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

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

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude