A Novel Information Lifecycle Management Scheme for a Web-Based Database System

Transcription

1 Journal of Scientific & Industrial Research Vol. 76, February 2017, pp A Novel Information Lifecycle Management Scheme for a Web-Based Database System Jui-Pin Yang 1 * and Ying-Chin Chen 2 *1 Department of Information Technology and Communication, Shih Chien University, Kaohsiung City, Taiwan 2 Department of Electrical Engineering, National Chung Cheng University, Chiayi City, Taiwan Received 28 October 2015; revised 22 August 2016; accepted 24 November 2016 In this paper, we provide details regarding the design, analysis and experiments of a new information lifecycle management scheme for a web-based database system referred to as Adaptive Information Lifecycle Management (AILM). AILM is a novel scheme because it simultaneously considers variations in time and data usage. In addition, AILM consists of four main components: data collection, information valuation, information classification, and growth prediction. In this paper, 2255 teaching cases over eight years are managed using the AILM scheme from the life.edu.tw web site. Using AILM, the growth in storage capacity increases by almost 6~7% per year, and the number of teaching cases is projected to increase by almost 160 per year in the next five years. To enhance the performance of the web-based database system, we further classify the data into three groups, namely, Class_1, Class_2 and Class_3. The experiments demonstrate that Class_1 includes approximately 50% of the teaching cases but only uses approximately 40% of the storage capacity. In addition, the hit rate has increased to 75%. Moreover, we can delete the teaching cases with a 2.5% hit rate in Class_3, which contributes to saving approximately 10% of the storage capacity. In other words, the AILM scheme is useful because it not only expedites search time but also improves storage utilization in a web-based database system. Keywords: Lifecycle Management, Database System, Information Valuation, Information Classification, Storage Utilization Introduction The Internet has been widely integrated into daily life, and therefore, the growth in data significantly increases every year. In general, users can easily share data, but it is difficult to efficiently utilize data. Based on such demand, a large number of web sites efficiently manage user data and also provide search functionality. Accordingly, users can quickly obtain their required data. To provide users with differentiated services, many web sites are capable of supporting systematic information management. The simplest way to resolve this issue is to establish a database system. Unfortunately, the importance of data changes over time, and therefore the information lifecycle management scheme plays a key role. An Information Lifecycle Management (ILM) scheme, which involved software and hardware changes, was proposed to address the data explosion 1. A file lifecycle management policy describes the design and innovative implementation of a file system in a storage area network 2. The policy is used to leverage a key abstraction in the file system and support the *Author for Correspondence juipinyang@gmail.com location independence of files by using an innovative implementation technique that combines the concurrent policy execution and the policy decisions cache. However, this policy is only suitable for billions of files under normal conditions. An automated information lifecycle management scheme differentiates information according to its value 3. Furthermore, it accounts for the value changes. This scheme first presents an information valuation approach that quantifies the value of a given piece of information based on its usage over time. Second, it captures the changing nature of the file value throughout its lifecycle, which reflects the individual values of different files. By mapping the value classes to corresponding sets of attributes, storage systems can predict the lifecycle steps of each file. Based on file valuation, classification, and class membership prediction, the storage system is optimized. Service- Oriented Architecture (SOA) was proposed and is widely viewed as an innovative business solution 4. Service is a fundamental component of SOA and represents the repeatable business functionalities to develop applications. To build a scalable and reliable service, service lifecycle management is one of the critical mechanisms to ensure service quality. To

