Authors:
Jie Liu
1
and
Genyuan Du
2
;
3
Affiliations:
1
Computer Science Division, Western Oregon University, Monmouth, Oregon, U.S.A.
;
2
School of Information Engineering, Xuchang University, Xuchang, Henan, China
;
3
Henan International Joint Laboratory of Polarization Sensing and Intelligent Signal Processing, Xuchang, Henan, China
Keyword(s):
Analytical Database Management Systems (ADBMSs), Performance, Cost, Scaling.
Abstract:
Analytical database management systems offer significant advantages for organizations practicing data-driven decision-making. ADBMSs rely on massively parallel processing for performance improvement, increased availability and other computation related resources, and improved scalability and stability. In this position paper, we argue that (1) Gustafson-Barsis’ Law aligns well with use cases suitable for Cloud-based ADBMS, still, neither Amdahl’s law nor Gustafson’s law is sufficient in guiding us on answer the question "how many processors should we use to gain better performance economically", and (2) ADBMS’s capability of utilizing parallel processing does not translate directly into easy scaling, specially scaling horizontally by adding more instances or nodes to distribute the workload at will, so when costs are somewhat controllable, allowing easy scaling should be by far the most critical consideration for choosing an ADBMS.