Personal Cloud Storage Benchmarks and Comparison

The large amount of space offered by personal cloud storage services (e.g., Dropbox and OneDrive), together with the possibility of synchronizing devices seamlessly, keep attracting customers to the cloud. Despite the high public interest, little information about system design and actual implications on performance is available when selecting a cloud storage service. Systematic benchmarks to assist in comparing services and understanding the effects of design choices are still lacking. This paper proposes a methodology to understand and benchmark personal cloud storage services. Our methodology unveils their architecture and capabilities. Moreover, by means of repeatable and customizable tests, it allows the measurement of performance metrics under different workloads. The effectiveness of the methodology is shown in a case study in which 11 services are compared under the same conditions. Our case study reveals interesting differences in design choices. Their implications are assessed in a series of benchmarks. Results show no clear winner, with all services having potential for improving performance. In some scenarios, the synchronization of the same files can take 20 times longer. In other cases, we observe a wastage of twice as much network capacity, questioning the design of some services. Our methodology and results are thus useful both as benchmarks and as guidelines for system design

Minimizing the Cost and Enhancing the Security for Cloud Hosting Service

Cloud computing is a new emerging technology which provides different kinds of services through internet and reduces maintenance cost as well as provides data reliability. Now a days many cloud service providers are providing services with different policies and prices. But it leads difficult to customers to choose cloud services with less price. That clients as a rule put their information into a single cloud and after that just trust to luckiness. Proposing new data hosting technique termed as CHARM which supports two strong functionalities.The first is selecting a few suitable clouds and a fitting excess system to store information with minimized financial expense and ensured accessibility.The second is setting off a move procedure to re-disperse information as indicated by the varieties of information access example and evaluating of clouds.In this project defining secret sharing algorithm to reduce the risk of data intrusion and the loss of service availability in the cloud and ensure data integrity. Finally proposed scheme not only saves the cost and also Maintains data and price adjustments

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

Network Traffic Measurements, Applications to Internet Services and Security

The Internet has become along the years a pervasive network interconnecting billions of users and is now playing the role of collector for a multitude of tasks, ranging from professional activities to personal interactions. From a technical standpoint, novel architectures, e.g., cloud-based services and content delivery networks, innovative devices, e.g., smartphones and connected wearables, and security threats, e.g., DDoS attacks, are posing new challenges in understanding network dynamics. In such complex scenario, network measurements play a central role to guide traffic management, improve network design, and evaluate application requirements. In addition, increasing importance is devoted to the quality of experience provided to final users, which requires thorough investigations on both the transport network and the design of Internet services. In this thesis, we stress the importance of users’ centrality by focusing on the traffic they exchange with the network. To do so, we design methodologies complementing passive and active measurements, as well as post-processing techniques belonging to the machine learning and statistics domains. Traffic exchanged by Internet users can be classified in three macro-groups: (i) Outbound, produced by users’ devices and pushed to the network; (ii) unsolicited, part of malicious attacks threatening users’ security; and (iii) inbound, directed to users’ devices and retrieved from remote servers. For each of the above categories, we address specific research topics consisting in the benchmarking of personal cloud storage services, the automatic identification of Internet threats, and the assessment of quality of experience in the Web domain, respectively. Results comprise several contributions in the scope of each research topic. In short, they shed light on (i) the interplay among design choices of cloud storage services, which severely impact the performance provided to end users; (ii) the feasibility of designing a general purpose classifier to detect malicious attacks, without chasing threat specificities; and (iii) the relevance of appropriate means to evaluate the perceived quality of Web pages delivery, strengthening the need of users’ feedbacks for a factual assessment

Dissecting UbuntuOne: Autopsy of a Global-scale Personal Cloud Back-end

Personal Cloud services, such as Dropbox or Box, have been widely adopted by users. Unfortunately, very little is known about the internal operation and general characteristics of Personal Clouds since they are proprietary services. In this paper, we focus on understanding the nature of Personal Clouds by presenting the internal structure and a measurement study of UbuntuOne (U1). We first detail the U$1$ architecture, core components involved in the U1 metadata service hosted in the datacenter of Canonical, as well as the interactions of U$1$ with Amazon S3 to outsource data storage. To our knowledge, this is the first research work to describe the internals of a large-scale Personal Cloud. Second, by means of tracing the U$1$ servers, we provide an extensive analysis of its back-end activity for one month. Our analysis includes the study of the storage workload, the user behavior and the performance of the U1 metadata store. Moreover, based on our analysis, we suggest improvements to U1 that can also benefit similar Personal Cloud systems. Finally, we contribute our dataset to the community, which is the first to contain the back-end activity of a large-scale Personal Cloud. We believe that our dataset provides unique opportunities for extending research in the field

