40,855 research outputs found
Cloud-based desktop services for thin clients
Cloud computing and ubiquitous network availability have renewed people's interest in the thin client concept. By executing applications in virtual desktops on cloud servers, users can access any application from any location with any device. For this to be a successful alternative to traditional offline applications, however, researchers must overcome important challenges. The thin client protocol must display audiovisual output fluidly, and the server executing the virtual desktop should have sufficient resources and ideally be close to the user's current location to limit network delay. From a service provider viewpoint, cost reduction is also an important issue
Power Load Management as a Computational Market
Power load management enables energy utilities to reduce peak loads and thereby save money. Due to the large number of different loads, power load management is a complicated optimization problem. We present a new decentralized approach to this problem by modeling direct load management as a computational market. Our simulation results demonstrate that our approach is very efficient with a superlinear rate of convergence to equilibrium and an excellent scalability, requiring few iterations even when the number of agents is in the order of one thousand. Aframework for analysis of this and similar problems is given which shows how nonlinear optimization and numerical mathematics can be exploited to characterize, compare, and tailor problem-solving strategies in market-oriented programming
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
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
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