Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation

A significant portion of the energy consumption in cloud data centres can be attributed to the inefficient utilization of available resources due to the lack of dynamic resource allocation techniques such as virtual machine migration and workload consolidation strategies to better optimize the utilization of resources. We present a new method for optimizing cloud data centre management by combining virtual machine migration with workload consolidation. Our proposed Energy Efficient Particle Swarm Optimization (EE-PSO) algorithm to improve resource utilization and reduce energy consumption. We carried out experimental evaluations with the Container CloudSim toolkit to demonstrate the effectiveness of the proposed EE-PSO algorithm in terms of energy consumption, quality of service guarantees, the number of newly created VMs, and container migrations

Container-based load balancing for energy efficiency in software-defined edge computing environment

The workload generated by the Internet of Things (IoT)-based infrastructure is often handled by the cloud data centers (DCs). However, in recent time, an exponential increase in the deployment of the IoT-based infrastructure has escalated the workload on the DCs. So, these DCs are not fully capable to meet the strict demand of IoT devices in regard to the lower latency as well as high data rate while provisioning IoT workloads. Therefore, to reinforce the latency-sensitive workloads, an intersection layer known as edge computing has successfully balanced the entire service provisioning landscape. In this IoT-edge-cloud ecosystem, large number of interactions and data transmissions among different layer can increase the load on underlying network infrastructure. So, software-defined edge computing has emerged as a viable solution to resolve these latency-sensitive workload issues. Additionally, energy consumption has been witnessed as a major challenge in resource-constrained edge systems. The existing solutions are not fully compatible in Software-defined Edge ecosystem for handling IoT workloads with an optimal trade-off between energy-efficiency and latency. Hence, this article proposes a lightweight and energy-efficient container-as-a-service (CaaS) approach based on the software-define edge computing to provision the workloads generated from the latency-sensitive IoT applications. A Stackelberg game is formulated for a two-period resource allocation between end-user/IoT devices and Edge devices considering the service level agreement. Furthermore, an energy-efficient ensemble for container allocation, consolidation and migration is also designed for load balancing in software-defined edge computing environment. The proposed approach is validated through a simulated environment with respect to CPU serve time, network serve time, overall delay, lastly energy consumption. The results obtained show the superiority of the proposed in comparison to the existing variants

CloudBench: an integrated evaluation of VM placement algorithms in clouds

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.This work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness under the Grant TIN2016-79637-P “Towards Unifcation of HPC and Big Data Paradigms” and by the Mexican Council of Science and Technology (CONACYT) through a Ph.D. Grant (No. 212677)