Patent Application: US-201314401513-A

Abstract:
smart devices including smart phones and tablets are getting more powerful and become versatile enough to replace conventional personal computers . despite the rapid evolution of capabilities of such devices , controlling peripherals such as networked printers is infeasible due to lack of dedicated drivers to communicate with peripherals . to immediately enable smart devices to operate peripherals , a cloud - powered system , cloudbridge , is suggested . a cloudbridge application miming on a smart device works as a tcp bridge relaying packets between two tcp tunnels connected to a networked peripheral on one side and a cloud server on the other side . through the bridge , issuing operations from a smart device without having drivers becomes possible by asking the cloud server to interpret the operations to a language that the peripheral can understand , cloudbridge further optimizes user experience by using data compression that is adaptively applied by a decision function . the system implemented in android phones and linux servers is demonstrated to control networked printers on smart phones and tablets . the decision function is shown to optimize qoe metrics , such as response time and energy consumption though extensive evaluations .

Description:
cloudbridge enables peripheral support in smart devices in a way that is universally applicable and completely independent from local pcs . it also provides indistinguishable experiences to users while controlling peripherals comparing to that from a conventional pc . the cloudbridge system includes an application 100 for a smart device 102 and a daemon 104 for a cloud server 106 . as shown in fig1 , application 100 running on smart device 102 sets up tcp sockets to peripheral 108 and to cloud server 106 with the cloudbridge daemon 104 . using the sockets connecting both sides , cloudbridge application 100 configures smart device 102 to operate as a transparent bridge , allowing cloud server 106 with an appropriate driver to talk to the peripheral 108 . since cloud server 106 is assumed to have drivers for all candidate peripherals in the market and is controlled by application 100 , the peripherals become immediately accessible from smart device users . the proposed system can be used to operate networked printers or any other peripheral accessible to smart devices over a network . despite its simple yet efficient system architecture , our implementation using android smart phones and linux pcs reveals critical challenges in the system for its practicality . when cloudbridge is used to control networked printers , cloud server 106 obtains the information of printer 108 discovered around smart device 102 and a file to print chosen in smart device 102 . then server 106 interprets the file into a print stream written in a printer language , pcl ( printer command language ) using a proper driver that the target printer can understand . a challenge observed in the system is that the size of the print stream becomes huge ranging from a few times to a few hundred times of the original file size . given that our system relies on networking to deliver the stream to the printer , the increased size directly affects the user experiences such as the total time duration to get the print outs and the energy consumption of smart device 102 . to address the challenge , we investigate the characteristics of the printer stream and adopt a compression technique , which shows the best compression efficiency for the printer streams out of a large number of candidates . the adoption of compression brings trade - offs in the total print time and the energy consumption because compression and decompression also demand time and energy . for the optimal use of compression , we model and present a decision function optimizing user experiences by determining whether to apply compression or not . we perform extensive experiments with more than a thousand printer models from major printer vendors , whose drivers are installed in our cloud server . the results show that the ratio between the total print time of our system and the time using a conventional pc converges to b p / b c + 1 as the file size gets larger , where b p and b c denote the network bandwidth to the printer and to the cloud server , respectively . also , our decision function is verified to provide the best experience to users in 95 % of the time . we propose a cloud - powered network system , cloudbridge enabling peripheral support in smart devices . we implement the system in android smart devices and linux pcs and demonstrate the efficacy of the system for printing . we identify a challenge in the printing system and suggest a practical solution to the challenge by adopting a compression technique and its optimal decision function . this document is organized as follows . in section 1 , we overview related work regarding existing approaches to enable peripheral support in smart devices . we present the detailed architecture and implementation issues of cloudbridge system in section 2 . in section 3 , we extensively evaluate our implemented system and propose a compression decision function in section 4 . finally , we conclude the work in section 5 . in this section , we give special focus on techniques providing a specific peripheral support , printing from smart