U.S. patent application number 13/234950 was filed with the patent office on 2012-11-01 for opportunistic block transmission with time constraints.
This patent application is currently assigned to Endeavors Technologies, Inc.. Invention is credited to Jeffrey de Vries, Arthur S. Hitomi.
Application Number | 20120278555 13/234950 |
Document ID | / |
Family ID | 40589329 |
Filed Date | 2012-11-01 |
United States Patent
Application |
20120278555 |
Kind Code |
A9 |
de Vries; Jeffrey ; et
al. |
November 1, 2012 |
OPPORTUNISTIC BLOCK TRANSMISSION WITH TIME CONSTRAINTS
Abstract
A technique for determining a data window size allows a set of
predicted blocks to be transmitted along with requested blocks. A
stream enabled application executing in a virtual execution
environment may use the blocks when needed.
Inventors: |
de Vries; Jeffrey;
(Sunnyvale, CA) ; Hitomi; Arthur S.; (Huntington
Beach, CA) |
Assignee: |
Endeavors Technologies,
Inc.
Irvine
CA
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20120096224 A1 |
April 19, 2012 |
|
|
Family ID: |
40589329 |
Appl. No.: |
13/234950 |
Filed: |
September 16, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12062789 |
Apr 4, 2008 |
8024523 |
|
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13234950 |
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11388381 |
Mar 23, 2006 |
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12062789 |
|
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60986261 |
Nov 7, 2007 |
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60664765 |
Mar 23, 2005 |
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Current U.S.
Class: |
711/118 ;
711/E12.017 |
Current CPC
Class: |
G06F 12/0802 20130101;
H04L 65/607 20130101; G06F 12/0862 20130101; G06F 12/0864 20130101;
H04L 67/2847 20130101; H04L 41/0896 20130101; H04L 65/4084
20130101; G06F 13/1673 20130101 |
Class at
Publication: |
711/118 ;
711/E12.017 |
International
Class: |
G06F 12/08 20060101
G06F012/08 |
Claims
1. A system comprising: a virtual execution environment; a block
granularity caching engine; a cache; wherein, in operation: a
process associated with a stream-enabled application is executed in
the virtual execution environment; the virtual execution
environment intercepts a request from the process executing in the
virtual execution environment; the virtual execution environment
identifies one or more blocks that are associated with the
resource; the virtual execution environment makes a block request
associated with the resource; the block granularity engine checks
the cache for blocks to satisfy the block request; the client block
granularity engine provides at least one predictively streamed
block to the virtual execution environment if the predictively
streamed block is found in the cache; the virtual execution
environment satisfies the resource request of the process using the
at least one predictively streamed block.
2-25. (canceled)
Description
RELATED APPLICATIONS
[0001] This application is a continuation application of U.S. Ser.
No. 12/062,789 filed Apr. 4, 2008 entitled "Opportunistic Block
Transmission With Time Constraints," which claims priority to U.S.
provisional Ser. No. 60/986,261 filed Nov. 7, 2007 entitled
"Opportunistic Block Transmission With Time Constraints," which is
incorporated herein by reference in its entirety.
BACKGROUND
[0002] In requesting and receiving blocks of a stream enabled
application, some blocks may be predicted as needed following other
blocks. When blocks are predicted, there may be uncertainty as to
how many predicted blocks to transmit. This may relate to an amount
of data to be transmitted. If too many blocks are sent then a delay
perceived by a user is increased, and/or network bandwidth is
wasted, which may be expensive to a streaming service provider. If
too few blocks are sent then bandwidth of a network connection may
be underutilized. If a system refrains from transmitting blocks
until a user actually requests them then the system must transmit
the requested blocks while the user waits, expending the user's
valuable time. In many cases users may desire to eliminate such
waiting time.
[0003] The foregoing examples of the related art and limitations
related therewith are intended to be illustrative and not
exclusive. Other limitations of the related art will become
apparent to those of skill in the art upon a reading of the
specification and a study of the drawings.
SUMMARY
[0004] The following embodiments and aspects thereof are described
and illustrated in conjunction with systems, tools, and methods
that are meant to be exemplary and illustrative, not limiting in
scope. In various embodiments, one or more of the above described
problems have been reduced or eliminated, while other embodiments
are directed to other improvements.
[0005] A technique for determining a data window size allows a set
of predicted blocks to be transmitted using surplus bandwidth.
Advantageously, predicted blocks are transmitted to a streaming
playback device before the device needs the blocks. A stream
enabled application executing in a virtual execution environment
may use the blocks when needed without having to wait for their
transmission, limiting user delays.
[0006] A system based on the technique may include a streaming
playback device and a streaming server. The streaming server may
provide the streaming playback device blocks that it will need
prior to the blocks actually being requested. The streaming
playback device may cache necessary blocks and then use them when
needed. A caching system may request only those blocks that are not
found in cache when requests for blocks include some but not all of
the blocks in the cache.
