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An Approach to Content Adaptation for Mobile Computing

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Content adaptation is about generating any content version from one single original version ... versions, and do dynamic adaptation hence the 'balance' ... – PowerPoint PPT presentation

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Title: An Approach to Content Adaptation for Mobile Computing


1
An Approach to Content Adaptation for Mobile
Computing
  • Francis C.M. Lau ( W.Y. Lum)
  • Department of Computer Science Information
    Systems The University of Hong Kong

2
The Next Gold Rush?
  • The current Internet gold rush will be
    dwarfed
    by what is about to happen with
    Wireless Internet
    Access. The Economist

3
Challenges of Mobile Computing
  • Device heterogeneity and constraints
  • Content heterogeneity
  • The network
  • The user

4
Content Adaptation
  • Why adapt contents?
  • Most contents for viewing are for the larger
    screens
  • Creating multiple versions a burden
  • Even if you dont mind, there are just too many
    possible devices
  • Different users want different things
  • Having one, original version is easier to manage
  • Content adaptation is about generating any
    content version from one single original version
  • Author-once-present-anywhere (AOPA)

5
Preadaptation
  • Keeping just the original version (any other
    version is runtime-generated) could be slow
  • Preadaptation
  • to create all possible versions, and do static
    selection at runtime, or
  • to create just a few essential versions, and do
    dynamic adaptation hence the balance

6
A User-Centric, Context-Sensitive Approach
  • Content adaptation is not just about adapting to
    the device, but also the user, the network,
  • Adapting to the user a user-centric approach
  • The user has preferences
  • speed (how much delay can I tolerate?), color (do
    I mind black-and-white?), scaling (is smaller
    text size ok?), modality (do I care what
    format?),
  • What the user most prefers however might not be
    feasible because of constraints of the context
  • the device, the network, the requested object

7
A Collaborative Environment
Content adaptation happens here
INTERMEDIATE PROXY SERVER
CONTENTS PROVIDER
INTERNET
USER DEVICE
THE CONNECTIONS
Preadapted versions stored here
8
Content Adaptation in Two Steps
  • Content negotiation
  • N(Ppreference, Pdevice, Pnetwork, Pcontent) ? V
  • V the recommended version
  • Note version ID or metadata, not the real
    content
  • Content realization
  • R(V) ? O
  • O the actual object returned to the client

9
(No Transcript)
10
Content Negotiation
  • Negotiation users preference against the
    context
  • Preferences represented by scores and stored as
    score nodes in an efficient data structure
  • To traverse the data structure from
    highest-scoring node until TRUE
  • TRUE FALSE ? decision(score-node, context)
  • where context (Pdevice, Pnetwork, Pcontent)
  • note that estimated rendering time is considered
    in the process

11
CONTENT NEGOTIATION
recommended version

12
Negotiating for the Best Version
13
Content Realization
  • To generate the object based on the desired
    version recommended by the negotiation module
  • Involves one or more transcoding steps from some
    optimal preadapted version
  • Tradeoff between
  • real-time transcoding overhead (CPU cost, or
    time)
  • storage overhead of preadaptation (I/O cost)

14
Transcoding Relation Graph
  • V the set of all possible content versions
  • The edge (vi, vj) means vj can be derived from vi
    through transcoding
  • vi could be (4-bit color, 75 scaling) vj could
    be (1-bit color, 50 scaling)
  • Transcoding (?) is a lossy operation
  • Edge labels are the time cost of the
    corresponding transcoding operations based on
    some cost model
  • At least v0, the original content version, should
    be present in the content server

15
To Build the Preadapted Set, Vpre
  • Constrained by total size allowed
  • Each vertex (white) not in Vpre must be pointed
    to by exactly one edge from a vertex (black) in
    Vpre
  • With least total edge cost (over all edges from a
    black vertex to a white vertex) among all the
    possibilities
  • NP-complete

16
The Greedy Algorithm (GREEDY)
Vpre ? initial set while not exceeding space
allowed select v ? Vpre such that C(v ? Vpre) is
minimized add v to Vpre
  • C(V) total edge (black-to-white) cost based on
    an optimal edge set for a given preadapted set V
  • We can take space into account as well to
    maximize C'( ) which is the aggregated
    transcoding cost saving per unit spatial
    consumption

17
Example
Space limit 850 Kbytes
Vpre C( ) space
v0 13.6 500
v0, v1 8.6 750
v0, v2 8.5 700
v0, v3 10.2 580
v0, v4 7.7 600
v0, v4, v1 3.5 850
v0, v4, v2 4.3 800
v0, v4, v3 6.8 680
18
How Good is GREEDY
  • Let A and B be the improvements (i.e., reduction
    in transcoding time over all content versions)
    due to the optimal solution (OPT) and GREEDY
    respectively then
  • where k and k' are the numbers of versions
    selected by GREEDY and OPT respectively
  • If k9 and k'8, then GREEDY is at least 70 of
    OPT in performance

19
How Good is GREEDY
  • Proof based on that for a greedy selection
    algorithm for datacubes by Harinarayan et al.
    SIGMOD96
  • Can we do better?
  • The greedy algorithm does as well as we can hope
    any deterministic polynomial-time algorithm to
    do according to some recent result on set
    cover

20
Experimentation
  • A prototype PDF document content adaptation
    system (simulation)
  • User preference in five domains color,
    downloading time, scaling, modality,
    segmentation, each having a range of 4 values
  • hence 45 1024 score nodes per user

21
Negotiating for the Best Version
22
Experimentation
  • We measured performance in terms of the following
    against preadaptation capacity
  • aggregated transcoding cost saving
  • content coverage ratio of selected versions /
    all versions
  • C'( ) performs better than C( ) in most
    situations
  • Please refer to our Mobicom paper for the graphs

23
Experimentation
  • Modality vs. downloading time all others kept
    constant
  • WAP device
  • (a) modality gt downloading time ? WBMP
  • (b) downloading time gt modality ? WML
  • PDA
  • (c) PDF, (d) BMP, (e) HTML

24
Experimentation
a
c
b
  • Setting maximum download time
  • (a) WBMP, (b) larger WML, (c) smaller WML
  • note how (c) is segmented/cropped and the use of
    the next anchor

25
Experimentation
a
c
b
  • Awareness of network delays
  • (a) 144 kbps ? PDF with 256 colors
  • (b) 19.6 kbps ? BMP with 16 colors
  • (c) 9.6 kbps ? HTML

26
Further Research
  • Versions weighted according to popularity
  • To exploit mutual dependencies between objects
  • Dynamic (re-)preadaptation
  • similar to caching
  • separate caching at the proxy?
  • Better algorithms than GREEDY
  • Automatic content augmentation pervasive
    authoring
  • Easily-transcodable contents
  • Do we really need that many versions?
  • User preferences how specified?
  • Collaborative design device-proxy-server
  • Adaptation of code

27
Related Publications
  • W.Y. Lum and F.C.M. Lau, User-centric Content
    Negotiation for Effective Adaptation Service in
    Mobile Computing, IEEE Transactions on Software
    Engineering, to appear.
  • W.Y. Lum and F.C.M. Lau, A Context-Aware
    Decision Engine for Content Adaptation, IEEE
    Pervasive Computing, Vol. 1, No. 3,
    July-September 2002, 41-49.
  • W.Y. Lum and F.C.M. Lau, On Balancing Between
    Transcoding Overhead and Spatial Consumption in
    Content Adaptation, Proc. Mobicom 2002, Atlanta,
    USA, September 2002, 239-250.
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