Energy Optimization through a Multidimensional Distributed Scheduling Approach
Valera Humberto, Leonel Isaac Guerrero Giraldo, Carlos Alejandro Sivira Mu˜Noz, Alan Alfredo Rojas Marroquin, Lucas Aguilar Damas, Marc Dalmau, Philippe Roose, Yudith Cardinale · 2024
Distributed systems, spanning pervasive, cloud, and edge computing, require robust scheduling for tasks and data management, facing challenges like hardware diversity, QoS, localization, and energy efficiency. Current solutions often lack a comprehensive approach, failing to achieve balanced load management, QoS, and notably in minimizing application energy use, a key modern concern. We propose a distributed scheduler using multidimensional spaces and data structures aimed at comprehensive load balancing, energy efficiency, and QoS. The scheduler indexes devices within the structure based on diverse criteria such as hardware availability, GPS, and energy features. It then organizes applications into abstract graphs, where nodes are containers and their data items (units of data accessed by the containers), and edges are the transfer rates among them. To migrate containers, the scheduler performs range queries within the structure for optimal devices. To organize data items, it finds the barycenter of the devices executing the containers linked to them. We evaluated our scheduler’s effectiveness using the PISCO simulator, comparing it against other energy-efficient schedulers.