BRIEF DESCRIPTION
[0007] FIG. 1 depicts a diagram of an example of a system for
streaming software.
[0008] FIG. 2 depicts a diagram of an example of a system for
efficiently transmitting blocks.
[0009] FIG. 3 depicts a flowchart of an example of a method for
efficiently transmitting blocks.
[0010] FIG. 4 depicts a diagram of an example of a streaming
playback device receiving blocks.
[0011] FIG. 5 depicts a flowchart of an example of a method for
receiving blocks.
[0012] FIG. 6 depicts a diagram of an example of a streaming server
transmitting blocks.
[0013] FIG. 7 depicts a flowchart of an example of a method for
transmitting blocks.
[0014] FIG. 8 depicts a diagram of an example of a logical
expression of a block probability table and a diagram of an example
of partially filled response buffer.
[0015] FIG. 9 depicts a diagram of an example of a device for
streaming software.
DETAILED DESCRIPTION
[0016] In the following description, several specific details are
presented to provide a thorough understanding. One skilled in the
relevant art will recognize, however, that the concepts and
techniques disclosed herein can be practiced without one or more of
the specific details, or in combination with other components, etc.
In other instances, well-known implementations or operations are
not shown or described in detail to avoid obscuring aspects of
various examples disclosed herein.
[0017] FIG. 1 depicts a diagram 100 of an example of a system for
streaming software. FIG. 1 includes software provider 102, stream
enabler 104, and opportunistic streaming software system 106.
[0018] In the example of FIG. 1, software provider 102 supplies a
software application. The software application may be provided as
deliverables such as data, executable code, and libraries. The
application may be provided by way of CD-ROM, DVD-ROM, download
over a network, from an input/output (I/O) device, or via any known
or convenient mechanism.
[0019] Resources, e.g. data, executable code, may be included in
the deliverables supplied by the software provider 102. An
application executing in a virtual execution environment may
request resources, and it may be necessary to transmit blocks
including resources to a streaming playback device for satisfaction
of the resource request. A user may be required to wait while
blocks including resources are transmitted to a streaming playback
device.
[0020] The software application may have an interactive threshold.
The interactive threshold may be an amount of time that a user is
willing to wait for a system to access resources. In a non-limiting
example, the interactivity threshold may be approximately 1/10 of a
second because a 1/10 of a second delay is noticeable to a human
being, and delays of greater than 1/10 of a second may decrease
user satisfaction with the software. The interactivity threshold
may be supplied by the software provider 102, may be determined
through trial and error, may be determined by systematically
executing the software application many times and averaging user
responses, may be set to a default value (e.g. 1/10 of a second) or
may be acquired by any method known or convenient.
[0021] In the example of FIG. 1, stream enabler 104 prepares the
non-stream enabled software application provided by software
provided 102 to be streamed. The stream enabler 104 breaks the
non-stream enabled software application into blocks of an optimal
block size. The optimal block size may be small so as to allow for
aggregation of blocks with fine granularity. The optimal block size
may be narrowed to a range, e.g. 512 bytes-32 k bytes. In some
cases the optimal block size may be larger or smaller than the
specified range and may be any size known or convenient. A
deliverable is broken up into many pieces. Each of the pieces may
be of the optimal block size, or the pieces may have a variable
block size, all of which are the optimal block size or smaller. In
some cases, it may even be desirable to increase block size to
greater than the optimal block size. In a non-limiting example, the
optimal block size is set to 4 kb and the non-stream enabled
software application is broken up into blocks of a stream-enabled
application each up to 4 kb in size, most being of 4 kb. An optimal
size of 4 k may have certain advantages because that is also the
page size of many computer systems. This can improve memory
allocation efficiency, improve simplicity, and/or have other
advantages.
[0022] In the example of FIG. 1, opportunistic streaming software
system 106 may include a streaming software server and a streaming
software playback device. A server may be either hardware or
software or may be a combination of both hardware software, and
firmware. The server may include blocks of the stream-enabled
application. The server may be coupled to the streaming software
playback device via a network. The network may have throughput and
latency characteristics. The throughput and latency characteristics
may be dynamically changing.
[0023] Alternatively, in lieu of or in addition to a server, an I/O
device that includes at last some blocks of the stream enabled
application could be used. In this case, the I/O interface may have
relevant throughput and latency characteristics.
[0024] FIG. 2 depicts a diagram 200 of an example of a system for
opportunistically transmitting blocks. FIG. 2 includes deliverables
202, stream enabler 204, and streaming software system 205.
[0025] In the example of FIG. 2, deliverables 202 may be
deliverables of a non-stream enabled software application. The
deliverables 202 may be the deliverables as discussed in reference
to FIG. 1.
[0026] In the example of FIG. 2, stream enabler 204 may be
hardware, software, or a combination of hardware and software. The
stream enabler may be capable of taking deliverables of a
non-stream enabled software application and breaking the
deliverables up into blocks as discussed in reference to FIG.
