TL;DR
A cyclic feedback scheduling optimization strategy is proposed and a reasonable termination condition for the cyclic strategy based on theoretical derivation is designed and Experimental results show that the proposed method can effectively shorten task scheduling time.
A random forest enhanced particle swarm optimization algorithm (RFPSO) is proposed, which implements intelligent initialization of resource allocation through a random forest model, which improves the efficiency of finding optimal solutions and ensures that critical tasks can prioritize access to higher-performance com...
Efficient energy scheduling in heterogeneous computing environments is a critical challenge, as task allocation decisions directly affect both energy consumption and execution performance. This work presents an energy aware scheduling framework based on a discretized grasshopper optimization algorithm (GOA), designed t...
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addres...
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Qualit...
This is the first application of DCOP to MPTCP scheduling, enabling fully distributed optimization of energy use and throughput without centralized coordination or offline training.
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
