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Sample applications bene ting from ofioading<br />
Two sample applications illustrate the benefits of<br />
offloading: a chess game and image retrieval.<br />
Chess is one of the world’s most popular games. A<br />
chessboard has 8 × 8 = 64 positions. Each player controls<br />
16 pieces at the beginning of the game. Chess is<br />
Markovian, meaning that the game is fully expressed by<br />
the current state. Each piece may be in one of the 64 possible<br />
locations and needs 6 bits to represent the location.<br />
(This is an overestimate: Some pieces have restrictions—<br />
for example, a bishop can move to only half of the board,<br />
that is, 32 possible locations). To represent a chess game’s<br />
current state, it is sufficient to state that 6 bits × 32 pieces<br />
= 192 bits = 24 bytes; this is smaller than the size of a<br />
typical wireless packet.<br />
The amount of computation for chess is very large; Claude<br />
Shannon and Victor Allis estimated the complexity of chess<br />
to exceed the number of atoms in the universe. Chess can be<br />
parallelized, 8 making the value of F in Equation 2 very large.<br />
Since the amount of computation C is extremely large, and D<br />
is very small, chess provides an example where offloading is<br />
beneficial for most wireless networks.<br />
An image retrieval application retrieves images similar<br />
in content to a query from an image collection. The<br />
program accomplishes this by comparing numerical representations<br />
of the images, called features. The features<br />
for the image collection can be computed in advance;<br />
for a query, the program computes its features during<br />
retrieval and compares these with the image collection.<br />
Since most of the computation is done in advance, less<br />
computation is performed online, and the value of C is<br />
small. D is large since considerable data must be sent. As<br />
a result, even if the values of F become fl, D/B might still<br />
be too large when compared to C/M in Equation 2. Thus,<br />
offloading saves energy only if B is very large—that is,<br />
at high bandwidths.<br />
The “Mobile Image Processing” sidebar has more detail<br />
on the advantages of mobile devices offloading image retrieval<br />
to the cloud.<br />
Making computation ofioading more attractive<br />
Analysis indicates that the energy saved by computation<br />
offloading depends on the wireless bandwidth B, the<br />
amount of computation to be performed C, and the amount<br />
of data to be transmitted D. Existing studies thus focus on<br />
determining whether to offload computation by predicting<br />
the relationships among these three factors.<br />
However, there is a fundamental assumption underlying<br />
this analysis with the client-server model: Because the<br />
server does not already contain the data, all the data must<br />
be sent to the service provider. The client must offload the<br />
program and data to the server. For example, typically a<br />
newly discovered server for computation offloading does<br />
not already contain a mobile user’s personal image collec-<br />
MOBILE IMAGE PROCESSING<br />
Mobile devices such as cell phones and PDAs are becoming<br />
increasingly popular. Most of these devices are equipped<br />
with cameras and have several gigabytes of ash storage<br />
capable of storing thousands of images. With such large image<br />
collections, two functionalities become important: accessing<br />
specićc sets of images from the collection, and transmitting the<br />
images over a wireless network to other devices and servers for<br />
storage.<br />
For accessing a specićc set of images, content-based image<br />
retrieval (CBIR) can be a better alternative than manually browsing<br />
through all of them. For example, a user might want to view<br />
all images containing a specićc person or captured at a specićc<br />
location. Mobile image retrieval allows the user to obtain the<br />
relevant pictures by comparing images and eliminating the<br />
irrelevant matches on the mobile system.<br />
Several studies propose performing CBIR on mobile<br />
devices. 1-4 Because these mobile devices are battery powered,<br />
energy conservation is important. 2-4 It is energy e cient to par -<br />
tition CBIR between the mobile device and server depending on<br />
the wireless bandwidth. 3 As the bandwidth increases, očoad -<br />
ing image retrieval saves more energy.<br />
Most of the energy consumption for očoaded applications<br />
is due to transmission. For image retrieval, transmitting the<br />
images over a wireless network consumes signićcant amounts<br />
of energy. The images may be preprocessed on the mobile<br />
device before transmission5 to reduce the transmission energy.<br />
This reduction in transmission energy is achieved by reducing<br />
the ćle sizes. However, the amount of energy saved depends on<br />
the wireless bandwidth and the image contents.<br />
Preprocessing the images saves energy if the reduction in<br />
transmission energy compensates for the energy spent due to<br />
preprocessing. If the wireless bandwidth is high, the value of the<br />
former reduces. Moreover, di erent images may have di erent<br />
values of the latter based on their contents. Hence preprocessing<br />
must be adaptive based on the wireless bandwidth and the<br />
image contents. Wireless transmission energy is the most signić -<br />
cant bottleneck to energy savings in mobile cloud <strong>computing</strong>,<br />
and such techniques will become increasingly signićcant as it<br />
becomes more popular.<br />
References<br />
1. J. Yang et al., “A Fast Image Retrieval System Using Index<br />
Lookup Table on Mobile Device,” Proc. 19th Int’l Conf. Pattern<br />
Recognition (ICPR 08), IEEE Press, 2008, pp. 265-271.<br />
2. C. Zhu et al., “iScope: Personalized Multimodality Image<br />
Search for Mobile Devices,” Proc. 7th Int’l Conf. Mobile Systems,<br />
Applications, and Services (Mobisys 09), ACM Press,<br />
2009, pp. 277-290.<br />
3. Y-J. Hong, K. Kumar, and Y-H. Lu, “Energy-Efficient Content-<br />
Based Image Retrieval for Mobile Systems,” Proc. IEEE Int’l<br />
Symp. Circuits and Systems (ISCAS 09), IEEE Press, 2009, pp.<br />
1673-1676.<br />
4. D. Chen et al., “Tree Histogram Coding for Mobile Image<br />
Matching,” Proc. 2009 Data Compression Conf., IEEE CS Press,<br />
2009, pp. 143-152.<br />
5. Y. Nimmagadda, K. Kumar, and Y-H. Lu, “Energy-Efficient<br />
Image Compression in Mobile Devices for Wireless Transmission,”<br />
Proc. IEEE Int’l Conf. Multimedia and Expo (ICME 09), IEEE<br />
Press, 2009, pp. 1278-1281.<br />
APRIL 2010<br />
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