Dynamic scheduling strategy with efficient node availability prediction for handling divisible loads in multi-cloud systems

TL;DR: Wang et al. as mentioned in this paper proposed a fault-tolerant cost-efficient workflow scheduling algorithm (FCWS) that minimizes application execution cost, time while ensuring their reliability, and they used Weibull distribution to analyze task execution reliability and hazard rate, which was used to duplicate task with high execution hazard rate.

TL;DR: This paper presents a comprehensive survey and an overview of the inter-cloud scheduling schemes aimed to allocate user-submitted tasks and workflows to the appropriate virtual machines on multiple clouds regarding various objectives and factors.

TL;DR: In this paper, a resource-aware dynamic task scheduling approach is proposed and implemented, which has revealed significant improvements in terms of attained ARUR, Throughput, and Makespan.

TL;DR: The proposed system reduces load balancing in Cloud Quantum Computation (CQC) and overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.

TL;DR: Three new heuristics, one for batch mode and two for immediate mode, are introduced as part of this research, revealing that the choice of which dynamic mapping heuristic to use in a given heterogeneous environment depends on parameters such as the structure of the heterogeneity among tasks and machines and the arrival rate of the tasks.

TL;DR: Experimental results demonstrate that the proposed prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in thecloud.

TL;DR: A measurement study of Netflix is performed to uncover its architecture and service strategy, and finds that Netflix employs a blend of data centers and Content Delivery Networks (CDNs) for content distribution.

TL;DR: In this article, the authors developed an efficient scheduling algorithm based on heuristic algorithms to schedule a set of TASKS on a multi-rocessor real-time system.