Patent Publication Number: US-10789213-B2

Title: Calculation of digest segmentations for input data using similar data in a data deduplication system

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application, listed as U.S. application Ser. No. 13/941,873, is cross-related to the following seventeen applications each listed as: U.S. application Ser. Nos. 13/941,703, 13/941,951, 13/941,782, 13/941,886, 13/941,896, 13/941,694, 13/941,711, 13/941,958, 13/941,714, 13/941,742, 13/941,769, 13/942,009, 13/941,982, 13/941,800, 13/941,999, 13/942,027, and 13/942,048, all of which are filed on the same day as the present invention, Jul. 15, 2013, and the entire contents of which are incorporated herein by reference and are relied upon for claiming the benefit of priority. 
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates in general to computers, and more particularly to calculation of digest segmentations for input data using similar data in a data deduplication system in a computing environment. 
     Description of the Related Art 
     In today&#39;s society, computer systems are commonplace. Computer systems may be found in the workplace, at home, or at school. Computer systems may include data storage systems, or disk storage systems, to process and store data. Large amounts of data have to be processed daily and the current trend suggests that these amounts will continue being ever-increasing in the foreseeable future. An efficient way to alleviate the problem is by using deduplication. The idea underlying a deduplication system is to exploit the fact that large parts of the available data are copied again and again, by locating repeated data and storing only its first occurrence. Subsequent copies are replaced with pointers to the stored occurrence, which significantly reduces the storage requirements if the data is indeed repetitive. 
     SUMMARY OF THE DESCRIBED EMBODIMENTS 
     In one embodiment, a method is provided for calculation of digest segmentations for input data using similar data in a data deduplication system using a processor device in a computing environment. In one embodiment, by way of example only, a stream of input data is partitioned into input data chunks. Similar repository intervals are calculated for each input data chunk. Anchor positions are determined between an input data chunk and the similar repository intervals, based on data matches between a previous input data chunk and previous similar repository intervals. Digest segmentations of the similar repository intervals are projected onto the input data chunk, starting at the anchor positions. 
     In another embodiment, a computer system is provided for calculation of digest segmentations for input data using similar data in a data deduplication system using a processor device, in a computing environment. The computer system includes a computer-readable medium and a processor in operable communication with the computer-readable medium. In one embodiment, by way of example only, the processor, partitions a stream of input data into input data chunks. Similar repository intervals are calculated for each input data chunk. Anchor positions are determined between an input data chunk and the similar repository intervals, based on data matches between a previous input data chunk and previous similar repository intervals. Digest segmentations of the similar repository intervals are projected onto the input data chunk, starting at the anchor positions. 
     In a further embodiment, a computer program product is provided for calculation of digest segmentations for input data using similar data in a data deduplication system using a processor device, in a computing environment. The computer-readable storage medium has computer-readable program code portions stored thereon. The computer-readable program code portions include a first executable portion that, partitions a stream of input data into input data chunks. Similar repository intervals are calculated for each input data chunk. Anchor positions are determined between an input data chunk and the similar repository intervals, based on data matches between a previous input data chunk and previous similar repository intervals. Digest segmentations of the similar repository intervals are projected onto the input data chunk, starting at the anchor positions. 
     In addition to the foregoing exemplary method embodiment, other exemplary system and computer product embodiments are provided and supply related advantages. The foregoing summary has been provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: 
         FIG. 1  is a block diagram illustrating a computing system environment having an example storage device in which aspects of the present invention may be realized; 
         FIG. 2  is a block diagram illustrating a hardware structure of data storage system in a computer system in which aspects of the present invention may be realized; 
         FIG. 3  is a flowchart illustrating an exemplary method for digest retrieval based on similarity search in deduplication processing in a data deduplication system in which aspects of the present invention may be realized; 
         FIG. 4  is a flowchart illustrating an exemplary alternative method for digest retrieval based on similarity search in deduplication processing in a data deduplication system in which aspects of the present invention may be realized; 
         FIG. 5  is a flowchart illustrating an exemplary method for efficient calculation of both similarity search values and boundaries of digest blocks using a single linear calculation of rolling hash values in a data deduplication system in which aspects of the present invention may be realized; 
         FIG. 6  is a flowchart illustrating an exemplary method  600  for deduplication processing of an input data chunk in a data deduplication system in which aspects of the present invention may be realized; and 
         FIG. 7  is a flowchart illustrating an exemplary method for calculating candidate segmentations for an input data chunk in a data deduplication system in which aspects of the present invention may be realized. 
     
    
    
     DETAILED DESCRIPTION OF THE DRAWINGS 
     Data deduplication is a highly important and vibrant field in computing storage systems. Data deduplication refers to the reduction and/or elimination of redundant data. In data deduplication, a data object, which may be a file, a data stream, or some other form of data, is broken down into one or more parts called chunks or blocks. In a data deduplication process, duplicate copies of data are reduced or eliminated, leaving a minimal amount of redundant copies, or a single copy of the data, respectively. The goal of a data deduplication system is to store a single copy of duplicated data, and the challenges in achieving this goal are efficiently finding the duplicate data patterns in a typically large repository, and storing the data patterns in a storage efficient deduplicated form. A significant challenge in deduplication storage systems is scaling to support very large repositories of data. Such large repositories can reach sizes of Petabytes (1 Petabyte=2 50  bytes) or more. Deduplication storage systems supporting such repository sizes, must provide efficient processing for finding duplicate data patterns within the repositories, where efficiency is measured in resource consumption for achieving deduplication (resources may be CPU cycles, RAM storage, persistent storage, networking, etc.). In one embodiment, a deduplication storage system may be based on maintaining a search optimized index of values known as fingerprints or digests, where a (small) fingerprint represents a (larger) block of data in the repository. The fingerprint values may be cryptographic hash values calculated based on the blocks&#39; data. In one embodiment, secure hash algorithm (SHA), e.g. SHA-1 or SHA-256, which are a family of cryptographic hash functions, may be used. Identifying fingerprint matches, using index lookup, enables to store references to data that already exists in a repository. In one embodiment, block boundaries may be determined based on the data itself. 
