The proposed adaptive workflow scheduling (AWS) approach involves initial static scheduling, resource monitoring and rescheduling with the aim to achieve the minimum execution time for workflow application.
They define utility based on response time and profit, realize dynamic and adaptive workflow optimization on top of Pegasus and DAGMan and present the feasibility of the approach on top of five workflow-based scenarios.
The immune system works as an integrated whole intelligent system and runs in adaptive workflow (see Figure 6).
The computational model is based on services, helpers, and core system modules; core modules are the controllers of the adaptive workflow and system processes.
Helpers are functional based architecture, for example, "self-checking, context sensing, system maturation, cleaning and tolerance support, scale and expansion unit, and effectors rates (growth, death,…)." Core system processes the adaptive workflow and manages and controls the integrate processes.
Building a cognitive intelligent agent framework, with adaptive workflow ability for executing system consequences as mentioned on the proposed computational model; and that framework should influenced by study of the nervous system, and immunology "neuroimmunology" from a cognitive perspectives, as an integrated model.
The application of intelligent agents for such flexible, adaptive and collaborative workflow monitoring is investigated through an intelligent monitoring system in securities trading.
In this study, we proposed the adaptive approach to schedule workflow tasks (dependent tasks) to the dynamic grid resources based on rescheduling method.
In recent years, major efforts have been made to develop sophisticated experimental and bioinformatic workflows for sequencing adaptive immune repertoires.
In [12], the authors apply autonomic computing concepts and utility functions on adaptive resource allocation for concurrent workflows.