devices . there have been various techniques enabling printing from smart devices , but the existing techniques do not perfectly achieve our goal , independent , complete , and universal peripheral support for smart devices . we survey the techniques and discuss them in detail . a printer driver is software that converts digital data into a bit stream , which can be understood by a printer . to operate a printer on a computer , a specific driver needs to be installed beforehand . since printer manufacturers do not provide dedicated printer drivers to smart devices , there is no intuitive way to operate printers from smart devices . to address the problem , various approaches have been tried . the fact that there are over 300 printing applications in the android market shows high demand for a solution from users . current printing solutions including mobile applications available in the market are classified into three categories : 1 ) printing through a pc connected to a printer , 2 ) printing through a mobile driver ported from the driver for a pc , and 3 ) directly printing from a file at the printer . most of the applications listed in markets of ios and android make use of a local computer connected with a printer via a wired or a wireless connection . such applications send a file to the computer and ask the computer to print . airprint [ 2 ] from apple and cloudprint [ 6 ] from google adopt this approach . many android applications providing printing ability exploit the cloudprint service from google . this approach requires a setup procedure by a user and a pc to be always on for printing . we consider this approach does not meet our requirement of independence . to enable independent operation from a pc when printing from smart devices , another approach has been tried . several companies such as printershare [ 12 ] and the printer manufacturer samsung [ 13 ] provide applications with printer drivers converted for smart devices from the drivers originally developed for pcs . this approach immediately allows smart devices to operate printers ; however , the number of supported printers is very limited . also , functional limitations may exist since the drivers are not dedicated especially when the conversion is not done by the manufacturer . some proprietary functions may not be accessible . this approach lacks universal support . another approach mainly provided by printer manufacturers is printing a file directly at a printer without any help of a pc or a smart device . printer manufacturers call the technique , driver - less printing and recently launched a set of printers supporting the feature . in this approach , a smart device is only required to send a file to a printer over a network , then the printer itself interprets the file into a bit stream it can understand . hp eprint printers [ 7 ], kodak hero series [ 10 ] and the latest epson models [ 5 ] are examples of driver - less printers . this approach is desirable in that it does not put any burden on smart devices . in addition , this approach cannot provide universal support as most of the printers without such a feature cannot be utilized at all . more importantly , the approach has a fundamental limitation in supporting various file types , lacking the completeness . we cannot expect a printer to support all possible advanced file formats specific to advanced applications such as cad , psd , mat , key , and pptx . printing such files still requires a pc . table i summarizes features of the existing printing techniques compared with our proposed system , cloudbridge . as shown in the table , our approach relying on a cloud server satisfies all key features , it is independent , complete , and universal . until most of the printers understand almost all popular file formats without help of a pc , our proposed system can benefit smart devices . mobile printing solutions in the market and their characteristic classification based on three features . cloudbridge satisfies all key features : it provides independent , complete , and universal peripheral support . in this section , we provide a high level overview of our proposed system and present its detailed architecture . descriptions for the key components of our system follow . the cloudbridge system includes two major entities , ( 1 ) a cloudbridge application for smart devices and ( 2 ) a cloudbridge daemon for a cloud server . in one exemplary implementation , our system includes the following 6 steps of operation : 1 ) a smart device broadcasts a printer discovery query to a subnet or to a specific ip address and 2 ) gets the response back from one or more printers . 3 ) the smart device asks a user to choose a printer and a file to print and then forwards the corresponding response from the selected printer along with the selected file to our cloud server using a tcp socket . 4 ) the cloud server interprets the file into the print stream that the selected printer can understand using the matching driver in its database and 5 ) sends the stream back to the smart device . 