Miguel Caballer

Dynamic Management of Virtual Infrastructures

Cloud 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.

An economic and energy-aware analysis of the viability of outsourcing cluster computing to a cloud

This paper compares the total cost of ownership of a physical cluster with the cost of a virtual cloud-based cluster. For that purpose, cost models for both a physical cluster and a cluster on a cloud have been developed. The model for the physical cluster takes into account previous works and incorporates a more detailed study of the costs related to energy consumption and the usage of energy-saving strategies. The model for the cluster on a cloud considers pricing options offered by Amazon EC2, such as reserving instances on a long-term basis, and also considers using tools for powering nodes on and off on demand, in order to avoid the costs associated to keeping idle nodes running. Using these cost models, a comparison is made of physical clusters with cloud clusters of a similar size and performance. The results show that cloud clusters are an interesting option for start-ups and other organizations with a high degree of uncertainty with respect to the computational requirements, while physical clusters are still more economically viable for organizations with a high usage rate.

An Energy Manager for High Performance Computer Clusters

This paper presents a general energy management system for HPC clusters and cloud infrastructures that powers off cluster nodes when they are not being used, and conversely powers them on when they are needed. This system can be integrated with different HPC cluster middleware, such as Batch-Queuing Systems or Cloud Management Systems, by using a set of connectors, and is also able to deal with different mechanisms for powering on and off the computing nodes (such as Wake-on-Lan, Power Device Units, Intelligent Platform Management Interface or other infrastructure-specific mechanisms). While some existing Batch-Queuing Systems provide energy saving mechanisms, other popular choices lack this feature. Cloud management middleware do not generally provide this feature out of the box, and incorporating it implies making modifications to the middleware. The advantage of our approach is that it can be integrated with different resource management middleware, without needing any modification of that middleware. The paper describes the successful integration of the system proposed with the popular Torque/PBS management system, and also with the OpenNebula open source cloud management tool. Two real use-cases are presented, involving two different HPC clusters. These use cases show significant energy/costs savings of 38% and 16%.

Elastic Memory Management of Virtualized Infrastructures for Applications with Dynamic Memory Requirements

Abstract This paper addresses the impact of vertical elasticity for applications with dynamic memory requirements when running on a virtualized environment. Vertical elasticity is the ability to scale up and scale down the capabilities of a Virtual Machine (VM). In particular, we focus on dynamic memory management to automatically fit at runtime the underlying computing in- frastructure to the application, thus adapting the memory size of the VM to the memory consumption pattern of the application. An architecture is described, together with a proof-of-concept implementation, that dynamically adapts the memory size of the VM to prevent thrashing while reducing the excess of unused VM memory. For the test case, a synthetic benchmark is em- ployed that reproduces different memory consumption patterns that arise on real scientific applications. The results show that vertical elasticity, in the shape of dynamic memory management, enables to mitigate memory overprovisioning with controlled application performance penalty.

Infrastructure Deployment Over the Cloud

With the advent of cloud technologies the scientists have access to different cloud infrastructures in order to deploy all the virtual machines they need to perform the computations required in their research works. This paper describes a software architecture and a description language to simplify the creation of all the needed resources, and the elastic evolution of the computing infrastructure depending on the application requirements and some QoS features.

EC3: Elastic Cloud Computing Cluster

This paper introduces Elastic Cloud Computing Cluster (EC3), a tool that creates elastic virtual clusters on top of Infrastructure as a Service (IaaS) Clouds. The clusters are self-managed entities that scale out to a larger number of nodes on demand, up to a maximum size specified by the user. Whenever idle resources are detected, the clusters automatically scale in, according to some predefined policies, in order to cut down the costs in the case of using a public Cloud provider. This creates the illusion of a real cluster without requiring an investment beyond the actual usage. Two different case studies are presented to assess the effectiveness of an elastic virtual cluster. The results show that the usage of self-managed elastic clusters represents an important economic saving when compared both to physical clusters and to static virtual clusters deployed on an IaaS Cloud, with a reduced penalty in the elasticity management.

