Time-Cost Scheduling Algorithm

Commerce processes are now commonly being defined in terms of workflows, encompassing complex performance models, inter-dependencies between tasks and restrictive Service Level Agreements (SLAs). Moreover, the Cloud Computing paradigm is gaining in popularity as it seeks to reduce the costs of hosting and managing expensive IT infrastructure in-house, while at the same time moving these costs from “infrastructure expenditure” to “operational costs”. The scheduling of business processes/workflows over an external (on-demand) large-scale, distributed Cloud/Grid computing infrastructure is a complex problem. The efficient scheduling of data-intensive applications in Cloud Computing environments is a tough challenge The research will focus on developing job scheduling and resource (compute, memory, bandwidth) allocation algorithms which take into consideration the data requirements of the applications, the inter-dependencies between sub-tasks, the location of replicate sites for data, the network topology and real-time QoS. These tasks may need to be co-hosted on a single platform or assigned to resources very close to each other on the network to minimize network load, costs and/or time for a transaction to complete.
This algorithm presents a novel dispensation-time-cost scheduling algorithm which considers the characteristics of cloud computing to accommodate order-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly. The simulation may be performed to demonstrate that the algorithm can cut down the mean execution cost by over considerable percentage (say 16%) while meeting the user-designated deadline or shorten the mean execution time by over reasonable percentage %  (about 19%) with in the user designated execution cost.