2 86 J SCI IND RES VOL 76 FEBRUARY 2017 achieve this goal, this study proposes a conceptual stakeholder analysis framework to associate stakeholders with different lifecycle stages. This method provides deep insight into service lifecycle management from the stakeholder s viewpoint. However, it is unsuitable for deployment in webbased storage systems. Industry Foundation Classes (IFC) provide useful information structures for data sharing 5. However, they do not specify how to capture, exchange or synchronize the information between distinct entities throughout the building lifecycle. In other words, this study only investigates existing messaging protocols to identify which is the most appropriate for supporting the construction of a lifecycle management system. Moreover, a platform was built on a university campus using the selected messaging protocol. Intelligent Storage Tiering Manager (ISTM) is an analytic storage tiering tool that automates load balancing on data across and within different storage tiers in virtualized storage environments 6. Using administrator policies, ISTM identifies data from specified performance profiles and then automatically migrates the data to the appropriate storage tier. As a result, ISTM can minimize the performance impact by limiting the overall migration load and keeping data accessible during migration. Furthermore, this study introduces existing solutions related to ILM and implements the ISTM scheme within a data center. This tool could consume a great deal of resources while executing data migration. Exponential increases in stored data are putting pressure on Information Technology (IT) infrastructure in terms of cost, performance, scalability and manageability. It has become imperative to identify an efficient system for storing and managing data that corresponds to the growing demands on IT systems. Consequently, organizations are attempting to store increasing quantities of data for the lowest possible cost while meeting increasingly stringent regulatory requirements for data retention and protection. Accordingly, the ILM scheme is a solution that can resolve these IT demands 7-8. Compared with existing schemes, AILM is the first system that considers the effect of information lifecycle management on a webbased database system. More importantly, AILM can efficiently manage the information lifecycle according to variations in time and data usage. We conclude that the proposed scheme has three main advantages. AILM is an effective and complete solution for information lifecycle management; it consists of data collection, information valuation, and information classification and growth prediction. In other words, we propose complete procedures to manage the information lifecycle. Experiments demonstrate that AILM achieves efficient information lifecycle management by evaluating the information values. AILM can automatically delete unnecessary data to enhance overall storage utilization. As web content accumulates in the database system, storage capacity should be extended on a yearly basis. This extension increases both costs and data search time. By using AILM, we can effectively manage unnecessary data; for instance, the data can be deleted or migrated to cheaper storage devices on demand. AILM uses a cache to expedite search time by efficiently caching the most frequently accessed data. Design, Analysis and Experiments of AILM There are two common methods for managing the information lifecycle; one is to use the latest rankings of recently uploaded data, which means that the users wish to obtain new information with the least amount of elapsed time; the other is the rankings of the most widely used data, which means that the user wants to obtain information with the highest hit rate. To satisfy the above requirements, we propose the AILM scheme, which efficiently performs information lifecycle management for existing database systems by considering the variations in both time and data usage. The details of the AILM scheme include four components, which differ from the proposal 3. There are two key differences. First, we consider the priority and storage policy of each class in the information classification component. Second, we further design a growth prediction component to enhance the accuracy of information after the information valuation component. Next, we describe each component in AILM as follows: Data Collection: The goal of this component is to collect the required information from the webbased database system and transfer it into a predefined format. Next, this information will be quantitated according to the values in the information valuation phase. Information Valuation: The goal of this component is to evaluate the formatted information and obtain quantitative trends. With these trends established, AILM can adjust the quantitated values further. Next, the results will be classified in the information classification phase.

