Showing papers on "Data Corruption published in 2015"

Public Integrity Auditing for Dynamic Data Sharing With Multiuser Modification

Abstract: In past years, the rapid development of cloud storage services makes it easier than ever for cloud users to share data with each other. To ensure users’ confidence of the integrity of their shared data on cloud, a number of techniques have been proposed for data integrity auditing with focuses on various practical features, e.g., the support of dynamic data, public integrity auditing, low communication/computational audit cost, and low storage overhead. However, most of these techniques consider that only the original data owner can modify the shared data, which limits these techniques to client read-only applications. Recently, a few attempts started considering more realistic scenarios by allowing multiple cloud users to modify data with integrity assurance. Nevertheless, these attempts are still far from practical due to the tremendous computational cost on cloud users, especially when high error detection probability is required by the system. In this paper, we propose a novel integrity auditing scheme for cloud data sharing services characterized by multiuser modification, public auditing, high error detection probability, efficient user revocation as well as practical computational/communication auditing performance. Our scheme can resist user impersonation attack, which is not considered in existing techniques that support multiuser modification. Batch auditing of multiple tasks is also efficiently supported in our scheme. Extensive experiments on Amazon EC2 cloud and different client devices (contemporary and mobile devices) show that our design allows the client to audit the integrity of a shared file with a constant computational cost of 340 ms on PC (4.6 s on mobile device) and a bounded communication cost of 77 kB for 99% error detection probability with data corruption rate of 1%.

Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity

Abstract: The rise of data-intensive "Web 2.0" Internet services has led to a range of popular new programming frameworks that collectively embody the latest incarnation of the vision of Object-Relational Mapping (ORM) systems, albeit at unprecedented scale. In this work, we empirically investigate modern ORM-backed applications' use and disuse of database concurrency control mechanisms. Specifically, we focus our study on the common use of feral, or application-level, mechanisms for maintaining database integrity, which, across a range of ORM systems, often take the form of declarative correctness criteria, or invariants. We quantitatively analyze the use of these mechanisms in a range of open source applications written using the Ruby on Rails ORM and find that feral invariants are the most popular means of ensuring integrity (and, by usage, are over 37 times more popular than transactions). We evaluate which of these feral invariants actually ensure integrity (by usage, up to 86.9%) and which---due to concurrency errors and lack of database support---may lead to data corruption (the remainder), which we experimentally quantify. In light of these findings, we present recommendations for database system designers for better supporting these modern ORM programming patterns, thus eliminating their adverse effects on application integrity.

Detecting and Correcting Data Corruption in Stencil Applications through Multivariate Interpolation

Abstract: High-performance computing is a powerful tool that allows scientists to study complex natural phenomena. Extreme-scale supercomputers promise orders of magnitude higher performance compared with that of current systems. However, power constrains in future exascale systems might limit the level of resilience of those machines. In particular, data could get corrupted silently, that is, without the hardware detecting the corruption. This situation is clearly unacceptable: simulation results must be within the error margin specified by the user. In this paper, we exploit multivariate interpolation in order to detect and correct data corruption in stencil applications. We evaluate this technique with a turbulent fluid application, and we demonstrate that the prediction error using multivariate interpolation is on the order of 0.01. Our results show that this mechanism can detect and correct most important corruptions and keep the error deviation under 1% during the entire execution while injecting one corruption per minute. In addition, we stress test the detector by injecting more than ten corruptions per minute and observe that our strategy allows the application to produce results with an error deviation under 10% in such a stressful scenario.

SampleClean: Fast and Reliable Analytics on Dirty Data.

Abstract: An important obstacle to accurate data analytics is dirty data in the form of missing, duplicate, incorrect, or inconsistent values. In the SampleClean project, we have developed a new suite of techniques to estimate the results of queries when only a sample of data can be cleaned. Some forms of data corruption, such as duplication, can affect sampling probabilities, and thus, new techniques have to be designed to ensure correctness of the approximate query results. We first describe our initial project on computing statistically bounded estimates of sum, count, and avg queries from samples of cleaned data. We subsequently explored how the same techniques could apply to other problems in database research, namely, materialized view maintenance. To avoid expensive incremental maintenance, we maintain only a sample of rows in a view, and then leverage SampleClean to approximate aggregate query results. Finally, we describe our work on a gradient-descent algorithm that extends the key ideas to the increasingly common Machine Learning-based analytics.

