DRACeo: A smart simulator to deploy energy saving methods in microservices based networks

Abstract

Nowadays, many researchers work to identify microservices-based application deployments and scheduling solutions to save energy without decreasing functional QoS. In this work, we present DRACeo: A simulator that allows facing this challenge in a simple and efficient way, enabling its users to focus uniquely on microservices deployment/scheduling algorithms and its hardware/software repercussions (load vs. energy consumption) without worrying about low-level network configurations or operating system issues. DRACeo is able to deploy and schedule (move, duplicate, start/stop) microservices and their dependencies on various devices with software and hardware heterogeneity (CPU, bandwidth, RAM, Battery, etc.), taking into account various scheduling heuristics algorithms: centralized vs noncentralized. To do this, DRACeo allows deploying custom network topologies based on client-server schemes or p2p distributions, where devices can (dis)appear, turn on/off obeying random circumstances or user strategies. Finally, the simulator performs relevant operations such as QoS definition, resource monitoring, calculation of energy saved and consumption tracking (at device and network level). We tested some ideas based on our previous work "Kaligreen" to demonstrate the effectiveness of DRACeo.

Energy Consumption in Microservices Architectures: A Systematic Literature Review

“…The study by Valera et al [52] focuses on identifying ideal host candidates for running microservices on a decentralized network, applying runtime scaling operations (migration or duplication) to reduce energy consumption. Another work by Valera et al [47] presents DRACeo, an intelligent simulator to implement energy-saving methods in networks based on microservices. The proposed approach allows data centre managers to make more informed decisions regarding energy usage, considering system elasticity and resource allocation.…”

Towards Greener Applications: Enabling Sustainable-aware Cloud Native Applications Design

“…From a design perspective, in [30] more specific guidelines are provided focusing only on data mining. Current research mainly focuses on dynamic resource allocation and scheduling according to energy efficiency optimization [4] [35]. All these techniques are not exploiting the differences between microservices and their interaction.…”

A decentralizedagent‐basedsemantic service control andself‐adaptationin smart health mobile applications

“…Valera et al [19] proposed DRACeo, a simulator for evaluating and managing any type of network and heterogeneous devices to run distributed mobile applications based on services or microservices. DRACeo provides reconfiguration facilities (add/remove/update) to enable developers understanding the efficiency of deployment techniques.…”

“…Several approaches and platforms [4-5; 8; 10-20] have been proposed for reconfigurable IoT systems in the field of smart health. The three main categories of approaches are semantic-based platforms [4-5;8;10-13], rules-based approaches [14], and metaheuristic-based approaches [15][16][17][18][19][20]. For example, Naqvi et al [4] have proposed a semantic-based context-driven approach with QoC (Quality of Context) for mobile Cloud computing.…”