6 ) finally , the smart device forwards the stream to the selected printer through another tcp socket established with the printer . in our system , the bidirectional tcp bridge between two sockets lying in both directions is the key component . smart phones with the bridge can handle more diverse operations , such as error messages delivered from the printer or the server . messages from printers will be forwarded to the server , where they are interpreted to human - readable message , and will come back to the smart device for notification . leveraging this bridge , our system provides immediate benefits to smart device users . however , potential performance degradation may appear because the interpreted data turns out to be much larger in size than the original data . for instance , when a file is converted to a print stream using a printer language such as pcl ( printer command language ) 4 or pcl 5 , the stream size becomes 50 to 100 times of the original file size depending on the file type and contents . the most recent version of printer language , pcl 6 reduces the inflation to less than 10 but the oversize issue still remains . to mitigate the impact of this issue , we adopt a decision function in the server to determine whether to compress the stream or not depending on the network bandwidth and the stream size . the decision function optimizes the end - to - end delay of a print job as well as energy consumption in the smart device . the function is trained from multiple runs of printing operations and once modeled from the training , it starts providing its decision . our extensive evaluation on the decision function is given in section 4 . the software architectures of two major components , cloudbridge application 100 for smart devices 102 and daemon 104 for a cloud server 106 are shown in fig2 . the cloudbridge application 100 configured to execute on smart device 102 is composed of three modules , which are a user - interface module 200 , networking module 202 , and stream handling module 204 . user - interface module 200 collects user input regarding printing options , such as color , duplex , and a preferred performance metric ( i . e ., energy , time ). user - interface module 200 also delivers messages from a printer as well as a server to a user in a human - readable format . networking module 202 includes two sub - modules , a peripheral discovery module 206 and a data relay module 208 . discovery module 206 uses service discovery protocol such as multicast - dns ( domain name system ), dns - sd ( dns - based service discovery ), ssdp ( simple service discovery protocol ) to find nearby printers , especially printers located in the same subnet . peripheral discovery module 206 can extend its discovery boundary by specifying an ip address of the target printer using snmp ( simple network management protocol ) queries . data relay module 208 bridges packets from the printer to the cloud server and vice versa . data relay module 208 listens on tcp sockets for both sides and whenever there are packets coming from one side , it extracts payloads from these packets , encapsulates them to new ones and then forward the packets to the other side . the process of encapsulating the packets into new packets includes header assembly of the new packets , where a new tcp header is created for each new packet and combining the header with the new packet payload . for packets destined for printer 108 , header assembly includes inserting the ip address of printer 108 in the packet header , inserting other header parameters , and computing and inserting the checksum . for packets destined for server 106 , header assembly includes adding the ip address of server 106 to the header , inserting the other header parameters , and computing and inserting the checksum . stream handling module 204 selectively works when the payloads in the packets are determined to be compressed . since the decision on compression is made at cloud server 106 , stream handling module 204 passively performs decompression . algorithm 1 below summarizes the work flow of cloudbridge application 100 running on a smart device . 102 set up tcp socket sock 1 with cloud server transmit target printer information and file to server set up tcp socket sock 2 with local printer if compressed print stream then receive entire data from server through sock 1 decompress data into raw print stream transmit stream to printer through sock 2 else while receive packet from server through sock 1 do transmit packet to printer through sock 2 end while end if return e ) cloudbridge daemon : cloudbridge daemon 104 running on cloud server 106 includes three modules , which are a networking module 210 , a stream handling module 212 , and a file handling module 214 . networking module 210 receives printer information along with a file to print from a smart device . once the print stream is ready from other modules , networking module 210 delivers the stream back to smart device 102 . stream handling module 212 is key to the cloudbridge daemon 104 . based on the printer information retrieved from smart device 102 , stream handling module 212 finds the matching driver in its database and generates the print stream with the options designated by the smart device user . upon generation of the print stream , stream handling module 212 makes a decision on whether to compress the stream or not applying a pre - trained decision function based on the network bandwidth and the stream size . operation of the compression decision function implemented by stream handing module 212 will be described in detail in section 4 . file handling module 214 only intervenes when