An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Abstract

Summary Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on‐demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first‐class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy‐aware manner. In this paper, we propose an energy‐aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

“…We use 4 types of PMs (150 PMs for each type) to construct our private cloud platform. And the energy consumption rate settings are similar to our previous work in [16][17][18]. Number of datasets {500, 1000, 1500, 2000}…”

“…And the tasks in the private clouds are also deployed on the PMs in P. The energy consumption in the private cloud for the execution of the privacy-aware applications mainly refers to the energy consumed by the PM base power, active VMs, and the unused VMs, and the energy consumption due to data transferring. The PMs in the sleep mode also consume a certain amount of power, but it is far less than the energy consumed by the active PMs in the order of magnitude, that could be neglected [16,17].…”

“…= MAX , = 1 (8) while ≤ | V | do (9) if V , can hold then (10) Calculate the total cost TC by (10) (11) if TC < then (12) = TC (13) else = + 1 (14) end if (15) end if (16) end while (17) U p d a t e with cost C (18) end if (19) PM 2 separately, and the data transferring between these two VMs only employs the switch 4 .…”

“…, } (1) Algorithm 1 Special VM identification for data access (2) for = 1 to | | do (3) if needs to be placed in private cloud then (4) for = 1 to do (5) if can hold then (6) A d d to (7) end if (8) end for (9) S o r t in the decreasing order of VM distance between the VM in svi and the VM in (10) Calculate the energy consumption ec 1 after allocating to ,1 by (16) (11) = ec 1 , num = 2 (12) while num ≤ | | do (13) Calculate the energy consumption ec num after allocating to ,num by Eq. (16) (14) if ec num < then (15) = ec num (16) end if (17) n u m= num + 1 (18) end while (19) U p d a t e according to MC and the relevant PM (20) end if We use 4 datasets with different scale of datasets that need to be placed in the hybrid cloud. And 20% of them are privacyaware data, which should be placed in the private cloud.…”

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

“…Furthermore, the implementation of cloud computing is broad in multiple domains. Dou et al introduce an energy‐aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. This method consisted of 2 main VM migration phases to reduce service prices and execution time, which had been evaluated by experiments.…”