FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud DOI
Manoj Kumar Patra, Bibhudatta Sahoo, Ashok Kumar Turuk

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2024, Номер 36(12)

Опубликована: Фев. 29, 2024

Abstract Containers in cloud computing provide a logical packaging technique for applications to be isolated from the environment which they actually execute, allowing efficient sharing of memory, processor, storage, and network resources at Operating System (OS) level. Since are so compact, container‐based clouds have recently gained significant popularity. In order maximize resource usage minimize energy consumption, container consolidation is widely employed environment. This work introduces that exploits Fractional Pelican Hawks Optimization (FPHO). Container as Service (CaaS) model, containers placed Virtual Machines (VMs), virtual machines hosted Physical (PMs) or servers. The proposed method consists two modules, namely, host status module module. module, PM's load predicted using Long Short Term Memory (LSTM) checked whether PM overloaded underloaded threshold. If it overloaded, selection algorithm performed, migration list also generated. created destination by an selector. same way, generated Finally, VM carried out considering multi‐objectives such load, cost, utilization, network, bandwidth optimally selected FHPO. Here, FHPO combination (FPO) Fire Hawk Optimizer (FHO). designed model achieved measures with minimum Level Agreement (SLA), Makespan 0.066, 0.019, 0.054, respectively setup one.

Язык: Английский

Experimental assessment of containers running on top of virtual machines DOI Creative Commons

Hossein Aqasizade,

Ehsan Ataie, Mostafa Bastam

и другие.

IET Networks, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 10, 2024

Abstract Over the past two decades, cloud computing paradigm has gradually attracted more popularity due to its efficient resource usage and simple service access model. Virtualisation technology is fundamental element of that brings several benefits users providers, such as workload isolation, energy efficiency, server consolidation, cost reduction. This paper examines combination operating system‐level virtualisation (containers) hardware‐level (virtual machines). To this end, performance containers running on top virtual machines experimentally compared with standalone based different hardware resources, including processor, main memory, disk, network in a real testbed by most commonly used benchmarks. Paravirtualisation full well type 1 2 hypervisors are covered study. In addition, three prevalent containerisation platforms examined.

Язык: Английский

Процитировано

1
FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud DOI
Manoj Kumar Patra, Bibhudatta Sahoo, Ashok Kumar Turuk

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2024, Номер 36(12)

Опубликована: Фев. 29, 2024

Abstract Containers in cloud computing provide a logical packaging technique for applications to be isolated from the environment which they actually execute, allowing efficient sharing of memory, processor, storage, and network resources at Operating System (OS) level. Since are so compact, container‐based clouds have recently gained significant popularity. In order maximize resource usage minimize energy consumption, container consolidation is widely employed environment. This work introduces that exploits Fractional Pelican Hawks Optimization (FPHO). Container as Service (CaaS) model, containers placed Virtual Machines (VMs), virtual machines hosted Physical (PMs) or servers. The proposed method consists two modules, namely, host status module module. module, PM's load predicted using Long Short Term Memory (LSTM) checked whether PM overloaded underloaded threshold. If it overloaded, selection algorithm performed, migration list also generated. created destination by an selector. same way, generated Finally, VM carried out considering multi‐objectives such load, cost, utilization, network, bandwidth optimally selected FHPO. Here, FHPO combination (FPO) Fire Hawk Optimizer (FHO). designed model achieved measures with minimum Level Agreement (SLA), Makespan 0.066, 0.019, 0.054, respectively setup one.

Язык: Английский

Процитировано

0