Popular Cloud Storage Services Research Articles

On the Synchronization Bottleneck of OpenStack Swift-Like Cloud Storage Systems

As one type of the most popular cloud storage services, OpenStack Swift and its follow-up systems replicate each object across multiple storage nodes and leverage object sync protocols to achieve high reliability and eventual consistency . The performance of object sync protocols heavily relies on two key parameters: $r$ (number of replicas for each object) and $n$ (number of objects hosted by each storage node). In existing tutorials and demos, the configurations are usually $r=3$ and $n by default, and the sync process seems to perform well. However, we discover in data-intensive scenarios, e.g., when $r>3$ and $n\gg 1,000$ , the sync process is significantly delayed and produces massive network overhead, referred to as the sync bottleneck problem . By reviewing the source code of OpenStack Swift, we find that its object sync protocol utilizes a fairly simple and network-intensive approach to check the consistency among replicas of objects. Hence in a sync round, the number of exchanged hash values per node is $\Theta (n\times r)$ . To tackle the problem, we propose a lightweight and practical object sync protocol, LightSync , which not only remarkably reduces the sync overhead, but also preserves high reliability and eventual consistency. LightSync derives this capability from three novel building blocks: 1) Hashing of Hashes , which aggregates all the $h$ hash values of each data partition into a single but representative hash value with the Merkle tree; 2) Circular Hash Checking , which checks the consistency of different partition replicas by only sending the aggregated hash value to the clockwise neighbor; and 3) Failed Neighbor Handling , which properly detects and handles node failures with moderate overhead to effectively strengthen the robustness of LightSync. The design of LightSync offers provable guarantee on reducing the per-node network overhead from $\Theta (n\times r)$ to $\Theta (\frac{n}{h})$ . Furthermore, we have implemented LightSync as an open-source patch and adopted it to OpenStack Swift, thus reducing the sync delay by up to 879 $\times$ and the network overhead by up to 47.5 $\times$ .

CoCloud: Enabling Efficient Cross-Cloud File Collaboration Based on Inefficient Web APIs

Cloud storage services such as Dropbox have been widely used for file collaboration among multiple users. However, this desirable functionality is yet restricted to the “walled-garden” of each service. At present, the only feasible approach to cross-cloud file collaboration seems to be using web APIs, whose performance is known to be highly unstable and unpredictable. Now that using inefficient web APIs is inevitable, in this paper we attempt to achieve sound user-perceived performance for cross-cloud file collaboration. This attempt is enabled by two key observations from real-world measurements. First, for each cloud, we are always able to deploy one or several nearby (client) proxies which can efficiently access the web APIs. Second, during file collaboration, significant similarity exists among different versions of a file. This can be exploited to substantially reduce inter-proxy traffic and thus shorten the data sync time. Guided by the observations, we design and implement an open-source prototype system called CoCloud. Currently, it supports file collaboration among four popular cloud storage services in the US and China. Its performance is well acceptable to users under representative workloads, even approaching or exceeding that of intra-cloud collaboration in many cases.

Cloud Storage for Data Sharing: Infrastructure as Service (IaaS) in Cloud Environment

Cloud Storage is a challenging task that provides Infrastructure as a Service (IaaS) in Cloud environment. Users should be aware of backing up their data, recovering their data when using cloud storage services. The cloud provider makes them to users online by keeping the uploaded files and downloaded files on cloud server. In this paper we compared the popular cloud storage services (DropBox, Google Drive, Box.net, Skydrive and Amazon cloud drive) and described the cloud storage types. To manage cloud storage company is precious, but it make sure to store data and provide services to users, users have no difficulty to access data, store data and provide convenience to users. The result described that cloud storage companies perform efficient storage to process the data.

