Cloud Database Emergence

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1 Abstract RDBMS technology is favorable in software based organizations for more than three decades. The corporate organizations had been transformed over the years with respect to adoption of information technology. Two tier software based applications have been modified with n-tier based software applications. These n-tier based software applications have a demand to carry server s allocation around the globe to use the resources power effectively like storage. The demand of electing Cloud technology enhanced because of its lower cost and pre-paid architecture. The major revolutions evolved inside/outside organizations after the growth of internet. Cloud database offering by database vendors are becoming popular as per database deployment model over the cloud. The data extraction and decision on extracted data automatically are the de-facto standard for cloud databases. The nature of software application on cloud is read centric most of the time. The most fundamental properties of relational databases are Atomicity, Consistency, Isolation and Durability. The paper presents an idea is to study the cloud based data model and its development to meet the users need. Keywords Cloud database; Relational Database; Cloud Layout I. INTRODUCTION The organizations support department is wholesale and responsible to facilitate reliable and feasible infrastructure in terms of server resources as well as backup at the cheapest cost. Small medium size enterprises is having impedance for investment in IT infrastructure. The objective of small scale organizations is to leverage the power of information s technology by keeping the minimal investment in software and hardware components. These kind of small scale organizations are the first choice for the adaptation of cloud environment. The other techniques like unification of servers, large and high speed storage devices and authentic networks are used by cloud based solutions. It is producing inexpensive setup with minimum investment and no cost for further support as well as scalable solutions. The organizations are having a platform like SAAS, PAAS and IAAS based on prepaid model from any location at any point of time. On demand basis the network is expanding in folded terms at any point of time from anywhere. This is changing the software organizations views for information s as well as concepts. It delivers the solutions with flexibility, high availability, scalability and pre-paid model. Cloud Database Emergence Vikas Khanna Therefore, it is sharply doing the reduction of cost in terms of costly infrastructure served and maintained by site providers. The allocation/de-allocation of latest software/hardware is the advantage along with less investment. These features are highly motivational factors for the selection of Cloud environment by audience. Cloud concept brings a move for technology side as well as in the database prospects. Cloud environment is raising a need for allocation of database services. Database allocation services over cloud are popularly known as a Database-as-a-Service. This paper presents several cloud databases model and identifies the difficulties from development point of view for these cloud databases. The paper has been framed with several sections and last section consolidates cloud databases followed by conclusions. II. CLOUD DATABASE The digital data is expanding at faster rate day by day basis, the demand of new kinds of data storage, better network for communication through high speed services offered by service vendor started demands for the development of cloud databases [1]. Several abbreviated word used for cloud data management like Cloud Storage, DaaS DBaaS. They differentiate in terms of data management and storage. The virtual storage is a cloud storage that provides platform to audience to store information in terms of documents and objects. The dominant file storage services over cloud are Google Drive, Rapid Share etc. [2]. Data as a service is to be utilized by user to store data on disk that kept into remote location and data access is available through Internet. The primary purpose is to do data backup and basic data management. This environment cannot sustain without basic data management services. The whole database features are served over network and users accessed the data that is kept at remote location hosted disks. It can be accessed and utilized at any point of time from anywhere location over internet. Google Big Table, Amazon RDS, Yahoo s, Microsoft s SQL Azure and Sherpa are the mostly used environment for hosting the database services over cloud. Cloud service providers are offering cloud database services over internet on basis of pre-paid model. The scalability is the first choice of provider. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 215

