Criteria to Compare Cloud Computing with Current Database Technology
IWSM/Metrikon/Mensura '08 Proceedings of the International Conferences on Software Process and Product Measurement
Database systems on virtual machines: How much do you lose?
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
How is the weather tomorrow?: towards a benchmark for the cloud
Proceedings of the Second International Workshop on Testing Database Systems
Benchmarking Database Performance in a Virtual Environment
Performance Evaluation and Benchmarking
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
An evaluation of alternative architectures for transaction processing in the cloud
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Runtime measurements in the cloud: observing, analyzing, and reducing variance
Proceedings of the VLDB Endowment
TPC-V: a benchmark for evaluating the performance of database applications in virtual environments
TPCTC'10 Proceedings of the Second TPC technology conference on Performance evaluation, measurement and characterization of complex systems
| Hi-index | 0.00 |
The current computing trend towards cloud-based Database-as-a-Service (DaaS) as an alternative to traditional on-site relational database management systems (RDBMSs) has largely been driven by the perceived simplicity and cost-effectiveness of migrating to a DaaS. However, customers that are attracted to these DaaS alternatives may find that the range of different services and pricing options available to them add an unexpected level of complexity to their decision making. Cloud service pricing models are typically ‘pay-as-you-go' in which the customer is charged based on resource usage such as CPU and memory utilization. Thus, customers considering different DaaS options must take into account how the performance and efficiency of the DaaS will ultimately impact their monthly bill. In this paper, we show that the current DaaS model can produce unpleasant surprises --- for example, the case study that we present in this paper illustrates a scenario in which a DaaS service powered by a DBMS that has a lower hourly rate actually costs more to the end user than a DaaS service that is powered by another DBMS that charges a higher hourly rate. Thus, what we need is a method for the end-user to get an accurate estimate of the true costs that will be incurred without worrying about the nuances of how the DaaS operates. One potential solution to this problem is for DaaS providers to offer a new service called Benchmark as a Service (BaaS) where in the user provides the parameters of their workload and SLA requirements, and get a price quote.