the daemon receives a file of an advanced format , for example , which cannot be interpreted by the printer driver . in such cases , file handling module 214 selects the corresponding file converter and converts the file into a file of a basic format such as pdf or ps . the print quality is guaranteed after conversion as the converter borrows the conversion ability from full - fledged applications on conventional computers . in some cases where the print file type is platform specific , e . g ., only supported on mac os x or windows , file handling module 214 performs the conversion in the corresponding os virtualized in the cloud server . once the conversion is done , the converted file is delivered to stream handling module 212 which generates the print stream subsequently . the work flow of cloudbridge daemon 104 is summarized in algorithm 2 below . detailed operations between cloudbridge application 100 , daemon 104 , and printer 108 are illustrated as a timeline diagram in fig3 . the operations are sequential in time following the arrows shown in the figure . ( 1 ) smart device application 100 broadcasts a discovery request to all hosts on the same subnet or sends query directly to an ip designated host . ( 2 ) on receiving the query , printers , such as printer 108 , send responses back containing their printer information . ( 3 ) on receiving the responses , application 100 asks the user to select a printer and a file to print , and ( 4 ) delivers them all together to cloud server daemon 104 . ( 5 ) on receiving the file and the printer information , daemon 104 first checks the file type . if the file type is of advanced applications , file handling module 214 converts the file into a basic format . ( 6 ) once the conversion is done , stream handling module 212 interprets it into a print stream . ( 7 ) stream handling module 212 determines whether to compress the stream or not based on the network bandwidth and the size of the stream . stream handling module 212 then sends the print stream , either after compression or not , back to smart device application 100 through the tcp connection established in ( 4 ). ( 8 ) on receiving the stream , smart device application 100 decompresses the print stream if the print stream is compressed . then , smart device application 100 delivers the raw stream to the printer by setting up a new tcp socket . ( 9 ) along with the processes ( 1 )-( 8 ), smart device application 100 periodically sends error queries to printer 108 to check the error status of printer 108 , such as lack of paper or toner or incidence of paper jam and provides instant feedback to the user . smart device application 108 also forwards printer errors to daemon 104 and gets error interpretations back , similar to what the stream handling module 212 does . in this section , we extensively evaluate our proposed system using various android smart devices , two actual networked printers , a huge number of emulated printers and a linux server . the smart devices include samsung galaxy s ii , lg g2x , and htc evo shift . the actual printers are hp laserjet 4250 and brother 2270dw , and the emulated printers include over 1 , 000 networked printers in total from all major printer manufacturers , such as hp , canon , epson , and samsung . according to a recent article from idc ( international data corporation ) [ 9 ] regarding the world printer market share as of 2011 , the major manufacturers cover 80 % of the market ( i . e ., hp ( 42 . 9 %), canon ( 18 . 1 %), epson ( 12 . 6 %), and samsung ( 5 . 7 %)). we installed drivers for 680 hp printers , 131 canon printers , 212 epson printers and 144 samsung printers , 1 , 167 in total , on the linux server for the performance emulation of our system . the smart devices and the printers are located in the u . s . whereas the linux server working as a remote cloud server is placed in a different continent . for energy measurement , we use a digital power meter from monsoon [ 11 ], which is able to dump the power consumption readings into a pc via usb connection . we use the lowest reading interval of the device , 200 microseconds , to measure the most accurate energy consumption in the smart devices . first , we evaluate the end - to - end operation time of the system . from the user &# 39 ; s point of view , the operation time that he or she may go through using the system might be the most important performance metric . analyzing time portion taken by different operations helps identify the performance bottleneck of the system . we split the total printing time into four time intervals as shown in fig5 , and summarize them in table ii . the initialization time represents the total time from the moment a print request is initiated by a user to the moment that a cloud server receives the file to print and the printer information . file conversion time denotes the time taken for converting an advanced file format to a basic file format . for instance , when the server gets an advanced file format such as pptx ( microsoft powerpoint ), it asks conversion module of file handler 214 running on a virtualized windows machine to convert it to a pdf file . virtual machine and platform specific application are used to assure indistinguishable quality in the print jobs . applications not dedicated to a file format may also give print outs but the details such as