     To provide reasonable deduplication in this approach, the mean size of the data blocks based on which fingerprints are generated must be limited to smaller sizes and may not be too large. The reason being that a change of a bit within a data block will probabilistically change the data block&#39;s corresponding fingerprint, and thus having large data blocks makes the scheme more sensitive to updates in the data as compared to having small blocks. A typical data block size may range from 4 KB to 64 KB, depending on the type of application and workload. Thus, by way of example only, small data blocks may range in sizes of up to 64 KB, and large data blocks are those data blocks having a size larger than 64 KB. 
     To support very large repositories scaling to Petabytes (e.g., repositories scaling to at least one Petabyte), the number of fingerprints to store coupled with the size of a fingerprint (ranging between 16 bytes and 64 bytes), becomes prohibitive. For example, for 1 Petabyte of deduplicated data, with a 4 KB mean data block size, and 32 bytes fingerprint size (e.g. of SHA-256), the storage required to store the fingerprints is 8 Terabytes. Maintaining a search optimized data structure for such volumes of fingerprints is difficult, and requires optimization techniques. However existing optimization techniques do not scale to these sizes while maintaining performance. For this reason, to provide reasonable performance, the supported repositories have to be relatively small (on the order of tens of TB). Even for such smaller sizes, considerable challenges and run-time costs arise due to the large scale of the fingerprint indexes, that create a bottle-neck (e.g., chunk look disk bottleneck) in deduplication processing. 
     To solve this problem, in one embodiment, a deduplication system may be based on a two-step approach for searching data patterns during deduplication. In the first step, a large chunk of incoming data (e.g. a few megabytes) is searched in the repository for similar (rather than identical) data chunks of existing data, and the incoming data chunk is partitioned accordingly into intervals and paired with corresponding (similar) repository intervals. In the second step, a byte-wise matching algorithm is applied on pairs of similar intervals, to identify identical sub-intervals, which are already stored in a repository of data. The matching algorithm of the second step relies on reading all the relevant similar data in the repository in order to compare it byte-wise to the input data. 
     Yet, a problem stemming from a byte-wise comparison of data underlying the matching algorithm of the second step, is that data of roughly the same size and rate as the incoming data should be read from the repository, for comparison purposes. For example, a system processing 1 GB of incoming data per second, should read about 1 GB of data per second from the repository for byte-wise comparison. This requires substantially high capacities of I/O per second of the storage devices storing the repository data, which in turn increases their cost. 
     Additional trends in information technology coinciding with the above problem are the following: (1) Improvements in the computing ability by increasing CPU speeds and the number of CPU cores. (2) Increase in disk density, while disk throughput remains relatively constant or improving only modestly. This means that there are fewer spindles relative to the data capacity, thus practically reducing the overall throughput. Due to the problem specified above, there is a need to design an alternative solution, to be integrated in a two step deduplication system embodiment specified above, that does not require reading from the repository in high rates/volumes. 
     Therefore, in one embodiment, by way of example only, additional embodiments address these problem, as well as shifts resource consumption from disks to the CPUs, to benefit from the above trends. The embodiments described herein are integrated within the two-step and scalable deduplication embodiments embodiment described above, and uses a similarity search to focus lookup of digests during deduplication. In one embodiment, a global similarity search is used as a basis for focusing the similarity search for digests of repository data that is most likely to match input data. 
     The embodiments described herein significantly reduce the capacity of I/O per second required of underlying disks, benefit from the increases in computing ability and in disk density, and considerably reduce the costs of processing, as well as maintenance costs and environmental overhead (e.g. power consumption). 
     In one embodiment, input data is segmented into small segments (e.g. 4 KB) and a digest (a cryptographic hash value, e.g. SHA1) is calculated for each such segment. First, a similarity search algorithm, as described above, is applied on an input chunk of data (e.g. 16 MB), and the positions of the most similar reference data in the repository are located and found. These positions are then used to lookup the digests of the similar reference data. The digests of all the data contained in the repository are stored and retrieved in a form that corresponds to their occurrence in the data. Given a position of a section of data contained in the repository, the digests associated with the section of data are efficiently located in the repository and retrieved. Next, these reference digests are loaded into memory, and instead of comparing data to find matches, the input digests and the loaded reference digests are matched. 
     The described embodiments provide a new fundamental approach for architecting a data deduplication system, which integrates a scalable two step approach of similarity search followed by a search of identical matching segments, with an efficient and cost effective digest/fingerprint based matching algorithm (instead of byte-wise data comparison). The digest/fingerprint based matching algorithm enables to read only a small fraction (1%) of the volume of data required by byte-wise data comparison. The present invention proposed herein, a deduplication system can provide high scalability to very large data repositories, in addition to high efficiency and performance, and reduced costs of processing and hardware. 
     In one embodiment, by way of example only, the term “similar data” may be referred to as: for any given input data, data which is similar to the input data is defined as data which is mostly the same (i.e. not entirely but at least 50% similar) as the input data. From looking at the data in a binary view (perspective), this means that similar data is data where most (i.e. not entirely but at least 50% similar) of the bytes are the same as the input data. 
     In one embodiment, by way of example only, the term “similar search” may be referred to as the process of searching for data which is similar to input data in a repository of data. In one embodiment, this process may be performed using a search structure of similarity elements, which is maintained and searched within. 
     In one embodiment, by way of example only, the term “similarity elements” may be calculated based on the data and facilitate a global search for data which is similar to input data in a repository of data. In general, one or more similarity elements are calculated, and represent, a large (e.g. at least 16 MB) chunk of data. 
     Thus, the various embodiments described herein provide various solutions for digest retrieval based on a similarity search in deduplication processing in a data deduplication system using a processor device in a computing environment. In one embodiment, by way of example only, input data is partitioned into fixed sized data chunks. Similarity elements, digest block boundaries and digest values are calculated for each of the fixed sized data chunks. Matching similarity elements are searched for in a search structure (i.e. index) containing the similarity elements for each of the fixed sized data chunks in a repository of data. Positions of similar data are located in a repository. The positions of the similar data are used to locate and load into the memory stored digest values and corresponding stored digest block boundaries of the similar data in the repository. The digest values and the corresponding digest block boundaries are matched with the stored digest values and the corresponding stored digest block boundaries to find data matches. 
