- The paper introduces Cloud WorkBench (CWB), an Infrastructure-as-Code (IaC) framework for automated, reproducible, and modular cloud service benchmarking.
- CWB leverages IaC and DevOps techniques to define, provision, and execute benchmarks across diverse cloud configurations with minimal manual intervention.
- A case study on Amazon EC2 demonstrates significant performance differences across instance types and storage, showing CWB's utility in evaluating cloud cost-performance tradeoffs.
Cloud WorkBench: Infrastructure-as-Code Based Cloud Benchmarking
The paper presents Cloud WorkBench (CWB), a web-based framework designed to streamline and automate the process of benchmarking cloud services, particularly within Infrastructure-as-a-Service (IaaS) environments. With the increasing complexity and variability of cloud configurations, users need robust methods to evaluate cost-performance tradeoffs across different cloud service options. CWB addresses this need by leveraging the concept of Infrastructure-as-Code (IaC), enabling reproducible and automated benchmarking processes.
Core Contributions
The core contributions of CWB revolve around the adoption of IaC principles in cloud benchmarking. Unlike traditional approaches, which often involve cumbersome and manual benchmark setups, CWB presents an architecture that facilitates modular, portable, and reproducible benchmark definitions. The framework incorporates DevOps techniques, using provisioning code to achieve idempotence in the setup and execution of benchmarks across diverse cloud configurations. This results in a system where benchmarks can be defined, scheduled, and executed with minimal manual intervention, providing experimenters with a high degree of control and flexibility.
Architecture and Methodology
The CWB architecture is composed of several integral components:
- Provisioning Service and Web Interface: These enable the definition and management of benchmarks, leveraging a clear and modular code base to specify cloud configurations.
- CWB Server: Central to the operation, the server handles the actual execution process, coordinating interactions between cloud VMs and managing the lifecycle of benchmarks.
- Provider API and Client Library: These support interactions with cloud environments, maintaining the integrity and consistency of benchmark setups.
Benchmarks are defined using IaC and executed automatically via a scheduler, which orchestrates the provisioning and execution phases. Notably, the system supports multi-VM setups, facilitated by the capability to query configuration states dynamically.
Case Study: Amazon EC2 IO Performance
To demonstrate practical applicability, the paper includes a case paper on disk IO performance across various Amazon EC2 instance types and storage configurations. The findings show significant differences in performance metrics, with larger instance types (e.g., m1.small and m3.medium) outperforming smaller ones (e.g., t1.micro) significantly in raw sequential disk write speed. Additionally, comparisons between standard and SSD-backed storage reveal varying levels of performance variability, informing decisions on balancing cost and performance for IO-intensive applications in the cloud.
Implications and Future Work
The implications of deploying IaC for benchmarking in cloud environments are substantial. Practically, CWB can simplify and standardize performance testing across different services and configurations, potentially leading to improved optimization strategies for cloud-based applications. Theoretically, the framework emphasizes the reproducibility and portability of benchmarks, addressing key challenges in cloud performance evaluation.
For future developments, the authors aim to enhance CWB with additional features, such as support for a wider range of cloud providers, automated metric collection, and integrated analysis tools. These capabilities would further consolidate benchmarking tasks into a cohesive framework, streamlining the process from definition to interpretation of results.
Conclusion
In summary, Cloud WorkBench represents a significant advancement in the field of cloud benchmarking, offering an infrastructure-as-code driven solution that emphasizes reproducibility, modularity, and automation. Its application in real-world scenarios demonstrates potential benefits both practically in terms of ease of evaluation, and theoretically in encouraging standardized benchmarking practices across diverse cloud ecosystems. The framework serves as a foundational tool for researchers and practitioners seeking to optimize cloud resource allocation and performance measurements systematically.