Do you have a drawer at home full of
cords? The challenge is that while the wireless charging market is in demand,
some strides still need to be made in terms of functionality. According to IHS,
the wireless power market is estimated to grow to one billion charging units by
2020. It’s not a new concept, and early adoption was seen with the
In 2017, Disney Research showcased the
ability to charge your device while the receiver is across the room, similar to
how WiFi works on a computer. Apple tried to enter the marketplace awhile back,
but because of their charging needs, the phones got too hot. Also, the length
of time it took to charge the device was originally an issue.
The way charging typically works is
that there is a charging dock that has a transmitting coil that induces current
into a receiver coil on your smart device that in turn charges that
battery. Early products required the exact positioning of the device on
the charger to work, but then ‘free positioning’ became a popular concept in
charging. The key to the first ‘free positioning’ concepts was the ability to
have a transmittable back surface on the device. Some smartphones have a
surface made of glass which helped, but the obvious drawback is when you drop
your phone. The industry continues to struggle with how to charge devices at a
It is easy to imagine a world where
wireless charging is available everywhere from hotels to airports. Some have
even imagined roads embedded with wireless charging to support electric cars.
Different solutions have hit the market
from chargeable phone cases to Disney’s vision of wireless charging hotspots.
Companies like Logitech and Corsair are currently selling wireless charging
technology that is transmitted via a mouse pad. Also, Apple recently re-entered
the market in submitting a patent that shows how devices can transmit wireless
power to nearby smart devices without using a wireless charging mat but instead
just wirelessly connecting to your home computer.
This year could be a hot patent space
for companies trying to find their foothold in this future market.
According to Gartner, the market for cloud computing will expand to 623 billion USD by 2030. With this growth, there is increased demand from cloud providers to make the best use of their resources in terms of performance efficiency. Three fairly common approaches to performance analysis include experiment-based performance analysis, discrete event-simulation-based performance analysis, and stochastic model-based performance analysis. However, the stochastic model-based performance analysis is the preferred method due to the lower cost point, as well as timeliness. Sakr and Gaber developed a simplified three-pool cloud architecture-based stochastic model to address some of the common barriers encountered with scaling.
Stochastic Model &
Three-Pool Cloud Architecture
Sakr and Gaber proposed a simplified
model from Markov’s stochastic model to support scalability and tractability
solution at a lower cost. Their model leverages three-pool cloud architecture
that has several interacting sub-models, including resource provisioning
decision engine, virtual machine sub-models, and pool sub-models. Sakr and
Gaber leveraged three-pool cloud architecture which includes the concepts of
the hot pool sub-model, warm pool sub-model, and cold pool sub-model. What
pool is used depends on how the response time and power consumption are
sorted. Each sub-model can be used to represent a machine in the pool. The
hot pool sub-model addresses the group needing maximum power with low response
times. The warm pool sub-model addresses machines in sleep model that are
waiting for the next run. Finally, the cold pool sub-model has machines that
are in the off state and have minimum power needs and longer response times.
Figure 1 shows Sakr and Gaber’s model of what happens when there is a service
request and how the resource provisioning decision engine tries to leverage the
various pool sub-models.
The biggest challenge to the model is
the potential service request rejection. When the buffer is at capacity or the
resource provisioning decision engine cannot find an available machine due to
capacity issues, request rejection is possible. However, there is some
understanding related to the service request probability that can be helpful.
For example, the longer the mean service time, the increased likelihood of
higher service requests. Therefore, if the capacity of the machines is
increased, the potential for service rejection can decrease.
Additional challenges for the
three-pool cloud architecture model to work effectively is that both service
requests and the machines have to have the same type. However, virtual
provisioning sub-models can be used with different machines where the machines
are grouped into classes, and each class is this represented by a pool so that
the individual pool is homogeneous.
Development of Applications
The advantage of the Stochastic Model
and pool architecture is that the pools can be strategically used to identify
performance bottleneck through what-if analysis and planning capacity. For
example, that Symbolic
Hierarchical Automated Reliability and Performance Evaluator (SHARPE)
is a modeling application that can look at performance, reliability, and
availability. This application has been installed at over 450 sites and lets
users choose different what-if analysis with alternative algorithms depending
on their objectives. Also, three-pool architecture can be useful for data
recovery efforts in the sense that mission-critical information can be in the
hot pool for faster recovery whereas less critical data can be stored in the
Cloud services are growing
exponentially in demand putting increased pressure on finding both timely and
cost-effective solutions to address performance issues. The Stochastic Model is
desirable because of its’ low cost and timeliness as compared to other models.
The Stochastic Model leverages a three-pool cloud architecture with pool
sub-models to process service requests. While there are some challenges related
to rejecting service requests and heterogeneous machines, the model can be
adapted to handle these challenges. Several applications have demonstrated the
promise of the pool architecture in strategically managing performance