Mechanism of Locating the Client's DaTa in the Clouds - Research Paper Example

?Other (s) Mechanism of locating the Client’s data in the Clouds Introduction Cloud computing is a service technology that enables clients to remotely store their data on servers found on the internet. The data is generally stored in a different location by a third party service provider and therefore the clients do not own the data centers and infrastructures but they basically rent it. The Cloud not only help clients and users to store, retrieve, delete and update their date within the cloud data centers, but the users are also able to access their stored data anywhere anytime. Owing to the current rapid prevalence of Cloud computing many clients are increasingly using the clouds to store sensitive information and this necessitates encrypting the data so as to protect the stored data against unsolicited access (Lasica, 76). A major challenge of data encryption in the clouds is that it makes it difficult for the Clients to locate their data. This is particularly with regard to the fact that the encryption of data in cloud computing significantly limits the ability of clients to use the traditional keyword searches in locating their data. Additionally data in the clouds usually require protection of their key word privacy to enhance the security of the stored data. This paper explores the potential use of string matching algorithms as a mechanism to enable clients using cloud computing effectively locates their stored data within the cloud. String matching (Fuzzy keyword) algorithms Generally cloud data systems usually consist of the client, data service provider and the cloud server. Advances in computing technology have enabled the use of networks and data identifier algorithms to build a mechanism that allows the clients to locate their data in the clouds based on string matching of the any data. Approximate string matching algorithms is an important technique that can potentially be used in finding a string of data that closely matches the search request (Myers, 396). The main advantage of this mechanism is that it allows users and clients to decrypt and locate data that is stored in the clouds while at the same time protecting it from unsolicited access. The fuzzy keyword data identifiers algorithms are usually designed to enable clients determine the location of their data in the cloud using the infrastructural network. These algorithms are also used to track and monitor the movement of data within the servers. Many methods can be used to determine the string familiarity of the cloud data. For example the edit distance measures string match of the given keywords. This not only allow the clients depending on cloud storage services to locate their data but it also help them to effectively correct potential errors and problems in their stored data. Additionally the similarity of the located data and the intended search may also reveal potential problems such as duplicate data and lack of uniformity in the content and format (Armbrust, 56). String matching algorithm as a data identifier mechanism also enhances the search correctness of the data being searched by the client. For example if the client needs to locate some data using an input that matches the data in the clouds, then the server will accurately locate the file using the keywords search request. The clients are therefore able to determine the location of their data within the virtual cloud network. On the other hand, any errors in spelling or inconsistencies in the format of the search keyword for the data being searched often bring the possible closest result based on the similarities of the keywords. How string matching data identifier algorithms work String matching algorithms function by allowing clients to locate data within the clouds using exact matching words. According to Abadi (33), the closeness of the match of the data being located is usually measured using the possible number of operations needed to convert the string into the exact match. The number is usually referred to as the edit distance. To allow the clients to be more specific, the edit distances are often employed to construct fuzzy word sets and eliminate the need to enumerate all the key word sets. For example an authorized client using string match algorithms to locate the encrypted data within the cloud is required to type in a request using the distinct keywords so as to selectively locate any data of his interest. On the other hand, the Cloud server is responsible for ensuring that the search request is mapped to the given set of data files which are each indexed by a specific file ID (Linked to the set of the keywords. The fuzzy keyword (string matching algorithms) search will then locate the data using a number of rules. One of the key rules employed in this mechanism of locating data in the cloud system is that when the client data request input matches the pre specified keyword in the cloud system, the server will locate the data files containing the keyword (Neal, 16). Another important rule is that when the client input search requests with format inconsistencies or typos, the cloud server will locate the approximate results based on pre-set similarity semantics. These rules are particularly important because the data on the cloud is usually stored in more than one location and this increases the risk of unsolicited access to the stored data. Additionally the fact that users of cloud computing technologies generally share the networks and storage of their data makes fuzzy keyword mechanism as the safest way of locating their data on the cloud. Generally the main principle behind the use of edit distance is that the smaller the distance of the keyword and the data, the more accurate the location of the data. The data identifier algorithms such as approximate string matching are generally achieved using dynamic computer programs that enable the client to locate their data within the cloud network. In these kinds of data identifier algorithms, different approximate matches may signify different constraints while other algorithms are often used to specify the variation between the data in the cloud and other some of the searches. Unlike the web data, data within the clouds are often solely stored by the clients and therefore an effective way of locating such data using string matching algorithms will enhance the efficiency of retrieval of the data by the respective clients and owners as well as protection of the data from any unauthorized access (Zobel, 338). The main limitation of using these string matching algorithms however is that the data stored in the cloud servers are often heterogeneous and the data are generally stored in a wide range of formats that make them difficult to locate (Fisher, 24). Consequently locating the data may require specific design of the data identifier algorithms depending on the particular categories of data stored by the client. With the current rapid growth of computing technologies, the cloud is increasingly being adopted as the main storage solution to both corporate organizations and individuals. This however results in an almost unrestricted computing primitive which is one of the major challenges facing the use of traditional data identifier algorithms in locating data within the clouds. Conclusion In conclusion, string matching algorithm (fuzzy keyword) is an effective data identifier mechanism that can be used to allow Clients to locate data within the cloud. This is primarily because data stored in the cloud is usually encrypted to enhance security and privacy and the clients are therefore unable to locate data within the cloud using traditional keyword searches. Finally this mechanism not only allows the clients to locate encrypted data on the cloud system but it also ensures the security of data in the clouds. Works Cited Abadi, Daniel. “Data management in the cloud: Limitations and opportunities”. IEEE Computer Society Technical Committee on Data Engineering,3(2009):17-22. Web.22, July, 2012. Armbrust, Michael., “A view of cloud computing,” Communications of the ACM, 53. 49(2010): 50-58.Print. Brynko, Barbara . “Cloud computing: Knowing the ground rules”. Information Today, 25.10(2008) 23-28. Print. Fisher, Steve. Service Computing: The AppExchange Platform. SCC,2.(2006): 24-32. Print. Lasica, Joseph, D. Identity in the age of cloud computing: The next-generation Internet's impact on business, governance and social interaction. Washington, D.C: Aspen Institute. 2009. Print. Myers, K., George. “A fast bit-vector algorithm for approximate string matching based on dynamic programming”. Journal of the ACM 46. 3(1999): 395-415. Navarro, Gonzalo. “Indexing Methods for Approximate String Matching”. IEEE Data Engineering Bulletin. 24 .4 (2001): 19-27. Neal, Leavitt, “Is cloud computing really ready for prime time?,” Computer,42.1(2009): 15-20. Print. Sellers, Peter, H. “The Theory and Computation of Evolutionary Distances: Pattern Recognition”. Journal of Algorithms 1.4(1991): 359-73. Zobel, P. Dart.“Finding approximate matches in large lexicons”. Software-Practice & Experience 25.3(2005): 331-345.Print. Read More