184 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
POEM: Pricing Longer for Edge Computing in the Device Cloud
Multiple access mobile edge computing has been proposed as a promising
technology to bring computation services close to end users, by making good use
of edge cloud servers. In mobile device clouds (MDC), idle end devices may act
as edge servers to offer computation services for busy end devices. Most
existing auction based incentive mechanisms in MDC focus on only one round
auction without considering the time correlation. Moreover, although existing
single round auctions can also be used for multiple times, users should trade
with higher bids to get more resources in the cascading rounds of auctions,
then their budgets will run out too early to participate in the next auction,
leading to auction failures and the whole benefit may suffer. In this paper, we
formulate the computation offloading problem as a social welfare optimization
problem with given budgets of mobile devices, and consider pricing longer of
mobile devices. This problem is a multiple-choice multi-dimensional 0-1
knapsack problem, which is a NP-hard problem. We propose an auction framework
named MAFL for long-term benefits that runs a single round resource auction in
each round. Extensive simulation results show that the proposed auction
mechanism outperforms the single round by about 55.6% on the revenue on average
and MAFL outperforms existing double auction by about 68.6% in terms of the
revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on
Algorithms and Architectures for Parallel Processing (ICA3PP
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