Dynamic Task Allocation and Scheduling for Energy Saving in Edge Nodes for IoT Applications

Abstract

Internet of Things with an Edge layer is a trending approach in areas such as healthcare, home, industry, and transportation. While scheduling the tasks of such applications, if the edge node utilizes its energy in computing latency-insensitive tasks then it might fail in executing the future latency-sensitive task due to low energy. Thus conserving the energy of the edge node is a key aspect to be considered while designing task allocation and scheduling policies. This can be done by exploiting the inactive state of the edge nodes which is due to less execution time taken than the predicted worst-case time. As this inactive node consumes energy, the best way is to utilize this energy by executing the other node’s task or by transiting to the zero energy state like shutdown. Managing the inactive interval in such a way also reduces the number of idle intervals in the schedule and the overall idle duration of the edge server which effectively reduces energy. In a homogeneous multi-edge (HME) system, techniques like Dynamic Procrastination (DP) combined with migration can help the edge node qualify for the shutdown. Other nodes can be slowed down to execute the tasks with later deadlines using the dynamic voltage/frequency scaling (DVFS) technique to further save energy. Migration combined with DP and DVFS effectively results in improved system utilization and reduced overall energy without affecting performance. This introduces challenges like dynamic allocation of tasks to edge nodes and meeting deadlines. In this work, we propose a dynamic task allocation and scheduling approach for an HME system that can decide on slowing down or shutting down the edge node. We observe that by decreasing the number of idle intervals and increasing the duration of the inactive state, our approach gives improved results for energy consumption over state-of-the-art energy reduction techniques.