Understanding Real World Data Corruptions in Cloud Systems

Abstract: Big data processing is one of the killer applications for cloud systems. MapReduce systems such as Hadoop are the most popular big data processing platforms used in the cloud system. Data corruption is one of the most critical problems in cloud data processing, which not only has serious impact on the integrity of individual application results but also affects the performance and availability of the whole data processing system. In this paper, we present a comprehensive study on 138 real world data corruption incidents reported in Hadoop bug repositories. We characterize those data corruption problems in four aspects: 1) what impact can data corruption have on the application and system? 2) how is data corruption detected? 3) what are the causes of the data corruption? and 4) what problems can occur while attempting to handle data corruption? Our study has made the following findings: 1) the impact of data corruption is not limited to data integrity, 2) existing data corruption detection schemes are quite insufficient: only 25% of data corruption problems are correctly reported, 42% are silent data corruption without any error message, and 21% receive imprecise error report. We also found the detection system raised 12% false alarms, 3) there are various causes of data corruption such as improper runtime checking, race conditions, inconsistent block states, improper network failure handling, and improper node crash handling, and 4) existing data corruption handling mechanisms (i.e., data replication, replica deletion, simple re-execution) make frequent mistakes including replicating corrupted data blocks, deleting uncorrupted data blocks, or causing undesirable resource hogging.

Preventing data corruption due to pre-existing split brain

Abstract: Various methods, systems, and processes to prevent data corruption caused by a pre-existing split brain condition in a cluster are presented. In response to determining that a node is no longer part of a sub-cluster, another node in the sub-cluster is designated as a racer node. The racer node accesses a matrix, and the matrix includes information to determine whether a majority of coordination points in a cluster are accessible by nodes in the sub-cluster. Based on the accessing, a determination is made that the information indicates that the majority of coordination points are accessible by the nodes. The information is then broadcasted.

Scalable error isolation for distributed systems

Abstract: In distributed systems, data corruption on a single node can propagate to other nodes in the system and cause severe outages. The probability of data corruption is already non-negligible today in large computer populations (e.g., in large datacenters). The resilience of processors is expected to decline in the near future, making it necessary to devise cost-effective software approaches to deal with data corruption. In this paper, we present SEI, an algorithm that tolerates Arbitrary State Corruption (ASC) faults and prevents data corruption from propagating across a distributed system. SEI scales in three dimensions: memory, number of processing threads, and development effort. To evaluate development effort, fault coverage, and performance with our library, we hardened two real-world applications: a DNS resolver and memcached. Hardening these applications required minimal changes to the existing code base, and the performance overhead is negligible in the case of applications that are not CPU-intensive, such as memcached. The memory overhead is negligible independent of the application when using ECC memory. Finally, SEI covers faults effectively: it detected all hardware-injected errors and reduced undetected errors from 44% down to only 0.15% of the software-injected computation errors in our experiments.

A Survey of Research on Data Corruption in Cyber–Physical Critical Infrastructure Systems

Abstract: Computer systems are present in every aspect of modern life. In many of these systems, corruption of data is unavoidable as a result of both intentional and unintentional means. In many systems, this erroneous data can result in severe consequences including financial loss, injury, or death. Critical infrastructure cyber–physical systems utilize intelligent control to improve performance; however, they are heavily data dependent. These systems have the potential to propagate corrupted data, leading to failure. This chapter presents a survey of work related to the propagation of corrupted data within critical infrastructure cyber–physical systems, including the sources of corrupted data and the structure of critical infrastructure cyber–physical systems. In addition, it presents a comparative analysis of various data corruption detection and mitigation techniques. Additionally, we discuss a number of studies on the negative effects of system execution on corrupted data. These key topics are essential to understanding how undetected corrupted data propagates through a critical infrastructure cyber–physical system.

Before the Repository: Defining the Preservation Threats to Research Data in the Lab

Abstract: This paper describes the results of a large survey designed to quantify the risks and threats to the preservation of the research data in the lab and to determine the mitigating actions of researchers. A total of 724 National Science Foundation awardees completed this survey. Identifying risks and threats to digital preservation has been a significant research stream. Much of this work has been within the context of a preservation technology infrastructure such as data archives for a digital repository. This study looks at the risks and threats to research data prior to its inclusion in a preservation technology infrastructure. The greatest threat to preservation is human error, followed by equipment malfunction, obsolete software, and data corruption. Lost and mislabeled media are not components in the threat taxonomies developed for repositories; however, they do represent an important threat to research data in the lab. Researchers have recognized the need to mitigate the risks inherent in maintaining digital data by implementing data management in their lab environments and have taken their responsibility as data managers seriously; however, they would still prefer to have professional data management support.