layout , styles , and font are not comparable with the one printed out from the genuine application . note that the file conversion time is optional . more general file formats such as pdf , ps , jpg , gif , and txt do not require any conversion . stream generation time is measured from the time when a print command is committed to the time when the server generates a complete print stream . stream forwarding time is the time duration from the moment the first stream packet is sent by the server to the time when the printer has received the last packet . if compression is processed , compression and decompression time will be included in stream forwarding time . to get a clearer picture of the time distribution among all sub - procedures , we execute the first few measurements using an actual printer , hp laserjet 4250 , excluding compression and decompression procedures . later on , we extend the measurement to over 1 , 000 printers for both cases with and without compression . time intervals divided for each major communication sequence during the cloudbridge operation we measure the time consumption of each sub - procedure using various file types which we classify into two major groups , basic and advanced file formats . text , pdf and ps files fall into the basic group and microsoft word , excel , and powerpoint files are categorized as the advanced group demanding conversion . fig4 shows the time distribution measured from the basic group . it clarifies that the initialization and forwarding time for all three file types in the basic group are linearly increasing along with file size . this is intuitively understandable as these two metrics are pure networking transfer time , which is highly dependent on the data size . since the print stream is larger than the original file , the stream forwarding sub - procedure takes longer than the initialization . for instance , the forwarding time is 4 . 3 times longer than the initialization time for a 5 mb test file , and 5 times longer than the initialization for a 2 mb pdf file . an exception is the ps file as shown in fig4 ( c ), which spends more time on initialization than forwarding , e . g ., initialization time is 1 . 5 times longer than forwarding time for a 4 mb ps file . this is because ps file format is already quite close to the print stream format , therefore the stream size is almost the same as the original file . when the file size is similar , the initialization time can take longer because of the asymmetry in uplink and downlink bandwidth of an internet connection . the initialization time is mostly involved in file uploading whereas the stream forwarding is in fact a downloading process . similar results are observed for the advanced group as shown in fig5 . the initialization and the stream forwarding time are again linearly correlated with the file size , while the conversion time is also positively correlated to file size , but not linearly . despite the fact that the stream forwarding time is linear with file size for a certain file type , stream forwarding time varies highly across file types . for instance , when printing a 1 mb file , the stream forwarding time for powerpoint file ( about 300 seconds ) is much longer than those for word ( about 25 seconds ) and excel files ( about 15 seconds ). this resulted from the larger complexity embedded in powerpoint files . powerpoint files usually contain a lot of graphical contents , such as images , tables , and equations , which contribute to generate much larger print stream . specifically , we find that the stream forwarding time becomes a dominant factor for advanced file types , especially for complex file types , such as ppt . this finding motivates our approach of data compression , which is discussed in detail in section 4 . for both groups of file types , an interesting finding is that the stream generation time is almost constant disregarding file sizes and file types . more importantly , it is negligible comparing to the time consumption of other sub - procedures . from the measurement , we can conclude that the stream forwarding time dominantly determines the user experience on printing time . as we pointed out previously , the stream forwarding time is affected by both the stream size and current network bandwidth . next , we investigate which factors impact on the stream size . in this analysis , we specially focus on inflation factor ( if ) that is defined as the ratio between the size of the stream and the size of the original file . we investigate two major factors determining if value , file type and printer type and quantify their impacts on if values in various settings . we first verify the impact of file type to if values through an actual printer , hp laserjet 4250 . pcl 6 , which generates smaller print streams than any other version , is applied in hp 4250 printer driver . we test several file formats such as txt , pdf , and ppt and investigate the correlation between the original file size and the corresponding stream size with a data set of about 100 files per each format . we observe that txt files show strong correlation between the stream size of original file size , with a correlation coefficient of 0 . 99 while the average if is around 10 . this is intuitive as txt format is so simple that interpretation overhead will monotonically increase as the original file size increases . pdf files give a quite low correlation coefficient , 0 . 