IntMA: Dynamic Interaction-aware resource allocation for containerized microservices in cloud environments

Joseph, Christina Terese; Chandrasekaran, K.

doi:10.1016/j.sysarc.2020.101785

Cited by 39 publications

(22 citation statements)

References 23 publications

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“…The Auto Scaling System can effectively guide the number of service cases according to the API Gateway stack. When its load exceeds the specified procedure, more instances are created with dynamism to balance the workload [16].…”

Section: Related Workmentioning

confidence: 99%

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Niazi¹,

Abbas²,

Soliman³

et al. 2023

Computers, Materials &Amp; Continua

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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.

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Section: Related Workmentioning

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

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“…So, it was found that the proposed elastic scheduling outperformed all the algorithms with respect to success ratio, auto scaling and better response time. [3] In this particular paper an INTMA algorithm which is Interaction Aware Resource Allocation algorithm was proposed for deploying the microservices. In order to deploy the microservices a new model was proposed which was called as Binary Quadratic Programming Problem.…”

Section: Auto Scaling Configurationmentioning

confidence: 99%

Dynamic Task Scheduling and Resource Allocation for Microservices in Cloud

Mugeraya¹,

Devadkar²

2022

J. Phys.: Conf. Ser.

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With the emergence of new companies and the expansion of the information technology sector, the need for Cloud Computing becomes apparent. Currently, the enterprises are rapidly transitioning from monolithic architecture to microservice-driven architecture. This research study has discovered that all task scheduling algorithms were designed for a specific (set) number of virtual machines, which resulted in the bottleneck problem, where multiple tasks were assigned to the microservice scheduler and the execution time of processing the tasks was significantly increased. Therefore, to address this issue, a novel model was designed based on the number of tasks and accordingly the number of virtual machines were dynamically generated to send the tasks to the microservice scheduler one by one, and the difficulties with execution time were also addressed. The study also discovered that due to the multiple workloads on the microservices, resource allocation becomes extremely difficult. To address this issue, containerized microservices were discovered. Here, the microservices would be distributed in containers. To implement the dynamic work scheduling technique, a cloud microservice translator would be developed, where a user may upload a text file and quickly get it dynamically translated. The main aim of this research work is to improve the task scheduling and resource allocation in microservices.

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“…They also stressed the need for more research work to optimize deployments at run time, especially through containers' initial deployment and the migration, rebalancing, or auto-scaling of clusters. Hoenisch et al [24] [13] Heuristic (LP-MKP) Virtual machine Network distance, resource fragmentation Multiple clouds [14] Heuristic (Genetic) Virtual machine Cost, Execution time Multiple clouds [15] Heuristic (Genetic) Virtual machine Monetary cost Multiple clouds [19] Heuristic (NSGA-II) Container Cost, Network latency, Reliability Multiple clouds [22] Heuristic (Genetic) Container Security Multiple clouds [24] Heuristic (NSGA-II) Container Cost, Deployment time Multiple clouds [25] Heuristic (LP) Container Cost, Deployment time Single cloud [26] Heuristic (Greedy) Container Energy Single cloud [27] Heuristic (RR+MST) Container Performance Kubernetes cluster [29] Profiling + Heuristic (LP) Virtual machine Tail addressed four-fold auto-scaling by formulating the scaling decision as a multi-object optimization problem. In this work, four dimensions of scaling were considered: VMs and containers can be adjusted horizontally (changes in the number of instances) and vertically (changes in the computational resources available to instances).…”

Section: Related Workmentioning

confidence: 99%

“…Piraghaj et al proposed a framework that consolidates containers on virtual machines to improve the energy efficiency of servers [26]. Joseph et al proposed a novel, robust heuristic approach called IntMA to deploy the microservices in an interaction-aware manner with the aid of the interaction information obtained from the interaction graph [27]. This work's remarkable thing is that the target environment was transited from multiple clouds to the Kubernetes cluster.…”

Section: Related Workmentioning

confidence: 99%

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

2020

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As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency. Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging. In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources. However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads. Besides, generalizing workloads' characteristics consisting of complex microservices is realistically challenging. Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical. As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling. Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way. To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements. Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results. Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don't.

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IntMA: Dynamic Interaction-aware resource allocation for containerized microservices in cloud environments

Cited by 39 publications

References 23 publications

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Dynamic Task Scheduling and Resource Allocation for Microservices in Cloud

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

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