     In one embodiment, the present invention provides a solution for utilizing a similarity search to load into memory the relevant digests from the repository, for efficient deduplication processing. In a data deduplication system, deduplication is performed by partitioning the data into large fixed sized chunks, and for each chunk calculating (2 things—similarity elements and digest blocks/digest values) hash values (digest block/digest value) for similarity search and digest values. The data deduplication system searches for matching similarity values of the chunks in a search structure of similarity values, and finds the positions of similar data in the repository. The data deduplication system uses these positions of similar data to locate and load into memory stored digests of the similar repository data, and matching input and repository digest values to find data matches. 
     In one embodiment, the present invention provides for efficient calculation of both similarity search values and segmentation (i.e. boundaries) of digest blocks using a single linear calculation of rolling hash values. In a data deduplication system, the input data is partitioned into chunks, and for each chunk a set of rolling hash values is calculated. A single linear scan of the rolling hash values produces both similarity search values and boundaries of the digest blocks of the chunk. Each rolling hash value corresponds to a consecutive window of bytes in byte offsets. The similarity search values are used to search for similar data in the repository. The digest blocks segmentation is used to calculate digest block boundaries and corresponding digest values of the chunk, for digests matching. Each rolling hash value contributes to the calculation of the similarity values and to the calculation of the digest blocks segmentations. Each rolling hash value may be discarded after contributing to the calculations. The described embodiment provides significant processing efficiency and reduction of CPU consumption, as well as considerable performance improvement. 
     Thus, as described above, the deduplication approach of the present invention uses a two-step process for searching data patterns during deduplication. In the first step, a large chunk of incoming data (e.g. 16 megabytes “MB”) is searched in the repository for similar (rather than identical) chunks of existing data, and the incoming chunk is partitioned accordingly into intervals, and paired with corresponding (similar) repository intervals. The similarity search structure (or “index”) used in the first step is compact and simple to maintain and search within, because the elements used for a similarity search are very compact relative to the data they represent (e.g. 16 bytes representing 4 megabytes). Further included in the first step, in addition to a calculation of similarity elements, is a calculation of digest segments and respective digest values for the input chunk of data. All these calculations are based on a single calculation of rolling hash values. In the second step, reference digests of the similar repository intervals are retrieved, and then the input digests are matched with the reference digests, to identify data matches. 
     In one embodiment, in the similarity based deduplication approach as described herein, a stream of input data is partitioned into chunks (e.g. at least 16 MB), and each chunk is processed in two main steps. In the first step a similarity search process is applied, and positions of the most similar reference data in the repository are found. Within this step both similarity search elements and digest segments boundaries are calculated for the input chunk, based on a single linear calculation of rolling hash values. Digest values are calculated for the input chunk based on the produced segmentation, and stored in memory in the sequence of their occurrence in the input data. The positions of similar data are then used to lookup the digests of the similar reference data and load these digests into memory, also in a sequential form. Then, the input digests are matched with the reference digests to form data matches. 
     When deduplication of an input chunk of data is complete, the digests associated with the input chunk of data are stored in the repository, to serve as reference digests for subsequent input data. The digests are stored in a linear form, which is independent of the deduplicated form by which the data these digests describe is stored, and in the sequence of their occurrence in the data. This method of storage enables efficient retrieval of sections of digests, independent of fragmentation characterizing deduplicated storage forms, and thus low on IO and computational resource consumption. 
     While the above process is efficient, specific properties of backup environments can be leveraged to further improve the efficiency of the solution. An important observation, which has been proven to be highly characteristic of backup environments, is that when an interval of repository data is identified as similar to a chunk of input data, there is considerably high probability that the data following this interval in the repository will be referenced shortly after by subsequent input data. This important property enables designing a solution where the similarity search step can be avoided as long as deduplication of the input data, based on previously determined references, is producing good results. In one embodiment, by way of example only, one example of good deduplication results is coverage of the input chunk of data, with matches to repository data, equal to and/or exceeding 70% of the size of the input chunk of data. This optimization saves considerable resources during run-time (e.g. IO operations, CPU consumption, networking consumption, serialization), as well as provides a layer of protection from possible spurious similarity search results (workloads which are more difficult for deduplication may produce at times spurious similarity search results), thus also improving the deduplication results. 
     Furthermore, in cases where the similarity search step is avoided, the calculation of the similarity elements is practically not required. Designing a way to also remove the need to calculate segmentation of the input data to digest segments, will enable to entirely avoid the calculation of the rolling hash values for chunks of input data. Since calculation of rolling hash values (i.e. a hash value for each seed at each byte offset of the input data), is a computationally intense operation, avoiding it will result in considerable improvement in the efficiency of the deduplication process. Therefore, an additional problem in this context is how to calculate digest segments and respective digest values for the input data, where similarity search is avoided. 
     The present invention provides a solution for both these problems. In one embodiment, the present invention provides a first algorithm enabling to reduce the frequency of applying similarity search for input data, and then a second algorithm enables to calculate digest segments and respective digest values for input data, without calculating rolling hash values. The algorithms of the present invention provide considerable additional efficiency and effectiveness of the deduplication process. 
     In one embodiment of the present invention, a stream of input data is partitioned into chunks (e.g. 16 MB), and the stream is assigned with a dedicated data structure, denoted as “reference set”, which contains a current set of positions into the repository data, where each position starts an interval of repository data identified as similar to the last chunk of data in the input stream. This data structure also contains a measure of the goodness of the deduplication result of the last chunk of data in the input stream. At initiation the set of positions is empty, and the measure of deduplication result is set to nil. 
