Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment

Recently developed IoT devices are capable of gathering, storing, and processing more data than ever before. This calls for the need for scalability. Through the use of edge computing, more processing functions can be relocated closer to where the data is gathered through the IoT devices. Here, processing tasks may be placed in the edge computing units (ECUs). Each ECU may host a cloudlet consisting of a number of virtual machines, where tasks could be executed. Due to the need to ensure near real-time response of the jobs, efficient job scheduling is required. Few recent works addressed this issue of job scheduling at the edges. However, many important constraints such as job dependency, job conflict along with heterogeneous edge infrastructure are not found to be considered. Accordingly, in this paper, we have proposed an optimal job scheduling approach based on the cuckoo search algorithm to handle these challenges subject to energy efficiency and resource utilization. The proposed algorithm is simulated and found to work notably well as compared to state-of-the-art edge-computing-based job scheduling techniques, especially when the jobs have conflict or dependency among them. The work is reported to achieve above 85% resource utilization even in the presence of job conflicts and dependencies.

Automated summary

IoT devices can gather, store, and process more data, requiring scalability. Edge computing allows processing functions to be moved closer to where data is gathered. Job scheduling at the edges is important for ensuring near real-time response, but existing techniques do not consider job dependency, conflict, and heterogeneous edge infrastructure. This paper proposes an optimal job scheduling approach based on the cuckoo search algorithm, achieving high resource utilization even with conflicts and dependencies.