2016 International Conference on Computing, Analytics and Security Trends (CAST) College of Engineering Pune, India. Dec 19-21, 2016
Big Data Architecture with Mobile Cloud in CDroid Operating System for Storing Huge Data Santanu Koley, Sudarshan Nandy
Palash Dutta, Sudipto Dhar, Tapashri Sur
Associate Professor Dept. of Computer Science & Engg. Budge Budge Institute of Technology Kolkata, India
[email protected],
[email protected]
Assistant Professor Dept. of Computer Science & Engg. Budge Budge Institute of Technology Kolkata, India
[email protected],
[email protected],
[email protected] dissimilar systems and moreover short point of time to market makes cloud systems exceptionally accepted.
Abstract—We are stepping frontward for an epoch of zeta bytes from Giga/Tera/Peta/Exa bytes in this era of computer science. The data storage in clouds is not the only preference as big data technology is obtainable for processing of both structured and unstructured data. Today a colossal sum of data is engendered by mobile phones (Smartphone) of both the composition types. For the sake of faster processing and elegant data utilization for gigantic quantity of data CDroid operating systems can be implemented with big data. Big data architecture is realized with mobile cloud for paramount deployment of wherewithal. The faster execution can be ended feasible for the make use of a new data centric architecture of MapReduce technology where as HDFS (Hadoop Distributed File System) also plays a big accountability in using data with dissimilar structures. As time advances the degree of data and information generated from smartphone augments and faster execution is call for the same. As per our research and development the only resolution for this mammoth amount of data is to put into practice Big data with CDroid scheme for best use of it. We believe this effort will budge a step ahead on the road to healthier civilization in close proximity to expectations.
We know Big Data is an excellent combination of both unstructured and structured data. The unstructured data resembling images of different formats including healthcare evidences akin to X-rays, ECG (Electro Cardio Gram), MRI (Magnetic Resonance Imaging) images, travel & logistics also portable document layouts, forms of diverse category, video as well as audio, documents similar to text, doc, rtf and other setups, manuals, contacts, automotive data, data related to security, attachments of electronic mail, energy/industry retails etc. Structured one tells us about tables of ancient database management systems of .CSV’s and .XLS’s where row and column is pertinent, as traditional DBMS portrays better. CDroid is an IaaS cloud-integrated mobile OS (Operating System); they can be used in Smartphones (e.g. Android). It is a system which has two different segments namely CDroid device (within Smartphone) and CDroid server {inside clouds (private/public)} [2].
Keywords— Big data; CDroid; Hadoop; MapReduce; Mobile Cloud.
II. CLOUD COMPUTING Cloud computing is the future of modern computer world. It is a centralized approach in terms of data storage, better processing, and omnipresent, well-located, on-demand network access with minimum expense. It follows a shared pool architecture where required networks, specific servers, massive storage, several applications and different services are included.
I. INTRODUCTION Today’s world is based on internet technology and billions of them are using mobile (cell) phone activated with pocket internet. Every second facebook, twitter, LinkedIn, blogs, WhatsApp and other internet sites generate vast amount of data throughout the world. The data produced in such a rate that former CEO of Google; Eric Schmidt once said about the huge data that is shaped by this internet world up to 2003 is around 5 Exabyte’s. Now this amount of data is creating in every second day which is escalating day by day in a multiplicative manner [1]. Big data and clouds are growing at a continuous pace because detail data confined by different organizations due to different reasons like improvement of sales, detail study, analysis, augmentation of social media , constant survey, collaborative projects, IoT (Internet of Things), multimedia etc.
Cloud as the name suggested, is a flexible service with service-oriented architecture. It uses the internet for providing certain services. The data, H/W and S/W all are shared in this construction. “Share and use of applications and resources of a network environment to get work done without concern about ownership and management of the resources and applications”- (M-S. E Scale, 2009) [4]. Now the serviceoriented architecture tells us about the services endow with by this technology which is nothing but dissimilar permutation of some exceptional technologies. The special model of cloud services furnishes service and deployment models [3]. Service model follows three different types of concepts like PaaS
Flexibility of use, payment as per the use of the different services provided, elimination of expensive computer hardware, stumpy investment on set up, relocate perils over
978-1-5090-1338-8/16/$31.00 ©2016 IEEE
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(Platform as a Service), SaaS (Software as a Service) and IaaS (Infrastructure as a Service). Services models are described as NIST (National Institute of Standards and Technology) model [5] for the above structural design. The discussion part for us is the IaaS cloud for the newly developed system.