     In one embodiment, a deduplication process receives a chunk of input data, associated with a specific input stream, and determines whether to activate similarity search for the input chunk, based on the information in the reference set data structure. If the measure of goodness of the deduplication result is low (e.g. below a predetermined deduplication result threshold) or nil, then similarity search is activated, and its results, namely the positions associated with the set of repository intervals which are identified as similar, are inserted into the reference set, replacing previous contents of the reference set if exists. Within the similarity search calculations, also digest segments boundaries and respective digest values are calculated for the input chunk, based on the calculated rolling hash values. If, on the other hand, the measure of goodness of the deduplication result is sufficiently high (e.g., above a predetermined deduplication result threshold), also implying that the set of positions in the reference set is not empty, then similarity search is avoided, and the positions in the reference set are updated, to reflect repository intervals immediately following the previous repository intervals. 
     In one embodiment, the positions of the similar repository data intervals are then used to lookup their respective digests and load these digests into memory for matching. When deduplication processing of an input chunk of data completes, the measure of goodness of the deduplication result is calculated for the input chunk, and updated in the reference set data structure. If the measure of goodness of the deduplication result of the input chunk is low, and the input chunk was processed without similarity search, then the input chunk is reprocessed with similarity search. 
     In cases where similarity search is avoided, digest segments boundaries are calculated by determining appropriate anchor positions in the input chunk and in the similar reference intervals, and projecting the segmentations of the reference intervals on the input chunk. For each projected segmentation, respective digest values are calculated, and then used for matching with the repository digests. Thus, the algorithms of the present invention considerably increase the efficiency, throughput and effectiveness of the deduplication process. 
     In one embodiment, focusing on a conditional activation of similarity search for an input chunk based on the deduplication result of a previous chunk in the input stream, a stream of input data is partitioned into chunks, and a determination is made as to whether to apply similarity search for an input chunk based on the deduplication result of a previous chunk in the input stream. If the deduplication result of a previous chunk in the input stream is not sufficiently high or does not exist, then similarity search is applied. If the deduplication result of a previous chunk in the input stream is sufficiently high then similarity search is avoided. In one embodiment, specifications of the similar intervals produced by similarity search are stored in a reference set data structure associated with the input stream, replacing any previous contents, if exists, in the reference set data structure. The positions of the current similar intervals in the repository are calculated based on the positions of previous similar intervals in the repository, by incrementing the positions of the previous similar intervals to reflect current similar intervals immediately following the previous similar intervals. The deduplication result of a previous chunk in the input stream is defined as the total matched size of the chunk divided by the total size of the chunk. The total matched size of a chunk in the input stream is defined as the total size of the portions of the chunk, which are covered by matches to repository data. If the deduplication result of a current input chunk is not sufficiently high after deduplication processing without similarity search, then the input chunk is reprocessed with similarity search applied. 
     In one embodiment, focusing on the calculation of candidate digest segmentations for an input chunk, based on information of similar repository intervals, and without calculating rolling hash values for the input chunk, the present invention activates the calculation algorithm in cases where similarity search is avoided, thus also avoiding the rolling hash calculation for the input chunk. In one embodiment, a stream of input data is partitioned into chunks, and repository intervals, which are similar to an input chunk, are determined. Then for each similar repository interval, its digest segmentation is projected onto the input chunk starting at an anchor position, thus forming a plurality of alternative digest segmentations for the input chunk. In one embodiment, an anchor position is defined as a pair of ending positions of a last data match, in the input data and in the repository data, calculated between a previous chunk in the same input stream and a previous similar repository interval, whose ending positions in the input stream and in the repository data are closest to the starting positions of the current input chunk and of the current similar repository interval respectively. The position and size of the last data match for each similar repository interval are stored for each input stream. The projection of a digest segmentation onto the input chunk is done based on the positions and sizes of the digest segments following an anchor position. Namely, the positions and sizes of the digest segments of a similar repository interval, starting at an anchor position, are projected onto the input chunk, starting at the respective anchor position. A candidate digest segmentation is calculated for the input chunk based on each one of the similar intervals, and respective digest values are computed using each candidate segmentation. The candidate segmentation that produced the best deduplication ratio for the input chunk is selected for storage. If the input chunk is partitioned into sub-sections, such that each sub-section has its own set of similar repository intervals, then the digest segmentations selected for each sub-section are concatenated into a single digest segmentation. 
     Turning now to  FIG. 1 , exemplary architecture  10  of a computing system environment is depicted. The computer system  10  includes central processing unit (CPU)  12 , which is connected to communication port  18  and memory device  16 . The communication port  18  is in communication with a communication network  20 . The communication network  20  and storage network may be configured to be in communication with server (hosts)  24  and storage systems, which may include storage devices  14 . The storage systems may include hard disk drive (HDD) devices, solid-state devices (SSD) etc., which may be configured in a redundant array of independent disks (RAID). The operations as described below may be executed on storage device(s)  14 , located in system  10  or elsewhere and may have multiple memory devices  16  working independently and/or in conjunction with other CPU devices  12 . Memory device  16  may include such memory as electrically erasable programmable read only memory (EEPROM) or a host of related devices. Memory device  16  and storage devices  14  are connected to CPU  12  via a signal-bearing medium. In addition, CPU  12  is connected through communication port  18  to a communication network  20 , having an attached plurality of additional computer host systems  24 . In addition, memory device  16  and the CPU  12  may be embedded and included in each component of the computing system  10 . Each storage system may also include separate and/or distinct memory devices  16  and CPU  12  that work in conjunction or as a separate memory device  16  and/or CPU  12 . 
       FIG. 2  is an exemplary block diagram  200  showing a hardware structure of a data storage system in a computer system according to the present invention. Host computers  210 ,  220 ,  225 , are shown, each acting as a central processing unit for performing data processing as part of a data storage system  200 . The cluster hosts/nodes (physical or virtual devices),  210 ,  220 , and  225  may be one or more new physical devices or logical devices to accomplish the purposes of the present invention in the data storage system  200 . In one embodiment, by way of example only, a data storage system  200  may be implemented as IBM® ProtecTIER® deduplication system TS7650G™. A Network connection  260  may be a fibre channel fabric, a fibre channel point to point link, a fibre channel over ethernet fabric or point to point link, a FICON or ESCON I/O interface, any other I/O interface type, a wireless network, a wired network, a LAN, a WAN, heterogeneous, homogeneous, public (i.e. the Internet), private, or any combination thereof. The hosts,  210 ,  220 , and  225  may be local or distributed among one or more locations and may be equipped with any type of fabric (or fabric channel) (not shown in  FIG. 2 ) or network adapter  260  to the storage controller  240 , such as Fibre channel, FICON, ESCON, Ethernet, fiber optic, wireless, or coaxial adapters. Data storage system  200  is accordingly equipped with a suitable fabric (not shown in  FIG. 2 ) or network adaptor  260  to communicate. Data storage system  200  is depicted in  FIG. 2  comprising storage controllers  240  and cluster hosts  210 ,  220 , and  225 . The cluster hosts  210 ,  220 , and  225  may include cluster nodes. 