1.
[0027] In the example of FIG. 2, streaming software system 205
includes streaming software playback device 206, and streaming
server 208. In the example of FIG. 2, streaming playback device 205
includes network data engine 210, block granularity caching engine
212, and virtual environment 208.
[0028] In the example of FIG. 2, the block granularity caching
engine 212 receives requested blocks as well as predictively
streamed blocks from streaming server 208. Predictively streamed
blocks may be blocks that were not requested, but were instead
predicted to be needed by a stream enabled application executing in
the virtual environment 224. Predictively streamed blocks may be
stored in a local cache until needed. When predictively streamed
blocks are needed resources included in the predictively streamed
blocks may be accessed without requesting the blocks from streaming
server 208.
[0029] In the example of FIG. 2, network data engine 210 may
transmit and receive information associated with network latency
and throughput to streaming server 208. It may be necessary for the
network data engine 210 to receive a packet of information that
includes a header having a timestamp, but no data. As will be
discussed in reference to FIG. 8, packets may be transmitted and
received in order to determine and update network latency
information. Similarly the network data engine 210 may transmit
data to the streaming server 208 to determine throughput.
[0030] In the example of FIG. 2, the virtual environment 224 may
allow a stream-enabled application to execute. The virtualized
execution environment 224 is a virtualized operating system
enabling a streamed application to execute on a streaming playback
device. A virtualized execution environment is discussed in U.S.
patent application Ser. No. 09/098,095 entitled "METHOD AND
APPARATUS TO ALLOW REMOTELY LOCATED COMPUTER PROGRAMS AND/OR DATA
TO BE ACCESSED ON A LOCAL COMPUTER IN A SECURE, TIME-LIMITED
MANNER, WITH PERSISTENT CACHING," which is incorporated by
reference.
[0031] In the example of FIG. 2, streaming server 208 includes
blocks 230, predictive block aggregator 232, and data window engine
234.
[0032] In the example of FIG. 2, the blocks 230 are blocks of a
stream-enabled application. Blocks 230 may be included in a local
storage, a database, or may be included in a non-local storage or a
non-local database. A database may be a database, may be a file, or
may be any known or convenient manner of storing data.
[0033] In the example of FIG. 2, the predictive block aggregator
232 may prioritize a number of blocks in order of likelihood of use
by the streaming playback device 206. The predictive block
aggregator 232 may add a number of blocks to a buffer in order of
priority. The priority of the blocks may be determined as discussed
in reference to FIG. 7A-B. Blocks may be added to the buffer until
the buffer has reached a data window size.
[0034] In the example of FIG. 2, the data window engine 234 sets a
data window size by considering the interactivity threshold of the
stream-enabled application, the network latency, and the network
throughput. Network latency and network throughput information may
be considered on a continuously updated basis, may be infrequently
updated, may be set only once, or may be disregarded. The
interactivity threshold may be used in reference to the latency and
throughput, or may be used independent of that information. The
data window size may be used to limit the size of the buffer filled
with predictively streamed blocks. In a non-limiting example, the
predictively streamed blocks are limited to 10 k and blocks are set
to 512 bytes. 20 predicted blocks are placed in the buffer, and the
blocks are transmitted.
[0035] In the example of FIG. 2, in operation, the deliverables 202
are received by the stream enabler 204 and broken up into blocks
and provided to streaming server 208 and stored in blocks 230. The
network data engine 210 sends a request for a block to the
streaming server 208 and the data window engine 234 calculates a
data window. The predictive block aggregator 232 prioritizes the
blocks 230 and places a number of the blocks 230 into a buffer up
to the data window size set by the data window engine 234. The
blocks in the buffer are transmitted to the streaming playback
device 206 as predictively streamed blocks. The block granularity
caching engine 212 stores the predictively streamed blocks in to a
local cache. An application executing in the virtual environment
224 requests a resource, and the resource is at least partially
satisfied from the blocks in the predictively streamed blocks in
the cache.
[0036] FIG. 3 depicts a flowchart 300 of an example of a method for
opportunistically transmitting blocks. The method is organized as a
sequence of modules in the flowchart 300. However, it should be
understood that these and modules associated with other methods
described herein may be reordered for parallel execution or into
different sequences of modules.
[0037] In the example of FIG. 3, the flowchart 300 starts at module
302 with executing a process associated with a stream-enabled
application that provides a first request for resources. The
resource may be needed for execution of a stream enabled
application within a virtualized execution environment. The
resources may be included in blocks of a stream enabled application
stored locally or remotely on a streaming system.
[0038] In the example of FIG. 3, the flowchart 300 continues to
module 304 with receiving, within a data window, one or more blocks
including resources used to satisfy the first request for resources
as well as one or more predictively streamed blocks. The data
window may limit the number of predictively streamed blocks that
may be transmitted. The resources in the one or more blocks may be
used to satisfy the first request for resources. The predictively
streamed blocks may be associated with a probability of being
requested by the stream enabled application in the future.
