handled by self diagnosis and recovery oriented computing provides error tolerance. The experimental analysis shows that this method gives improved security ...
Secured Data Storage and Error Tolerant Design in Cloud Computing M. R. Sumalatha1, C. Selvakumar2, G. Jeeva Rathnam3 1, 2 & 3
Department of Information Technology Anna University Chennai, Tamil Nadu, India Email :{sumalatha.ramachandran1, selvac2, suji.jr3}@gmail.com
Abstract—Enormous amount of data are being stored and transferred between various heterogeneous databases across the globe. Data stored in the databases are vulnerable to threats and attacks. Data loss during transmission and corruption are also inevitable. In order to provide a sustainable storage environment handling such limitations, systems should be reliable and fault tolerant. In this research work, an innovative mechanism which provides secure data storage with recovery mechanisms during faults is developed. Distributed data storage security is rendered by data partitioning technique and the failed processes are handled by self diagnosis and recovery oriented computing provides error tolerance. The experimental analysis shows that this method gives improved security when compared with existing system.
complication, fault tolerance mechanism is working as a medium of replacing the resource / recovery to continue the work or resume the next task to reach the TAT.
Index Terms—Fault tolerance, Security, Data partitioning, Recovery mechanisms.
I. INTRODUCTION In order to store the large volume of data, cloud storage systems use many small-scale independent storage systems. These systems together form the entire cloud storage. To store the data using cloud storage has multiple advantages. Few of them are data stored using an account can be synced in multiple devices using the same account. There are lot of conflicting replicas are available in cloud storage. Users can use minimal amount of storage space by avoiding the replicas. The cloud computing has many features to the users like communication media, file storage and computations, keep mirroring of highly important information, etc., Basically, user data are stored in various storage locations like local servers and cloud. An overview of cloud storage system is shown in Fig.1. In the large scale applications, to reduce execution time and to improve performance the works are distributed to many resources. This will help users to achieve the turnaround time (TAT) as earlier than the threshold timing for the completion of a job. Sometimes the resources will be jammed due to unexpected failures. In this scenario handling the work load will be complex to the end users. If the resources are independent, failures will not have much effect. Suppose the work done by resources which is dependent will be awaiting resultant value from neighboring resource, the execution process will be questionable. In order to avoid this
Fig.1. Overview of Cloud Storage System
Secure data storage with recovery mechanism during faults is mandatory as shown in Fig [2]. For storage servers, such systems are necessary as large amount of data handling is done and at the same time a great number of end users are simultaneously seeking service.
Fig.2. Fault Tolerance in Cloud
Though the data are stored in cloud servers, the user information which is accessible by the cloud service provider (CSP) is not secured. The privacy is lacking in cloud environment even it is providing easy storage and flexible scaling trend effectively. To provide efficient privacy to the users, the encryption mechanism has been incorporated with the storage computation module. Here the user credential data is not in readable form. In order to identify the user credentials, the third parties including CSP needs to have the key to recognize the plain text. The cloud computation is mainly used for processing the large volume of data; this can be stored in cloud server. But the existing security mechanism for data storage is not giving the full assurance due to the unknown threatens and attacks. To handle this problem, the Partitioning mechanism is being added to enhance data storage security in this work. This part of the work is being carried out in cloud before the big data get involved completely in computational part. The detailed discussions and implementation issues are explained in the forthcoming sections. The rest of the paper is organized as follows: Section II presents the related work in the respective field of research. Section III gives the detailed proposed architecture of the secured data storage and error tolerant design of the work. Section IV presents the implementation issues of the proposed work. Section V provides the performance analysis of the proposed system in comparison with the existing work. Section VI finally provides the conclusion and the future enhancements.
Sherif Sakr et al. [4] analysis about open issues pertaining to finding the right balance between scalability, consistency, economical aspects of the trade-off design decisions. Cong Wang et al. [5], in their design allow users to audit the cloud storage with very lightweight communication and computation cost. They also support secure and efficient dynamic operations on user data which includes block insertion, deletion and updation. However, to ensure security the complete file is encrypted here. Instead in our proposed work the data is segmented and encrypted based on size, and it is stored in cloud to enhance the security and reduce the computation time whereby improving the performance. In the paper [6] author has shown the importance of fault tolerant in cloud and in the article [8], author has described about the study of how work migration can improve task completion in the midst of failures by maintaining low monetary costs. The study shows the importance of handling failure cases in spot instance. However, the anlyzation is not carried out for handling complete failure. In the proposed work two types of scenario, redundancy mechanism and recovery oriented computing is shown for handling the same by using prediction method. Ravi Jhawar et al. [7], in their work proposed handling fault tolerant techniques with an high level approach by designing a separate service layer. Fault tolerance is further strengthened by providing integrated diagnostic approach and isolation of the node recovery mechanisms in the proposed work. Solutions were also provided to have an effective and secured fault tolerant data storage issues by analyzing the data.