• Variety: Variety denotes the category of data i.e. in different forms. In addition, difference sources will produce big data such as sensors, devices, social networks, the web, mobile phones, etc. For example, data could be web logs, RFID (Radio Frequency Identification) sensor readings, unstructured social networking data, streamed video and audio.
The incredibly well-liked Cloud computing is known as its salient features as pay-per-use model which makes it low cost, resource pooling, easy to install and utilization capability, provision of services as needed by the user and farm out ability, QoS (Quality of Service), wide network access, suppleness at a prompt tempo, self stipulation, determined overhaul of service, scalable, steadfastness, effortless maintenance with up gradation, squat fence energy etc [6].
• Velocity: This means how frequently the data is generated i.e. data analysis. For example, every millisecond, second, minute, hour, day, week, month, year. Processing frequency may also differ from the user requirements. Several data need to be processed real-time and some may not. • Veracity: Veracity is the data in doubt i.e. uncertain, untrusted, and unclean. The uncertainty is due to data inconsistency and incompleteness, ambiguities, latency, deception, model approximations. It is the management of the reliability and predictability of inherently imprecise data types.
“MCC (Mobile Cloud Computing) at its simplest refers to an infrastructure where both the data storage and the data processing happen outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just Smartphone users but a much broader range of mobile subscribers” [7][8]. The processing task is not done by the mobile device the power and memory consumption is also less in this area and eventually the mobile device became very fast [9].
There is another factor called variability which can be a problem for those who analyze the data. This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
IaaS cloud technology is the recommended method for storing data on mobile phones, Apps etc in the form of Second code segment or CDroid Server scheme. To accomplish the low cost terminology we ought to locate the cloud server in a geographical neighbourhood where fujitsu server will be positioned must be lowest electricity cost for each unit in this globe [10][11].
Big data are characterized by different aspects: (a) data sources (b) data are numerous; (c) data cannot be categorized into regular relational databases (d) content format and (e) data stores (f) data staging (g) data are generated, captured, and processed rapidly. Distributed file system structure is used to store in Big data uses two different methods like HDFS and MapReduce programming framework. Both of them are used to store and maintain the whole data structure.
III. BIG DATA
A. HDFS
Big data is a concept to store, process and analyze huge amount (e.g. Exabyte’s) of data which is nearly impossible with traditional RDBMS (Relational DataBase Management System) system. This is because besides structured data it deals with semi or unstructured data. Big data is classified into several categories like data sources, content format, data stores, data staging, and data processing.
HDFS is accountable to accumulate the data in chunks. Here data is splitting into blocks of 64 MB each. It is made compatible for executing its file system on a hardware platform where price is lowest. It can put up with exceedingly blemish system as it keeps up block replication. The search engine for the web application as named Apache-Nutch project was the reason for creation of this architecture. Apache-Hadoop is the well prologue as a sub project of subsequent search engine. An instance of HDFS is made of big number of server computers; sometimes the quantity can go away few thousands too. These servers stockpile the data of the said file system. They are not prepared for single interaction by the users but for batch processing. The data files are much bigger beyond assessment, as they reached the size near to TB’s. Here the low latency of data access is overlooked but high throughput is enhanced and that is much necessary for system like HDFS. Data coherency and this throughput is foundation of the concept of ‘write once and read numerous times’ concept for the files.