[0039] In the example of FIG. 3, the flowchart 300 continues to
module 306 with storing one or more predictively streamed blocks in
a cache. The blocks may be blocks that include resources that have
a high likelihood of being used by the stream enabled application
for a subsequent request for resources. A streaming system may
predictively stream the blocks in advance of the stream enabled
application requesting resources included in the blocks.
[0040] In the example of FIG. 3, the flowchart 300 continues to
module 308 with providing a second request for resources. The
second request for resources may be associated with one or more
blocks that were predictively streamed in advance of the second
request for resources.
[0041] In the example of FIG. 3, the flowchart 300 continues to
module 310 with checking the cache to find the one or more
predictively streamed blocks to satisfy the block request. If
blocks including the resource were predictively streamed in
advance, the resource may be found in blocks in the cache. However,
blocks including the resource may not have been predictively
streamed, or may have gone unused for a sufficient amount of time
for the blocks to have been replaced with other blocks that have
been more recently predictively streamed.
[0042] In the example of FIG. 3, the flowchart 300 continues to
module 312 with at least partially satisfying the request for the
resource using the one or more predictively streamed blocks in the
cache. In some cases the resource may be spread across more than
one block. One or more blocks including the resource may not be in
the cache when needed. Blocks not found in the cache must be
requested so that all blocks necessary are available so that the
resource may be provided. In the case that the resource is entirely
included in blocks in the cache, the entire resource is produced
from the blocks in the cache, and the resource request is
satisfied. Having satisfied the request for the resource, the
flowchart terminates.
[0043] FIG. 4 depicts a diagram 400 of an example of a streaming
playback device receiving blocks. FIG. 4 includes streaming
playback device 402, streaming system 412, and blocks 414.
[0044] In the example of FIG. 4, the streaming playback device 402
includes virtual environment 404, cache 406, block granularity
caching engine 408, and interface 410.
[0045] In the example of FIG. 4, the virtual environment 404 may be
a virtualized operating system enabling a streamed application to
execute on a computing device as discussed in reference to FIG.
2.
[0046] In the example of FIG. 4, the cache 406 may store one or
more blocks, the blocks including one or more resources. Blocks may
be stored in a last-in-first-out (LIFO), associative cache, or any
caching system known or convenient. Blocks that are used may be
maintained in the cache 406 while blocks that are not used may be
replaced by more recent predictively streamed blocks.
[0047] In the example of FIG. 4, the block granularity caching
engine 408 may receive predicted blocks as they are transmitted to
the streaming playback device 402. The blocks may be stored in the
cache 406 by the block granularity caching engine 408. The block
granularity caching engine 408 may intercept block requests from
the virtual execution environment 404, and inspect the cache 406 to
determine if some, all or none of the blocks are found in the cache
406. If some of the blocks are found in the cache 406, then the
block granularity caching engine 408 may create a modified block
request including a request for blocks that were requested by the
virtual execution environment 404, but not found in the cache 406.
If none of the blocks are found in the cache 406, the block
granularity caching engine 408 may create a block request for all
blocks requested by the virtual execution environment 404. If all
of the blocks are found in the cache, the block granularity caching
engine 408 may provide all blocks requested directly to the virtual
execution environment 404, and may refrain from transmitting any
request for blocks.
[0048] In the example of FIG. 4, the interface 410 may provide
block requests to the streaming system 412. The interface 410 may
receive blocks from the streaming system 412 and provide the blocks
to the block granularity caching engine 408. An interface should be
afforded a broad enough interpretation to include a bus within a
single computing system, a wireless or wired connection between
computing systems, or any known or convenient manner of
transmitting blocks of a stream enabled application. The interface
could be an input/output device, for reading a fixed media such as
a CD-ROM, DVD-ROM, or other computer readable media.
[0049] In the example of FIG. 4, the streaming system 412 may be a
separate computing device from streaming playback device 402, may
be a fixed media from which a stream-enabled application is read,
or may be any source of a stream enabled application. The streaming
system 412 may include the blocks 414, or may be coupled to a
computing device including the blocks 414. In the case of a fixed
media, the streaming playback device 402 reads blocks from the
input output device such as a CD-ROM, DVD-ROM, or other known or
convenient computer readable medium.
[0050] In the example of FIG. 4, the blocks 414 may include blocks
of a stream enabled application. Blocks 414 may be a file, a group
of files, a database, a data store, or any manner known or
convenient of storing a stream enabled application.