Preventing data corruption and single point of failure in fault-tolerant memory fabrics

Abstract: An example device in accordance with an aspect of the present disclosure includes a redundancy controller and/or memory module to prevent data corruption and single point of failure in a fault-tolerant memory fabric. Devices include engines to issue and/or respond to primitive requests, identify failures and/or fault conditions, and receive and/or issue containment mode indications.

A data-driven cyber-physical detection and defense strategy against data integrity attacks in smart grid systems

Abstract: I consider a cyber-physical perspective to the problem of detecting and defending against data integrity attacks in smart grid systems. In this paper, the problem of transient stability with distributed control using real-time data from geographically distributed Phasor Measurement Units (PMUs) is studied via a flocking-based modeling paradigm. I demonstrate how cyber corruption can be identified through the effective use of telltale physical couplings within the power system. I also develop a data-driven real-time cyber-physical detection and defense strategy with distributed control using real-time data from PMUs. The proposed strategy leverages the physical coherence to probe and detect PMU data corruption and estimate the true information values for attack mitigation.

Preventing data corruption and single point of failure in a fault-tolerant memory

Abstract: According to an example, data corruption and single point of failure is prevented in a fault-tolerant memory fabric with multiple redundancy controllers by granting, by a parity media controller, a lock of a stripe to a redundancy controller to perform a sequence on the stripe. The lock may be broken in response to determining a failure of the redundancy controller prior to completing the sequence. In response to breaking the lock, the parity cacheline of the stripe may be flagged as invalid. Also, a journal may be updated to document the breaking of the lock.

Data resiliency in a shared memory pool

Abstract: Various systems to achieve data resiliency in a shared memory pool are presented. Multiple memory modules are associated with multiple data interfaces, one or multiple erasure-coding interfaces are communicatively connected with the multiple data interfaces, and multiple compute elements are communicatively connected with one or multiple erasure-coding interfaces. Data sets are erasure-coded, and the resulting fragments are stored in random access memory modules distributed throughout the system. Storage in RAM allows real-time fetching of fragments using random-access read cycles and streaming of fragments using random-access write cycles, in which read operations include reconstruction of data sets from fetched data fragments, and write operations allow conversion of data sets into fragments which are then streamed and distributively stored. Distributed memory creates data resiliency to reconstruct original data sets in cases such as data corruption, failure of a memory module, failure of a data interface, or failure of a compute element.

Regenerating-Codes-Based Efficient Remote Data Checking and Repairing in Cloud Storage

Abstract: The dramatic development of cloud storage services has led growing companies and individuals to outsource their data to cloud. However, users still concern about the availability and integrity of the data stored in cloud. To relieve these concerns, data redundancy is introduced into cloud storage systems, and data integrity verification schemes are used to check whether data is corrupted. Once data corruption is detected, the repair operations should be executed. However, most of the existing schemes based on erasure codes or network coding techniques either introduce high computation cost or cannot efficiently support remote data repairing. In this paper, we propose an efficient Remote Data Checking and Repairing (RDCR) scheme based on the minimum bandwidth regenerating codes. Our scheme reduces data owners' burden of checking data integrity by enabling a third party to perform the public integrity verification. In addition, unlike previous schemes, our scheme supports exact repair of corrupted data so that the computation cost is further reduced. We implement our scheme and the experiment results show that, compared with the existing schemes, RDCR has lower computational overhead and communication cost.

Cache based recovery of corrupted or missing data

Abstract: Systems and methods for recovering corrupted data or missing data from a cache are provided. When a data corruption is discovered in a storage system, the cache may be searched to determine if a valid copy of the corrupted data can be recovered from the cache.

Data recovery method and apparatus for COW type file system

Abstract: The present invention relates to a data recovery method and an apparatus for COW type file system, which are used to recover damaged original data in the COW type file system. The recovery method comprises steps of: 1) performing backup on an original data storage medium; 2) scanning the original data storage medium, to acquire a viable recovery point that is closest to a modification time; and 3) performing data recovery according to the viable recovery point obtained from step 2. Compared to the prior art, a data recovery method with respect to a COW (Copy On Write) type file system provided by the present invention comprises: firstly performing backup to data, and then performing scanning and analysis on metadata (superblock) of the file system, and maximally and completely recovering damaged data caused by subjective or objective factors into available data on the storage medium, thereby avoiding or greatly reducing losses caused by data corruption.