Data sources are named as web and social media, content format is already known as structured, semi-structured or unstructured format. The data format document oriented, column oriented, graph based and key values whereas data staging can be described as cleaning, normalization and transformation. Finally data processing can be done with the help of two different techniques like batch processing and real-time processing. Big data can describe with four principles of ‘V’ as Volume, Variety, Velocity and Veracity. • Volume: Volume speaks about the size of the data generated from different sources and ready to expand such as TB (Terabytes), PB (Petabytes), ZB (Zettabytes), etc. Information can be created with detail data analysis and Smartphone’s are biggest creator of such kind of longitudinal data [12].
The Hadoop architecture contains data sources, Hadoop framework and big data insight. The data sources contain website clickstream data, content management system, external web content and user generated content. The Hadoop framework includes HDFS that is restrained with Big data landing zone and Map reduce algorithms. These algorithms
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are controlled by keyword research, content classifications or themes and user segmentations. The Big data insight holds keyword relevant content rich user targeted landing pages.
MapReduce is a data processing paradigm where big amount of data turned into small. This programming model is coupled with the operation for doling out and engendering bulky data sets with a parallel, distributed algorithm on a cluster.[13][14] Hadoop is a system that can store and manipulate large amounts of data very easily, based on simple master-slave architecture. It is the core of hadoop. Conceptually similar approaches have been very well known since 1995 with the Message Passing Interface [15] standard having reduced [16] and scatter operations [17].
HDFS is prepared by make use of java technology at its best. Structural design of this file system is given name of NameNode-DataNode tracked on master-slave construction. Serving read-write operation/application from the file systems client is performed by the duo. The instruction from NameNode is carry out by performing block creation, deletion and replication. They are intended to execute on commodity systems which may be OS as GNU (General Public License)/Linux as NameNode is kind of software same as DataNode. NameNode runs file system namespace operations like opening, closing, and renaming files and directories. It is a master server and administrates the file system namespace and controls access to files by clients.
MapReduce is divided with several applications, trends, success stories, uses, functions, features and implementations and others. Uses like queries and analytics, functions as Map and Reduce, features in the vein of a programming model, large scale distributed data processing, simple but restricted, parallel programming, extensible, inspired in functional programming but not equivalent and sometimes think in recursive solutions. Implementations resembling with Google, apache-hadoop and many different technologies are used successfully in MapR. The other maps reduce techniques similar to Signalcollect and Storm is used here.
NameNode conclude the mapping of diverse blocks to DataNodes. To ensure the DataNodes working accurately, that is normally single per cluster the NameNode entertains time to time updating of heartbeat and a block report from DataNodes in a regular interval. DataNode may be called as store keeper for the node its deals with, as a group of DataNode is conscientious for stockpiling of several blocks. These blocks contain splitting files scattered in altered blocks. HDFS portrays a file system namespace and set aside user data to be stored into these files are in blocks. Master
Slave
Task Tracker MapReduce Layer
HDFS Layer
IV. CDROID OS Cdroid OS is a special kind of OS, works only for mobile devices like smart-phones and tabs where the system is installed inside phone memory as well as IaaS cloud servers. A. CDroid Devices This part will handle all the operations done by the Smartphone like calling, SMS (Short Message Service)/ MMS (Multimedia Message Service), internet access, App management etc. and maintain all those using log files. The CDroid device sends all the collected data and information to the cloud side CDroid server as shown in the picture. This process is completely a piggybacking method [18].
Task Tracker
Job Tracker
Name Node
B. CDroid Server This is the cloud-side system which handles a reliable connection. It protects the user’s privacy as mobile ad blocker; push notification handler is being used. They also handle mobile computation offloading and data backup synchronization handler, remote code executor [19] for better mobile user access.
Data Node
Data node
Multi-Node Cluster
Fig.1.
Master-Slave architecture of HDFS.
CDroid part inside the Smartphone handles the operative load environment within it, which works with the other part installed in the clouds as IaaS. The cloud service provider itself is responsible for all synchronization, communication with the Smartphone. Thus a lot of work is reduced by the Smartphone and it become faster with enhanced battery life. The rest of the parts are already installed in the Smartphone to communicate with the cloud. We assume the internet connection is on when this system works. A set of user id and password is provided to every Smartphone connected with the cloud network. CDroid has used in Android based mobile phones for switching to wi-fi, Bluetooth or data service.
NameNode does not flood with user data; it is a negotiator for every bit of HDFS metadata. HDFS is designed such a way that it has a single NameNode in a cluster to the highest degree simplifies the architecture of the system. Hadoop framework has the layer namely HDFS layer and MapReduce layer. The second one is known execution engine in a multinode cluster. A job tracker acquaintances task trackers in both master and slave part; on the other hand name node in HDFS layer associates data nodes in those part as shown in the diagram. B. MapReduce
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Fig.2.
EDGE (Enhanced Data GSM Environment) protocol that is numerous times faster (around 512 Kbit/s or more) than the primeval GPRS (General Packet Radio Services) speed at about 56Kbit/s based on wireless reliability [21]. The digital transmission method includes 3G (Third Generation) and beyond (3.5G) cell phone network protocols like UMTS (Universal Mobile Telecommunication Service) that is based on GSM (Global System for Mobile communication) standards. This protocol normally uses WCDMA (Wideband Code Division Multiple Access) to use the bandwidth better and augmented spectral efficiency. There is another protocol named HSDPA (High Speed Downlink Packet Access) used for 3G technology, sometimes the downlink speed is nearby 21 MBPS (Mega Bits Per Second) [22], and EV-DO (Evolution Data Maximized) with EV-DV (Evolution Data and voice) too. These technologies provide a maximum data transfer speeds of up to 3 Mbps. The big sized web pages, watching .mp4, .avi, .3gp type data (video) or live Television (news, games etc.), play games and much more. IP connectivity in this technology is completely packet based. There is another protocol used to uplink of data is namely HSUPA (High Speed Uplink Packet access). This protocol is based on UMTS/W-CDMA uplink technology and used as a complimentary of HSDPA. It boosts the uplinking of data from a minimum of 1.4 Mbps to maximum 5.8 Mbps. These technologies are known as 3.5G or beyond.
CDroid Basic Architecture.
The solution is there with the CDroid system as provided. CDroid systems first code segment is on the cloud, so a number of background processes execute on the cloud server. It solves several problems like hanging off a cell phone system and saves the energy that was supposed to consume from phone battery. The heat problem of mobile hardware is also solved in this way. The cloud providers use several antimalware applications, solves the problems when downloading anything from Google play or somewhere else.
C. System Architecture Data transmission commences on user end where mobile devices like smartphones or other cell phone, laptop, and desktops associated. Smartphone transmits at the cost of 0.63.0 watts. The wireless networks initiate the transfer of data; where non-connected hexagon cells bring into play analogous frequency.
V. THE SYSTEM APPROACH Big data application in CDroid OS needs some systematic approach. All types of data like structured, unstructured and semi-structured is stored & processed here. The communication strategy for the system approach is the smartest in comparison to the existing system.
This process saves battery as well as frequency will linger within the cell. Every cluster has a base station and they are connected to MSC (Mobile Switching Centre) or switching center of mobile; that hoards the details of subscribers. PSTN (Public Switched Telephone Network) in central switching center hooks up MSC’s of changed clusters. MSC entertains the phone calls and its directions. MTSO (Mobile Telephone Switching Office) situated in a city for a single service provider handles all of the phone connections to the normal land-based phone system, and controls all of the base stations in the region. Base Stations have their transceivers for serving radio signal to and from antennas with radio frequency between 800MHz (Mega Hertz) and 2200MHz, plus signal amplifiers and a system controller. The CDMA technology used here is using divergent frequencies of the given range [24]. This architecture is ready to use CDMA2000 1xEV-DV where the forward link it supports is 3.08 Mbps and a reverse link nearly 1 Mbps [25]. The Cloud RAN (Radio Access Network) can be used by the recommended system as also for fault tolerance [26].
