Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Abstract

Currently, data centers energy consumption in the cloud is attracting a lot of interest. One of the most approaches to optimize energy and cost in data centers is virtualization. Recently, a new type of container-based virtualization has appeared, containers are considered very light and modular virtual machines, they offer great flexibility and the possibility of migration from one environment to another, which allows optimizing applications for the cloud. Another approach to saving energy is to consolidate the workload, which is the amount of processing that the computer has to perform at any given time. In this article, we will study the container placement algorithm that takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, we proposed a Hybrid approach for managing resources and workload based on ant colony optimization (ACO) and the first-fit decreasing (FFD) algorithm to avoid unnecessary power consumption. The results of the experiment indicate that using the first-fit decreasing algorithm (FFD) for container placement is better than ant colony optimization especially in a homogeneous systems. On the other hand the ant colony optimization shows very satisfying results in the case of workload management.

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

“…Table 5 presents the results obtained from the application of the Genetic Reinforcement Learning Algorithm (GRLA) in both homogeneous and heterogeneous data center environments from Table 4, alongside comparative metrics from traditional metaheuristic techniques: A Genetic Algorithm (GA) [38], Ant Colony Optimization (ACO) [39], and Simulated Annealing (SA) [40]. Additionally, we include results from the First Fit Decreasing (FFD) heuristic [38] for a comprehensive comparison.…”

Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers

“…In our case, we will evaluate this algorithm using container instances to compare it with our genetic algorithm (GA). More of this we will compare the approach proposed in [33] which applies the Ant Colony Optimization (ACO) for container placement with our genetic algorithm (GA).…”

“…More of that, they can adapt to problems with a high complexity and which require a huge calculation. Another point that encouraged us more to compare our Genetic Algorithm with Simulated Annealing and the ACO is that the works [19] and [33] have the same vision regarding the placement of virtual instances by optimizing the use of material resources and uses the same procedure, except that the work [19] are based on virtual machines instead of containers. For the FFD, we will evaluate it with First-Fit (FF) and Random-Fit (RF) which are algorithms of the Bin Packing problem such as the FFD.…”