TY - JOUR
T1 - Live migration of video analytics applications in edge computing
AU - Rong, Chenghao
AU - Wang, Jessie Hui
AU - Wang, Jilong
AU - Zhou, Yipeng
AU - Zhang, Jun
N1 - Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2024/3
Y1 - 2024/3
N2 - In order to schedule resources efficiently or maintain applications'
continuity for mobile customers, edge platforms often need to adaptively
migrate the applications on them. However, our measurement shows that
existing migration solutions cannot solve the issue of migrating video
analytics applications in edge computing because the memory states of
video analytics applications have different characteristics from other
applications. We conduct a breakdown analysis of the memory states of
video analytics applications, and propose to treat three types of states
separately with three different techniques,
i.e.
, warm-up, sync, and replay, to minimize the negative influence of
migrations on application performance. Based on this idea, we implement a
prototype system in which two new components,
i.e.
,
state store
and
sidecar
, are designed to achieve near-transparent live migration with minimal
application code modifications. Evaluation experiments demonstrate that
the time of application interruption caused by migrating a video
analytics application with our solution is less than 405ms, and our
solution does not consume much resources.
AB - In order to schedule resources efficiently or maintain applications'
continuity for mobile customers, edge platforms often need to adaptively
migrate the applications on them. However, our measurement shows that
existing migration solutions cannot solve the issue of migrating video
analytics applications in edge computing because the memory states of
video analytics applications have different characteristics from other
applications. We conduct a breakdown analysis of the memory states of
video analytics applications, and propose to treat three types of states
separately with three different techniques,
i.e.
, warm-up, sync, and replay, to minimize the negative influence of
migrations on application performance. Based on this idea, we implement a
prototype system in which two new components,
i.e.
,
state store
and
sidecar
, are designed to achieve near-transparent live migration with minimal
application code modifications. Evaluation experiments demonstrate that
the time of application interruption caused by migrating a video
analytics application with our solution is less than 405ms, and our
solution does not consume much resources.
UR - http://www.scopus.com/inward/record.url?scp=85149365729&partnerID=8YFLogxK
U2 - 10.1109/TMC.2023.3246539
DO - 10.1109/TMC.2023.3246539
M3 - Article
AN - SCOPUS:85149365729
SN - 1536-1233
VL - 23
SP - 2078
EP - 2092
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
IS - 3
ER -