2,249 research outputs found
Cloud WorkBench - Infrastructure-as-Code Based Cloud Benchmarking
To optimally deploy their applications, users of Infrastructure-as-a-Service
clouds are required to evaluate the costs and performance of different
combinations of cloud configurations to find out which combination provides the
best service level for their specific application. Unfortunately, benchmarking
cloud services is cumbersome and error-prone. In this paper, we propose an
architecture and concrete implementation of a cloud benchmarking Web service,
which fosters the definition of reusable and representative benchmarks. In
distinction to existing work, our system is based on the notion of
Infrastructure-as-Code, which is a state of the art concept to define IT
infrastructure in a reproducible, well-defined, and testable way. We
demonstrate our system based on an illustrative case study, in which we measure
and compare the disk IO speeds of different instance and storage types in
Amazon EC2
Digital curation and the cloud
Digital curation involves a wide range of activities, many of which could benefit from cloud
deployment to a greater or lesser extent. These range from infrequent, resource-intensive tasks
which benefit from the ability to rapidly provision resources to day-to-day collaborative activities
which can be facilitated by networked cloud services. Associated benefits are offset by risks
such as loss of data or service level, legal and governance incompatibilities and transfer
bottlenecks. There is considerable variability across both risks and benefits according to the
service and deployment models being adopted and the context in which activities are
performed. Some risks, such as legal liabilities, are mitigated by the use of alternative, e.g.,
private cloud models, but this is typically at the expense of benefits such as resource elasticity
and economies of scale. Infrastructure as a Service model may provide a basis on which more
specialised software services may be provided.
There is considerable work to be done in helping institutions understand the cloud and its
associated costs, risks and benefits, and how these compare to their current working methods,
in order that the most beneficial uses of cloud technologies may be identified. Specific
proposals, echoing recent work coordinated by EPSRC and JISC are the development of
advisory, costing and brokering services to facilitate appropriate cloud deployments, the
exploration of opportunities for certifying or accrediting cloud preservation providers, and
the targeted publicity of outputs from pilot studies to the full range of stakeholders within the
curation lifecycle, including data creators and owners, repositories, institutional IT support
professionals and senior manager
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
Assessing database and network threats in traditional and cloud computing
Cloud Computing is currently one of the most widely-spoken terms in IT. While it offers a range of technological and financial benefits, its wide acceptance by organizations is not yet wide spread. Security concerns are a main reason for this and this paper studies the data and network threats posed in both traditional and cloud paradigms in an effort to assert in which areas cloud computing addresses security issues and where it does introduce new ones. This evaluation is based on Microsoftâs STRIDE threat model and discusses the stakeholders, the impact and recommendations for tackling each threat
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
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
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