     To facilitate a clearer understanding of the methods described herein, storage controller  240  is shown in  FIG. 2  as a single processing unit, including a microprocessor  242 , system memory  243  and nonvolatile storage (“NVS”)  216 . It is noted that in some embodiments, storage controller  240  is comprised of multiple processing units, each with their own processor complex and system memory, and interconnected by a dedicated network within data storage system  200 . Storage  230  (labeled as  230   a ,  230   b , and  230   n  in  FIG. 3 ) may be comprised of one or more storage devices, such as storage arrays, which are connected to storage controller  240  (by a storage network) with one or more cluster hosts  210 ,  220 , and  225  connected to each storage controller  240 . 
     In some embodiments, the devices included in storage  230  may be connected in a loop architecture. Storage controller  240  manages storage  230  and facilitates the processing of write and read requests intended for storage  230 . The system memory  243  of storage controller  240  stores program instructions and data, which the processor  242  may access for executing functions and method steps of the present invention for executing and managing storage  230  as described herein. In one embodiment, system memory  243  includes, is in association with, or is in communication with the operation software  250  for performing methods and operations described herein. As shown in  FIG. 2 , system memory  243  may also include or be in communication with a cache  245  for storage  230 , also referred to herein as a “cache memory”, for buffering “write data” and “read data”, which respectively refer to write/read requests and their associated data. In one embodiment, cache  245  is allocated in a device external to system memory  243 , yet remains accessible by microprocessor  242  and may serve to provide additional security against data loss, in addition to carrying out the operations as described in herein. 
     In some embodiments, cache  245  is implemented with a volatile memory and non-volatile memory and coupled to microprocessor  242  via a local bus (not shown in  FIG. 2 ) for enhanced performance of data storage system  200 . The NVS  216  included in data storage controller is accessible by microprocessor  242  and serves to provide additional support for operations and execution of the present invention as described in other figures. The NVS  216 , may also referred to as a “persistent” cache, or “cache memory” and is implemented with nonvolatile memory that may or may not utilize external power to retain data stored therein. The NVS may be stored in and with the cache  245  for any purposes suited to accomplish the objectives of the present invention. In some embodiments, a backup power source (not shown in  FIG. 2 ), such as a battery, supplies NVS  216  with sufficient power to retain the data stored therein in case of power loss to data storage system  200 . In certain embodiments, the capacity of NVS  216  is less than or equal to the total capacity of cache  245 . 
     Storage  230  may be physically comprised of one or more storage devices, such as storage arrays. A storage array is a logical grouping of individual storage devices, such as a hard disk. In certain embodiments, storage  230  is comprised of a JBOD (Just a Bunch of Disks) array or a RAID (Redundant Array of Independent Disks) array. A collection of physical storage arrays may be further combined to form a rank, which dissociates the physical storage from the logical configuration. The storage space in a rank may be allocated into logical volumes, which define the storage location specified in a write/read request. 
     In one embodiment, by way of example only, the storage system as shown in  FIG. 2  may include a logical volume, or simply “volume,” may have different kinds of allocations. Storage  230   a ,  230   b  and  230   n  are shown as ranks in data storage system  200 , and are referred to herein as rank  230   a ,  230   b  and  230   n . Ranks may be local to data storage system  200 , or may be located at a physically remote location. In other words, a local storage controller may connect with a remote storage controller and manage storage at the remote location. Rank  230   a  is shown configured with two entire volumes,  234  and  236 , as well as one partial volume  232   a . Rank  230   b  is shown with another partial volume  232   b . Thus volume  232  is allocated across ranks  230   a  and  230   b . Rank  230   n  is shown as being fully allocated to volume  238 —that is, rank  230   n  refers to the entire physical storage for volume  238 . From the above examples, it will be appreciated that a rank may be configured to include one or more partial and/or entire volumes. Volumes and ranks may further be divided into so-called “tracks,” which represent a fixed block of storage. A track is therefore associated with a given volume and may be given a given rank. 
     The storage controller  240  may include a data duplication module  255 , a similarity index module  257  (e.g., a similarity search structure), a similarity search module  259 , and a data structure module  260  (e.g., a reference set data structure). The data duplication module  255 , the similarity index module  257 , the similarity search module  259 , and the data structure module  260  may work in conjunction with each and every component of the storage controller  240 , the hosts  210 ,  220 ,  225 , and storage devices  230 . The data duplication module  255 , the similarity index module  257 , the similarity search module  259 , and the data structure module  260  may be structurally one complete module or may be associated and/or included with other individual modules. The data duplication module  255 , the similarity index module  257 , the similarity search module  259 , and the data structure module  260  may also be located in the cache  245  or other components. 
     The storage controller  240  includes a control switch  241  for controlling the fiber channel protocol to the host computers  210 ,  220 ,  225 , a microprocessor  242  for controlling all the storage controller  240 , a nonvolatile control memory  243  for storing a microprogram (operation software)  250  for controlling the operation of storage controller  240 , data for control, cache  245  for temporarily storing (buffering) data, and buffers  244  for assisting the cache  245  to read and write data, a control switch  241  for controlling a protocol to control data transfer to or from the storage devices  230 , the data duplication module  255 , the similarity index module  257 , and the similarity search module  259 , in which information may be set. Multiple buffers  244  may be implemented with the present invention to assist with the operations as described herein. In one embodiment, the cluster hosts/nodes,  210 ,  220 ,  225  and the storage controller  240  are connected through a network adaptor (this could be a fibre channel)  260  as an interface i.e., via at least one switch called “fabric.” 
