A Genetic Algorithm-Based Energy-Efficient Container Placement Strategy in CaaS

Abstract

Container placement (CP) is a nontrivial problem in Container as a Service (CaaS). Many works in the literature solve it by using linear server energy-consumption models. However, the solutions of using a linear model makes different CPs indistinguishable with regard to energy consumption in a homogeneous host environment that has a same amount of active hosts. As such, these solutions are energy inefficient. In this paper, we demonstrate that an energy-saving gain can be achieved by optimizing the placement of containers under a nonlinear energy consumption model. Specifically, we leverage a strategy based on genetic algorithm (GA) to search the optimal solution. Unfortunately, the conventional GA incurs performance degradation when the virtual machine (VM) resource utilization is high. In order to solve this problem, we propose an improved genetic algorithm called IGA for efficiently searching the optimal CP solution by introducing two different exchange mutation operations and constructing a function as the control parameter to selectively control the usage of the two operations. Extensive experiments are carried out under different settings, and their results show that our strategy is better than the existing CP strategies, i.e., spread and binpack, on energy efficiency target. In addition, the introduced IGA is experimentally proved to be more effective compared with the First Fit, Particle Swarm Optimization (PSO) algorithm and conventional GA. Moreover, the results validate that our proposed strategy can search new CP solutions with better fitness and alleviate the performance degradation caused by the conventional GA when the VM resource utilization is high.

Be Good Neighbors: A Novel Application Isolation Metric Used to Optimize the Initial Container Placement in CaaS

“…As demonstrated in our previous work [10], a linear power consumption models of the resource utilization makes different container placements undistinguishable with regard to energy consumption in a homogeneous PM environment that has the same numbers of active PMs. However, energy savings can be achieved by optimizing the placement of containers under a nonlinear energy consumption model.…”

“…The crossover and mutation operations of the GA play important roles in the population evolution phase. Note that the resource utilizations are very high after using the FF algorithm for container placement, and this may lead to an inefficient evolution for the original GA. Fortunately, our previous work [10] has showed that the mutation operation we proposed is well suited for solving the container placement problem when the resource utilizations are high. The mutation operation consists of two kinds of exchange operations and one control parameter to select the operation.…”

“…It prefers to perform a small local search in the early stage of the search to make the search near the initial solution more even, and perform more intense mutations to jump out of the local optimal in the mid-stage, and then perform a small local search in the later stage. More details can be seen in [10].…”

“…Our previous work [10] indicates that the container placement optimization plays an essential role in energy-saving problem. However, few studies in the literature take both the two objectives into consideration for initial container placement (ICP) in CaaS.…”

SDN Enhanced Resource Orchestration of Containerized Edge Applications for Industrial IoT

“…On one hand, this is a consequence of their lightweight architecture, which enables less resource utilization and low bootstrapping time. On the other hand, their technical compliance with various deployment platforms, such as Virtual Machines (VMs), Physical Machines (PMs), and cloud environments makes the adoption easier in both the academia and the industries [10]. Primarily, containers are designed to provide a standardized isolation platform for application deployment which allows developers to isolate their applications from the environment.…”