Power consumption of virtual machines with network transactions: Measurement and improvements

Abstract

Abstract-There have been significant studies on virtual machines (VMs), including their power consumption in performing different types of tasks. The VM's power consumption with network transactions, however, has seldom been examined. This paper presents an empirical study on the power consumption of typical virtualization packages while performing network tasks. We find that both Hardware Virtualization and Paravirtualization add considerable energy overhead, affecting both sending and receiving, and a busy virtualized web-server may consume 40% more energy than its non-virtualized counterparts. Our detailed profiling on packet path reveals that a VM can take 5 times more cycles to deliver a packet than a bare-metal machine, and is also much less efficient on caching. Without fundamental changes to the hypervisor-based VM architecture, we show that the use of adaptive packet buffering potentially reduces the overhead. Its practicality and effectiveness in power saving are validated through driver-level implementation and experiments.

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

“…Moreover, the parameters used in Table I led to about 40% PC increment within each virtualised server at maximum workload. This increment was real-time measured in [12], which represents the cost of over-utilising the server due to the existence of many VMs that share it's units. However, the proposed model is not constrained to only yielding this amount of percentage, but rather, is valid for any type of server through adjusting the model parameters.…”

“…In a virtualisation environment, a single VM requires π times more delay to process a packet compared to the traditional counterparts. This is due to increased accessing calls and interrupts among VM-HV and HV-server's unit, where π can reach up to 5 [12]. Modelling this concept requires introducing a factor called MCS index (mcs) to describe the linear relationship between the RBs and execution time in a bare BBU server (τ bare ), where τ bare = τ init + (mcs * RB n ), τ init denotes the initial BBU delay due to other BBU functions, rather than MCS.…”

“…The formulation of such problem is described as follows: ind holds the speed of light (sol) inside the optical fiber, and ind is the refractive index of the optical fiber, which is assumed to be identical for all RRHs-BBU pool links. Through the third constraint (12), the latency of the server due to one VM (τ n v ) can be controlled and the latter as well as the fixed value of initial HV delay π can control the delay τ bare . The latter, in turn, affects the number of processed RBs (RB n ) of each VM.…”

“…However, this might be possible when the server is running offline applications, the delay of which is relaxed. Because a virtualised server with 1 VM may take about 5 times more execution time delay to process a packet compared to its traditional counterparts [12], such delay in online services can not be avoided. This delay originates from each VM only being able to own a scarce amount of the host server's resources as it has to share these with other VMs.…”

“…The reasons of such consumption is due to higher computation levels, generated I/O instructions, and compound accessing for the device resources by the aggregated VMs' applications. Consequently, a busy virtualised server may consume about 40% more power than traditional counterparts [12]. This increament requires further investigations regarding modelling the PC of virtualised servers.…”