[0051] In the example of FIG. 4, in operation, a stream enabled
application executing within the virtual execution environment 404
may request, for example, a resource in terms of a file offset and
length. The virtual execution environment 404 may interpret the
resource request as block 7. The block granularity caching engine
408 may then inspect the cache 406 to determine whether the
requested block 7 is in the cache or not. If the requested block 7
is in the cache, then the block granularity caching engine 408 may
provide the requested block to the virtual execution environment
404. If the requested block is not in the cache 406 then the block
granularity caching engine 408 may then request the block 7 from
the streaming system 412. The streaming system 412 may reply with
block 7 and perhaps, by way of example, but not limitation,
additional predictive blocks 7, 8, & 9. The additional blocks 8
and 9 may have a high likelihood of being needed after block 7 is
requested. The block granularity caching engine 408 may provide the
requested block 7 to the virtual execution environment 404, and may
cache the predictively streamed blocks 8 and 9, as well as the
requested block 7. The resources in the requested block 7 may be
provided by the virtual execution environment 404 to the stream
enabled application in terms of a file, offset and length.
[0052] FIG. 5 depicts a flowchart 500 of an example of a method for
receiving blocks. The method is organized as a sequence of modules
in the flowchart 500. However, it should be understood that these
and modules associated with other methods described herein may be
reordered for parallel execution or into different sequences of
modules.
[0053] In the example of FIG. 5, the flowchart 500 starts at module
502 with requesting a resource of a stream enabled application. A
stream enabled application may request the resource in terms of a
file, offset and length.
[0054] In the example of FIG. 5, the flowchart 500 continues to
module 504 with translating the request for the resource into a
block ID of a block including the resource. The resource may be
included in a block, or may be spread across one or more blocks.
The translation may thus include a plurality of blocks or merely a
single block.
[0055] In the example of FIG. 5, the flowchart 500 continues to
module 506 with inspecting a cache for the block. It may be that
the block has been previously received, and is stored in cache. The
block may have been previously requested, or may have been
predictively streamed because it was likely that the resource
included in the block would be requested. If a block is present,
then the block is locked, or otherwise prevented from being deleted
until the resource request is responded to. A locked block may be
unlocked after the resource request is responded to.
[0056] In the example of FIG. 5, the flowchart 500 continues to
module 508 with transmitting a request for the block. If the block
was not found in the cache it may be necessary to request the
block.
[0057] In the example of FIG. 5, the flowchart 500 continues to
module 510 with receiving the block as well as additional
predictively streamed blocks. In the case that one or more blocks
are likely to be requested after the block, predictively streamed
blocks may be transmitted along with the block, taking into
consideration, a window for transmission based on the amount of
time that a user may be willing to wait as well as the amount of
data that may be transmitted in the time.
[0058] In the example of FIG. 5, the flowchart 500 continues to
module 512 with caching the additional predictively streamed
blocks. These additional predictively streamed blocks may be stored
for future use. Blocks in the cache that are not needed may be
replaced by predictively streamed blocks. Any locked blocks may not
be replaced by additional predictively streamed blocks. Having
cached the additional predictively streamed blocks, the flowchart
terminates.
[0059] FIG. 6 depicts a diagram 600 of an example of a streaming
server transmitting blocks. FIG. 6 includes streaming server 602,
and streaming playback device 616.
[0060] In the example of FIG. 6, streaming server 602 includes
predictor 604, blocks 608, predicted block aggregation engine 612,
output buffer 610, and interface 614.
[0061] In the example of FIG. 6, the predictor 604 determines a
likelihood of blocks being requested. The predictor creates
probability data. The predictor is discussed in more depth in U.S.
patent application Ser. No. 10/988,014 entitled "SYSTEM AND METHOD
FOR PREDICTIVE STREAMING" by Jeffrey de Vries, incorporated herein
by reference.
[0062] In the example of FIG. 6, the probability data 606 includes
probabilities of blocks being requested after blocks that have
already been requested, such as is discussed in reference to FIG.
7. The probability data may be expressed as a logical block
probability table, as a data store of probabilities, or in any
manner known or convenient.
[0063] In the example of FIG. 6, the output buffer 610 may be any
computer readable medium capable of storing data. The output buffer
610 may be volatile, or non-volatile. In a non-limiting example,
the output buffer may include random access memory. The output
buffer 610 may store blocks prior to transmission.
[0064] In the example of FIG. 6, the predicted block aggregation
engine 612 includes functionality to fill an output buffer with
blocks from blocks 608 based on block probability data 606. The
predictive block aggregation engine 612 may add blocks to the
output buffer up to a limit set by a data window size as discussed
in reference to FIG. 7. The data window size may be limited by user
responsiveness requirements and a maximum throughput between
interface 614 and streaming playback device 616. An engine normally
includes a processor and memory including instructions for
execution by the processor.
[0065] Notably, below, two examples are provided of pseudo code
that could be implemented to fill the output buffer 610 with
blocks. However, neither the first example, nor the second example
are limiting. Any known or convenient manner of filling the output
buffer 610 with blocks may be used.
[0066] In a non-limiting example, the predicted block aggregation
engine 612 may implement a priority queue to fill the output buffer
with blocks. Consider a priority queue PQ. PQ contains tuples
(pq,bq) each containing a probability (pq) and a block number (bq).
