multimedia applications with the aim of reduc ing storage and/or transmission costs, while preserving reliable visual quality. In this research, we propose a novel hybrid image compression technique based on staggering downsampling combined with discrete cosine transform (DCT). The proposed approach not only overlaps downsample images to reduce data re dundancy but also utilizes the energy compaction properties of DCT for efficient compression. The proposed method performance on benchmark grayscale im ages such as Lena, House, and other refernce images were evaluated by means of image quality assessment metrics, namely, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), visual information fidelity (VIF); and com pression efficiency metrics: bitrate and compression ratio. The results clearly show that the proposed algorithm outperforms JPEG and Set partitioning in hi erarchical trees (SPIHT) + discrete wavelet transform (DWT) method, with the following results: PSNR of 45.02, MSSIM of 0.9856, VIF of 0.8271, Bitrate of 0.12 bpp and a Compression Ratio of 64.00 (i.e. a reduction of 64 times). The suggested hybrid image compression method optimizes bug multimedia stor age and transmission by minimizing storage space and bandwidth usage while maintaining image quality. It, therefore, achieves a balance between percep tual quality and compression efficiency, making it the best option for resource constrained applications such as remote sensing, embedded systems, video com pression, and medical image archival.