Database Reference
In-Depth Information
computing tasks into a distributed environment does not pay off because network
traffic fees outnumber savings in processing power. In principle, calculating the
tradeoff between basic computing services can be useful to get a general idea of
the economies involved. This method can easily be applied to the pricing schemes
of cloud computing providers (e.g Amazon, Google). Florescu and Kossmann [
133
]
have also argued in the new large scale web applications, the requirement to provide
100 % read and write availability for all users has overshadowed the importance of
the ACID paradigm as the gold standard for data consistency. In these applications,
no user is ever allowed to be blocked. Hence, consistency has turned to be an
optimization goal in modern data management systems in order to minimize the cost
of resolving inconsistencies and not a constraint as in traditional database systems.
Therefore, it is better to design a system that it deals with resolving inconsistencies
rather than having a system that prevents inconsistencies under all circumstances.
Kossmann et al. [
162
] conducted an end-to-end experimental evaluation for the
performance and cost of running enterprise web applications with OLTP workloads
on alternative cloud services (e.g. RDS, SimpleDB, S3, Google AppEngine, Azure).
The results of the experiments showed that the alternative services varied greatly
both in cost and performance. Most services had significant scalability issues. They
confirmed the observation that public clouds lack of support for uploading large data
volumes. It was difficult for them to upload 1 TB or more of raw data through the
APIs provided by the providers. With regard to cost, they concluded that Google
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 trade-offs cloud data
management systems, the Yahoo! Cloud Serving Benchmarks, YCSB [
54
,
112
] and
YCSB
CC
[
53
,
192
], have been presented as frameworks and core set of benchmarks
for NoSQL systems. The benchmarking tools have been made available via open
source in order 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.
3.7
Discussion and Conclusions
For more than a quarter of a century, the relational database management systems
(RDBMS) have been the dominant model for database management. They provide
an extremely attractive interface for managing and accessing data, and have proven
to be wildly successful in many financial, business and Internet applications.
However, with the new trends of Web scale data management, they started to suffer
from some serious limitations [
116
]:
Database systems are difficult to scale
. Most database systems have hard limits
beyond which they do not easily scale. Once users reach these scalability limits,
time consuming and expensive manual partitioning, data migration, and load
balancing are the only recourse.
