Emerging Edge Computing (EC) technology has shown promise for many delay-sensitive Deep Learning (DL) based applications of smart cities in terms of improved Quality-of-Service (QoS). EC requires judicious decisions which jointly consider the limited capacity of the edge servers and provided QoS of DL-dependent services. In a smart city environment, tasks may have varying priorities in terms of when and how to serve them; thus, priorities of the tasks have to be considered when making resource management decisions. In this paper, we focus on finding optimal offloading decisions in a three-tier user-edge-cloud architecture while considering different priority classes for the DL-based services and making a trade-off between a task’s completion time and the provided accuracy by the DL-based service. We cast the optimization problem as an Integer Linear Program (ILP) where the objective is to maximize a function called gain of system (GoS) defined based on provided QoS and priority of the tasks. We prove the problem is NP-hard. We then propose an efficient offloading algorithm, called PGUS, that is shown to achieve near-optimal results in terms of the provided GoS. Finally, we compare our proposed algorithm, PGUS, with heuristics and a state-of-the-art algorithm, called GUS, using both numerical analysis and real-world implementation. Our results show that PGUS outperforms GUS by a factor of 45% in average in terms of serving the top 25% higher priority classes of the tasks while still keeping the overall percentage of the dropped tasks minimal and the overall gain of system maximized.
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Efficient service handoff across edge servers via docker container migration
Supporting smooth movement of mobile clients is important when
offloading services on an edge computing platform. Interruption free
client mobility demands seamless migration of the offloading
service to nearby edge servers. However, fast migration of
offloading services across edge servers in a WAN environment
poses significant challenges to the handoff service design. In this
paper, we present a novel service handoff system which seamlessly
migrates offloading services to the nearest edge server, while the
mobile client is moving. Service handoff is achieved via container
migration. We identify an important performance problem during
Docker container migration. Based on our systematic study
of container layer management and image stacking, we propose a
migration method which leverages the layered storage system to
reduce file system synchronization overhead, without dependence
on the distributed file system. We implement a prototype system
and conduct experiments using real world product applications.
Evaluation results reveal that compared to state-of-the-art service
handoff systems designed for edge computing platforms, our system
reduces the total duration of service handoff time by 80% (56%)
with network bandwidth 5Mbps (20Mbps).
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- Award ID(s):
- 1741635
- NSF-PAR ID:
- 10077232
- Date Published:
- Journal Name:
- ACM/IEEE Symposium on Edge Computing
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3132211.3134460
