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result of this, the software application is unable to fully utilize the elastic feature of
the cloud environment.
Xiong et al. [66] have presented a provider-centric approach for intelligently man-
aging the computing resources in a shared multi-tenant database system at the virtual
machine level. The proposed approach consists of two main components:
1. The system modeling module that uses machine learning techniques to
learn a model that describes the potential profit margins for each tenant
under different resource allocations. The learned model considers many
factors of the environment such as SLA cost, client workload, infrastructure
cost, and action cost.
2. The resource allocation decision module dynamically adjusts the resource
allocations, based on the information of the learned model, of the different
tenants to achieve the optimum profits.
Tatemura et al. [61] proposed a declarative approach for achieving elastic OLTP
workloads. The approach is based on defining the following two main components:
1. The transaction classes required for the application.
2. The actual workload with references to the transaction classes.
Using this information, a formal model can be defined to analyze elasticity of
the workload with transaction classes specified. In general, we believe that there is
a lack of flexible and powerful consumer-centric elasticity mechanisms that enable
software application to have more control on allocating the computing resources for
the database tier of their applications over the application running time and make
the best use of the elasticity feature of the cloud computing environments. More
attention from the research community is required to address these issues in future
work.
9.6.2 D
ata
r
ePliCation
anD
C
onsistenCy
m
anagement
In general, stateless services are easy to scale since any new
replicas
of these ser-
vices can operate completely independently of other instances. In contrast, scaling
stateful services, such as a
database system
, needs to guarantee a consistent view of
the system for users of the service. However, the cost of maintaining several data-
base replicas that are always strongly consistent is very high. As we have previously
described, according to the
CAP
theorem, most of the NoSQL systems overcome the
difficulties of distributed replication by relaxing the consistency guarantees of the
system and supporting various forms of weaker consistency models (e.g., eventual
consistency [63]). In practice, a common feature of the
NoSQL
and
DaaS
cloud offer-
ings is the creation and management of multiple replicas (usually 3) of the stored
data while a replication architecture is running behind-the-scenes to enable auto-
matic failover management and ensure high availability of the service. In general,
replicating for performance differs significantly from replicating for availability or
fault tolerance. The distinction between the two situations is mainly reflected by the
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