Mengsong Zou

Exploring Models and Mechanisms for Exchanging Resources in a Federated Cloud

One of the key benefits of Cloud systems is their ability to provide elastic, on-demand (seemingly infinite) computing capability and performance for supporting service delivery. With the resource availability in single data centres proving to be limited, the option of obtaining extra-resources from a collection of Cloud providers has appeared as an efficacious solution. The ability to utilize resources from multiple Cloud providers is also often mentioned as a means to: (i) prevent vendor lock in, (ii) to enable in house capacity to be combined with an external Cloud provider, (iii) combine specialist capability from multiple Cloud vendors (especially when one vendor does not offer such capability or where such capability may come at a higher price). Such federation of Cloud systems can therefore overcome a limit in capacity and enable providers to dynamically increase the availability of resources to serve requests. We describe and evaluate the establishment of such a federation using a CometCloud based implementation, and consider a number of federation policies with associated scenarios and determine the impact of such policies on the overall status of our system. CometCloud provides an overlay that enables multiple types of Cloud systems (both public and private) to be federated through the use of specialist gateways. We describe how two physical sites, in the UK and the US, can be federated in a seamless way using this system.

Integrating Software Defined Networks within a Cloud Federation

Cloud computing has generally involved the use of specialist data centres to support computation and data storage at a central site (or a limited number of sites). The motivation for this has come from the need to provide economies of scale (and subsequent reduction in cost) for supporting large scale computation for multiple user applications over (generally) a shared, multi-tenancy infrastructure. The use of such infrastructures requires moving data to a central location (data may be pre-staged to such a location prior to processing using terrestrial delivery channels and does not always require the use of a network-based transfer), undertaking processing on the data, and subsequently enabling users to download results of analysis. We extend this model using software defined networks (SDNs), whereby capability within the network can be used to support in-transit processing while data is in movement from source to destination. Using a smart building infrastructure scenario, consisting of sensors and actuators embedded within a built environment, we describe how an SDN-based architecture can be used to support real time data processing. This significantly influences the processing times to support energy optimisation of the building and reduces costs. We describe an architecture for such a distributed, multi-layered Cloud system and discuss a prototype that has been implemented using the CometCloud system, deployed across three sites in the UK and the US. Wevalidate the prototype using data from sensors within a Sports facility and making use of EnergyPlus.

Deadline constrained video analysis via in-transit computational environments

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

Market Models for Federated Clouds

Multi-cloud systems have enabled resource and service providers to co-exist in a market where the relationship between clients and services depends on the nature of an application and can be subject to a variety of different Quality of Service (QoS) constraints. Deciding whether a cloud provider should host (or finds it profitable to host) a service in the long-term would be influenced by parameters such as the service price, the QoS guarantees required by customers, the deployment cost (taking into account both cost of resource provisioning and operational expenditure, e.g. energy costs) and the constraints over which these guarantees should be met. In a federated cloud system users can combine specialist capabilities offered by a limited number of providers, at particular cost bands-such as availability of specialist co-processors and software libraries. In addition, federation also enables applications to be scaled on-demand and restricts lock in to the capabilities of a particular provider. We devise a market model to support federated clouds and investigate its efficiency in two real application scenarios:(i) energy optimisation in built environments and (ii) cancer image processing both requiring significant computational resources to execute simulations. We describe and evaluate the establishment of such an application based federation and identify a cost-decision based mechanism to determine when tasks should be outsourced to external sites in the federation. The following contributions are provided: (i) understanding the criteria for accessing sites within a federated cloud dynamically, taking into account factors such as performance, cost, user perceived value, and specific application requirements; (ii) developing and deploying a cost based federated cloud framework for supporting real applications over three federated sites at Cardiff (UK), Rutgers and Indiana (USA), (iii) a performance analysis of the application scenarios to determine how task submission could be supported across these three sites, subject to particular revenue targets.

Enabling autonomic computing on federated advanced cyberinfrastructures

We present a federation model to support the dynamic federation of resources and autonomic management mechanisms that coordinate multiple workflows to use resources based on objectives. We illustrate the effectiveness of the proposed framework and autonomic mechanisms through the discussion of representative use case application scenarios, and from these experiences, we discuss that such a federation model can support new types of application formulations.

