ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

Abstract

Cloud computing has been one of the most popular distributed computing paradigms. Elasticity is a crucial feature that distinguishes cloud computing from other distributed computing models. It considers the resource provisioning and allocation processes can be implemented automatically and dynamically. Elasticity feature allows cloud platforms to handle different loads efficiently without disrupting the normal behavior of the application. Therefore, providing a resource elasticity analytical model can play a significant role in cloud resource management. This paper presents Controlling Elasticity (ControCity) framework for controlling resources elasticity through using ''buffer management'' and ''elasticity management''. In the proposed framework, there are two essential components called buffer manager and elasticity manager in the application layer and middleware layer, respectively. The buffer management controls the input queue of the user's request and the elasticity management controls the elasticity of the cloud platform using learning automata technique. In the application layer, applications are received by cloud applications and, then, placed in the control of the buffer. Buffer manager controls the queue of requests, and elasticity manager of the middleware layer using the learning automata provides a solution for controlling the elasticity of the cloud platform. The experimental results indicate that the ControCity reduces the response time by up to 3.7%, and increases the resource utilization and elasticity by up to 8.4% and 5.4%, respectively, compared with the other approaches.

Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

“…A possible approach exploits the ability of CPUs to vary their working frequency at run-time to match the workload (Guenter et al 2011). However, this prevents from taking advantage of the flexibility offered by cloud frameworks built on top of servers partitioned into virtual machines (VMs) and from the use of middleware layers to control access to resources (Ghobaei-Arani et al 2019). Hence, to pursue the vision of efficient cloud datacenters, the preferred solution aims at finding the best mapping of VMs over servers or other computing resources, referred to as physical machines (PMs) in the following, according to some performance criteria.…”

One-to-Many Negotiation QoE Management Mechanism for End-User Satisfaction

“…For this reason, user satisfaction management or service acceptability rate are not taken into account. Another paper [51] proposes a Controlling Elasticity (ControCity) framework to control the elasticity of the resources using two essential components called buffer management to control the input queue of user's request at the application layer and elasticity management to control the elasticity of the cloud platform with learning automata technique at middleware layer.…”

Analysing the Impact of Scaling Out SaaS Software on Response Time

“…Salah et al [16] presented an analytical model that can be used to guarantee proper elasticity for cloud-hosted applications and services, in order to satisfy particular performance requirements. Ghobaei-Arani et al [17] presented a framework called ControCity for controlling resources elasticity through using buffer management and elasticity management by leveraging the learning automata technique. e aforementioned works mainly considered how to adjust the resources to meet the performance improvement requirement of the scalable software.…”