2 They ensure high availability, of resources as well as optimal resource allocation and multi- tenancy. Relational databases like MySQL and SQL database Server can also be treated as a cloud database. All operations like installation, configuration and maintenance of database can be done either by the user himself or by the service provider over cloud. The first mechanism is known as a Do-it-Yourself. There are several providers offer readymade database services like Xeround s MySQL [4]. The developers have to ensure reliability and elasticity service as a manual activity in Do-it-Yourself mechanism. The database as a service solution will simplify complexity which in turn results to cost reduction to run database. It separates the developer from the daunting database management tasks. Cloud databases support features of relational databases like improved data availability, data scaling and data performance with complete flexibility at low cost. Relational Database Management System focused with structured data that is kept in databases by keeping data s metadata. On other side Cloud databases can be used for structured as well as for different natures of data. The unstructured nature of data kept incomplete meta data stored in various files type. The unstructured data is having the more storage requirements under any kind of applications that can be dealt by cloud database effectively. The major adoptable storage architecture is Shared-nothing and shared-disk in database systems. a) Shared-nothing Storage Architecture SNS architecture does not share memory or disk storage. It kept data distribution into independent sets. Independent data sets are kept on different servers physically which participates in database execution. Each server is responsible for processing and maintaining its database exclusively. The applications designed in such a manner with respect to scalability on SNS architecture over Cloud. This architecture does not worked properly for the data partitioning over cloud. Shared-nothing database is almost difficult to virtualize. It is not simple and data partitioning maintenance is very hard. It required a component at middle to send request to database server. Repartitioning is required after addition of more servers. In this case huge number of operations required to be done otherwise data send from one node to another node and joining the data will be tedious. These operations will results towards more latency and bandwidth will be choked. The performance of database will be greatly reduced. SNS architecture is mainly used for those kinds of application that required more data intensive tasks. The first Shared-nothing implementation released by IBM in year 1990 in the form of DB2. Another example of shared-nothing architecture is Amazon s SimpleDB, Hadoop Distributed File System and PNUTS of Yahoo [3-4]. b) Shared-disk Database Architecture SDD architecture comprises of one device called Storage Area Network and database completely resides on it. This architecture can be accessed by all nodes throughout the network. Low cost servers can be utilized at node side. Node virtualization can be done in this manner easily. The storage calculation can be done in isolation. Multiple nodes can work on whole data separately with other nodes. The data access from servers is independent of component involved in application architecture. Each client/node has access to all of the shared disk data. This architecture favors OLTP systems. The major vendors follow this kind of architecture are Sybase, Oracle RAC, IBM DB2 purescale [5]. III. RELATIONAL DATABASES AND CLOUD DATABASES Two decades back transaction processing oriented applications were on more demand. Over the period database developed by vendors are getting maturity and audience started using those in their daily lives. Analytics based applications is the requirement of enterprises. Enterprises wanted to store data for transaction processing as well as to analyze data with respect to consumer trends and needs to handle stakeholder s requirements. Relational databases have an ability to handle transaction oriented data. Top most database providers like IBM and Microsoft added extra capabilities into relational databases for analysis purpose. Over the period variety of databases like Column, Document, Key-value, Graph, Multi-model databases, built [12-13]. But none of these chase the capabilities of the relational databases. In parallel Internet revolution and web 2.0 oriented applications started producing huge amount of data without having meta data information. RDBMS cannot handle these types of data. The necessity started to store and handle this kind of unstructured data defined the importance of NoSQL data model in the field of database technology as a Cloud databases. RDBMs (SQL Model) and NOSQL model are briefly explained below: a) SQL Model The origin of relational databases is almost more than four decades old. It works well with limited set of resource on a machine in terms of memory, disk storage, processing speed and network. The databases are commonly keeping objects like tables, columns, indexes, relationships and schema. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 216

3 Tables are used for storage of data and relationship between tables enforced the integrity of data. The access of data can be faster after applying indexes. The database design and maintenance operations can be performed by data base administrator. This model is more applicable for transactional type of databases. They contain confidential/ sensitive and operational data such as financial data to deal with serious operations on those. Cloud environment is not well suitable for this type of data architecture and challenging to scale after a certain boundary [6-7]. b) NoSQL Model A NoSQL database is not based on relational model theory. It provides storage and retrieval of data in another fashion not like relational tables used in RDBMS. It does not share anything so it is horizontally scalable database without Atomicity, Consistency, Integrity and Durability (ACID properties of transaction). The examples of NoSQL are as follows Key/value stores Document stores Object database Tuple stores Graph stores Column stores Multi model They can manage big data. Different stores have different kind of implementation for data arrangement. These databases provided improvement in terms of scalability and also no need of joins from different tables.. IV. CLOUD DATABASE CHALLENGES Cloud computing technology cannot be addressed and equipped with features without consideration of cloud databases that is almost daunting task. The cloud databases problems are discussed below: a) Scalability The essential requirement of Cloud is scalability which tells that resources can be added and removes dynamically without service delay. It keeps pressure on human resource for database development. They have to support and take care of infinite number of simultaneous users and data gradually increasing. Organizations have to take care of high data volumes. On demand basis addition of servers can resolve the scalability issue. Transaction based data is not have a requirement of scalability in contrast to data analytics. b) High availability and Fault Tolerance The database availability deals with the database servers should be up, database running and available all times throughout year. This is a prerequisite to do replication of data across large geographic locations to provide high data, availability. The vendor like Facebook is providing high availability and replication services without any trouble. c) Heterogeneous Environment Audience can access application from any location at any point of time. These applications can be access using multi devices listed below: Mobile phone Tablets Notepads Computers The applications and its data either structured or unstructured can be of different kind it is almost not possible to predict how system will be used by user. d) Data Consistency and Integrity All business applications top requirement is data integrity and it is well achieved through database constraints. Unexpected outcomes will produce in absence of database integrity. Cloud databases approach is listed below: Basically Available Soft state Eventually consistent This approach is in opposite to ACID (Atomicity, Consistency, Isolation and Durability) property. Data availability at different locations favors the consistency of data over cloud databases. Transaction consistency is very difficult task inside RDBMS to cater the transactional data demands.. For such case cloud databases approach can be used with caution. Data integrity should not be overruled by cloud databases. e) Simplified Query Interface The distributed nature of Cloud Database puts a major challenge for cloud developers for querying the database. The query needs to be distributed over multiple nodes of cloud database for data query. It s a requirement for simple and standard query interface for database query. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 217