CloudMe forensics: A case of big data forensic investigation

SummaryThe significant increase in the volume, variety, and velocity of data complicates cloud forensic efforts, and such (big) evidential data will, at some point, become too (computationally) expensive to be fully identified, collected, and analysed in a timely manner. Thus, it is important for digital forensic practitioners to have an up‐to‐date knowledge of relevant data artefacts that could be forensically recovered from the cloud product under investigation. In this paper, CloudMe, a popular cloud storage service, is studied. The types and locations of the artefacts relating to the installation and uninstallation of CloudMe client application, logging in and out, and file synchronization events from the computer desktop and mobile clients are described. Findings from this research will also help inform future development of tools and techniques (e.g., data mining techniques) for cloud‐enabled big data endpoint forensics investigation.

The Impact of Content Sharing on Cloud Storage Bandwidth Consumption

Cloud storage offers a popular environment for users to store and share content. Yet, content sharing leads to multiple downloads of the same content when users synchronize devices. These downloads contribute to bandwidth waste and increase server workloads. To what extent does this occur? Would network caches alleviate the problem? The authors address these questions by investigating the traffic generated by Dropbox, a popular cloud storage service. Using data collected from four networks, they show that a large fraction (57-70 percent) of downloads generated by Dropbox users is associated with content shared among multiple devices. Moreover, despite optimizations such as the LAN Sync protocol, up to 25 percent of Dropbox download traffic comes from content replicas. The authors then evaluate an alternative synchronization architecture that uses caches to offload storage servers from such downloads. Their experiments show that the approach cost-effectively avoids most repetitive downloads, benefiting service providers, the network, and end users.

SugarSync forensic analysis

Cloud storage services are popular with both individuals and businesses as they offer cost-effective, large capacity storage and multi-functional services on a wide range of devices such as personal computers (PCs), Mac computers, and smart mobile devices (e.g. iPhones). However, cloud services have also been known to be exploited by criminals, and digital forensics in the cloud remains a challenge, partly due to the diverse range of cloud services and devices that can be used to access such services. Using SugarSync (a popular cloud storage service) as a case study, research was undertaken to determine the types and nature of volatile and non-volatile data that can be recovered from Windows 8, Mac OS X 10.9, Android 4 and iOS 7 devices when a user has carried out different activities such as upload and download of files and folders. We then document the various digital artefacts that could be recovered from the respective devices.

FAST CLOUD: Pushing the Envelope on Delay Performance of Cloud Storage With Coding

Our paper presents solutions that can significantly improve the delay performance of putting and retrieving data in and out of cloud storage. We first focus on measuring the delay performance of a very popular cloud storage service Amazon S3. We establish that there is significant randomness in service times for reading and writing small and medium size objects when assigned distinct keys. We further demonstrate that using erasure coding, parallel connections to storage cloud and limited chunking (i.e., dividing the object into a few smaller objects) together pushes the envelope on service time distributions significantly (e.g., 76%, 80%, and 85% reductions in mean, 90th, and 99th percentiles for 2-MB files) at the expense of additional storage (e.g., 1.75 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\times$</tex></formula> ). However, chunking and erasure coding increase the load and hence the queuing delays while reducing the supportable rate region in number of requests per second per node. Thus, in the second part of our paper, we focus on analyzing the delay performance when chunking, forward error correction (FEC), and parallel connections are used together. Based on this analysis, we develop load-adaptive algorithms that can pick the best code rate on a per-request basis by using offline computed queue backlog thresholds. The solutions work with homogeneous services with fixed object sizes, chunk sizes, operation type (e.g., read or write) as well as heterogeneous services with mixture of object sizes, chunk sizes, and operation types. We also present a simple greedy solution that opportunistically uses idle connections and picks the erasure coding rate accordingly on the fly. Both backlog-based and greedy solutions support the full rate region and provide best mean delay performance when compared to the best fixed coding rate policy. Our evaluations show that backlog-based solutions achieve better delay performance at higher percentile values than the greedy solution.

Security Concerns in Popular Cloud Storage Services

The authors provide a systematic security analysis on the sharing methods of three major cloud storage and synchronization services: Dropbox, Google Drive, and Microsoft SkyDrive. They show that all three services have security weaknesses that may result in data leakage without users' awareness.