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seems to be more interested in small applications with light workloads whereas
Azure is currently the most affordable service for medium to large services.
With the goal of facilitating performance comparisons of the tradeoffs cloud data
management systems, the Yahoo! Cloud Serving Benchmarks, YCSB* [26] and
YCSB++ † [52], have been presented as frameworks and core set of benchmarks for
NoSQL systems. The benchmarking tools have been made available via open-source
to allow extensible development of additional cloud benchmark suites that represent
different classes of applications and to facilitate the evaluation of different cloud data
management systems.
9.6 CHALLENGES OF THE NEW WAVE OF NoSQL SYSTEMS
In this section, we shed the lights on a set of novel research challenges, that have
been introduced by the new wave of NoSQL systems that need to be addressed to
ensure that the vision of designing and implementing successful scalable data man-
agement solutions can be achieved.
9.6.1 t rue e lastiCity
A common characteristic of internet-scale applications and services is that they
can be used by large numbers of end users and highly variable load spikes in the
demand for services that can occur depending on the day and the time of year and
the popularity of the application. In addition, the workload characteristic could vary
significantly from one application type to another where possible fluctuations on the
workload characteristics that could be of several orders of magnitude on the same
business day may also occur [13]. In principle, elasticity and horizontal scalability
are considered to be of the most important features that are provided by NoSQL
systems [59]. In practice, both of the commercial NoSQL offerings (e.g., Amazon
SimpleDB) and commercial DaaS offerings (e.g., Amazon RDS, Microsoft SQL
Azure) do not provide their users with any flexibility to dynamically increase or
decrease the allocated computing resources of their applications. While NoSQL
offerings claim to provide elastic services of their tenants, they do not provide any
guarantee that their provider-side elasticity management will provide scalable per-
formance with increasing workloads [10]. Moreover, commercial DaaS pricing mod-
els require their users to predetermine the computing capacity that will be allocated
to their database instance as they provide standard packages of computing resources
(e.g., Micro, Small, Large , and Extra Large DB Instances). In practice, predicting
the workload behavior (e.g., arrival pattern, I/O behavior, service time distribution)
and consequently accurate planning of the computing resource requirements with
consideration of their monetary costs are very challenging tasks. Therefore, the user
might still tend to overprovide the allocated computing resources for the database
tier of their application to ensure satisfactory performance for their workloads. As a
* http://wiki.github.com/brianfrankcooper/YCSB/.
† http://www.pdl.cmu.edu/ycsb++/index.shtml.