Database Reference
In-Depth Information
general public like a utility [
191
]. Recently, the technology industry has matured
to the point where there is now an emerging mass market for this rental model.
Hence, cloud computing is not a revolutionary new development. However, it is an
evolution that has taken place over several decades and different technologies such
as virtualization, grid computing, utility computing and autonomic computing.
From the data management and processing point of view, there are two general
archetypes of data-intensive applications:
1.
On-Line Analytical Processing (OLAP)
: is characterized by relatively low
volume of transactions. Queries are often complex, involve aggregations. and
require accessing historical and multi-dimensional data with the purpose of
analyzing it and reporting certain figures. For OLAP systems, queries usually
runs in a batch processing mode.
2.
On-line Transaction Processing (OLTP)
: is characterized by a large number of
short transactions. The main emphasis for OLTP systems is put on very fast
query processing, maintaining data integrity in multi-access environments and
an effectiveness measured by number of transactions per second.
In general, successful cloud data management systems are normally designed to
satisfy as much as possible from the following
wish list
[
58
,
110
]:
Availability
: They must be always accessible even on the occasions where
there is a network failure or a whole datacenter has gone offline. Towards this
goal, the concept of Communication as a Service (CaaS) emerged to support
such requirements, as well as network security, dynamic provisioning of virtual
overlays for traffic isolation or dedicated bandwidth, guaranteed message delay,
communication encryption, and network monitoring [
235
].
Scalability
: They must be able to support very large databases with very high
request rates at very low latency. They should be able to take on new tenants
or handle growing tenants without much effort beyond that of adding more
hardware. In particular, the system must be able to automatically redistribute
data to take advantage of the new hardware.
Elasticity
: They must be able to satisfy changing application requirements in
both directions (scaling up or scaling down). Moreover, the system must be able
to gracefully respond to these changing requirements and quickly recover to its
steady state.
Performance
: On public cloud computing platforms, pricing is structured in a
way such that one pays only for what one uses, so the vendor price increases
linearly with the requisite storage, network bandwidth, and compute power.
Hence, the system performance has a direct effect on its costs. Thus, efficient
system performance is a crucial requirement to save money.
Multitenancy
: They must be able to support many applications (tenants) on
the same hardware and software infrastructure. However, the performance of
these tenant must be isolated from each another. Adding a new tenant should
require little or no effort beyond that of ensuring that enough system capacity
has been provisioned for the new load.