The priority queue may be ordered by probability. A minimum
probability tuple may always sit at the top of the queue. Initially
PQ is empty. Q may be a working queue of tuples (pw,bw) each
containing a probability (pw) and a block number (bw). PROB may be
an indexed probability table 606 storing a probability of needing a
predicted block having seen one or more previous blocks. The
probability may be set in the range of 0-1. Let N be the max number
of blocks that you can put into the output buffer=data window
size/block size.
[0067] In continuing the non-limiting example, the following
pseudo-code could be implemented to fill an output buffer with
predicted blocks up to a maximum data window:
TABLE-US-00001 PUSH (1.0, requested block) onto working queue Q
WHILE working queue Q is not empty DO POP (pw,bw) with probability
pw and block number bw from working queue Q IF (PQ.size = N) (i.e.
PQ already has N entries) DO IF (pw <= PQ.min (i.e. the min
probability at the top of the PQ)) DO CONTINUE back at the WHILE
(i.e. skip this block) END IF IF (PQ already contains an entry for
block number bw) DO IF (the probability in the entry for block bw
in PQ >= pw) DO CONTINUE back at the WHILE (i.e. keep existing
entry) ELSE REMOVE the entry for block bw from PQ END IF END IF END
IF PUSH (pw, bw) onto priority queue PQ (updating the min entry as
necessary) FOR EACH predicted block bp for which PROB[bw][bp] >
0 DO PUSH (pw * PROB[bw][bp], bp) onto working queue Q END FOR LOOP
END WHILE LOOP FOR EACH block in the priority queue DO go read the
block data and put the block number and the block data into the
output buffer END FOR LOOP SEND output buffer containing requested
block + top N-1 blocks based on probability
[0068] Notably, one could use as many stages of look ahead as is
desirable. One could have PROB[seen1][seen2] . . . [seen n]
[predicted block]=the probability of seeing the predicted block
give the sequence of previously seen blocks seen 1 . . . seen n.
The additional stages of look ahead may provide better focused
predictions of blocks. In another non-limiting example, the
following pseudo code could be implemented to fill the output
buffer with blocks. Q is a working queue of (probability, block
number) tuples, sorted by order of decreasing probability,
initially empty. Let probability table be same as discussed above
relative to the first example.
TABLE-US-00002 PUSH (1.0, requested block) onto working queue Q
WHILE working queue Q is not empty AND output buffer is not full DO
POP (pw,bw) with probability pw and block number bw from working
queue Q IF output buffer doesn't already contain block bw ADD block
number bw and data for block bw to output buffer END IF FOR EACH
predicted block bp for which PROB[bw][bp] > 0 DO IF output
buffer doesn't already contain block bp INSERT (pw * PROB[bw][bp],
bp), sorted by decreasing probability, into working queue Q END IF
END FOR LOOP END WHILE LOOP SEND output buffer
[0069] In the example of FIG. 6, the data window engine 613 sets a
data window size by considering the interactivity threshold of the
stream-enabled application, the network latency, and the network
throughput. Network latency and network throughput information may
be considered on a continuously updated basis, may be infrequently
updated, may be set only once, or may be disregarded. The
interactivity threshold may be used in reference to the latency and
throughput, or may be used independent of that information. The
data window size may be used to limit the size of the buffer filled
with predictively streamed blocks.
[0070] In the example of FIG. 6, the interface 614 may be an
interface as discussed in reference to FIG. 9. It should be
appreciated that the interface may be interpreted broadly enough to
include connections within a single computing system. Additionally,
the interface may any means of transmitting data, e.g., optical
network, ethernet, or any means known or convenient.
[0071] In the example of FIG. 6, the streaming playback device 616
may include a virtualized environment for a stream enabled
application. Streaming playback device 616 may include a processor,
non-volatile memory, and an interface. The streaming playback
device 616 may include a virtual execution environment executing a
stream enabled application that requests resources. A resource
request may be translated into a block request.
[0072] In the example of FIG. 6, in operation, the streaming
playback device 616 creates a request for a block. The request is
provided to streaming server 602. The predictor 604 predicts a
number of blocks that are likely to be requested after the block
and stores the predictions in probability data 606. Predicted block
aggregation engine 612 identifies the blocks in probability data
606 that are likely to be requested after the block, and adds zero
or more blocks from the blocks 608 until the output buffer 610 has
reached a data window size. Once the output buffer 610 has reached
the data window size, the output buffer is provided, via the
interface 614, to the streaming playback device 616.
[0073] FIG. 7 depicts a flowchart 700 of an example of a method for
transmitting blocks. The method is organized as a sequence of
modules in the flowchart 700. However, it should be understood that
these and modules associated with other methods described herein
may be reordered for parallel execution or into different sequences
of modules.
[0074] In the example of FIG. 7, the flowchart 700 starts at module
702 with predicting one or more blocks that are associated with
resources that will be requested by a stream-enabled application.