Multi-bit soft error tolerable L1 data cache based on characteristic of data value

Abstract: Due to continuous decreasing feature size and increasing device density, on-chip caches have been becoming susceptible to single event upsets, which will result in multi-bit soft errors. The increasing rate of multi-bit errors could result in high risk of data corruption and even application program crashing. Traditionally, L1 D-caches have been protected from soft errors using simple parity to detect errors, and recover errors by reading correct data from L2 cache, which will induce performance penalty. This work proposes to exploit the redundancy based on the characteristic of data values. In the case of a small data value, the replica is stored in the upper half of the word. The replica of a big data value is stored in a dedicated cache line, which will sacrifice some capacity of the data cache. Experiment results show that the reliability of L1 D-cache has been improved by 65% at the cost of 1% in performance.

Local file system integrated cloud storage service method

Abstract: The invention relates to the technical field of cloud storage, in particular to a local file system integrated cloud storage service method. The cloud storage service comprises three modules, namely a file verification module, a view manager and a cloud storage assist device; file verification service is used for performing verification value management on files; the view manager maintains two views, namely moving views and frozen views, and is mainly used for managing state change conditions of local files; the cloud storage assist device is used for processing communication service between a local file system and a remote cloud storage platform and maintaining synchronous views during file synchronization. The method can effectively prevent data loss caused by data corruption, hardware crash and other conditions of a local operating system to avoid the problem of inconsistency of data at a cloud storage server and the problem of inconsistency spread between different clients in the same cloud storage platform, and the method can be widely applied to various cloud storage platforms.

Characterizing and exploiting application behavior under data corruption

Abstract: Shrinking semiconductor technologies come at the cost of higher susceptibility to hardware faults that render the systems unreliable. Traditionally, reliability solutions are aimed to protect equally and exhaustively all hardware parts of a system. This is in order to maintain the illusion of a correctly operating hardware. Due to the increasing error rates that induce higher reliability costs, this approach can no longer be sustainable. It is a fact that hardware faults can be masked by various levels of fault-masking effects. Therefore, not all hardware faults manifest as the same outcome on an application’s execution. Motivated by this fact, we propose a shift to vulnerabilitydriven unequal protection of a given structure (or same-level structures), where the less-vulnerable parts of a structure are protected less than their more-vulnerable counterparts. For that purpose, in this thesis, we quantitatively investigate how the effect of hardware-induced data corruptions on application behavior varies. We develop a portable software-implemented fault-injection (SWIFI) tool. On top of performing singlebit fault injections to capture their effects on application behavior, our tool is also datalevel aware and tracks the corrupted data to obtain more of their characteristics. This enables to analyze the effects of single-bit data corruptions in relation to the corrupted data characteristics and the executing workload. After a set of extensive fault-injection experiments on programs from the NAS Parallel Benchmarks suite, we obtain detailed insight on how the vulnerability varies; among others, for different application data types and for different bit locations within the data. The results show that we can characterize the vulnerability of data based on their high-level characteristics (e.g. usage type, size, user and memory space location). Moreover, we conclude that application data are vulnerable in parts. All these show that there is potential in exploiting the application behavior under data corruption. The exhaustive equal protection can be avoided by safely shifting to vulnerability-driven unequal protection within given structures. This can reduce the reliability overheads, without a significant impact on the fault coverage. For that purpose, we demonstrate the potential benefits of exploiting the varying vulnerability characteristics of application data in the case of a data cache.

How can we design a cost-effective model that ensures a high level of data integrity protection?

Abstract: Everyone agree that data is more secured locally than when it is outsourced far away from their owners. But the growth of local data annually implies extra charges for the customers, which makes their business slowing down. Cloud computing paradigm comes with new technologies that offer a very economic and cost-effective solutions, but at the expense of security. So designing a lightweight system that can achieve a balance between cost and data security is really important. Several schemes and techniques has been proposed for securing, checking and repairing data, but unfortunately the majority doesn't respect and preserve the cost efficiency and profitability of cloud offers. In this paper we try to answer the question: how can we design a model that enables a high level of integrity check while preserving a minimum cost? We try also to analyze a new threat model regards the tracking of a file's fragments during a repair or a download operation, which can cause the total loss of customers data. The solution given in this paper is based on redistributing fragments locations after every data operation using a set of random values generated by a chaotic map. Finally, we provide a data loss insurance (data corruption as well) approach based on user estimation of data importance level that helps in reducing user concerns about data loss.

