23,769 research outputs found
Delay-aware power optimization model for mobile edge computing systems
Reducing the total power consumption and network
delay are among the most interesting issues facing
large-scale Mobile Cloud Computing (MCC) systems and
their ability to satisfy the Service Level Agreement (SLA).
Such systems utilize cloud computing infrastructure to support
offloading some of user’s computationally heavy tasks
to the cloud’s datacenters. However, the delay incurred
by such offloading process lead the use of servers (called
cloudlets) placed in the physical proximity of the users, creating
what is known as Mobile Edge Computing (MEC).
The cloudlet-based infrastructure has its challenges such
as the limited capabilities of the cloudlet system (in terms
of the ability to serve different request types from users
in vast geographical regions). To cover the users demand
for different types of services and in vast geographical
regions, cloudlets cooperate among each other by passing
user requests from one cloudlet to another. This cooperation
affects both power consumption and delay. In this work,
we present a mixed integer linear programming (MILP
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
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