33,712 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
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
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