With the explosion of intelligent and latency-sensitive applications
such as AR/VR, remote health and autonomous driving, mobile
edge computing (MEC) has emerged as a promising solution to
mitigate the high end-to-end latency of mobile cloud computing
(MCC). However, the edge servers have significantly less computing
capability compared to the resourceful central cloud. Therefore, a
collaborative cloud-edge-local offloading scheme is necessary to
accommodate both computationally intensive and latency-sensitive
mobile applications. The coexistence of central cloud, edge servers
and the mobile device (MD), forming a multi-tiered heterogeneous
architecture, makes the optimal application deployment very challenging
especially for multi-component applications with component
dependencies. This paper addresses the problem of energy and
latency efficient application offloading in a collaborative cloud-edgelocal
environment. We formulate a multi-objective mixed integer
linear program (MILP) with the goal of minimizing the systemwide
energy consumption and application end-to-end latency. An
approximation algorithm based on LP relaxation and rounding is
proposed to address the time complexity. We demonstrate that our
approach outperforms existing strategies in terms of application
request acceptance ratio, latency and system energy consumption.
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Load Profiling and Migration for Effective Cyber Foraging in Disaster Scenarios with FORMICA
Cyber foraging techniques have been proposed in edge computing to support resource-intensive and latency-sensitive mobile applications. In a natural or man-made disaster scenario, all cyber foraging challenges are exacerbated by two problems: edge nodes are scarce and hence easily overloaded and failures are common due to the ad-hoc hostile conditions. In this paper, we study the use of efficient load profiling and migration strategies to mitigate such problems. In particular, we propose FORMICA, an architecture for cyber foraging orchestration, whose goal is to minimize the completion time of a set of jobs offloaded from mobile devices. Existing service offloading solutions are mainly concerned with outsourcing a job out of the mobile responsibility. Our architecture supports both mobile-based offloading and backend-driven onloading i.e., the offloading decision is taken by the edge infrastructure and not by the mobile node. FORMICA leverages Gelenbe networks to estimate the load profile of each node of the edge computing infrastructure to make proactive load profiling decisions. Our evaluation on a proof-of-concept implementation shows the benefits of our policy-based architecture in several (challenged disaster) scenarios but its applicability is broad to other IoT-based latency-sensitive applications.
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- Award ID(s):
- 1647084
- NSF-PAR ID:
- 10082119
- Date Published:
- Journal Name:
- 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)
- Page Range / eLocation ID:
- 80 to 87
- 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/10.1109/NETSOFT.2018.8459971
