Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

A formal method for rule analysis and validation in distributed data aggregation service

The usage of Cloud Serviced has increased rapidly in the last years. Data management systems, behind any Cloud Service, are a major concern when it comes to scalability, flexibility and reliability due to being implemented in a distributed way. A Distributed Data Aggregation Service relying on a storage system meets these demands and serves as a repository back-end for complex analysis and automatic mining of any type of data. In this paper we continue our previous work on data management in Cloud storage. We present a formal approach to express retrieval and aggregation rules with a compact, yet powerful tool called Rule Markup Language. Our extended solution proposes a standard form to schemes and uses the tool to match the rules to the XML form of the structured data in order to obtain the unstructured entries from BlobSeer data storage system. This allows the Distributed Data Aggregation Service (DDAS) to bypass several steps when processing a retrieval request. Our new architecture is more loosely-coupled with a separate module, the new tool, used fo

Towards an MPI-like Framework for Azure Cloud Platform

Message passing interface (MPI) has been widely used for implementing parallel and distributed applications. The emergence of cloud computing offers a scalable, fault-tolerant, on-demand al-ternative to traditional on-premise clusters. In this thesis, we investigate the possibility of adopt-ing the cloud platform as an alternative to conventional MPI-based solutions. We show that cloud platform can exhibit competitive performance and benefit the users of this platform with its fault-tolerant architecture and on-demand access for a robust solution. Extensive research is done to identify the difficulties of designing and implementing an MPI-like framework for Azure cloud platform. We present the details of the key components required for implementing such a framework along with our experimental results for benchmarking multiple basic operations of MPI standard implemented in the cloud and its practical application in solving well-known large-scale algorithmic problems

Scientific High Performance Computing (HPC) Applications On The Azure Cloud Platform

Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations

Security aspects of software development in the Microsoft Azure cloud

Tato práce představí aspekty vývoje v cloudu, které jsou zajímavé z hlediska bezpečnosti. Konkrétně budou ukázány na platformě Microsoft Azure. Některé služby provozované na Azure budou představeny se zaměřením na jejich bezpečnostní možnosti. Mimo jiné to budou způsoby dlouhodobého ukládání dat a autentizační služba Azure Active Directory. V druhé polovině bude představeno několik problémů, které se mohou vyskytnout během vývoje cloudových služeb. Mezi ně budou patřit zejména operace se soukromými daty a ochrana přístupových bodů k internetu. Tyto problémy budou podrobně prozkoumány a poté budou poskytnuta řešení nezávislá na platformě i taková, která využívají některých dalších služeb provozovaných Microsoftem na Azure.This work reviews security-related aspects of cloud development. The Microsoft Azure platform will be used for demonstration of these aspects. To that end, some services from Azure were investigated for the security measures they provide. This includes storage and identity services provided by Microsoft, as well as their respective security features. In the second half, several problematic areas of cloud development will be introduced. Those will be mainly handling confidential data and application secrets, and properly securing Internet-facing endpoints in the cloud. They will be examined closely and the thesis will provide platform-agnostic solutions as well as solutions utilizing some services provided by Azure

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201