Amazon EC2 And S3 As A Cloud-Based Distributed Computing System


In all industries, the exponential expansion of data quantities necessitates the development of novel storage technology. Cloud technology can spread documents, block storage, or cache across numerous physical servers for highly available data backup and disaster recovery. The networked power storage underpins Amazon S3 and large data pools with distributed computing data centers.

What is Distributed Storage, and how does it work?

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A distributed computing file system is a type of architecture that allows data to be shared among numerous physical servers and, in some cases, multiple data centers. It normally consists of a storage cluster with data synchronization and coordination mechanism between cluster nodes.

Highly scalable cloud service storage technologies like Amazon S3 & Microsoft’s Cloud Blob Storage and distributed storage solutions like Cloudian Hyperstore rely on distributed storage.

Several types of data can be stored in distributed storage systems:

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Files—a distributed computing allows devices to install a virtual disk, with the files spread over multiple workstations.

Block storage is a type of data storage that stores information in a volume called blocks. This is a performance-enhancing alternative to an archive structure of distributed computing. A cloud service Storage Area Network is a common dispersed block storage system (SAN).

Objects—in a distributed memory storage system, data is wrapped into objects with a unique Number or hashed.

There are various advantages of using a distributed storage system:

The distributed computing fundamental motive for spreading storage is to represent the predicted, adding more storage space to the cluster by installing more storage nodes.

Redundancy—for high reliability, backup, and disaster recovery, distributed file systems can store several copies of the same data.

Cost—distributed storage allows you to store vast amounts of data on less expensive commodity hardware.

Performance—In some cases, distributed storage can outperform a single server, for example, by storing data closer to its users or allowing highly parallel access to big files.

Features and Limitations of Distributed Storage:

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The majority of distributed computing storage systems include one or more of the following characteristics:

Partitioning is the ability to redistribute data among cluster nodes and allow clients to retrieve data from various nodes simultaneously.

Replication refers to duplicating the same piece of data across different cluster nodes while keeping the data consistent while clients update it.

Fault tolerance refers to keeping data accessible even if one or more nodes in a distributed storage cluster fail.

Elastic scalability allows data consumers to request additional storage space as needed and storage systestrators to scale storage up or down by adding or deleting storage units from the cluster.

The CAP theorem defines an intrinsic limitation for distributed storage systems. According to the theorem, a distributed system cannot sustain Consistency, Availability, and Partition Tolerance. At least a few of these three traits must be sacrificed. While many distributed storage systems ensure availability and partition tolerance, they sacrifice consistency.

An instance of a distributed file system is Amazon S3.

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S3 stands for Amazon Simple Storage Service, a parallel processing storage system. Objects in S3 are made of metadata. The metadata is a moniker pair collection that offers information about the item, such as the most recent modification date. S3 accepts both standard marking and user-defined metadata.

Buckets are used to organize objects. Users of Amazon S3 must construct buckets and select which buckets to store and retrieve objects from. Users can organize their data using buckets, which are logical structures. The actual data may be spread among many file nodes in several Amazon AWS Availability Zones (AZ) in the same region behind the scenes. A bucket in Amazon S3 is always associated with a certain geographical area instance, Unit ed States 1 (North Virginia)—and items cannot leave that region.

A container, a password, and a version ID are used to identify each object in S3. The password is a one-of-a-kind identifier for each object in the bucket. Each object in S3 has numerous versions, as denoted by the edition ID.

Amazon AWS provide highly available partition tolerance because of the CAP theorem. However, it cannot ensure consistency. Instead, it proposes the following model of eventual consistency:

If users PUT or Delete the data in Amazon S3, it is securely stored, but change may take some time to propagate throughout Amazon S3.

Clients reading the data immediately after a change will still see the old copy of the data until the modification is disseminated.

S3 guarantees atomicity: when clients read an object, they will see either the old or new copy, but never a damaged or partial version.

Cloudian’s On-Premises Distributed Storage

Cloudian HyperStore is the enterprise storage system with a completely distributed design that eliminates vulnerabilities and allows for easy scaling from thousands of Terabytes through Exabytes. It works flawlessly with S3 API.

The HyperStore software is based on three or more nodes, allowing you to replicate your items for high availability. It allows you to add as many hard disks as you require, and the extra differences will immediately join the elastic storage pool.

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