173 research outputs found
Safe and Secure Wireless Power Transfer Networks: Challenges and Opportunities in RF-Based Systems
RF-based wireless power transfer networks (WPTNs) are deployed to transfer
power to embedded devices over the air via RF waves. Up until now, a
considerable amount of effort has been devoted by researchers to design WPTNs
that maximize several objectives such as harvested power, energy outage and
charging delay. However, inherent security and safety issues are generally
overlooked and these need to be solved if WPTNs are to be become widespread.
This article focuses on safety and security problems related WPTNs and
highlight their cruciality in terms of efficient and dependable operation of
RF-based WPTNs. We provide a overview of new research opportunities in this
emerging domain.Comment: Removed some references, added new references, corrected typos,
revised some sections (mostly I-B and III-C
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
Room-Temperature Electrochemical Reduction of Epitaxial Bi₂O₃ Films to Epitaxial Bi Films
This work reports a new facile approach to fabricate high-quality epitaxial Bi thin films by direct electrochemical reduction of epitaxial δ-Bi2O3 thin films on Au single crystals in aqueous solution at room-temperature. The as-produced Bi thin films (without any post-annealing process) exhibit large grain sizes, continuous microstructures, and enhanced magnetotransport properties
Differentiate Quality of Experience Scheduling for Deep Learning Inferences with Docker Containers in the Cloud
With the prevalence of big-data-driven applications, such as face recognition
on smartphones and tailored recommendations from Google Ads, we are on the road
to a lifestyle with significantly more intelligence than ever before. Various
neural network powered models are running at the back end of their intelligence
to enable quick responses to users. Supporting those models requires lots of
cloud-based computational resources, e.g., CPUs and GPUs. The cloud providers
charge their clients by the amount of resources that they occupy. Clients have
to balance the budget and quality of experiences (e.g., response time). The
budget leans on individual business owners, and the required Quality of
Experience (QoE) depends on usage scenarios of different applications. For
instance, an autonomous vehicle requires an real-time response, but unlocking
your smartphone can tolerate delays. However, cloud providers fail to offer a
QoE-based option to their clients. In this paper, we propose DQoES,
differentiated quality of experience scheduler for deep learning inferences.
DQoES accepts clients' specifications on targeted QoEs, and dynamically adjusts
resources to approach their targets. Through the extensive cloud-based
experiments, DQoES demonstrates that it can schedule multiple concurrent jobs
with respect to various QoEs and achieve up to 8x times more satisfied models
when compared to the existing syste
Load-balancing distributed outer joins through operator decomposition
High-performance data analytics largely relies on being able to efficiently execute various distributed data operators such as distributed joins. So far, large amounts of join methods have been proposed and evaluated in parallel and distributed environments.
However, most of them focus on inner joins, and there is little published work providing the detailed implementations and analysis
of outer joins. In this work, we present POPI (Partial Outer join & Partial Inner join), a novel method to load-balance large parallel
outer joins by decomposing them into two operations: a large outer join over data that does not present significant skew in the input
and an inner join over data presenting significant skew. We present the detailed implementation of our approach and show that
POPI is implementable over a variety of architectures and underlying join implementations. Moreover, our experimental evaluation
over a distributed memory platform also demonstrates that the proposed method is able to improve outer join performance under
varying data skew and present excellent load-balancing properties, compared to current approaches
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