II. RELATED WORK III. PROPOSED WORK A survey has been carried out with existing systems to design an effective secured data storage and error tolerant design. To accomplish the competent data dynamics, the present evidence of storage framework is enhanced in various ways. In the paper [1] they have discussed about a protocol which ensures public verifiability without help of a third party auditor. This mechanism provides the data correctness and security. This protocol does not support data level dynamics and fault tolerance. R. Sanchez et al. [2] have discussed about privacy enhanced and trust-aware IdM architecture that enables to keep to a trace-off between user’s privacy and degree of tracking to obtain an adequate personalization degree in the different services. This framework is designed based on the importance of cloud consumers. User information security analysis providing Identity management and authentication is learnt from this paper. Hence an innovative mechanism which provides secured data storage and fault tolerance is handled in the proposed work. Elena Ferrari and Bhavani Thuraisingham [3] have shown the importance of securing the data using Data and Applications Security and Privacy (DASPY) framework from unauthorized access due to the explosion of sensitive or private data. This work gives insights of how to integrate value-added security characteristics into today’s cloud storage services.
The proposed work aims in the design of secured data storage and error tolerant design in cloud computing. The user data stored in cloud storage environment is not complex to the cloud users, even though it involves lot of computational part and takes time to execute the result during storage. In our work partition methodology is proposed to split the user input file into multiple files based on the file size and record count. Hence multiple data are submitted in an instant involving parallel execution process. The execution time is reduced henceforth compared with single process execution. Let us assume the user data size is 10GB, the computational time it takes for cloud storage in secured manner is around 20 minutes in the existing system. When the same file is processed through our SDSETD mechanism, the file will be split into 10 numbers of small file, with smaller size of 1GB.These files are submitted for parallel computation process which saves around 70% of time. The data partitioning method handles the files in an effective manner. If there is any failure in the midst of this process, the proctor unit which monitors the system for data loss and correctness reports to the fault tolerant system. Fault tolerant component initially, self diagnosis the system, if the fault is before the run time of the process the faulty node is isolated otherwise it searches for the redundancy node. Apart from the traditional redundancy fault tolerant mechanism, the
proposed SDSETD uses integrated diagnostic approach where the dependent process involved in the computation is analyzed effectively and the process job is migrated to another non faulty node to carry out the work. The data correctness is around 78 % here. The computational time for the entire Fault tolerant is effectively reduced by this mechanism, providing secured cloud storage and data recovery in spite of failures. The complete framework of SDSETD is shown in the Fig. 3
different recovery methods have been implemented. First method is to identify data errors, i.e., during partition, if the file is not split correctly, the process will be failed. Proctor monitors this situation, and it resubmits the task for the correct file, whereby recovering the file without any data loss. The second issue is for handling processor failures. Here, proctor shifts the failed or unexecuted task to some other processor to improve performance and thereby avoiding delay. IV. IMPLEMENTATION ISSUES SDSETD prototype is designed is evaluated considering minimum number of processes initially for a task in a single cloud environment. The input file makes use of the following algorithms to provide secured cloud data storage and error tolerant design.
Fig.3. Framework of SDSETD System
A. File Partitioning Component The large scale applications are using large volume of data taking much time for execution at the computation unit. To reduce the latency during execution, the data can be split into multiple files and can be assigned to processing part. This partitioning mechanism work is based on the file size and the record separator. The files have large number of records and when it is segmented based on the file size, the last record of some files might be partially segmented. To avoid this messed up splitting, our partition mechanism is designed effectively. This will check for data records and splits it based on the file size using record separator. Hence, the partitioned small unit of file will be delivered as a meaningful data. B. Fault Tolerant Component Traditional system allows user to process multiple tasks in sequence. The parallel processing allows user to utilize the full computational power of the system to complete the task easier and quickly. The Parallel group processing in this design allows multiple processes run concurrently through shared memory technique. In general this technique is used for multitasking system to complete the process as earlier. Also it ensures the proper utilization of the computing power which increases the efficiency of the system. The system does not remain idle at any point of time hence the resources are also effectively utilized. Hence, the time required to process the large volume of data, is greatly reduced when compared to traditional single processing system. In order to handle big data the user have some private concurrence with third party auditors maintaining some special agreements to check whether the data is well formed and processed well, etc., Our system monitors the work that is running in shared memory whether the multiple processes are running fine or not. To handle errors efficiently in the cloud system which makes use of data intensive parallel computations, two
Algorithm 1: File partitioning 1: Procedure 2: Read Input File “InF” 3: Let threshold = “1 GB” 4: Let i = 0; 5: Compute InSize = sizeOf(InF) 6: if (InSize > threshold) then 7: while (input = ) do 8: Compute i = i + 1 9: if (sizeOf(input) < threshold) then 10: store “InF_i”