99 , and an average if of 3 . this is possibly due to the fact that pdf files usually contain many graphics which is not aligned thus leads to various stream sizes . the results also show that even with a high level printer language , e . g ., pcl 6 , the if values fluctuate from 2 to 10 on average . we subsequently present more of a detailed investigation on if values for over 1 , 000 printers from all major printer manufactures . to investigate the generalized if for various printers , we emulate aforementioned 1 , 167 printers whose drivers are installed in our cloud server . fig6 shows cdfs ( cumulative distribution function ) of if values for major manufacturers . from the test results using a text file of the size 1 kb , we observe that hp laserjet 4250 shows an if of 8 . 8 and the other printers give if values widely varying from 2 to 700 . our results indicate that the ifs are typically large . only 20 % of printers show if values less than 10 , while more than 40 % shows if values larger than 100 . we also verify that small if values less than 10 mostly come from printers with pcl 6 , as pcl 6 adopt smaller and more compact commands than previous versions . on the other hand , higher if values larger than 100 come from printers using previous pcl versions . it is natural to get a very large stream size when using low pcl version such as pcl 5 or 4 , because they are originally designed to convert a file into a raster image , which expresses a content on a paper using a bit sequence . for instance , when a printer only supports black and white color mode , 1 in the bit sequence corresponds to a black dot and 0 corresponds to no dot . in this representation , even a single character requires a large number of dots , thus leading to a large stream even when the file content is quite simple . fig7 shows experimental results using different file type , ps , which gives lower average if values compare to txt file type . over 80 % of the printers generate print streams smaller than ˜ 10 × of the original file size . based on our observation , print streams generated from ps files have low if due to the nature of file type which relies on a printer - friendly language . if for ps files can be considered as lower bound of if . however , one should note that higher versions of pcl do not always guarantee a lower if . according to a technical report from hp [ 8 ], there are several cases where lower pcl versions give smaller if than higher pcl versions . the difference comes from the characteristics of the languages . higher pcl versions remembers the objects as vectors whereas lower pcl versions record the locations of dots . recall that our aim is to enable printing from smart devices , the large if values of various printers however , limits the practicality of the system . to further optimize the performance , we consider to adopt a compression technique for the print stream . among various lossless compression techniques in the literature , we focus on ppm ( prediction by partial matching ) algorithm which is theoretically proven to be optimal in the compression ratio [ 15 ]-[ 18 ], where the compression ratio is defined by the size of the original data and the compressed data . fig8 shows the if values from the same set of printers for the same text file used in fig6 after applying ppm compression . on average , we get a compression ratio of 10 which indicates that the stream size reduced to 1 / 10 . beneficial from the high compression ratio , only 30 % out of 1 , 167 printers have if values of 20 or higher after compression . particularly , only 10 % of samsung printers give if values larger than 20 as they are applying higher version of pcl than printers from other manufactures . compression of the print stream , despite the obvious benefit , could also degrade the performance since compression and decompression take time and the decompression operation on a smart device is computation - intensive , leading to high energy consumption . to determine whether to compress the stream or not , we design a decision function configured to operate on cloud server 106 side . more particularly , as set forth above , the compression decision function may be implemented by stream handling module 212 of cloudprint daemon 104 . the decision function is designed to optimize user experience in terms of the total print time and the energy consumption of a smart device based on the parameters listed in table iii . we model each of the parameters using a few independent variables and create decision criteria for the print time and the energy consumption . note that the modeling process is embedded in cloudbridge application 100 and cloudbridge daemon 104 . when users of a specific smart device model run cloudbridge service multiple times , our system aggregates the log data and trains the models automatically . once the system collects enough training data , it stops collecting data and starts applying the decision criteria calculated from the models . parameters and their units used in decision function modeling . we consider x and b d as independent variables and model other parameters using independent variables . we first model the compression time t c at cloud server 106 and the decompression time t d at smart device 102 . fig9 and 10 show the measurement results from about 100 experimental runs with various files . the figures also show the best fitting regression equations for the measurement results . for an arbitrary stream size x , a linux server with intel quad - core i7 cpu shows t c = 1 . 