     In one embodiment, the host computers or one or more physical or virtual devices,  210 ,  220 ,  225  and the storage controller  240  are connected through a network (this could be a fibre channel)  260  as an interface i.e., via at least one switch called “fabric.” In one embodiment, the operation of the system shown in  FIG. 2  will be described. The microprocessor  242  may control the memory  243  to store command information from the host device (physical or virtual)  210  and information for identifying the host device (physical or virtual)  210 . The control switch  241 , the buffers  244 , the cache  245 , the operating software  250 , the microprocessor  242 , memory  243 , NVS  216 , data duplication module  255 , the similarity index module  257 , the similarity search module  259 , and the data structure module  260  are in communication with each other and may be separate or one individual component(s). Also, several, if not all of the components, such as the operation software  250  may be included with the memory  243 . Each of the components within the devices shown may be linked together and may be in communication with each other for purposes suited to the present invention. As mentioned above, the data duplication module  255 , the similarity index module  257 , the similarity search module  259 , and the data structure module  260  may also be located in the cache  245  or other components. As such, the data duplication module  255 , the similarity index module  257 , the similarity search module  259 , and the data structure module  260  maybe used as needed, based upon the storage architecture and users preferences. 
     As mentioned above, in one embodiment, the input data is partitioned into large fixed size chunks (e.g. 16 MB), and a similarity search procedure is applied for each input chunk. A similarity search procedure calculates compact similarity elements, which may also be referred to as distinguishing characteristics (DCs), based on the input chunk of data, and searches for matching similarity elements stored in a compact search structure (i.e. index) in the repository. The size of the similarity elements stored per each chunk of data is typically 32 bytes (where the chunk size is a few megabytes), thus making the search structure storing the similarity elements very compact and simple to maintain and search within. 
     The similarity elements are calculated by calculating rolling hash values on the chunk&#39;s data, namely producing a rolling hash value for each consecutive window of bytes in a byte offset, and then selecting specific hash values and associated positions (not necessarily the exact positions of these hash values) to be the similarity elements of the chunk. 
     One important aspect and novelty provided by the present invention is that a single linear calculation of rolling hash values, which is a computationally expensive operation, serves as basis for calculating both the similarity elements of a chunk (for a similarity search) and the segmentation of the chunk&#39;s data into digest blocks (for finding exact matches). Each rolling hash value is added to the calculation of the similarity elements as well as to the calculation of the digest blocks segmentation. After being added to the two calculations, a rolling hash value can be discarded, as the need to store the rolling hash values is minimized or eliminated. This algorithmic element provides significant efficiency and reduction of CPU consumption, as well as considerable performance improvement. 
     In one embodiment, the similarity search procedure of the present invention produces two types of output. The first type of output is a set of positions of the most similar reference data in the repository. The second type of output is the digests of the input chunk, comprising of the segmentation to digest blocks and the digest values corresponding to the digest blocks, where the digest values are calculated based on the data of the digest blocks. 
     In one embodiment, the digests are stored in the repository in a form that corresponds to the digests occurrence in the data. Given a position in the repository and size of a section of data, the location in the repository of the digests corresponding to that interval of data is efficiently determined. The positions produced by the similarity search procedure are then used to lookup the stored digests of the similar reference data, and to load these reference digests into memory. Then, rather than comparing data, the input digests and the loaded reference digests are matched. The matching process is performed by loading the reference digests into a compact search structure of digests in memory, and then for each input digest, querying the search structure of digests for existence of that digest value. Search in the search structure of digests is performed based on the digest values. If a match is found, then the input data associated with that digest is determined to be found in the repository and the position of the input data in the repository is determined based on the reference digest&#39;s position in the repository. In this case, the identity between the input data covered by the input digest, and the repository data covered by the matching reference digest, is recorded. If a match is not found then the input data associated with that digest is determined to be not found in the repository, and is recorded as new data. In one embodiment, the similarity search structure is a global search structure of similarity elements, and a memory search structure of digests&#39; is a local search structure of digests in memory. The search in the memory search structure of digests is performed by the digest values. 
       FIG. 3  is a flowchart illustrating an exemplary method  300  for digest retrieval based on similarity search in deduplication processing in a data deduplication system in which aspects of the present invention may be realized. The method  300  begins (step  302 ). The method  300  partitions input data into data chunks (step  304 ). The input data may be partitioned into fixed sized data chunks. The method  300  calculates, for each of the data chunks, similarity elements, digest block boundaries, and corresponding digest values are calculated (step  306 ). The method  300  searches for matching similarity elements in a search structure (i.e. index) for each of the data chunks (which may be fixed size data chunks) (step  308 ). The positions of the similar data in a repository (e.g., a repository of data) are located (step  310 ). The method  300  uses the positions of the similar data to locate and load into memory stored digest values and corresponding stored digest block boundaries of the similar data in the repository (step  312 ). The method  300  matches the digest values and the corresponding digest block boundaries of the input data with the stored digest values and the corresponding stored digest block boundaries to find data matches (step  314 ). The method  300  ends (step  316 ). 
       FIG. 4  is a flowchart illustrating an exemplary alternative method  400  for digest retrieval based on similarity search in deduplication processing in a data deduplication system in which aspects of the present invention may be realized. The method  400  begins (step  402 ). The method  400  partitions the input data into chunks (e.g., partitions the input data into large fixed size chunks) (step  404 ), and for an input data chunk calculates rolling hash values, similarity elements, digest block boundaries, and digest values based on data of the input data chunk (step  406 ). The method  400  searches for similarity elements of the input data chunk in a similarity search structure (i.e. index) (step  408  and  410 ). The method  400  determines if there are enough or a sufficient amount of matching similarity elements (step  412 ). If not enough matching similarity elements are found then the method  400  determines that no similar data is found in the repository for the input data chunk, and the data of the input chunk is stored in a repository (step  414 ) and then the method  400  ends (step  438 ). If enough similarity elements are found, then for each similar data interval found in a repository, the method  400  determines the position and size of each similar data interval in the repository (step  416 ). The method  400  locates the digests representing the similar data interval in the repository (step  418 ). The method  400  loads these digests into a search data structure of digests in memory (step  420 ). The method  400  determines if there are any additional similar data intervals (step  422 ). If yes, the method  400  returns to step  416 . If no, the method  400  considers each digest of the input data chunk (step  424 ). The method  400  determines if the digest value exists in the memory search structure of digests (step  426 ). If yes, the method  400  records the identity between the input data covered by the digest and the repository data having the matching digest value (step  428 ). If no, the method  400  records that the input data covered by the digest is not found in the repository (step  430 ). From both steps  428  and  430 , the method  400  determines if there are additional digests of the input data chunk (step  432 ). If yes, the method  400  returns to step  424 . If no, method  400  removes the similarity elements of the matched data in the repository from the similarity search structure (step  434  and step  410 ). The method  400  adds the similarity elements of the input data chunk to the similarity search structure (step  436 ). The method  400  ends (step  438 ). 
