Database Reference
In-Depth Information
Table 7.2
Number of
provisioned database replicas
r l a
/
le
1
2
3
4
80/20
4
3
5
5
50/50
5
4
7
6
replicas. This trade-off shows that there is no clear winner between the two
approaches and we can not favour one approach over the other. However, the
declarative SLA-based approach empowers the cloud consumer with a more
convenient and flexible mechanism for controlling and achieving their policies in
dynamic environments such as the Cloud.
7.6
Related work
Several approaches have been proposed for dynamic provisioning of computing
resources based on their effective utilization [
115
,
190
,
232
]. These approaches
are mainly geared towards the perspective of cloud providers. Wood et. al. [
232
]
have presented an approach for dynamic provisioning of virtual machines. They
define a unique metric based on the data consumption of the three physical
computing resources: CPU, network and memory to make the provisioning decision.
Padala et.al. [
190
] carried out black box profiling of the applications and built
an approximated model which relates performance attributes such as the response
time to the fraction of processor allocated to the virtual machine on which the
application is running. Dolly [
96
] is a virtual machine cloning technique to
spawn database replicas and provisioning shared-nothing replicated databases in
the cloud. The technique proposes database provisioning cost models to adapt the
provisioning policy to the low-level cloud resources according to the application
requirements. Rogers et al. [
200
] proposed two approaches for managing the
resource provisioning challenge for cloud databases. The Black-box provisioning
uses end-to-end performance results of sample query executions, whereas white-
box provisioning uses a finer grained approach that relies on the DBMS optimizer
to predict the physical resource (e.g., I/O, memory, CPU) consumption for each
query. Floratou et al. [
131
] have studied the performance and cost in the relational
database as a service environments. The results show that given a range of
pricing models and the flexibility of the allocation of resources in cloud-based
environments, it is hard for a user to figure out their actual monthly cost upfront.
Soror et al. [
211
] introduced a virtualization design advisor that uses information
about the database workloads to provide offline recommendations of workload-
specific virtual machines configurations. To the best of our knowledge, our approach
is the first to tackle the problem of dynamic provisioning the cloud resources of the
database tier based on consumer-centric and application-defined SLA metrics.