45e − 4 x 2 + 0 . 1379x + 0 . 1464 . a smart device , the samsung galaxy s ii shows t d = 9 . 51e 3 x 2 + 7 . 26e − 2 x + 0 . 7218 . the size of the compressed stream , x c becomes 0 . 5x on average . note that the compression ratio becomes smaller than the ratio shown in fig8 , because of the limited capability of a ppm library for android smart devices . the compression ratios shown in fig8 can be considered as upper bounds of ppm method . we measure the downlink bandwidth between the server and the android device using probing packets at the beginning of the print stream transfer . a small portion of print stream is used for the bandwidth probing , if the server decides not to use the compression , then it transmits the rest of the raw print stream . otherwise , it compresses the rest and transmits the compressed print stream with an indicator that notifies smart device 102 that the print stream is compressed . we derive a decision function for the total print time indicating the time taken for end - to - end operation . eq . 1 shows the function determining whether to compress or not based on the total print time . the inequality inside the indicator function represents the total print time when the compression is applied ( right side ) or not ( left side ). note that the time for common sub - procedures , e . g ., initialization and stream generation , are cancelled out at both sides . applying regression equations for t c , t d , and x c simplifies the decision criteria and gets to eq . 2 . according to the simplified decision criteria , for a given stream size x , when the downlink bandwidth b d is lower than the value of 0 . 5x /( 0 . 020137x 2 − 0 . 11958x + 2 . 188 ), compression is recommended . this implies that when the downlink bandwidth is very high , sending a large print stream without compression provides better performance as it eliminates the compression and decompression time . we plot the decision function and ground truth data from a large number of experimental runs . to collect ground truth data , we configured a cloud server to always compress the stream and a smart device to decompress the stream . by collecting the timing information when the compression is applied , we can easily infer what will be the total print time in the case where the compression is not applied . note that cloudbridge application 100 is designed to report the collected timing information to the server when printing is not performed . we mark the ground truth to be o or x in the plot . the mark o stands for the ground truth where the compression gives shorter print time while x indicates the opposite case . the o marks over the decision criteria line are considered as false negatives ( fns ) and the x marks below the criteria are regarded as false positives ( fps ). we evaluate the accuracy of the decision criteria using fps and fns from ground truth data . for more practical evaluation , we define tolerance as a threshold value of the difference between the total print time with and without compression . we apply the tolerance to mitigate the strictness of fps and fns . for instance , when the tolerance is 1 second , we do not consider ground truth data having less than 1 second of difference in total print time as fps or fns . when the tolerance becomes 0 , fp and fn follow their conventional definition . the accuracy is measured by a well - known metric , mean squared error ( mse ) that averages out the squared errors for each incident . in our case , when fp or fn happens , we consider the error is 1 and is 0 otherwise . fig1 shows the mse of our decision criteria for various tolerance values . the figure tells that when users allow 5 seconds of tolerance , our decision criteria gives mse of 0 . 024 , telling that the decision is correct for about 97 . 6 % of the trials . we also design decision criteria in the aspect of energy consumption in a smart device . designing a decision function for energy consumption requires measurements on average power consumption for each of operations in the smart device . the current smart devices have no ability to measure the accurate power consumption from the device , so we measure the power using a digital power meter from monsoon [ 11 ]. fig1 illustrates measurement results of running the cloudbridge application on an android smart phone , samsung galaxy s ii . fig1 ( a ) represents the operation using compression while fig1 ( b ) shows the result without compression . we measure the power level from the beginning of the operations until the device forwards the received stream to the local printer . due to the redundancy , we omit the forwarding operation in the result . ( forwarding the stream from device to printer consumes the same time and power in both cases .) as shown in the graph , power consumption during data transmission and reception are high . from more than 10 times of runs , the average of p r is measured to be 717 mw . the average of p i is measured to be 415 mw . the power consumption while decompressing the stream is much higher . p d is measured to be about 2014 