Paper Info
Title
Facilitating Application-Aware Bandwidth Allocation in the Cloud with One-Step-Ahead Traffic Information
Abstract
Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AppBag</italic> , an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><italic>App</italic></underline> lication-aware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><italic>Ba</italic></underline> ndwidth <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><italic>g</italic></underline> uarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AppBag</italic> can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.
Year
DOI
Venue
2020
10.1109/TSC.2019.2922176
IEEE Transactions on Services Computing
Keywords
DocType
Volume
Bandwidth,Channel allocation,Computational modeling,Data centers,Cloud computing,Hoses,Resource management
Journal
13
Issue
ISSN
Citations 
2
1939-1374
3
PageRank 
References 
Authors
0.52
0
6
Name
Order
Citations
PageRank
Dian Shen131.20
Junzhou Luo21257153.97
Fang Dong320235.44
Jin Jiahui48816.84
Junxue Zhang5372.83
Jun Shen6208.82