A Diversity-Based Approach for Communication Integrity in Critical Embedded Systems

Abstract: We present, in this paper, a fault-tolerant approach to cope with accidental communication data corruption in critical embedded systems. One of the classical integrity approaches is the redundancy-based approach that consists particularly in replicating the message and sending all copies via the same communication channel consecutively or sending them via replicated communication channels. Yet, such approach is vulnerable to some cases of Common-Mode Failure. So, we propose to diversify the copies to be sent via two independent proposals: i) diversifying either the error detection function (which generate the check bits) or ii) the data payload. This paper focus on the first proposal by presenting experiments and results to validate its effectiveness. Besides, it describes basic theoretical concepts of the second proposal. Our case study is the Flight Control System (FCS). Yet, our approach could be deployed in other systems for which we describe the key properties.

Distributed life cycle scheduling for cascaded data processing

Abstract: According to current market trends, there is a huge demand to process large volume, velocity and variety of data. Real time and static data processing needs to be compliant to big data standards to provide a promising future. For data processing based business solutions, at each intermediate stage of processing, data correctness is important. However in real life scenario various sources of data corruption can be encountered. In this paper we are targeting on solution that can handle data corruption, ensures fault tolerance to a maximum extent and provides synchronization among processing entities in distributed environment. Here we have prototyped our proposal on top of an open source framework Trident, a big data technology to process real time data. The proposal can be efficiently used for cascaded data processing use cases (output of one processing chain is input to another processing chain). Common use cases adhering to data correctness are data interpolation, statistics management, billing software, data fraud analysis etc. Current paper describes an architecture which enhances the data processing capability of current Trident implementation in distributed environment for cascaded or interdependent data processing use cases. Our solution drives life cycle initiation in a transactional manner for the processing flow based on the status of individual processing nodes. This approach provides global chain level initialization and avoids data loss or data duplication due to external entities. Here we have focused on corruption due to initialization latency which can occur due to external entity connection discrepancy/external protocol response dependency/ third party software initialization dependency. The proposed solution can prove useful to handle both real time and static data for providing synchronization, fault tolerance and reliability.

Design Data Management in Model-Based Design

Abstract: One of the keys to successfully harnessing the benefits of graphical modeling in ModelBased Design is the effective management of associated design data. Design data in the form of model parameters and simulation configuration settings are as important to the simulation as the algorithm itself. Together they determine the form and range of the simulation outputs and internal states of the model. However, control engineers have been inclined towards graphical editing and layout than design data management leading to several key design issues especially for large scale systems. These are separation, logical partitioning, change detection, dependency analysis and traceability. In this paper, we present an approach to address these issues using a demonstration within Simulink, an exemplary Model-Based Design environment. A tradeoff decision on embedding of design parameters in the graphical model as opposed to keeping them separate can determine the ease of carrying out a parameter sweep. Another advantage is in the case of modular design platforms where the control variables required to switch variant configurations must be separated out from the design data. Moreover, the use of multiple configuration settings can be used to optimize the performance of the simulation depending on the inherent dynamics present in the model. Lack of logical partitioning of the data can present understandability and data corruption issues within a collaborative environment. Parallel development of graphical components without clear data separation increases the risk of design error. Furthermore, innovative partitioning schemes require a careful understanding of sharing and logical relationships among the data which can be independent of the graphical model componentization. Another decision point in logical partitioning is regarding the storage of design data in a centralized or a decentralized repository system with each offering unique advantages. For example, a decentralized system consisting of multiple files can be put under source control and differenced to earlier versions to detect changes. A change detection workflow that integrates changes in the graphical design with those in the data is key to getting a complete understanding of the changes in the system. As the design begins to scale, it may become necessary to store the design data in a decentralized repository system. However, this does present additional challenges. For example, the tracking of the interdependencies between the graphical model components and the design data partitions becomes critical. An analysis of such dependencies must be carried out to identify any missing components from the project. At a lower level, the traceability of the individual design data to the components is critical to establish the necessary and sufficient conditions for executing a component. Such analysis can also help with the logical partitioning schemes and removing redundant data. One of the big challenges of utilizing the recommendations presented above is in migrating a legacy system to use them. Such migration presents constraints on how much porting can be done in practice. In this paper, we outline several recommendations for addressing these key questions. In conclusion, we offer a set of best practices that engineers can use to manage their data effectively within a team-based environment.

