A placement architecture for a container as a service (CaaS) in a cloud environment

Abstract

Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

“…The ACO algorithm has been proven effective as an optimization meta-heuristic for several NP-hard research problems, including the traveling salesman and job shop scheduling problems [32]. Furthermore, the ACO algorithm has been used in similar scheduling problems in the cloud, including scheduling virtual machines on cloud resources [33], [34] and scheduling tasks on virtual machines with the goal of load balancing the tasks on the virtual machines and reducing the response time of the tasks [35], [36]. Moreover, the ACO algorithm is used for scheduling IoT tasks on the cloud [37].…”

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

“…Hussein et al [14] design a fitness function to evaluate the resource waste of VMs and hosts. Based on this, two heuristic algorithms, namely Best Fit (BF) and Max Fit (MF), are given, and a meta-heuristic algorithm, namely Ant Colony Optimization based on Best Fit (ACO-BF) algorithm is proposed.…”

“…( ) = ( )⁄ (13) If its virtualization overhead is . Then, the minimum number of VMs needed to deploy all the containers in can be got as (14).…”

Intelligent cloud workflow management and scheduling method for big data applications

“…In [20], the author developed an open source tool called DRomics, which can be used as an R-package or a web-based service; it has no concentration dependence or high variability and can identify the best model for describing a concentration response curve. In [21,22], the author improved the resource utilization ratio in terms of the number of CPU cores and the memory size of virtual machines (VMs) and physical machines (PMs) and minimized the number of virtual machines and active PMs instantiated in the cloud environment. In [23], the author proposed a framework that supports mobile applications with a context-aware computing offloading function and proposed an estimation model to automatically select the cloud resources to be offloaded.…”