       FIG. 5  is a flowchart illustrating an exemplary method  500  for efficient calculation of both similarity search values and boundaries of digest blocks using a single linear calculation of rolling hash values in a data deduplication system in which aspects of the present invention may be realized. The method  500  begins (step  502 ). The method  500  partitions input data into data chunks (steps  504 ). The data chunks may be fixed sized data chunks. The method  500  considers each consecutive window of bytes in a byte offset in the input data (step  506 ). The method  500  determines if there is an additional consecutive window of bytes to be processed (step  508 ). If yes, the method  500  calculates a rolling hash value based on the data of the consecutive window of bytes (step  510 ). The method  500  contributes the rolling hash value to the calculation of the similarity values and to the calculation of the digest blocks segmentations (i.e., the digest block boundaries) (step  512 ). The method  500  discards the rolling hash value (step  514 ), and returns to step  506 . If no, the method  500  concludes the calculation of the similarity elements and of the digest blocks segmentation, producing the final similarity elements and digest blocks segmentation of the input data (step  516 ). The method  500  calculates digest values based on the digest blocks segmentation, wherein each digest block is assigned with a corresponding digest value (step  518 ). The similarity elements are used to search for similar data in the repository (step  520 ). The digest blocks and corresponding digest values are used for matching with digest blocks and corresponding digest values stored in a repository for determining data in the repository which is identical to the input data (step  522 ). The method  500  ends (step  524 ). 
     As mentioned above, in one embodiment, each input stream of data is assigned with a dedicated data structure, denoted as a “reference set”, which contains a current set of positions into the repository data, where each position starts an interval of repository data identified as similar to the last chunk of data in the input stream. This data structure also contains a measure of the goodness of the deduplication result of the previous chunk of data in the input stream, which is defined as the deduplication ratio of the previous chunk, namely the total size of the portions of the previous chunk which are covered by matches to repository data divided by the total size of the previous chunk. In one embodiment, the deduplication ratio value is defined as sufficiently good if it is not less than a predefined threshold. An example of a predefined threshold would be 70%. If the deduplication ratio value is less than the predefined threshold then the deduplication ratio value is defined as not sufficiently good (e.g., less than the predefined threshold). 
     A deduplication process receives a chunk of input data, associated with a specific input stream, and determines whether to activate similarity search for the input chunk, based on the information in the reference set data structure. In one embodiment, two states are defined as follows: A “reference set recalculation state” is applied at the beginning of an input stream, and in cases where deduplication processing of an input chunk did not yield a sufficiently good deduplication result. This state triggers activation of similarity search for an input chunk. A “valid reference set state” is applied when processing an input chunk which is not at the beginning of an input stream, and where deduplication processing of the previous chunk in the input stream provided a sufficiently good deduplication result. In this state, similarity search is avoided. The state changes from reference set recalculation to valid reference set when the deduplication result of an input chunk is sufficiently good; and changes back to reference set recalculation when the deduplication result of an input chunk is not sufficiently good. 
     As described in  FIG. 6 , below, the method of the present invention receives as input an input chunk of data associated with a specific stream of input data, and a reference set data structure associated with the input stream. If the measure of deduplication result in the reference set is not sufficiently high or nil, then the method performs the following: activates a similarity search for the input chunk, and obtains a list of similar repository intervals, where each interval is specified by a position and size; and stores the specifications of the similar intervals in the reference set data structure, replacing any previous contents in the reference set data structure if exists. Within similarity search, the following are calculated for the input chunk (based on calculation of rolling hash values): similarity elements, digest segments boundaries and respective digests values. 
     If the measure of deduplication result in the reference set is sufficiently high, the method advances the positions in the reference set data structure to reflect repository intervals immediately following the current repository intervals, and calculates digest segments boundaries and respective digest values for the input chunk based on information of the calculated similar repository intervals. The method to achieve this is elaborated in the following. 
     In one embodiment, the similar intervals specified in the reference set data structure are scanned and their associated digests are loaded from the repository into memory. The digests of separate repository intervals are read in parallel. The method of the present invention then matches the input and the repository digests to find data matches, and calculates a measure of goodness of the deduplication result of the current input chunk. The measure is defined as the deduplication ratio of the chunk, which is the total size of the portions of the chunk that are matched with repository data divided by the total size of the chunk. The measure value is stored in the reference set data structure. If the measure of goodness of the deduplication result is not sufficiently high and the input chunk was processed without similarity search, then the method of the present invention reprocesses the input chunk with application of similarity search. 
     If the measure of goodness of the deduplication result is sufficiently high or the input chunk was processed with similarity search, then the method of the present invention stores the digest segments boundaries and respective digest values of the input chunk in the repository, and also stores the data of the input chunk in the repository in a deduplicated form, using the data matches and mismatches calculated for the input chunk. Specifically, mismatched input data is stored, and matched input data is recorded as references to matched repository data. 