As many stages of look ahead as is desirable may be used to provide
predictions of future blocks. In a non-limiting example, given that
the streaming playback device requested block 4, a predictor could
predict that the next three blocks that will be requested by a
streaming playback device will be, in order of probability, 7, 15,
and 24.
[0075] In the example of FIG. 7, the flowchart 700 continues to
module 704 with adding predicted blocks to an output buffer in
order of priority until an output buffer has reached the data
window size. The blocks may be added in priority, by likelihood of
being requested, or by any known or convenient manner. In a
non-limiting example, the blocks added may begin with the block
requested followed by the block most likely be requested, until the
maximum, e.g., 32K of blocks have been added to the buffer.
[0076] In the example of FIG. 7, the flowchart 700 continues to
module 706 with transmitting the blocks in the output buffer. The
blocks in the buffer are provided to the streaming software client.
Having transmitted the blocks in the output buffer, the flowchart
terminates.
[0077] FIG. 8 depicts a diagram 800A of an example of a logical
expression of a block probability table and a diagram 800B of an
example of partially filled response buffer.
[0078] In the example of FIG. 8, the diagram 800A includes
probabilities of a streaming playback device requesting blocks.
Each row of this table includes the probability of each block being
needed after this block. Diagonally square (7,7), through square
(13,13) include no probabilities as such would calculate the
probability of a block being requested immediately after the block,
itself, is requested. In the example of FIG. 8, for example, the
probability of block 11 being requested after block 7 is 0.1, or
10%. In a non-limiting example, this might be because block 8 is
only used by the streamed application when an error condition
occurs, and that error condition only occurs about 10% of the time,
e.g., a misspelled file name.
[0079] In the example of FIG. 8, the diagram 800B includes the
partially filled response buffer storing a requested block as well
as a plurality of predicted blocks. It may not be necessary or
possible to fill the response buffer completely with blocks. In a
non-limiting example, if block 7 is requested, and no blocks are
predicted to follow block 7, then it may not be possible to fill a
10-block-long output buffer because there are not enough blocks to
send.
[0080] In determining a data window size, the following
non-limiting examples an interactive threshold may be calculated,
however, any known or convenient method of calculating a data
window size may be used. The following non-limiting examples are
provided for clarity.
[0081] In a non-limiting example of a method for calculating a data
window size MAX_WINDOW may be an upper limit of the largest amount
of data that the response buffer may hold. Throughput may be a
value representing the amount of data per unit of time that may be
transmitted on a network connecting a streaming server with a
streaming playback device. The data window size may thus be
calculated: data window=MIN[MAXIMUM_WINDOW, throughput*interactive
threshold].
[0082] In a non-limiting example, an interactive threshold is a
limit on the amount of time that an individual may be willing to
wait for a block. A latency may be a time delay between deciding to
transmit a block and transmitting the first bit of the block. A
throughput may be a value representing the amount of data per unit
of time that may be transmitted on a network connecting a streaming
server with a streaming playback device. The data window size may
thus be calculated: data window=(interactive threshold-round trip
latency)*throughput.
[0083] In a non-limiting example a MIN_WINDOW_SIZE may be a lower
limit of the smallest amount of data that the response buffer may
hold. An interactive threshold and a latency may be as described
above. The data window may be thus calculated data window=MAX
[MIN_WINDOW_SIZE, (interactive threshold-(round trip
latency))*throughput].
[0084] Any manner of determining a latency known or convenient may
be used. Latency may be important because an interactive threshold
is decreased by subtracting a latency from the time allowed for the
interactive threshold. Any manner of determining a latency known or
convenient may be used. Latency may be continuously recalculated
throughout a streaming session so as to provide an accurate data
window size.
[0085] Throughput may be dynamically determined by, at the
beginning of the session, sending test buffers between a server and
a client. The throughput may be calculated, based on the time
required to transmit the buffers, and the data size of the buffers.
The throughput could be run through a low pass filter to obtain an
average throughput, to get a constant updated throughput. If
something in the network switches routes, then we may adapt our
window size. Alternatively, any manner of determining a throughput
known or convenient may be used.
[0086] FIG. 9 depicts a diagram 900 of an example of a device for
streaming software. The computing system 900 may be a computing
system that can be used as a client computing system, such as a
wireless client or a workstation, or a server computing system. The
computing system 900 includes a computer 902, and a display device
906. The computer 902 includes a processor 908, interface 910,
memory 912, display controller 914, and non-volatile storage 916.
The computer 902 may be coupled to or include display device
906.
[0087] The computer 902 interfaces to external systems through the
interface 910, which may include a modem, network interface, CD-ROM
drive, DVD-ROM drive, or any known or convenient interface. An
interface may include one or more input-output devices. Interface
910 may include one or more interfaces. An interface may include a
device for reading a fixed media. An interface may receive
deliverables. An interface may transmit a stream-enabled
application. It will be appreciated that the interface 910 can be
considered to be part of the computing system 900 or a part of the
computer 902. The interface 910 can be an analog modem, ISDN modem,
cable modem, token ring interface, satellite transmission interface
(e.g. "direct PC"), or other interface for coupling a computing
system to other computing systems.
