Reducing cloud infrastructure costs through task management
Oleg N. Galchonkov, Mykola Babych, Andrey V. Plachinda, Anastasia R. Majorova · Applied Aspects of Information Technology · 2021
The transition of more and more companies from their own computing infrastructure to the clouds is due to a decrease in thecost of maintaining it, the broadest scalability, and the presence of a large number of tools for automating activities. Accordingly,cloud providers provide an increasing number of different computing resources and tools for working in the clouds. In turn, this givesrise to the problem of the rational choice of the types of cloud services in accordance with the peculiarities of the tasks to be solved.One of the most popular areas of effort for cloud consumers is to reduce rental costs. The main base of this direction is the use of spotresources. The article proposes a method for reducing the cost of renting computing resources in the cloud by dynamically managingthe placement of computational tasks, which takes into account the possible underutilization of planned resources, the forecast of theappearance of spot resources and their cost. For each task, a state vector is generated that takes into account the duration of the taskand the required deadline. Accordingly, for a suitable set of computing resources, an availability forecast vectors are formed at agiven time interval, counting from the current moment in time. The technique proposes to calculate at each discrete moment of timethe most rational option for placing the task on one of the resources and the delay in starting the task on it. The placement option andlaunch delays are determined by minimizing the rental cost function over the time interval using a genetic algorithm. One of the features of using spot resources is the auction mechanism for their provision by a cloud provider. This means that if there are more preferable rental prices from any consumer, then the provider can warn you about the disconnection of the resource and make this disconnection after the announced time. To minimize the consequences of such a shutdown, the technique involves preliminary preparation of tasks by dividing them into substages with the ability to quickly save the current results in memory and then restart from thepoint of stop. In addition, to increase the likelihood that the task will not be interrupted, a price forecast for the types of resourcesused is used and a slightly higher price is offered for the auction of the cloud provider, compared to the forecast. Using the exampleof using the Elastic Cloud Computing (EC2) environment of the cloud provider AWS, the effectiveness of the proposed method isshown.