       FIG. 6  is a flowchart illustrating an exemplary method  600  for deduplication processing of an input data chunk in a data deduplication system in which aspects of the present invention may be realized. In other words,  FIG. 6  is flowchart illustrating an exemplary method  600  for deduplication processing of an input chunk with conditional activation of similarity search. The method  600  begins (step  602 ). The method  600  assigns each input stream of data with a dedicated data structure, denoted as a “reference set,” which contains a current set of positions into the repository data, where each position starts an interval of repository data identified as similar to the last chunk of data in the input stream, and also contains a measure of the goodness of the deduplication result of the last chunk of data in the input stream. The method  600  receives as input an input chunk of data associated with a specific stream of input data, and a reference set data structure associated with the input stream (step  604 ). The method  600  determines if the measure value of deduplication result in the reference set data structure is not sufficiently high (e.g., below a predetermined deduplication result threshold) or nil (step  606 ). An example value of a predetermined threshold for the deduplication result is 70%. If no, the method  600  advances the positions in the reference set data structure to reflect repository intervals immediately following the current repository intervals (step  608 ). The method  600  then calculates digest segments boundaries and respective digest values for the input chunk based on information of the calculated similar repository intervals (step  610 ). Returning to step  606 , if yes, the method  600  activates a similarity search for the input data chunk and obtains a list of similar repository intervals (step  612 ). Each interval is specified by a position and a size. The method  600  then stores the specifications of the similar intervals in the reference set data structure and replaces any previous contents, if existing, in the reference set data structure (step  614 ). The method  600  calculates for the input chunk, within the similarity search, based on calculated rolling hash values, similarity elements, digest segments boundaries, and respective digests values (step  616 ). From both step  616  and  610 , the method  600  then scans the similar intervals specified in the reference set data structure, and loads the digests associated with the similar intervals from the repository into a memory, and reads the digests of separate repository intervals in parallel (step  618 ). The method  600  matches the input and the repository digests to find data matches (step  620 ). The method  600  calculates a measure value of the goodness of the deduplication result of the current input chunk, and stores the measure value in the reference set data structure (step  622 ). This measure is defined, in one embodiment, as the deduplication ratio of the chunk, which is the total size of the portions of the chunk that are covered by matches to repository data divided by the total size of the chunk. The method  600  then determines if the following conditions apply: the measure of goodness of the deduplication results is not sufficiently high and the input data chunk was processed without similarity search (step  624 ). If yes, the method  600  returns to step  612 . If no, the method  600  stores the digest segments boundaries and respective digest values of the input data chunk in the repository (step  626 ). The method  600  stores the data of the input chunk in the repository in a deduplicated form, using the data matches and mismatches calculated for the input chunk. Mismatched input data is stored, and matched input data is recorded as references to matched repository data (step  628 ). The method  600  ends (step  630 ). 
     A further problem which should be solved is how to calculate digest segments boundaries and respective digest values for an input chunk based on information of the identified similar repository intervals, and without calculating rolling hash values for the input chunk (rolling hash values normally serve as basis for calculating digest segments, which then enable to calculate respective digest values). A method to solve this problem is required for step  610  (see  FIG. 6  step  610 ) in the algorithm specified above, and will be activated in cases where similarity search is avoided, thus also avoiding the rolling hash calculation for the input chunk. 
     In one embodiment, the present invention provides an algorithm to solve this problem. Turning now to  FIG. 7  a block diagram illustrating an exemplary method  700  for calculating candidate segmentations for an input data chunk in a data deduplication system in which aspects of the present invention may be realized, is illustrated. For each similar repository interval  718 B and  718 D, an anchor position  714  (illustrated in  FIG. 7  with  714 A-B) is identified based on the information of data matches  710  (illustrated in  FIG. 7  with  710 A-D) previously calculated. In  FIG. 7 , the two rectangles at the left side of the bottom tips of lines  714 A-B are marked as  710 C and  710 D. These are the portions of the input data covered by the matches whose repository portions are marked with  710 A and  710 B. An anchor position  714  is defined, in one embodiment, as a pair of ending positions of a data match in the input data and in the repository data  714 A-B, calculated between a previous chunk in the same input stream  706  and a previous similar repository interval  718 A and  718 C, whose ending positions in the input stream and in the repository data  714 A-B are closest to the starting positions of the input chunk  704  and of the similar repository interval  718 B and  718 D respectively. The 3 vertical lines  730 ,  740 , and  750  partition in half the 3 horizontal lines  708 A-C), where the horizontal lines  708  represent data intervals, the bottom line  750  is the input data and the top two lines ( 730  and  740 ) are similar repository data. The vertical lines  730 ,  750  partition the data intervals to the current input chunk and similar data—on the right side, and the previous input chunk and similar data—on the left side. 
     The specifications (i.e. position and size) of the last data match  710 A-D for each similar repository interval  718 B and  718 D are stored in a reference set data structure for each similar repository interval, upon completion of deduplication of each input chunk  704 . This information is then available for usage by this algorithm for the next input chunk, in cases where similarity search is avoided. For each similar repository interval  718 B and  718 D, an anchor position  714 A and  714 B is identified as specified above, and then the digest segmentation of the repository data  712 A and  712 B starting at the anchor position  714 A and  714 B is projected onto the input chunk  704  to form a segmentation on the input chunk  720 A and  720 B. Projection is done based on the positions and sizes of the digest segments  712 A and  712 B following an anchor position  714 A and  714 B. Therefore, for each similar interval  718 B and  718 D, a candidate digest segmentation  720 A and  720 B is calculated for the input chunk  704 , and respective digest values are computed. In the next step, the digests of the candidate segmentations  720 A and  720 B calculated for the input chunk  704  are matched with the digests of the similar repository intervals  718 B and  718 D, and data matches are calculated (step  620  in  FIG. 6 ). 
     In one embodiment of the present invention, in the algorithm of calculating candidate segmentations  720 , all the candidate segmentations of the input chunk  704  are essentially equivalent in their importance. So an arising problem is which segmentation of the input chunk should be stored, to serve as basis for deduplication of subsequent input chunks. In one embodiment, in the algorithm of the present invention, the candidate segmentation  720  that produced the most comprehensive coverage of the input chunk  704  with matches (i.e. produced the best deduplication ratio) is selected for storage. If the input chunk  704  is partitioned into sub-sections, such that each sub-section has its own set of similar repository intervals  718 B and  718 D, then the segmentations selected for each sub-section are concatenated into a single segmentation. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention have been described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that may direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the above figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.