A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments

2021 ◽  
pp. 207-223
Author(s):  
Gabriel-Cosmin Apostol ◽  
Luminita Borcea ◽  
Ciprian Dobre ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis
2010 ◽  
Vol 51 (1) ◽  
pp. 1-2
Author(s):  
Jesus Carretero ◽  
J. Daniel Garcia

Author(s):  
Ahmet Artu Yıldırım ◽  
Dan Watson

Major Internet services are required to process a tremendous amount of data at real time. As we put these services under the magnifying glass, It's seen that distributed object storage systems play an important role at back-end in achieving this success. In this chapter, overall information of the current state-of –the-art storage systems are given which are used for reliable, high performance and scalable storage needs in data centers and cloud. Then, an experimental distributed object storage system (CADOS) is introduced for retrieving large data, such as hundreds of megabytes, efficiently through HTML5-enabled web browsers over big data – terabytes of data – in cloud infrastructure. The objective of the system is to minimize latency and propose a scalable storage system on the cloud using a thin RESTful web service and modern HTML5 capabilities.


2021 ◽  
Author(s):  
David Andrew Lloyd Tenty

As we approach the limits of Moore’s law the Cloud computing landscape is becoming ever more heterogeneous in order to extract more performance from available resources. Meanwhile, the container-based cloud is of growing importance as a lightweight way to deploy applications. A unified heterogeneous systems framework for use with container-based applications in the heterogeneous cloud is required. We present a bytecode-based framework and it’s implementation called Man O’ War, which allows for the creation of novel, portable LLVM bitcode-based containers for use in the heterogeneous cloud. Containers in Man O’ War enabled systems can be efficiently specialized for the available hardware within the Cloud and expand the frontiers for optimization in heterogeneous cloud environments. We demonstrate that a framework utilizing portable bytecode-based containers eases optimizations such as heterogeneous scaling which have the potential to improve resource utilization and significantly lower costs for users of the public cloud.


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