PEFT-based Trade-off Schedule Plan for Executing IoT Applications in Cloud Environment

Abstract

Internet of Things (IoT) consists of physical things or objects that are connected through a network of sensors, software, and electronics. Mainly, IoT devices are of restricted processing and storage capacities. On the other hand, infinite storage and processing capacities are available over the cloud. Thus by merging these two methods, the mutual advantages have been discussed in the past years. Due to heterogeneous resource requirements of IoT application’s task, it is not so easy to schedule these over the cloud. Existing heuristic-based scheduling strategies had mainly developed considering either minimization of makespan or minimization of cost. But, for the present scenario, an efficient schedule plan is required to schedule IoT applications over cloud that minimizes the execution time along with execution cost while preserving the task dependencies constraints as well as user-defined constraints like budget and deadline. In this paper, Cost–time computational intelligent heuristics based upon PEFT i.e. CTPEFT has been proposed to give a trade-off schedule plan between computational cost and time beneath the user-specified deadline and budget constraints. To validate the effectiveness of the proposed heuristic, extensive simulation experiments have been conducted while considering different synthetic IoT application tasks. CTPEFT has been compared with our previously proposed algorithm, i.e. BDHEFT as well as with other competing algorithms like BHEFT, and HCPPEFT. Simulation results shows that CTPEFT heuristic can generate better cost-makespan trade-off plan as compared to BDHEFT, BHEFT, and HCPPEFT.

KEYWORDS:

• BDHEFT
• Cloud
• HEFT
• IoT
• PEFT
• Workflow

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Amandeep Verma

Amandeep Verma is an assistant professor (IT) in UIET, Panjab University, Chandigarh, India. She has done her PhD in workflow scheduling in cloud computing. Her area of interest includes parallel and distributed computing, computer networks, and cloud computing.