Academic journal article Journal of Digital Information Management

Cellular DBMS: An Attempt towards Biologically-Inspired Data Management

Academic journal article Journal of Digital Information Management

Cellular DBMS: An Attempt towards Biologically-Inspired Data Management

Article excerpt

I. Introduction and Motivation

In the past, database research got motivations from arrival of new hardware, software, and applications for further progress. These motivations are still there and will persist in the future. Desire for improvements keeps researchers busy for finding the breakthrough in prevailing requirements. Sometimes we get many breakthroughs in a short period and sometimes we wait for decades to get few. In the prevailing era, we have explosion in the data growth and usage scenarios, because of wide spread usage of internet and advent of new applications (e.g., social networking, virtual worlds). Hardware trends are changing and the processing and storage unit cost have reduced. Many assumptions about secondary storage and mainmemory, etc., made in past are no longer valid and many bottlenecks, such as network communication cost have changed. Leading database researchers found a consensus on the need of revisiting database engines, accommodating architectural shifts in computing hardware and platforms, and finding solutions for new usage scenarios [1]. Cellular DBMS1 is an effort to contribute to database research in the above-mentioned directions.

Existing data management solutions are complex. These solutions have evolved over time and now they provide a multitude of functionalities. These functionalities are tightly coupled within their monolithic architecture [2]. Due to complexity, their performance is less predictable, i.e., the consistency of performance with the increase of functionality and the data growth is not certain and it is difficult to assess, how performance will vary for different hardware, workload, and operating systems, etc. Continuous administration and maintenance is needed to keep them performing at an optimal level, which results in high administrative and maintenance cost. Existing database management systems (DBMS) have dozens of tuning knobs. Internal sub-systems are tightly coupled. Effect of tuning a knob on other knobs and their performance is less predictable [2, 3]. Furthermore, existing DBMS architectures and solutions were designed decades ago considering legacy hardware and their bottlenecks. Now many opportunities exist to redesign existing data management architectures for exploiting features of new hardware.

Database researchers have suggested transition of DBMS from monolithic to a diversified architecture with small, simple, and reusable components of limited functionality with clean inter-component interaction [1, 2]. The Cellular DBMS architecture is designed by considering these suggestions. The Cellular DBMS architecture takes inspiration from biological systems. We want to utilize the mechanisms that exist in biological systems for data management. Using these mechanisms, we want to develop highly customizable and autonomous DBMS with more predictable performance. The vision for Cellular DBMS predictability is shown in Figure 1, i.e., a DBMS should be consistently predictable with the data growth and addition of functionalities. To achieve these goals in Cellular DBMS, we envision integration of techniques from different relevant fields, such as software engineering, distributed data management, computer networks, and parallel processing.

This paper is organized as follows: Section 2 introduces the related concepts required for background information and technical discussion. A detailed related work is provided in Section 3. Cellular DBMS architecture and its design principles are explained in Section 4. Section 5 presents the implementation details. Sample implementation scenarios are discussed in Section 6. Section 7 concludes the paper with some directions to future work.

2. Related Concepts

In this section, we will introduce the related concepts of DBMS, Software Engineering, and Autonomy that are important for improving the understandability of the reader for the topic.

[FIGURE 1 OMITTED]

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