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(VR3-VR5) and PaaS (platform as a service) (VR6, VR7); separate virtualized
resources or services (VR1, VR2); two interacting campuses, A  and B; and
interconnecting them to a network infrastructure that in many cases may
need to use dedicated network links for guaranteed performance.
Efficient operation of such infrastructure will require both overall infra-
structure management and individual services and infrastructure segments
to interact between themselves. This task is typically out of the scope of the
existing cloud service provider models but will be required to support per-
ceived benefits of the future cloud-based e-SDI. These topics are a subject for
us in other research on the intercloud architecture framework (ICAF) [37-39].
The ICAF provides a common basis for building adaptive and on-demand
provisioned multiprovider cloud-based infrastructure services.
Besides the general cloud-based infrastructure services (storage, compute,
infrastructure/virtual machine [VM] management), the following specific
applications and services are required to support big data and other data-centric
applications [40]:
• Cluster services
• Hadoop-related services and tools
• Specialist data analytics tools (logs, events, data mining, etc.)
• Databases/servers SQL, NoSQL
• MPP databases
• Big data management tools
• Registries, indexing/search, semantics, namespaces
• Security infrastructure (access control, policy enforcement, confi-
dentiality, trust, availability, privacy)
• Collaborative environment (groups management)
Big data analytics tools are currently offered by the major cloud services
providers, such as Amazon Elastic MapReduce and Dynamo [41], Microsoft
Azure HDInsight [42], IBM Big Data Analytics [43]. HPCC Systems by
LexisNexis [44], Scalable Hadoop, and data analytics tools services are
offered by a few companies that position themselves as big data companies,
such as Cloudera [45] and a few others [46].
2.7 Security Infrastructure for Big Data
2.7.1 Security and Trust in Cloud-Based Infrastructure
Ensuring data veracity in big data infrastructure and applications requires
deeper analysis of all factors affecting data security and trustworthiness
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