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.

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.