25,198 research outputs found
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud
Electronic Health (e-Health) technology has brought the world with
significant transformation from traditional paper-based medical practice to
Information and Communication Technologies (ICT)-based systems for automatic
management (storage, processing, and archiving) of information. Traditionally
e-Health systems have been designed to operate within stovepipes on dedicated
networks, physical computers, and locally managed software platforms that make
it susceptible to many serious limitations including: 1) lack of on-demand
scalability during critical situations; 2) high administrative overheads and
costs; and 3) in-efficient resource utilization and energy consumption due to
lack of automation. In this paper, we present an approach to migrate the ICT
systems in the e-Health sector from traditional in-house Client/Server (C/S)
architecture to the virtualised cloud computing environment. To this end, we
developed two cloud-based e-Health applications (Medical Practice Management
System and Telemedicine Practice System) for demonstrating how cloud services
can be leveraged for developing and deploying such applications. The Windows
Azure cloud computing platform is selected as an example public cloud platform
for our study. We conducted several performance evaluation experiments to
understand the Quality Service (QoS) tradeoffs of our applications under
variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green
Computing (CGC 2013
Cold Storage Data Archives: More Than Just a Bunch of Tapes
The abundance of available sensor and derived data from large scientific
experiments, such as earth observation programs, radio astronomy sky surveys,
and high-energy physics already exceeds the storage hardware globally
fabricated per year. To that end, cold storage data archives are the---often
overlooked---spearheads of modern big data analytics in scientific,
data-intensive application domains. While high-performance data analytics has
received much attention from the research community, the growing number of
problems in designing and deploying cold storage archives has only received
very little attention.
In this paper, we take the first step towards bridging this gap in knowledge
by presenting an analysis of four real-world cold storage archives from three
different application domains. In doing so, we highlight (i) workload
characteristics that differentiate these archives from traditional,
performance-sensitive data analytics, (ii) design trade-offs involved in
building cold storage systems for these archives, and (iii) deployment
trade-offs with respect to migration to the public cloud. Based on our
analysis, we discuss several other important research challenges that need to
be addressed by the data management community
Cloudbus Toolkit for Market-Oriented Cloud Computing
This keynote paper: (1) presents the 21st century vision of computing and
identifies various IT paradigms promising to deliver computing as a utility;
(2) defines the architecture for creating market-oriented Clouds and computing
atmosphere by leveraging technologies such as virtual machines; (3) provides
thoughts on market-based resource management strategies that encompass both
customer-driven service management and computational risk management to sustain
SLA-oriented resource allocation; (4) presents the work carried out as part of
our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a
Service software system containing SDK (Software Development Kit) for
construction of Cloud applications and deployment on private or public Clouds,
in addition to supporting market-oriented resource management; (ii)
internetworking of Clouds for dynamic creation of federated computing
environments for scaling of elastic applications; (iii) creation of 3rd party
Cloud brokering services for building content delivery networks and e-Science
applications and their deployment on capabilities of IaaS providers such as
Amazon along with Grid mashups; (iv) CloudSim supporting modelling and
simulation of Clouds for performance studies; (v) Energy Efficient Resource
Allocation Mechanisms and Techniques for creation and management of Green
Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
Advanced Cloud Privacy Threat Modeling
Privacy-preservation for sensitive data has become a challenging issue in
cloud computing. Threat modeling as a part of requirements engineering in
secure software development provides a structured approach for identifying
attacks and proposing countermeasures against the exploitation of
vulnerabilities in a system . This paper describes an extension of Cloud
Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in
relation to processing sensitive data in cloud computing environments. It
describes the modeling methodology that involved applying Method Engineering to
specify characteristics of a cloud privacy threat modeling methodology,
different steps in the proposed methodology and corresponding products. We
believe that the extended methodology facilitates the application of a
privacy-preserving cloud software development approach from requirements
engineering to design
Advanced Cyberinfrastructure for Science, Engineering, and Public Policy
Progress in many domains increasingly benefits from our ability to view the
systems through a computational lens, i.e., using computational abstractions of
the domains; and our ability to acquire, share, integrate, and analyze
disparate types of data. These advances would not be possible without the
advanced data and computational cyberinfrastructure and tools for data capture,
integration, analysis, modeling, and simulation. However, despite, and perhaps
because of, advances in "big data" technologies for data acquisition,
management and analytics, the other largely manual, and labor-intensive aspects
of the decision making process, e.g., formulating questions, designing studies,
organizing, curating, connecting, correlating and integrating crossdomain data,
drawing inferences and interpreting results, have become the rate-limiting
steps to progress. Advancing the capability and capacity for evidence-based
improvements in science, engineering, and public policy requires support for
(1) computational abstractions of the relevant domains coupled with
computational methods and tools for their analysis, synthesis, simulation,
visualization, sharing, and integration; (2) cognitive tools that leverage and
extend the reach of human intellect, and partner with humans on all aspects of
the activity; (3) nimble and trustworthy data cyber-infrastructures that
connect, manage a variety of instruments, multiple interrelated data types and
associated metadata, data representations, processes, protocols and workflows;
and enforce applicable security and data access and use policies; and (4)
organizational and social structures and processes for collaborative and
coordinated activity across disciplinary and institutional boundaries.Comment: A Computing Community Consortium (CCC) white paper, 9 pages. arXiv
admin note: text overlap with arXiv:1604.0200
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