スケーラビリティの概要

スケーラビリティの概要:

定義: スケーラビリティとは、利用者や仕事の増大に適応できる度合いを指し、電気通信やソフトウェア工学においてシステムやネットワーク、アルゴリズムの望ましい特性の一つとされています。
具体的な例: 小規模なシステムを大規模に拡張する際に、システム全体を交換するのではなく、リソース（主にハードウェア）を追加することで透過的に規模を拡張できる能力がスケーラビリティの一形態です。

スケーラビリティの特性:

スケーラブルなシステム: リソースの量に比例して全体のスループットが向上するシステムは「スケーラブル」または「スケーラビリティのあるシステム」と呼ばれます。
評価尺度: スケーラビリティの高さは規模透過性、負荷に応じたリソースの拡大・縮小の容易さ（位置透過性）、異種な機器やソフトウェアを無意識に管理・利用できる能力（異種透過性）などの尺度で評価されます。

スケールアップとスケールアウト:

スケーラビリティの高め方:

Overview of Scalability:

Definition: Scalability refers to the ability or degree to adapt to an increase in users or workload. It is considered a desirable characteristic in telecommunications and software engineering, representing a form of expandability.
Concrete Example: Scalability involves the capacity to transparently expand the scale, not by replacing the entire system when transitioning from a small to a large system (analogous to moving to a larger building), but by adding resources, especially hardware (analogous to expanding or adding a new building).

Characteristics of Scalability:

Scalable Systems: Systems where the overall throughput improves proportionally to the amount of resources are termed "scalable" or possess scalability.
Evaluation Metrics: The degree of scalability is often evaluated based on characteristics such as scale transparency, the ease of expanding or contracting resources in response to load variations (location transparency), and the ability to manage and utilize different devices and software seamlessly (heterogeneous transparency).

Scale-Up and Scale-Out:

Scale-Up: Adding resources to a single node of a system, involving enhancements to CPU, memory, or replacing the entire computer.
Scale-Out: Adding nodes to a system, often employing parallel computing technologies like clusters. In reality, a combination of both approaches is often used.

Enhancing Scalability:

Importance of System Design: It is often advised to focus on enhancing hardware-related scalability rather than simply increasing processing capabilities when designing systems.
Evaluation of Scalability: The degree of scalability is measured by evaluating scale transparency, location transparency, heterogeneous transparency, and other criteria.
Comparison for Performance Improvement: Increasing the number of nodes in a system is generally more cost-effective for achieving performance improvement than enhancing the processing capabilities of individual nodes. However, the effectiveness of this approach tends to diminish as the number of nodes increases. This issue is addressed in the field of performance engineering. As an example, considering a program that can be partially parallelized for speed-up, assuming 70% of the program can be parallelized, scaling out from 1 to 4 processors yields a maximum speed-up of approximately 2.105, while scaling out to 8 processors results in a maximum speed-up of approximately 2.581.

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