3 YANG & CHEN: A NOVEL INFORMATION LIFECYCLE MANAGEMENT SCHEME 87 Information Classification: The goal of this component is to classify the results from the information valuation phase, and hence, AILM determines the priority and adequate storage device for each class. Growth Prediction: The goal of this component is to analyze the quantitative trends and results of classification. With this step, AILM can adjust the quantitative trends and reduce the processing time under the Data Collection phase. The experimental data are collected from the Life Education Learning web site in Taiwan. In addition, the database system uses MySQL, and the operating system is Linux. After systematically describing AILM, we will explain the details of the proposed scheme as follows. Data Collection Before collecting the required information, we introduce the sources of information and their meanings. In this paper, AILM is used to execute efficient information lifecycle management on the database system by considering the variations in time and data usage. Moreover, the proposed scheme can improve storage utilization and expedite the search time; therefore, it needs to obtain information related to time, storage capacity and data usage. The information associated with time is the creation time, modification time, and last access time. In addition, the information associated with storage capacity is the file size. Both types of information can be obtained through the operating system. In practice, we develop a program to obtain the required information by utilizing the commands of the Linux operating system. Furthermore, this program can transfer the results into a predefined format. Finally, the information related to data usage is the statistics concerning hits. All information can be obtained from MySQL. Similarly, we utilize the commands of MySQL to obtain the required attributes including file number, file name, hit rate and creation time. In our experiments, the total number of hits is , and the amount of teaching cases is In addition, the file size is 1742 MB, and the average existing duration of each teaching case is 5.6 years. Furthermore, the average file size is 0.77 MB, and the average hit for each teaching case is 175. Information Valuation After the data collection component, AILM evaluates the information value by quantification. Avg.hit denotes the average hit for each teaching case per year. Accordingly, Avg.hit is in our experiments. The accumulated percentage of teaching cases, storage capacity and the approaching curve of storage capacity versus Avg.hit are depicted in Fig. 1. In Fig. 1, the purple curve represents the variations in the accumulated teaching case ratio versus Avg.hit, and the blue curve represents the variations in storage capacity versus Avg.hit. AILM derives a function to address the changes in storage capacity, namely, the AH function, which is depicted as a red dashed curve. In addition, f denotes a function. In particular, the AH function in (1) represents the square of the standard deviation over 0.99 associated with the blue curve. AH=f(Avg.hit)=0.0003*Avg.hit *Avg.hit *Avg.hit (1) Information Classification When the storage space is insufficient to accommodate new teaching cases, some teaching cases should be deleted according to their values. Therefore, AILM needs to estimate the data growth. The changes in storage capacity per year are analyzed, and an S function is used to predict the growth rate in the next five years. The S function is shown in (2), and the experimental results are depicted in Table 1. Furthermore, n denotes the nth year, and ln denotes a natural logarithm. According to Table 1, there will be an approximately 6~7% increase in capacity demand per year. The average file size is 0.77 MB, so the increment of teaching cases is approximately 160 per year. If we do not increase the available storage capacity, then 7% of the storage capacity should be deleted to make room for the new, incoming teaching cases. S=f(n)=892.32*ln(n) (2) Fig. 1 Accumulated percentage of teaching cases and storage capacity and an approaching curve of storage capacity versus Avg.hit

4 88 J SCI IND RES VOL 76 FEBRUARY 2017 Next, we analyze the relationship between the AH value and storage capacity as depicted in Fig. 2. When the AH value is 2.5, the storage capacity is 8.71%. In other words, we can satisfy the storage capacity demand in the future without adding new storage devices. Relationship between the AH value and storage capacity To enhance the efficiency of the cache, we consider the file access probability in the cache. H denotes the probability of file access; hence, H is equal to the hit number in the cache divided by total hits. Assume that the amount of teaching cases is ma and there are ml teaching cases in the cache. The value of ma can be obtained from the statistics of the database system. Assume that the access time in the cache is t1 and the access time in the storage devices is t2. We then obtain the expected processing time of T, where T is equal to H*ml*t1+(1-H)*(ma-m1)*t2 divided by ma. Here, we consider