mw on average . it is important to note that the power consumption values may vary for different models of smart devices as they use diverse processors and wifi modem chipsets . hence , the decision criteria requires a set of power consumption values from different states per smart device . table iv gives the measurement results for three different android devices and shows diversity of the power level consumed on each device . average power level measured for each operation period on three different andriod devices . p i , p r , and p d denote for average power for idle state , data reception , and decompression , respectively . note that samsung galaxy s ii with a dual core pocessor consumes much higher power than others during decompression . once the average power consumptions are obtained , we can apply the values to the operation times to get a decision function on energy consumption , eq . 3 . a simplified criteria is given at eq . 4 , which determines whether to compress or not using b d and x . experimental results showing the ground truth data along with the decision criteria are illustrated in fig1 . fig1 again shows the mse for various tolerance values represented in mwh . the graph indicates that if a user can tolerate 0 . 5 mwh of energy difference , the decision function makes only 4 . 1 % errors over 770 trials . 0 . 5 mwh corresponds to 0 . 01 % of the total battery capacity of a typical smart phone ( e . g ., 5000 mwh ). according to the error ratio of two decision functions , eq . 2 and eq . 4 , our decision functions turn out to give highly accurate predictions . here we discuss the decision making for both criteria at the same time . when we overlap the decision functions together as shown in fig1 , it interestingly classifies the regions into 3 cases . when b d and x fall into the cases included in the lower region or the upper region , the decision making becomes easy . in those regions , the compression benefits both time and energy at the lower region whereas no compression benefits both in the upper region . the region in the middle is equivocal . the compression gives a benefit to the total print time , but gives a penalty to the energy . this is due to the nature of print stream which gives large variance depending on the file type , file size and the printer type . to avoid confusion of the system , we put a preference field in the cloudbridge application , which asks a user about her preference on energy and time . our decision functions in the cloud server fetch her preference from the application to provide the best experience to the user , even for the equivocal regions . as described above , the subject matter described herein includes a cloud - powered system , cloudbridge enabling peripheral support on smart devices . to prove the concept , we focus on printing from smart devices . cloudbridge is universally applicable to all smart devices and all networked printers or other network - accessible peripherals and is completely independent from personal computers . more importantly , it also provides indistinguishable experiences ( i . e ., quality of print outs ) to smart devices users from that of conventional computers . for practicality , we adopt decision functions to our system , optimizing performance metrics closely related to user experiences such as total print time and energy consumption in smart devices . through extensive measurements and training from the measurement data , the decision functions are shown to provide accurate decisions on whether to compress data or not with more than 95 % of probability . the cloudbridge system architecture relying on cloud servers for the intelligence of interpreting languages to communicate with peripherals has high flexibility and scalability by its nature . we expect our system architecture brings more practical solutions to smart devices helping such devices overcome functional limitations over conventional computers . as stated above , the subject matter described herein may be used to interface with printers without requiring driver software on the smart device . however , the subject matter described herein is not limited to interfacing with printers . other types of peripherals that the subject matter described herein may be used to interface with include fax machines , scanners , wireless mice , and cameras . in addition , the subject matter described herein is not limited to relaying the data directly to the peripheral . for example , in an alternate implementation of the subject matter described herein , the application on one smart device may relay the print data to an application on another smart device for storage or for direct interfacing with a peripheral , such as a printer . according to another aspect of the subject matter described herein , the peripheral and the mobile device desiring to access the peripheral may be located behind a firewall or network address translator ( nat ). in such a case , the server may communicate with the mobile device through the firewall using a pinhole created by connection between the mobile device and the server . the disclosure of each of the references below is incorporated herein by reference in its entirety : amazon web services , amazon elastic compute cloud ( amazon ec2 ). http :// aws . amazon . com / ec2 /. 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