Scalable distributed evolutionary algorithm orchestration using Docker containers

Dziurzański, Piotr; Zhao, Shuai; Przewoźniczek, Michał Witold; Komarnicki, Marcin M.; Indrusiak, Leandro Soares

doi:10.1016/j.jocs.2019.101069

Cited by 17 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Containers are small virtual instances in terms of hardware and software resources [25]- [27]. They provide a virtual platform such as Docker, with which multiple users can drive and run their applications or images of operating systems directly on the physical machine [27], [26].…”

Section: Workload Placementmentioning

confidence: 99%

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

Bouaouda¹,

Afdel²,

Abounacer³

2023

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The increase of servers in data centers has become a significant problem in recent years that leads to a rise in energy consumption. The problem of high energy consumed by data centers is always related to the active hardware especially the servers that use virtualization to create a cloud workspace for the users. For this reason, workload placement such as virtual machines or containers in servers is an essential operation that requires the adoption of techniques that offer practical and best solutions for the workload placement that guarantees an optimization in the use of material resources and energy consumption in the cloud. In this article, we propose an approach that uses heuristics and meta-heuristics to predict cloud container placement and power consumption in data centers using a Genetic Algorithm (GA) and First Fit Decreasing (FFD). Our algorithms have been tested on CloudSim and the results showed that our methods gave better and more efficient solutions, especially the Genetic Algorithm after comparing them with Ant Colony Optimization (ACO) and Simulated Annealing (SA).

show abstract

Section: Workload Placementmentioning

confidence: 99%

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

Bouaouda¹,

Afdel²,

Abounacer³

2023

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Cloud-native architectures soon evolved to use computing nodes for which the startup time and the overall cost were more lightweight and used isolated, "containerized", operating system images; these were initially called by the same name as the company that proposed them, Docker, but are now an open standard supervised by the Open Computing Initiative. These container-based architectures are nowadays mainstream [34]; from the point of view of scientific computing, they enable replicability by not fully defining the infrastructure in which the experiment can run, thus creating "frozen" workflows that can be directly reused in new experiments on-premises, in paid infrastructure and even on your laptop. These methodologies and technologies eventually landed in the evolutionary computing field via the work published by Salza and Ferrucci [35].…”

Section: State Of the Artmentioning

confidence: 99%

“…This work uses a primary/subordinate parallel execution with an event-driven architecture using the Kubernetes orchestration technology. A similar approach is followed by Dziurzanski et al [34], also using Kubernetes and an autoscaler but implementing an island model using a multi-objective genetic algorithm. Two real-world smart factory optimization scenarios are used as test cases, and the system is deployed on a Kubernetes cluster.…”

Section: State Of the Artmentioning

confidence: 99%

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

et al. 2023

View full text Add to dashboard Cite

Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

show abstract

“…Finally, it allows managers to prepare the management of resources to enhance worker skills and provide a stable working atmosphere. A new way of connected manufacturing that revolves objects, processes and people was characterized by advancements in these three fields [11]. Big data is divided into structured, unstructured, and semi-structured data.…”

Section: Introductionmentioning

confidence: 99%

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Niazi¹,

Abbas²,

Soliman³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Kubernetes is an open-source container management tool which automates container deployment, container load balancing and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals beside the corporate sector. It provides cost-effective infrastructure, platform and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment.

show abstract

Scalable distributed evolutionary algorithm orchestration using Docker containers

Cited by 17 publications

References 34 publications

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

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

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Contact Info

Product

Resources

About