Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Ismaeel et al. Journal of Cloud Computing: Advances, Systems and Applications (2018) 7:10 https://doi.org/10.1186/s13677-018-0111-x Journal of Cloud Computing: Advances, Systems and Applications RESEARCH Open Access Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres Salam Ismaeel , Raed Karim and Ali Miri* Abstract Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through VirtualMachine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process. Keywords: Cloud computing, Data centre management, Workload prediction, Energy efficiency, VM placement, Review Introduction Recent years has seen an exponential increase in the use of the cloud computing industry in satisfying Information Technology (IT) requirements. Data center power usage has been among one of the large commodity IT service expenditure for many organizations. The global data center electricity usage in 2012 was around 300 − 400 TWh, about 2% of global electricity usage and it is expected to triple by 2020 [16, 181], see Fig. 1 [136]. With up to 88% of this power going to powering and cooling IT equipment’s, any energy use reduction can result in major power and cost savings. For example, an estimate by Amazon shows the cost of energy for its data centers has reached 42% of total cost of its operation [139]. In addition, according to Environmental Protection Agency, each 1000kWh of power consumption emits 0.72 tons of CO2 [171]. Hence, the reduction of energy usage has become one of the key objectives in the design of any modern data centers. Today Data centres often consist of a large number of Physical Machines (PMs), which are grouped into multiple management clusters. Each of these clusters manages and controls a large number of PMs. A cluster can be homogeneous in that all of its managed PMs are identical, or it * Correspondence: Department of Computer Science, Ryerson University, 350 Victoria St, Toronto, ON M5B 2K3, Canada could be heterogeneous in that it manages PMs with different resource make and capacities [44]. Virtual Machines (VMs) are virtualized environments with predetermined virtual resources such as CPU, memory storage and bandwidth configured with an operating system and/or middle-ware and one or more application programs. VMs can execute workloads like any PM. Cloud service providers offer their computing resources to their clients based on Service Level Agreement (SLA). Services provided are typically in a form of VMs, which place on different PMs to carry out various tasks. The virtualization ability not only enables service providers to charge their clients based on their usage in a pay as-you-go scheme, but also it provides clients the ability to scale up or scale down resource utilization, as their needs vary. These advantageous partially stem from the fact that virtualization technology enables multiple virtual servers to run on the same PM, resulting in better resource utilization and reduction of aggregate power consumption [68, 89, 90, 102]. Data centre energy efficiency measures reduction of energy used by hardware or software equipment in data centres for a given service or level of activity. Hardware equipment includes both IT equipment (e.g. network and servers) and supporting equipment (e.g. power supply, cooling and data center building itself ), whereas © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Ismaeel et al. Journal of Cloud Computing: Advances, Systems and Applications (2018) 7:10 Fig. 1 Projection of data centres electricity use software equipment may include Cloud Management Systems (CMSs) used to manage the entire data centre or end-users’ applications [66, 125]. Given that a large part of power consumption of data centers is in their hardware equipment, this paper focuses on the problem of reducing energy consumption through efficient management of PMs and VMs in Cloud Data Centre (CDC) [18, 53]. We consider four different strategies: – VM Resizing is the process of changing the number of resources reserved for VMs through either adding or removing resource elements, or increasing or decreasing the capacity of each resource element in a VM. All these processes will be done without executing a reboot, an application restart, reconfiguration or recreation of a VM [28]. This will attempt to adjust PMs to their actual load and typically results in a reduction of power use [27, 73, 105, 163]. – Optimal initial placement seeks to optimally assign VM or group of VMs to servers - as part of an initial state such that the mapping minimizes the total inter-rack PMs used or traffic load in the network to reduce energy [58]. Deterministic Algorithms will discuss these algorithms, such as those in ( [112, 144, 168]). – Overbooking of physical resources refers to the strategy of overlaying requested virtual resources onto physical resources at a higher ratio than 1:1 [135]. This strategy can result in better utilization of PM idle resources, which might have been otherwise reserved. However, special care must be taken to reduce risks associated with unmet Quality of Service (QoS) demand over peak PM resource utilization [13, 116]. – VM Consolidation is the process of using minimum active PMs as possible through migrating VMs over time in an optimal fashion to reduce resource consumption [109, 118, 142]. There are two general types of VM consolidation: static and dynamic. In static consolidation, sizing and placement of VMs on PMs are pre-determined when a Page 2 of 28 job arrives and the placement does not change over a period of time. This type of VM consolidation therefore is often suitable for short running jobs for a couple of hours, where PMs resources for different types of VMs are predefined [157]. Energy reduction will be mostly based on simple heuristics or historical VMs demand patterns. Although this may result in an increase of the cost of application provider during low demand resource period, whereas during high utilization periods, the available resources may be insufficient [183]. Dynamic VM consolidation can result in utilization of fewer PMs by re-allocation (...truncated)