Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Novel Cost Efficient Resource Allocation Technique Based on Deadline and Budget Constraints for Edge Users

Wireless Personal Communications - The edge computing paradigm has experienced quick development in recent years. This paradigm is featured by pushing the storage and computational resources closer...     

Low Voltage Low Power Single Ended Operational Transconductance Amplifier for Low Frequency Applications

Wireless Personal Communications - This paper presents the designing of a low voltage low power single ended operational transconductance amplifier (OTA) for low frequency application. The designed...     

Efficient JPEG Encoding Using Bernoulli Shift Map for Secure Communication

To ensure confidentiality and efficient network bandwidth, digital data must be compressed and encrypted. In most communication systems, these two factors are critical for information processing. Image compression and encryption may result in lower restoration quality and performance. Secure-JPEG is an effort to create a compression and encryption technique for digital data. This approach is based on the JPEG compression standard, which is the most extensively used lossy compression scheme. It enhances the usual JPEG compression algorithm to encrypt data during compression. The Secure-JPEG approach encrypts the data while it is compressed, and it may be easily modified to offer near lossless compression. Lossless compression, on the other hand, has a lower compression ratio and is only useful in certain situations. The paper addresses the issue of insufficient security as a result of the usage of a simple random number generator that is not cryptographically safe. The enhanced security characteristics are provided via the Generalized Bernoulli Shift Map, which has a chaotic system with proven security. Several cryptographic tests are used to validate the algorithm's security, and the chaotic system's behavior is also examined.