Understanding the Effects of Data Corruption on Application Behavior Based on Data Characteristics

Abstract: In this paper, the results of an experimental study on the error sensitivities of application data are presented. We develop a portable software-implemented fault-injection SWIFI tool that, on top of performing single-bit flip fault injections and capturing their effects on application behavior, is also data-level aware and tracks the corrupted application data to report their high-level characteristics usage type, size, user, memory space location. After extensive testing of NPB-serial 7.8M fault injections, we are able to characterize the sensitivities of data based on their high-level characteristics. Moreover, we conclude that application data are error sensitive in parts; depending on their type, they have distinct and wide less-sensitive bit ranges either at the MSBs or LSBs. Among other uses, such insight could drive the development of sensitivity-aware protection mechanisms of application data.

Factor Analysis of Fraud Causes by the Perception of Auditors in the Province of North Sulawesi

Abstract: Fraud is defined as foul or scam in finance, which is not only problems faced by business es and industr ies , but also haunt s the implementation of government conduct around the world, also in Indonesia. The data by KPK shows Indonesia n corruption cases from 2011 to 2014 (as of October 31, 2014) . T he perpetrators of corruption based on positions are as many as 193 actors, based on the type of case as many as 207 cases , and as many as 206 cases based agencies. In North Sulawesi, according to data Corruption Court Class 1 A, the number of cases of Corruption during 2011 to October 2014 are as many as 125 cases. The purpose of this research is to identify and analyze the factors that cause the perception of fraud according to the auditor. This study used 105 respondents by the Judgement sampling technique and exploratory factor analysis method was used . The results show s greed and fear of losing office factors does not determine the occurrence of fraud . T here are five new factor determinant s of fraud that ar e classified as individual behavior, lack of supervision, lack of attention boss, financial pressures and working comfort factors . Role of synergy between the Internal Auditor, Independent Auditor, Government Auditor and Tax Auditor with the Government and leaders of the business community and local government in North Sulawesi province is expected to prevent fraud by making fraud prevention action plan . Keywords: fraud, perception, auditor

Data synchronization method and navigation equipment

Abstract: The invention discloses a data synchronization method and navigation equipment. The method comprises steps as follows: after a first account logging in the navigation equipment quits, data corresponding to the first account are stored locally in the navigation equipment; a second account logs in the navigation equipment, and if the first account is forced to quit, the second account is not allowed to check the data which correspond to the first account and are stored locally; if the first account is manually quitted by a user, after data synchronization of the second account and cloud equipment is realized, cloud data and set new local data are locally processed together to form data corresponding to the second account. With the adoption of the method, data corruption caused by synchronization of different accounts on the same navigation equipment can be avoided, and the user privacy is effectively protected.

Recovery from data corruption in a storage array

Abstract: Recovery from data corruption in a storage array is disclosed. One example is a computing device including a host bus adapter that communicates with a host device to process a data request from the host device by performing an operation on a data block, the data request including one of a write request and a read request, and communicates with a persistent storage to issue the data request to the persistent storage. An interconnect communicates with a plurality of control nodes in the storage array, each control node including a data cache, and the operation performed in a data cache in a control node of the plurality of control nodes. A processor determines, during the operation, if the data block is corrupted, and upon a determination that the data block is corrupted, initiates data recovery of the corrupted data block based on an I/O state and a data state.

Pipelined Messaging Gateway

Abstract: The SMS gateway EMG uses separate threads for each connection. This requires a large number of locks to avoid data corruption, and causes problems when scaling to thousands of connections. The CPU ...

Certain Investigation on Error Detecting and Correcting Codes in Memories

Abstract: The radiation in the space environment creates multiple upsets in the memories, when the datas are travelling from one system to another system. These upsets can become a serious problem in terms of accuracy and performance of the digital system. The reliability of data transmission gets severely affected by these errors.so it is essential to detect and correct the errors and protect the memories from data corruption. The various Error detecting and correcting codes are used to detect and correct the upsets.