Load Balancing Using Dynamic Ant Colony System Based Fault Tolerance in Grid Computing

Load balancing is often disregarded when implementing fault tolerance capability in grid computing. Effective load balancing ensures that a fair amount of load is assigned to each resource, based on its fitness rather than assigning a majority of tasks to the most fitting resources. Proper load balancing in a fault tolerance system would also reduce the bottleneck at the most fit resources and increase utilization of other resources. This paper presents a fault tolerance algorithm based on ant colony system, that considers load balancing based on resource fitness with resubmission and checkpoint technique, to improve fault tolerance capability in grid computing. Experimental results indicated that the proposed fault tolerance algorithm has better execution time, throughput, makespan, latency, load balancing and success rate

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications

Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions

Edge and Fog computing paradigms utilise distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of the fast-evolving IoT applications. Due to the fine-grained modularity of the microservices along with their independently deployable and scalable nature, MSA exhibits great potential in harnessing both Fog and Cloud resources to meet diverse QoS requirements of the IoT application services, thus giving rise to novel paradigms like Osmotic computing. However, efficient and scalable scheduling algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. To this end, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications scheduling in Edge and Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the scheduling problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey