International Journal of Advance Foundation and Research in Computer (IJAFRC) Volume 2, Special Issue (NCRTIT 2015), January 2015.

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1 Literature Survey On Cloud Data Recovery Techniques. Mrs. D.A Phalke, Onkar Kulkarni, AbhijitFulsagar,HarishRane, AjinkyaChothave Department of Computer Engineering, D.Y.Patil College of Engineering, Akurdi, Pune , Maharashtra, India. ABSTRACT -The cloud computing is increasing day by day as its advantages overcome the disadvantage of various early computing techniques. Cloud provides online data storage where data is stored. If in case, file deletion or if the cloud gets destroyed due to any reason the data stored at cloud gets lost. Hence to overcome this problem various data recovery techniques have been developed in cloud computing. In this literature paper, we explore some existing techniques that are previously implemented to tackle this problem. The objective of this paper is to give the brief explanation of the existing techniques. Keywords- Parity Cloud Service (PCS), Semantic Overlay Network (SON), Seed Block (Si), Virtual Cloud Manager (VCM), etc. I. INTRODUCTION In today s world, cloud computing is most exciting technologies because of its capacity to reduce cost associated with computing while increasing flexibility and scalability [1].The data available from the various sources is huge and in the electronic form. So to store this data requires lots of volume of data storage devices. Hence consumers usually prefer to store data on the cloud. If the cloud gets corrupted or damaged or if file deletion happens accidentally by consumer, which leads to loss of important data. Thus back-up of the uploaded data must be taken and if loss of data happens we can recover that data from the data back-up. Considering this situation, many techniques have been developed is cloud computing to recover the data such as HSDRT [4], ERGOT [6], PCS [5], Rent out Resource Service [7], old/hot backup strategy, Linux Box etc [1][2]. The performance of these techniques is based on level of complexity of implementation, cost of implementation, reliability, data backup limit, security, privacy etc. A. Cloud Cloud is basically a combination of servers at very high level. The combination of servers is virtually said to be in space, so it is called as cloud. The job is to store data. As cloud is combination of various servers, thus its storing capacity is also very high and with it many issues also arises with it such as security, performance, easiness, etc. B. Data Backup Server As the name suggests, it is similar to normal server, wherein data is stored. It is called as data backup server because, originally data is stored at main server and from there it is stored at another server which acts like backup server. C. Data Back-Up Server In Cloud , IJAFRC and NCRTIT 2015 All Rights Reserved

2 Here implementation takes place at very high level. Since many users now opt for cloud to store their data, thus it is important to provide security and backup. Backup because if cloud gets destroyed due to any reason then client/user can retrieve their data back. Thus whatever data is stored at cloud of n no of clients; whole of that data is stored at another server called as data backup server. Cloud computing provides on demand resources to the consumer as it requires the management of resources among each and every client/user [3]. Such management includes various aspects of proper utilization of the resources. The resources can be any hardware or software. Cloud computing must be able to provide reliability such that users can upload their sensitive and important data. The cost-effective approach is the main concern while implementing any cloud. During the study of cloud computing, we found various advantages of it. In its positive points, we found that the cloud is capable enough to store the huge amount of data of various different clients with complete security such that Internet Service Provider (ISP) provides a huge storage in a cloud to the user client and users are allow safely to upload there private and important data to the main cloud. And at the same time we found critical issue regarding this storage i.e. if any of the client s data is missing or disappeared for some reason or the cloud get destroyed either due to any natural hazardous things like flood, earthquake etc. then for back-up and recovery consumer/client has to depend on service provider which means the data has to be stored in the server. To overcome this problem, it requires an efficient technique for data backup and recovery so that the client can able to contact the backup server where private data is stored with high reliability and whenever a main cloud fails to provide the user s data. These techniques must possess the low cost of implementation and can easily recover the data after any disaster. That s why, the need of the back-up and recovery techniques for cloud computing arises due to heavy storage of its clients. II. DATA RECOVERY TECHNIQUES There was many data recovery techniques in cloud computing. They are as follows: A. HS-DRT The HS-DRT file backup mechanism has three principal components as shown in figure and those are Data Centre, Supervisory Server and various client nodes [4]. Figure 1 The Basic Configuration Of HS-DRT System The basic procedure is as follows: , IJAFRC and NCRTIT 2015 All Rights Reserved

3 1. Backup sequence: When the Data Centre receives the data which is to be backed up, it encrypts it, scrambles it, and divides it into fragments, and thereafter replicates the data. The Data Centre encrypts the fragments again in the second stage and distributes them to the client nodes in a random order. At the same time, the Data Centre sends the metadata used for deciphering the series of fragments to the Supervisory Server. The metadata comprises encryption keys (for both the first and second stages), and several items of information related to fragmentation, replication, and distribution. 2. Recovery sequence: When a disaster occurs, the Supervisory Server initiates the recovery sequence. The Supervisory Server collects the encrypted fragments from various appropriate clients in a manner similar to a rake reception procedure. Then they are decrypted, merged, and descrambled in the reverse order of that performed at the Data Centre and the decryption is then complete. Thus, the Supervisory Server can recover the original data that has been backed-up. 3. Security Level of HS-DRT: The Security level of the HS-DRT depends on spatial scrambling, fragmentation/replication, and the shuffling algorithm. Because of these three factors, nobody is able to decrypt without collecting all relevant fragments, selecting a unique set of fragments, and sorting the fragments into the correct order. Even if some fragments are intercepted, nobody is able to decrypt parts of the original data from such fragments. i. Spatial scrambling: The spatial scrambling procedure can be implemented using C simple algorithm. for (i=1;i<imax;i++) {buf[i]=buf[i]+buf[i-1];} buf [0] =buf[0] +buf[imax-1]; The array buf [] consist of data to be scrambled and imax is the size of the buf array. This computation process should be repeated at least six times. To descramble, it is only necessary to perform the same operations in the reverse order. By using spatial scrambling technology, it is almost impossible for a third party to decipher the data by comparing and combining the encrypted fragments. ii. Fragmentation/replication: Fragmentations means dividing data in small pieces. Even if a hacker captured all raw packets between the data centre and the client nodes, it would be extremely difficult to assemble all the packets in the correct order, and to determine whether all packets are there are not. Also, the proposed backup mechanism replicates each fragment and encrypts each copy of the fragment with a different encryption key. So even if a pair of encrypted fragments is the available different encryption key is required to decrypt the data. iii. Shuffling: HS-DRT mainly uses a shuffling method with pseudorandom number generators for the distribution to the client nodes. Distribution of encrypted fragments is done in a shuffled order, since we predetermine the destination client nodes from the shuffled table in advance. B. Parity Cloud Service (PCS) Parity cloud service is the technique based on parity recovery service [5].PCS has low cost of recovery and recovers data with high probability. For data backup, PCS creates the virtual disk in users system for , IJAFRC and NCRTIT 2015 All Rights Reserved

4 data backup, make parity groups and store parity data of group in cloud. The PCS algorithm works using Exclusive OR for creating parity information. 1. Initial parity generation: First the seed block (Si) is generated for virtual disk of client. PCS server sends the initialize message to each Recovery Manager in the group. After sending the message, the server sends temporary random block (r) to the first node. After receiving the r block, the first node generates an intermediate parity block via r Exclusive-OR S1 and sends it to its successor, 2nd node. Then second node generates an intermediate parity block via XORing the received parity block with its seed block, S2, and sends it to its successor, third node and so on. The final block transferred to the PCS server from node 4 is XORed with the temporary random block, r, again, to generate the seed parity block across all seed blocks (((((r Exclusive-OR S1) Exclusive-OR S2)Exclusive-OR S3) Exclusive-OR S4) r = S1 Exclusive-OR S2Exclusive-OR S3Exclusive-OR S4). For each group the initialization process occur only at once. 2. Parity Block Update: The Manager in PCS agent maintains map to indicate whether the parity block for each data block in the virtual disk has been generated or not which is parity generation bitmap (PG-bitmap).All fields of bitmap is initialized (set to 0) after the initialization process for any data block in the virtual disk. The PG-bitmap is referred when a parity block is updated. When a block (Bold) in node is to be updated to a new block (Bnew), the Storage Manager refers to the corresponding value in the PG-bitmap. If it is 0, then the Storage Manager generates an intermediate parity block (Pt) by XORing the new block with the seed block (Pt = BnewSi), and set the corresponding value in the PG-bitmap to 1. Otherwise, the intermediate parity block is generated by XORing the new block and the old block (Pt = Bnew-Bold). For each VDPG, the PCS server also maintains the PG-bitmap. 3. Data Block Recovery: When a data block is corrupted, it can be recovered using the parity block provided by the PCS server and encoded data blocks provided by other nodes in the parity group. Assume that the n-th data block in nodei, Bin, has been corrupted. Nodei sends a recovery request message to the PCS server. On receiving the recovery request message, the PCS server identifies to which VDPG the node belongs to and reads the corresponding parity block, Pn. Then, it generates a temporary random block, r, and a temporary parity block, Pr, for recovery process. When the size of the VDPG is even, Pr= Pnr. Otherwise, Pr= Pn. The PCS server sends Pr along with the list of nodes that will send their encoded data block to nodei for recovery along with the IP address of nodei to all other nodes in the group. If there are any off-line nodes, the PCS server sends the message when they become on-line. On receiving the message, each node generates their own encoded data block, E, by XORing the n-the data block with r (Ej = Bin r, for each node j VDPG, j i) and sends to nodei. Then, the nodei recovers the corrupted data block by Bin = PrE1 Ei-1 Ei+1 E VDPG. C. ERGOT The increasing demands for online services needs distributed architecture for promoting its scalability and semantics to enable to their efficient retrieval [6]. So by using two different approaches towards goal are Semantic Overlay Network (SON) and Distributed Hash Tables (DHT). Semantic based discovery is done in distributed infrastructure such as grids and clouds. By combining SON and DHT, ERGOT (Efficient routing grounded on taxonomy) is introduced. ERGOT takes advantage in two ways first is services are advertised in DHT on basis of their annotations and , IJAFRC and NCRTIT 2015 All Rights Reserved

5 another one is annotations enable semantic based service match making, so these approaches enables us for accuracy of search and network traffic. Service oriented architecture (SOA) paradigm is model for modular composition and reuse of third party software component on large scale. SOA model aims that the developers can dynamically discover and assemble software module to fulfil their needs. As an example, think a user is looking for a service that offers car-selling information. A possibly provided service description request may add terms such as price, vehicle model, year and so forth. Using Chord, relevant services would only be retrieved if their semantic profiles have exactly the same keywords that used in the query. However, it can be perfect possible that in the specifications related terms are used instead, for instance automobile instead of car and car type instead of car model. In this case session, ERGOT s semantic similarity approach will still be able to return correct relevant services, while Chord would face failure to find any matches related to that. We have explored the viability of these ideas in the particular case where peers are autonomous and distributed semantic service registries. D. Rent Out The Rented Resourses The architecture of Virtual Cloud is a client-server model. It consists of two main components i.e. Virtual Cloud Manager (VCM) and Host Manager (HM)[7]. Virtual Cloud Manager is a server type component, functioning on the cloud vendor s infrastructure. Host Manager is a component installed at each host machine. 1. Virtual Cloud Manager: Virtual cloud manager consists of 3 layers i.e. Client Service API, Virtual Cloud Middleware and Communication Layer. Figure 2. Interaction model for Virtual Cloud Service Utilization a. Client Service API: Client service API is an interface layer to the Virtual Cloud Middleware. It enables different clients to request different types of services from Virtual Cloud Manager. It understands different types of messages and service requests , IJAFRC and NCRTIT 2015 All Rights Reserved

6 b. Virtual Cloud Middleware: Virtual Cloud Middleware is the core part of the Virtual Cloud Manager. The functionality of these components is given below. c. Distributor: It is the central point of the Virtual Cloud Middleware and is responsible for task assignment and communication among all its components. It receives a request from a client through client service API and route it to the appropriate component responsible for the desired task. d. AAA Server: It is a module responsible for the Authentication, Authorization, and Auditing of the clients. Authentication is required to use the cloud service. It verifies the correct use of services and resources by the users. e. Metering Service: It is an accounting service for both the users and hosts. It calculates the billing for the users on the basis of account and metering settings. User can choose among different types of packages depending upon his needs and offers made by virtual cloud vendor. It also calculates the billing for hosts on the basis of type of contract and resource utilization. f. Client Directory: All the users/clients information is registered in the user directory. g. Service Directory: It keeps the record of the available cloud services at a particular time. h. Resource Directory: It keeps record of all the hosts and the information about their infrastructure resources. It also has a record of resources from other cloud vendors. i. Host Controller: It is responsible for managing the hosts. It has a capability to perform certain node management tasks, including the node creation, termination, assignment/acquisition, release etc. j. Federation Manager/Service: It is responsible for cross-cloud connection with other virtual cloud vendors. It has the record about all the cloud vendors in its federation. It has two main tasks to do. The first is to periodically advertise its own shareable resources and services to the other cloud vendors in its federation. The second task is to receive advertisements from the other virtual cloud vendors for their available resources. It records the details about the resources into the Resource Directory and details about the services in the Service Directory. Federation Manager uses the Instant Messaging and Presence Service (IMPS) protocol to advertise its resources. If the desired resource is not available locally within the Virtual Cloud then federation manager requests its peer federation manager located on the remote Virtual Cloud for the resources. k. Logging Service: It is responsible for keeping record of all the events occurring in the VCM manager. 2. Communication Layer: Communication layer is responsible for communication with the clients, hosts and possibly with other VCM in case of cross-cloud federation. It is also understand different communication protocols and technologies , IJAFRC and NCRTIT 2015 All Rights Reserved

7 a. Host Manager: It creates a virtualized environment with the help of a hypervisor, to provide a set of cloud services. It has three components i.e. Metering Service Sensor, Communication Layer and Low Level Services. b. Metering Service Sensor: It is responsible for the accounting of resource usage. Metering Service at VCM does the billing for the user on the basis of this information. c. Communication Layer: Communication layer is responsible for communication with the clients, Virtual Cloud Manager, and other hosts d. Low Level Services: It is responsible for interaction with the external environment of the host manager. e. Execution Process: o Client first request for a service to virtual cloud vendor. o The request is first forwarded through Client Service API, which allows the access from different types of client platforms and message formats. o Then the request if send to distributor, which checks the service validity (in which domain service lies and it can be provided at time or not). o Then authentication of user. o Then it checks from the resource directory that where the resource for the particular service does exists. o If the Distributor manages to find a resource to perform the task, it asks the Host controller to assign a host node to the service. But if the Distributor cannot find an existing resource/host for the service, then it asks the Host Controller to first create/register a host node and then assign that host to the service. o Host node creation and termination record is updated in the Resource Directory and host assignment and release is updated in Metering Service. o At the time of service assignment, a unique service assignment ID (SaID) is generated, which shows that a particular client is using a particular service. o If the distributor fails to find an existing resource or to create a new host then it asks federation manager to contact the remote federation manager for a service assignment. o Host node serves the required service to the client/user, on the basis of service request from the VCM. It creates a direct communication link with the client and uses the SaID for reference. When a client wants to finish the service utilization, it asks the VCM. Host controller at VCM requests the host node to terminate the service and it releases the node from the service. Then distributor updates the resource directory that the node has been released and also informs metering service to stop servicing. III. VARIOUS TOOLS USED FOR CLOUD DISASTER RECOVERY There are various tools which helps in data recovery, and those are as follows [8]: 1. AppSim: Internet Level application and efficiently used for multitenant nonfunctional requirement. 2. Google App Engine: Google App Engine is a Java and Python based platform that affords web application, resource storage and application hosting, data storage. 3. OpenNebula: It is open source toolkit which makes public, private, and hybrid cloud. This tool support VM migration , IJAFRC and NCRTIT 2015 All Rights Reserved

