77,682 research outputs found
The Virtual Machine (VM) Scaler: An Infrastructure Manager Supporting Environmental Modeling on IaaS Clouds
Infrastructure-as-a-service (IaaS) clouds provide a new medium for deployment of environmental modeling applications. Harnessing advancements in virtualization, IaaS clouds can provide dynamic scalable infrastructure to better support scientific modeling computational demands. Providing scientific modeling as-a-service requires dynamic scaling of server infrastructure to adapt to changing user workloads. This paper presents the Virtual Machine (VM) Scaler, an autonomic resource manager for IaaS Clouds. We have developed VM-Scaler, a REST/JSON-based web services application which supports infrastructure provisioning and management to support scientific modeling for the Cloud Services Innovation Platform (CSIP) [Lloyd et al. 2012]. VM-Scaler harnesses the Amazon Elastic Compute Cloud (EC2) application programming interface to support model- service scalability, cloud management, and infrastructure configuration for supporting modeling workloads. VM-Scaler provides cloud control while abstracting the underlying IaaS cloud from the end user. VM-Scaler is extensible to support any EC2 compatible cloud and currently supports the Amazon public cloud and Eucalyptus private clouds versions 3.1 and 3.3. VM-Scaler provides a platform to improve scientific model deployment by supporting experimentation with: hot spot detection schemes, VM management and placement approaches, and model job scheduling/proxy services
The Virtual Machine (VM) Scaler: An Infrastructure Manager Supporting Environmental Modeling on IaaS Clouds
Infrastructure-as-a-service (IaaS) clouds provide a new medium for deployment of environmental modeling applications. Harnessing advancements in virtualization, IaaS clouds can provide dynamic scalable infrastructure to better support scientific modeling computational demands. Providing scientific modeling as-a-service requires dynamic scaling of server infrastructure to adapt to changing user workloads. This paper presents the Virtual Machine (VM) Scaler, an autonomic resource manager for IaaS Clouds. We have developed VM-Scaler, a REST/JSON-based web services application which supports infrastructure provisioning and management to support scientific modeling for the Cloud Services Innovation Platform (CSIP) [Lloyd et al. 2012]. VM-Scaler harnesses the Amazon Elastic Compute Cloud (EC2) application programming interface to support model- service scalability, cloud management, and infrastructure configuration for supporting modeling workloads. VM-Scaler provides cloud control while abstracting the underlying IaaS cloud from the end user. VM-Scaler is extensible to support any EC2 compatible cloud and currently supports the Amazon public cloud and Eucalyptus private clouds versions 3.1 and 3.3. VM-Scaler provides a platform to improve scientific model deployment by supporting experimentation with: hot spot detection schemes, VM management and placement approaches, and model job scheduling/proxy services
Resource management for software defined data centers for heterogeneous infrastructures
Software Defined Data Center (SDDC) provides more
resource management flexibility since everything is defined as a
software, including the network as Software Defined Network
(SDN).Typically, cloud providers overlook the network, which is
configured in static way. SDN can help to meet applications goals
with dynamic network configuration and provide best-efforts for QoS.
Additionally, SDDC might benefit by instead of be composed by
heavy Virtual Machines, use light-weight OS Containers. Despite the
advantages of SDDC and OS Containers, it brings more complexity
for resource provisioning. The goal of this project is to optimize the
management of container based workloads deployed on Software defined
Data Centers enabled with heterogeneous network fabrics
through the use of network-aware placement algorithms that are
driven by performance models
Dynamic management of virtual infrastructures
The final publication is available at Springer via http://dx.doi.org/10.1007/s10723-014-9296-5Cloud infrastructures are becoming an appropriate solution to address the computational needs of scientific applications. However, the use of public or on-premises Infrastructure as a Service (IaaS) clouds requires users to have non-trivial system administration skills. Resource provisioning systems provide facilities to choose the most suitable Virtual Machine Images (VMI) and basic configuration of multiple instances and subnetworks. Other tasks such as the configuration of cluster services, computational frameworks or specific applications are not trivial on the cloud, and normally users have to manually select the VMI that best fits, including undesired additional services and software packages. This paper presents a set of components that ease the access and the usability of IaaS clouds by automating the VMI selection, deployment, configuration, software installation, monitoring and update of Virtual Appliances. It supports APIs from a large number of virtual platforms, making user applications cloud-agnostic. In addition it integrates a contextualization system to enable the installation and configuration of all the user required applications providing the user with a fully functional infrastructure. Therefore, golden VMIs and configuration recipes can be easily reused across different deployments. Moreover, the contextualization agent included in the framework supports horizontal (increase/decrease the number of resources) and vertical (increase/decrease resources within a running Virtual Machine) by properly reconfiguring the software installed, considering the configuration of the multiple resources running. This paves the way for automatic virtual infrastructure deployment, customization and elastic modification at runtime for IaaS clouds.The authors would like to thank to thank the financial support received from the Ministerio de Economia y Competitividad for the project CodeCloud (TIN2010-17804).Caballer Fernández, M.; Blanquer Espert, I.; Moltó, G.; Alfonso Laguna, CD. (2015). Dynamic management of virtual infrastructures. Journal of Grid Computing. 13(1):53-70. https://doi.org/10.1007/s10723-014-9296-5S5370131de Alfonso, C., Caballer, M., Alvarruiz, F., Molto, G., Hernández, V.: Infrastructure deployment over the cloud. In: 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, pp. 517–521. IEEE. (2011). doi: 10.1109/CloudCom.2011.77Alvarruiz, F., De Alfonso, C., Caballer, M., Hernández, V.: An energy manager for high performance computer clusters. 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Performance-oriented Cloud Provisioning: Taxonomy and Survey
Cloud computing is being viewed as the technology of today and the future.
Through this paradigm, the customers gain access to shared computing resources
located in remote data centers that are hosted by cloud providers (CP). This
technology allows for provisioning of various resources such as virtual
machines (VM), physical machines, processors, memory, network, storage and
software as per the needs of customers. Application providers (AP), who are
customers of the CP, deploy applications on the cloud infrastructure and then
these applications are used by the end-users. To meet the fluctuating
application workload demands, dynamic provisioning is essential and this
article provides a detailed literature survey of dynamic provisioning within
cloud systems with focus on application performance. The well-known types of
provisioning and the associated problems are clearly and pictorially explained
and the provisioning terminology is clarified. A very detailed and general
cloud provisioning classification is presented, which views provisioning from
different perspectives, aiding in understanding the process inside-out. Cloud
dynamic provisioning is explained by considering resources, stakeholders,
techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table
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