Database Reference
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operating costs by tenant consolidation. In general, increasing the degree of multi-
tenancy (number of tenants per server) is normally expected to decrease per-tenant
allocated resources and thus performance, but on the other hand, it also reduces the
overall operating cost for the DaaS provider and vice versa. Therefore, it is neces-
sary, but challenging for the DaaS providers to balance between the performance
that they can deliver to their tenants and the data center's operating costs. Several
provider-centric approaches have been proposed to tackle this challenge. Chi et
al. [ 102 ] have proposed a cost-aware query scheduling algorithm, called iCBS , that
takes the query costs derived from the service level agreements (SLA) between
the service provider and its customers (in terms of response time) into account to
make cost-aware scheduling decisions that aims to minimize the total expected cost.
SLA-tree is another approach that have been proposed to efficiently support profit-
oriented decision making of query scheduling. SLA-tree uses the information about
the buffered queries which are waiting to be executed in addition to the SLA for each
query that indicates the different profits for the query for varying query response
times and provides support for the answering of certain profit-oriented what if type
of questions. Lang et al. [ 170 ] presented a framework that takes as input the tenant
workloads, their performance SLA, and the server hardware that is available to
the DaaS provider, and produces server characterizing models that can be used
to provide constraints into an optimization module. By solving this optimization
problem, the framework provides a cost-effective hardware provisioning policy and
a tenant scheduling policy on each hardware resource. The main limitation of this
approach is that the input information of the tenant workloads is not always easy to
specify and model accurately. PIQL [ 67 ]( P erformance I nsightful Q uery L anguage)
is a declarative language that has been proposed with a SLA compliance prediction
model. The PIQL query compiler uses static analysis to select only query plans
where it can calculate the number of operations to be performed at every step in their
execution. In particular, PIQL extends SQL to allow developers to provide extra
bounding information to the compiler. In contrast to traditional query optimizers,
the objective of the query compiler is not to find the fastest plan but to avoid
performance degradation. Thus, the compiler choose a potentially slower bounded
plan over an unbounded plan that happens to be faster given the current database
statistics. If the PIQL compiler cannot create a bounded plan for a query, it warns
the developer and suggests possible ways to bound the computation.
In general, adequate SLA monitoring strategies and timely detection of SLA
violations represent challenging research issues in the cloud computing environ-
ments. Salman [ 75 ] has suggested that it may be necessary, in the future, for cloud
providers to offer performance based SLAs for their services with a tiered pricing
model, and charge a premium for guaranteed performance. While this could be one
of the directions to solve this problem, we believe that it is a very challenging goal
to delegate the management of the fine-granular SLA requirements of the consumer
applications to the side of the cloud service provider due to the wide heterogeneity
in the workload characteristics, details and granularity of SLA requirements, and
cost management objectives of the very large number of consumer applications
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