A. Hardware Approach This proposed architecture shows how huge amount of data stores in DataNode. Bigdata is associated with HDFS in the mobile cloud. Data comes from different nodes (desktop, laptop, smartphone or tablets) using wired or wireless connection. Routers, Linux gateways are the arbitrator to transmit data in the direction of cloud data centers. Electricity guzzle is not an imperative factor for our discussion for above devices [20]. The radio frequency is utilized to relocate data to HDFS. CDroid cloud server (Fujitsu) hooks up conversion server and data capture system. They pour data into exploiting Big-Data with indexes where disconnecting storage is used for accumulating huge data as required. B. System Framework The actual system application framework is evaluated with generations of internet technology as it evolves better from its previous generations. The proposed system comprises IEEE (Institute of Electrical and Electronics Engineers) 802.11 standard for WLAN (Wireless Local Area Network) with
Big data architecture is primarily associated with massive data travels from end nodes with the combination of radio transmission and telephone network technologies as ripened system as individual [23]. Firewall system impedes every (almost) unnecessary traffic on or after internet structure as it
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fortifies with Linux OS. Now data arrive at an enterprise server with all possible safekeeping. VPN channel remits data traffic to business application server toil as intranet system. Now they convey them towards our main cloud server runs on CDroid OS. The Fujitsu server with resource pool architecture Wireless Networks
Mobile Devices
executes this process inside the cloud with smartest ever provision endow with this server. If intranet is not included then we can bypass the business application server and enterprise server where best ever overhaul can be realized. In this case, data travels directly from internet system.
Internet Firewall
Laptop & Desktop PC too
Business Application Server
Enterprise Server
VPN Channel
Cloud Server Business Intranet
Data Capture System Video files
E-mail
Content classification Data extraction Data validation Export
ECM Repository Data Capture system Databases
FTP Sites
File Shares Conversion Functionality Full page searchable PDF Format conversion Multiple workflow XML output
Conversion Server
Touch Screen Capability One word Index Multiple line Index Bar code index Multilanguage support Database lookups
Touch Screen Index User Interface Fig.3.
Searchable PDF (Portable Document Format) file that most of us are familiar with. Another way to automatically index is via Data Capture technology where an only selective field such as an invoice number and vendor name is extracted from an invoice instead of the full-page text. Data Capture is particularly useful for providing 'relevant index' versus all index values. An example is if an organization processes contracts. In this case, there is no need to index all the terms
Proposed System Architecture.
Exploiting big data services with indexes separate servers used as there are repositories like ECM (Enterprise Content Management), e-mail, video files, databases, FTP (File Transfer Protocol) sites, other file servers used. There are many effective ways to capture indexes. A common automated method to index the content itself is to make a
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and conditions of the contract agreement. Only the 'relevant index' values such as the parties involved in the agreement, the date and maybe a few other pertinent pieces of information. Also, new methods to offer simplified automation of indexing can be to utilize touch-screen devices for indexing fields from images. Touch indexing makes the user experience much more enjoyable and therefore encourages indexing by persons most familiar with the content.
[6]
[7] [8]
A well-designed solution can perform indexing at the time content is introduced to the system, or after the fact on the server or a combination of both. There are three different parts associates big data namely capture, index, and exploit. The proper architecture of an effective solution to Exploit Big Data with Indexes will depend on individual organizations requirements and needs. One of the most important things to know is now, more than ever, there are many ways to achieve a highly efficient system while also delivering these capabilities at an affordable cost. The benefits of exploiting Big Data are tremendous for organizations in many different industries.
[9]
[10]
[11]
[12]
VI. CONCLUSION
[13]
The system will reduce the data storage problems in terms of huge data-centric applications and CDroid OS will be faster as data processing tasks are performed by cloud servers. The Fujitsu server in the cloud is fastest within the world as proven by Fujitsu corp. Structured data processing is not a problem as many database applications are provided. Semi-structured, even unstructured data is well maintained, accessed properly to and from clouds. Implementation of the real system will start a revolution.
[14] [15]
[16] [17]
VII. FUTURE SCOPE
[18]
Things are needed to be channelizing properly to build the system with the proper sponsor as costly Fujitsu server and cloud application setup will come onto play. This can be a successful project for Govt. of India with efficient manpower and finalize make in India.
[19]
[20]
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