8 4. CloudSim: Enable modeling and simulation. It also creates virtual machine. 5. Cloud Analyst: Cloud Analyst is competent produces graphical output results of simulation in the form charts and tables. The advantage of this tool is that it supports Virtualization. 6. Open stack: This is suitable for Public and Hybrid cloud. The tool is progressing with improvements to support wide of requests from the growing user community. Table 1.Comparison between different approaches S.N Approach Advantage Disadvantage 1 HSDRT This technique can be used by movable clients like Smartphone s, laptop etc. High implementation cost. 2 Parity service cloud Provides reliable service Privacy to data Low implementation cost High Implementation complexity 3 ERGOT Fast and exact match data retrieval Privacy to data High time and implementation complexity 4 Rent out the Rented Resources Cost depends on infrastructure utilization Implementation gets complex Resources must be kept under special attention due to rented concept IV. CONCLUSION Thus conclusion is PCS generates a virtual disk in user system for data backup, make parity groups across virtual disk, and store parity data of parity group in cloud. It uses the Exclusive OR () for creating Parity information. It is comparatively reliable, simple, easy to use and more convenient for data recovery, but it contains complexities. HS-DRT provides an efficient way for movable clients but fails to manage implementation cost. It effectively uses wide distributed data transfer mechanism and high encryption. ER-GOT is based on the semantic analysis and unable to focus on time and implementation complexity. Rent out the Rented Resources is based on renting the resources to clients in form of cloud service. V. REFERENSES [1] Ms. Kruti Sharma, Prof. Kavita R Singh, Seed Block Algorithm: A Remote Smart Data Back-up Technique for Cloud Computing, 2013 International Conference on Communication Systems and Network Technologies. [2] Ms..KrutiSharma,ProfK.R.Singh, 2012, Online data Backup And Disaster Recovery techniques in cloud computing:a review, IJEIT, Vol.2, Issue , IJAFRC and NCRTIT 2015 All Rights Reserved

9 [3] ChintureenaThingom, Cloud Disaster Management With Scheduling Strategy, International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 3, Issue 5, May 2014, ISSN: [4] Yoichiro Ueno, NoriharuMiyaho, Shuichi Suzuki,MuzaiGakuendai, Inzai-shi, Chiba,Kazuo Ichihara, Performance Evaluation of a Disaster Recovery System and Practical Network System Applications, Fifth International Conference on Systems and Networks Communications, 2010, pp [5] Chi-won Song, Sungmin Park, Dong-wook Kim, Sooyong Kang, 2011, Parity Cloud Service: A Privacy-Protected Personal Data Recovery Service, International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11. [6] Giuseppe Pirr o, Paolo Trunfio, Domenico Talia, Paolo Missier and Carole Goble, 2010, ERGOT: A Semantic-based System for Service Discovery in Distributed Infrastructures, 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. [7] Sheheryar Malik, FabriceHuet, 2011, Virtual Cloud: Rent out the Rented Resourses, 6th IEEE International Conference for Internet Technology and Secured Transactions (ICITST-2011). [8] ChintureenaThingom, A Study on Tools for Cloud Disaster Management, International Journal of Interdisciplinary and Multidisciplinary Studies (IJIMS), 2014, Vol 1, No.4, , IJAFRC and NCRTIT 2015 All Rights Reserved

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