a worse condition, where t1=t2, while the total number of teaching cases Table 1 Storage capacity and prediction percentages nth year Capacity Prediction Percentage % % % % % % % % % % % % Fig. 2 Relationship between AH value and storage capacity is Accordingly, we obtain variations in T, which are related to AH, H and ml in Table 2. From Table 2, when the cache is at 30% storage capacity, the processing time can be reduced by approximately 47%. Therefore, we suggest that the cache size be set at 30% storage capacity and the AH value be Based on the analysis, we classify the files into three levels, whose characteristics are described as follows. Class_1:AH is larger than 32.67, and the storage capacity is 522 MB. In addition, H is 60.1%, and the number of total teaching cases is 784. Class_2:AH is between 2.5 and 32.67, and the storage capacity is 1068 MB. In addition, H is 39.84%, and the number of total teaching cases is 421. Class_3:AH is smaller than 2.5, and the storage capacity is 152 MB. In addition, H is 0.06%, and the number of total teaching cases is 50. Growth Prediction In the information classification component, the data are classified according to their importance. When the storage space is insufficient, AILM will delete the teaching cases belonging to Class_3. However, this action could change the distributions of teaching cases. As a result, the AH function should be modified accordingly. A common method is to invoke the Data Collection component, but this method is too complicated to implement. To design a useful method, we list the changes under different number percentages in Fig. 3. From Fig. 3, if the deletion ratio increases from 0% to 20%, the required storage capacity decreases relatively. Based on these observations, we would like to consider the deletion ratio in the AH function. d denotes the deletion ratio, and D(d) denotes the curve of the AH function when the deletion ratio is d. In addition, we obtain the Table 2 Expected processing time related to AH, H and m1 space AH H ml T 0% % % 10% % % 20% % % 30% % % 40% % % 50% % % 60% % % 70% % % 80% % % 90% % % 100% % %

5 YANG & CHEN: A NOVEL INFORMATION LIFECYCLE MANAGEMENT SCHEME 89 cases amount to approximately 160 per year. Additionally, AILM predicts case distributions, which is helpful for modifying the AH equation. Briefly, AILM is very useful for web-based database systems and can expedite search time and reduce storage space. In the future, we would like to consider the access behavior of users, types of teaching cases, and storage cost and subsequently develop a more efficient and practical information lifecycle management scheme for web-based database systems. Fig. 3 Statistics of deletion ratio changes of the R function within D (0%)-D (20%). Finally, we derive the R function that is associated with the AH value using (3). R=-0.004*AH (3) Conclusion In this paper, we propose an efficient and novel information lifecycle management scheme, namely, AILM. This paper offers two important contributions via AILM. First, AILM considers variations in both time and storage usage so that it can effectively improve system performance. Second, AILM consists of four phases: data collection, information valuation, information classification and growth prediction. In other words, AILM is a complete solution to achieve effective information lifecycle management. Based on the AILM analysis, data belonging to Class_1 should be placed in the cache, and data belonging to Class_2 should be placed in general storage devices. Finally, data belonging to Class_3 can be deleted on demand while considering the required storage space over the next few years. According to the function S, AILM predicts that the annual growth rate of the storage space is approximately 6 7% and that new teaching Acknowledgments The author acknowledges financial support from Shih Chien University under grant number USC References 1 Govil J, Kaur N, Kaur H & Govil J, Data/Information lifecycle management: a solution for taming data beast, In Proc Int Conf on Info Technol: New Gen, (2008) Beigi M, Devarakonda M, Jain R, Kaplan M, Pease D, Rubas J, Sharma U & Verma A, Policy-based information lifecycle management in a large-scale file system, In Proc IEEE Pol, (2005) Chen Y, Information valuation for information lifecycle management, In Proc IEEE Int Conf on Auto Comput, (2005) Rong W, Wu Q, Ouyang Y, Liu K & Xiong Z, Prioritised stakeholder analysis for software service lifecycle management, In Proc IEEE Int Conf on Services Comput, (2013) Kubler S, Madhikermi M, Buda A, Framling K, Derigent W & Thomas A, Towards data exchange interoperability in building lifecycle management, In Proc IEEE Int Conf on Emerg Technol and Fact Auto, (2014) Alatorre G, Singh a, Mandagere N, Butler E, Gopisetty S & Song Y, Intelligent information lifecycle management in virtualized storage environments, In Proc SRII Glob Conf, (2014) Implementing information lifecycle management (ILM) with Oracle Database 12c, Oracle White Paper, (2015) SAP Information lifecycle management to reduce business risk and costs, SAP, (2104) 1-26.