Supporting Data-Intensive Workflows in Software-Defined Federated Multi-Clouds

Cloud computing is emerging as a viable platform for scientific exploration. Elastic and on-demand access to resources (and other services), the abstraction of “unlimited” resources, and attractive pricing models provide incentives for scientists to move their workflows into clouds. Generalizing these concepts beyond a single virtualized datacenter, it is possible to create federated marketplaces where different types of resources (e.g., clouds, HPC grids, supercomputers) that may be geographically distributed, are collectively exposed as a single elastic infrastructure. This presents opportunities for optimizing the execution of application workflows with heterogeneous and dynamic requirements, and tackling larger scale problems. In this paper, we introduce a framework to manage the end-to-end execution of data-intensive application workflows in dynamic software-defined resource federation. This framework enables the autonomic execution of workflows by elastically provisioning an appropriate set of resources that meet application requirements, and by adapting this set of resources at runtime as the requirements change. It also allows users to customize scheduling policies that drive the way resources federated and used. To demonstrate the benefits of our approach, we study the execution of two different data-intensive scientific workflows in a multi-cloud federation using different policies and objective functions.

Realizing the Potential of IoT Using Software-Defined Ecosystems

Pervasive computational ecosystems that combine data sources and computing/communication resources in self-managed environments, such as the ones powered by Internet of Things (IoT) devices, have the potential to automate and facilitate many aspects of our lives, and impact a variety of applications, from the management of extreme events to the optimization of everyday processes. However, this vision remains mostly unrealized despite the fact that the technology to achieve it exists, largely because of the gap between our ability to collect data and our ability to gain insight from it. In this paper, we discuss the challenges associated with providing a pervasive computational ecosystem. We then present our vision of how to best support data-driven computational ecosystems and propose a conceptual architecture that leverages ideas from software-defined environments in order to combine data, computing, and communication resources. In addition, we show how this proposed architecture enables the execution of data-driven workflows on top of these resources.

A Framework for Realizing Software-Defined Federations for Scientific Workflows

Federated computing has been shown to be an effective model for harnessing the capabilities and capacities of geographically- distributed resources in order to solve large science and en- gineering problems. However, traditional High Performance Computing (HPC) based federation models can be restrictive as they present users with a pre-defined set of resources and do not allow federations to evolve in response to changing resources or application needs. As emerging application workflows and the underlying resources become increasingly dynamic and exhibit changing requirements and constraints, they cannot be easily supported by such federation models. Instead, new federation models that are capable of dynamically adapting to these emerging needs are required. In this paper, we present a programmable dynamic federation model that uses software-defined environment concepts to drive the federation process and seamlessly adapt resource compositions at runtime. The resulting software-defined federation adapts to meet both requirements and constraints set by the user, application, and/or resource providers. In this paper we present the design and prototype implementation of such software-defined federation model, and demonstrate its operation and performance through a use case where heterogeneous, geographically distributed resources are federated based on user specifications, and the federation evolves over time following the requirements and constraints defined by the user.

A computational model to support in-network data analysis in federated ecosystems

Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data analysis and manipulation within data intensive applications. SDN technology allows us to separate the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage software-defined networking (SDN) to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. This strategy allows us to minimize waiting times at the destination data center and to cope with spikes in demand for computational capability. We validate our approach using a smart building application in a multi-cloud infrastructure. Results show how the in-transit processing strategy increases the computational capabilities of the infrastructure and influences the percentage of job completion without significantly impacting costs and overheads.

Leveraging In-Transit Computational Capabilities in Federated Ecosystems

Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data analysis and manipulation within data intensive applications. SDN technology allows us to separate the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage software defined networking to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. This strategy allows us to minimize waiting times at the destination data center and to cope with spikes in demand for computational capability. We validate our approach using a smart building application in a multi cloud infrastructure. We show how the in-transit processing strategy increases the computational capabilities of the infrastructure and influences the percentage of job completion without significantly impacting costs and overheads.