4 f) Database Security and Privacy Multitenant model of cloud database requires a challenge to keep the database schema compliance with respect to local rules and regulations country polices and data should adhere to compliance level. Unauthorized hosts lead to potential risks for storage of data. Illegal access can be avoided by applying the encryption over the data before the placement on cloud. The cloud should not keep the proficiency to directly decrypt the data before accessed by authorized users. It is also a daunting task to provide security and privacy to different databases modeled as multitenant. Cloud databases should possess the capability for interfacing with intelligence tools [8-9]. V. CLOUD DATABASE PRACTICES Low cost hardware can be used for cloud databases usually expressed as commodity hardware. The increase in load can be easily accommodated using commodity hardware. The cloud database example is Amazon simple storage service. The Objects storage capacity is in gigabyte size by keeping Meta data for each object in kilo bytes. All top database vendors like IBM, Oracle and MySQL are using amazon cloud services. All RDBMS like MySQL, Oracle or SQL Server database engines have the capabilities access to run under Amazon RDS database instance [10-11]. Another cloud database is Amazon Simple DB which can be consumed by non-relational databases. It has to work closely with Amazon different services for storing, processing s and querying data sets on the cloud. It consists of domains, items, attributes and values. A domain can be represented as a table e.g. Order table. Items further classified in form of attribute-value pairs. Data replication, indexing and performance tuning can be managed [12]. Another cloud approach is Map Reduce that is based on parallel architecture programming model for high scalability. It operates in a distributed manner and the abstraction applied using mapper and reducer framework. The traditional data base system is not comparable to this model. [15].Map Reduce is programming framework that runs across large collection of servers. Hadoop supports the big data through its programming framework. Hadoop Distributed File System have extended its implementation through inclusion of HBase column store database [16]. One of the most used databases is MongoDB over cloud and it is based on document model. Dynamic schemas and data can be stored as a JSON. It provides the speed and scalability of key-value stores and rich functionality like indexes and dynamic queries of relational databases. Horizontal scalability can be achieved with this cloud [22]. VI. CONCLUSION The increased volume of data in almost any kind of applications has changed the database architecture in terms of usage. The unstructured data handling can be achieved by cloud databases. It is not possible for every organization to implement data over cloud database since it involves the huge cost for the data center development. The cloud databases had gained its markets in shorter span. The major features i.e. Atomicity, Consistency, Integrity and durability are lacking in Cloud databases. The cloud databases gives a positive reply for the handling of unstructured data handled by applications with full of loads. This paper gives a clear idea of cloud databases and its features, implementation and challenges. It also explains the comparative study with other types of databases available into market. REFERENCES [1] Sarathy et al, Next generation cloud computing architecture -enabling real-time dynamism for shared distributed physical infrastructure, 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE 10) [2] Wu Jiyi,Ping Lingdi,Pan Xuezeng.Cloud Computing: Concept and Platform, Telecommunications Science, 12:23-30, [3] Daniel J. Abadi, Data Management in the Cloud: Limitations and Opportunities [4] Web-Scale Data Serving at Yahoo [5] Donald Kossmann, Tim Kraska, Simon Loesing, "An Evaluation of Alternative Architectures for Transaction Processing in the Cloud", SIGMOD 10, June [6] Thakur Ramjiram Singh, Cloud Computing: An Analysis, International Journal of Enterprise Computing and Business Systems, Vol. 1, issue 2, July 2011, pp [7] Rick Cattell, Scalable SQL and NoSQL Data Stores [8] Mihaela Ion, Enforcing multi-user access policies to encrypted cloud databases, IEEE International Symposium on Policies for Distributed Systems and Networks, 2011, pp [9] Maggiani, R. Cloud computing is changing how we communicate, IPCC 2009, 2009, pp [10] [11] [12] [13] [14] F. Chang et al., Bigtable: A Distributed Storage System for Structured Data, in 7th Usenix Symp. Operating Systems Design and Implementation (OSDI 06), Usenix Assoc., 2006, pp [15] Dawei Jiang et al., MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 9, [16] D. Borthakur, The Hadoop Distributed File System: Architecture and Design, Apache Software Foundation, l last accessed on May 27, Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 218

5 [17] Troy Davis, Cloud Computing Use Cases and Considerations, Cloud Computing Talk.pdf last accessed on June 10, 2012 [18] [19] esearch.yahoo.com/files/ycsb.pdf [20] [21] Jason Baker et al., Megastore: Providing Scalable, Highly Available Storage for Interactive Services, in 5th Biennial Conference on Innovative Data Systems Research (CIDR 11) [22] Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 219

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