[0088] The processor 908 may be, for example, a microprocessor such
as an Intel Pentium microprocessor or Motorola power PC
microprocessor. The memory 912 is coupled to the processor 908 by a
bus 920. The memory 912 can be Dynamic Random Access Memory (DRAM)
and can also include Static RAM (SRAM). The bus 920 couples the
processor 908 to the memory 912, also to the non-volatile storage
916, and to the display controller 914.
[0089] The non-volatile storage 916 is often a magnetic hard disk,
an optical disk, or another form of storage for large amounts of
data. Some of this data is often written, by a direct memory access
process, into memory 912 during execution of software in the
computer 902. One of skill in the art will immediately recognize
that the terms "machine-readable medium" or "computer-readable
medium" includes any type of storage device that is accessible by
the processor 908 and also encompasses a carrier wave that encodes
a data signal.
[0090] The computing system 900 is one example of many possible
computing systems which have different architectures. For example,
personal computers based on an Intel microprocessor often have
multiple buses, one of which can be an I/O bus for the peripherals
and one that directly connects the processor 908 and the memory 912
(often referred to as a memory bus). The buses are connected
together through bridge components that perform any necessary
translation due to differing bus protocols.
[0091] Network computers are another type of computing system that
can be used in conjunction with the teachings provided herein.
Network computers do not usually include a hard disk or other mass
storage, and the executable programs are loaded from a network
connection into the memory 912 for execution by the processor 908.
A Web TV system, which is known in the art, is also considered to
be a computing system, but it may lack some of the features shown
in FIG. 9, such as certain input or output devices. A typical
computing system will usually include at least a processor, memory,
and a bus coupling the memory to the processor.
[0092] In addition, the computing system 900 is controlled by
operating system software which includes a file management system,
such as a disk operating system, which is part of the operating
system software. One example of operating system software with its
associated file management system software is the family of
operating systems known as Windows.RTM. from Microsoft Corporation
of Redmond, Wash., and their associated file management systems.
Another example of operating system software with its associated
file management system software is the Linux operating system and
its associated file management system. The file management system
is typically stored in the non-volatile storage 916 and causes the
processor 908 to execute the various acts required by the operating
system to input and output data and to store data in memory,
including storing files on the non-volatile storage 916.
[0093] Some portions of the detailed description are presented in
terms of algorithms and symbolic representations of operations on
data bits within a computer memory. These algorithmic descriptions
and representations are the means used by those skilled in the data
processing arts to most effectively convey the substance of their
work to others skilled in the art. An algorithm is here, and
generally, conceived to be a self-consistent sequence of operations
leading to a desired result. The operations are those requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these quantifies take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared, and otherwise manipulated. It has proven convenient at
times, principally for reasons of common usage, to refer to these
signals as bits, values, elements, symbols, characters, terms,
numbers, or the like.
[0094] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the following discussion, it is appreciated that throughout the
description, discussions utilizing terms such as "processing" or
"computing" or "calculating" or "determining" or "displaying" or
the like, refer to the action and processes of a computing system,
or similar electronic computing device, that manipulates and
transforms data represented as physical (electronic) quantities
within the computing system's registers and memories into other
data similarly represented as physical quantities within the
computing system memories or registers or other such information
storage, transmission or display devices.
[0095] The teachings included herein also relate to an apparatus
for performing the operations herein. This apparatus may be
specially constructed for the required purposes, or it may comprise
a general purpose computer selectively activated or reconfigured by
a computer program stored in the computer. Such a computer program
may be stored in a computer readable storage medium, such as, but
is not limited to, read-only memories (ROMs), random access
memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, any
type of disk including floppy disks, optical disks, CD-ROMs, and
magnetic-optical disks, or any type of media suitable for storing
electronic instructions, and each coupled to a computing system
bus.
[0096] The algorithms and displays presented herein are not
inherently related to any particular computer or other apparatus.
Various general purpose systems may be used with programs in
accordance with the teachings herein, or it may prove convenient to
construct more specialized apparatus to perform the required method
steps. The required structure for a variety of these systems will
appear from the description below. In addition, there is no
reference to any particular programming language, and various
examples may be implemented using a variety of programming
languages.
[0097] It will be appreciated to those skilled in the art that the
preceding examples and embodiments are exemplary and not limiting
in scope. It is intended that all permutations, enhancements,
equivalents, and improvements thereto that are apparent to those
skilled in the art upon a reading of the specification and a study
of the drawings are included within the true spirit and scope of
these teachings. It is therefore intended that the following
appended claims include all such modifications, permutations, and
equivalents as fall within the true spirit and scope of these
teachings.
* * * * *