Improving performance and capacity utilization in cloud storage for content delivery and sharing services

Content delivery and sharing (CDS) is a popular and cost effective cloud-based service for organizations to deliver/share contents to/with end-users, partners and insider users. This type of service improves the data availability and I/O performance by producing and distributing replicas of shared contents. However, such a technique increases the storage/network resources utilization. This paper introduces a threefold methodology to improve the trade-off between I/O performance and capacity utilization of cloud storage for CDS services. This methodology includes: i) Definition of a classification model for identifying types of users and contents by analyzing their consumption/ demand and sharing patterns, ii) Usage of the classification model for defining content availability and load balancing schemes, and iii) Integration of a dynamic availability scheme into a cloud based CDS system. Our method was implemented ¿This work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant TIN2016-79637-P ”Towards Unification of HPC and Big Data Paradigms

A PARTIAL REPLICATION LOAD BALANCING TECHNIQUE FOR DISTRIBUTED DATA AS A SERVICE ON THE CLOUD

Data as a service (DaaS) is an important model on the Cloud, as DaaS provides clients with different types of large files and data sets in fields like finance, science, health, geography, astronomy, and many others. This includes all types of files with varying sizes from a few kilobytes to hundreds of terabytes. DaaS can be implemented and provided using multiple data centers located at different locations and usually connected via the Internet. When data is provided using multiple data centers it is referred to as distributed DaaS. DaaS providers must ensure that their services are fast, reliable, and efficient. However, ensuring these requirements needs to be done while considering the cost associated and will be carried by the DaaS provider and most likely by the users as well. One traditional approach to support a large number of clients is to replicate the services on different servers. However, this requires full replication of all stored data sets, which requires a huge amount of storage. The huge storage consumption will result in increased costs. Therefore, the aim of this research is to provide a fast, efficient distributed DaaS for the clients, while reducing the storage consumption on the Cloud servers used by the DaaS providers. The method I utilize in this research for fast distributed DaaS is the collaborative dual-direction download of a file or dataset partitions from multiple servers to the client, which will enhance the speed of the download process significantly. Moreover, I partially replicate the file partitions among Cloud servers using the previous download experiences I obtain for each partition. As a result, I generate partial sections of the data sets that will collectively be smaller than the total size needed if full replicas are stored on each server. My method is self-managed; and operates only when more storage is needed. I evaluated my approach against other existing approaches and demonstrated that it provides an important enhancement to current approaches in both download performance and storage consumption. I also developed and analyzed the mathematical model supporting my approach and validated its accuracy

Building Distributed File Systems on Commercial Cloud Storage Services

With the increase in bandwidths available for internet users, cloud storage services have emerged to offer home users an easy way to share files and extend the storage space available for them. Most systems offer a limited free storage quota and combining these resources from multiple providers could be intriguing to cost-oriented users. In this study, we will implement a virtual file system that utilizes multiple different commercial cloud storage services (Dropbox, Google Drive, Microsoft OneDrive) to store its data. The data will be distributed among the different services and the structure of the data will be managed locally by the file system. The file system will be run in user space using FUSE and will use APIs provided by the cloud storage services to access the data. Our goal is to show that it is feasible to combine the free space offered by multiple services into a single easily accessible storage medium. Building such a system requires making design choices in multiple problem areas ranging from data distribution and performance to data integrity and data security. We will show how our file system is designed to address these requirements and will then conduct several tests to measure and analyze the level of performance provided by our system in different file system operation scenarios. The results will also be compared to the performance of using the distinct cloud storage services directly without distributing the data. This will help us to estimate the overhead or possible gain in performance caused by the distribution of data. It will also help us to locate the bottlenecks of the system. Finally, we will discuss some of the ways that could be used to improve the system based on test results and examples from existing distributed file systems