Energy-Aware Migration of Virtual Machines Driven by Predictive Data Mining Models

Abstract

Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason it is extensively studied. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. Experimental results, performed on data of a real Cloud data center, show encouraging benefits in terms of energy saving.

Efficient and scalable execution of smart city parallel applications

“…15 The rapid and correct prediction of Internet data traffic has several applicative impacts in smart city scenarios. 34 A second application, recently described in Altomare et al, 36 is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices. 34 A second application, recently described in Altomare et al, 36 is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices.…”

“…() A first example concerns to the so‐called infotainment applications , such as community services (ie, point‐of‐interest notifications, electronic and financial services, media downloading, and parking zone management), which can require the transmission of a huge amount of data among users and road units for an efficient provisioning of such services . A second application, recently described in Altomare et al, is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices . Finally, usage pattern prediction of requests can be also used to influence the admission/denial of service demands made by priority and nonpriority users, in order to match their respective Quality of Service agreements.…”