An energy management system for cluster infrastructures

This paper presents a general energy management system for High Performance Computing (HPC) clusters and cloud infrastructures that powers off cluster nodes when they are not being used, and conversely powers them on when they are needed. This system can be integrated with different HPC cluster middleware, such as Batch-Queuing Systems or Cloud Management Systems, and can also use different mechanisms for powering on and off the computing nodes. The presented system makes it possible to implement different energy-saving policies depending on the priorities and particularities of the cluster. It also provides a hook system to extend the functionality, and a sensor system in order to take into account environmental information. The paper describes the successful integration of the system proposed with some popular Batch-Queuing Systems, and also with some Cloud Management middlewares, presenting two real use-cases that show significant energy/costs savings of 27% and 17%.

CodeCloud: A Platform to Enable Execution of Programming Models on the Clouds

This paper presents a platform that supports the execution of scientic applications covering dierent programming models (such as Master/Slave, Parallel/MPI, MapReduce and Workows) on Cloud infrastructures. The platform includes i) a high-level declarative language to express the requirements of the applications featuring software customization at runtime; ii) an approach based on virtual containers to encapsulate the logic of the dierent programming models; iii) an infrastructure manager to interact with dierent IaaS backends; iv) a conguration software to dynamically congure the provisioned resources and v) a catalog and repository of virtual machine images. By using this platform, an application developer can adapt, deploy and execute parallel applications agnostic to the cloud backend.

Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures

In this study, we describe the ?further development of Elastic Cloud Computing Cluster (EC3), a tool ?for creating self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) clouds. By using spot ?instances and checkpointing techniques, EC3 can significantly reduce the total ?execution cost as well as facilitating automatic fault tolerance. Moreover, EC3 can deploy and manage hybrid clusters across on-premises and public ?cloud resources, thereby introducing ?cloud bursting capabilities. ?We present the results of a case study that we conducted to assess the effectiveness of the tool ?based on the structural dynamic analysis of buildings. In addition, we evaluated the checkpointing algorithms in a real ?cloud environment with existing workloads to study their effectiveness. The results ?demonstrate the feasibility and benefits of this type of ?cluster for computationally intensive applications. Cost-efficient hybrid elastic virtual clusters are deployed across clouds.Spot instances and checkpointing reduce the costs of execution.Hybrid clusters reduce the total execution time by employing cloud bursting.Computationally intensive applications are executed easily with EC3.

Automatic memory-based vertical elasticity and oversubscription on cloud platforms

Hypervisors and Operating Systems support vertical elasticity techniques such as memory ballooning to dynamically assign the memory of Virtual Machines (VMs). However, current Cloud Management Platforms (CMPs), such as OpenNebula or OpenStack, do not currently support dynamic vertical elasticity. This paper describes a system that integrates with the CMP to provide automatic vertical elasticity to adapt the memory size of the VMs to their current memory consumption, featuring live migration to prevent overload scenarios, without downtime for the VMs. This enables an enhanced VM-per-host consolidation ratio while maintaining the Quality of Service for VMs, since their memory is dynamically increased as necessary. The feasibility of the development is assessed via two case studies based on OpenNebula featuring (i) horizontal and vertical elastic virtual clusters on a production Grid infrastructure and (ii) elastic multi-tenant VMs that run Docker containers coupled with live migration techniques. The results show that memory oversubscription can be integrated on CMPs to deliver automatic memory management without severely impacting the performance of the VMs. This results in a memory management framework for on-premises Clouds that features live migration to safely enable transient oversubscription of physical resources in a CMP. We describe a memory oversubscription framework for Cloud Management Platforms.Transient overcommitment of physical hosts increases consolidation.Automatic vertical elasticity is managed via memory ballooning and live migration.Horizontal and vertical elastic virtual clusters are used in production.