An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-Art (1312.6170v1)

Abstract: Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite challenging considering a large number of heterogeneous cloud resources. Further, considering the fact that at each point of time, there will be a different and specific cloud service which may be massively required. Hence, cloud monitoring tools can assist a cloud providers or application developers in: (i) keeping their resources and applications operating at peak efficiency; (ii) detecting variations in resource and application performance; (iii) accounting the Service Level Agreement (SLA) violations of certain QoS parameters; and (iv) tracking the leave and join operations of cloud resources due to failures and other dynamic configuration changes. In this paper, we identify and discuss the major research dimensions and design issues related to engineering cloud monitoring tools. We further discuss how aforementioned research dimensions and design issues are handled by current academic research as well as by commercial monitoring tools.

• The paper analyzes commercial cloud monitoring tools by reviewing research dimensions, design issues, and the state-of-the-art, proposing an evaluation framework covering architecture, interoperability, QoS, visibility, and programming interfaces.
• Key challenges in cloud monitoring include layer-specific application tracking, extracting relevant information, and predictive status assessment of applications within dynamic cloud environments.
• A comparative analysis of tools highlights varying capabilities across architecture, interoperability, and visibility, suggesting future directions involve standardization and incorporating ML for enhanced monitoring.

An Analytical Examination of Commercial Cloud Monitoring Tools

The paper An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-Art presents a comprehensive discourse on the various facets and intricacies surrounding cloud monitoring tools in the commercial domain. Authored by Khalid Alhamazani and colleagues from esteemed institutions across the globe, the research delves deeply into the architecture, functionality, and evaluation criteria of these tools as applied to modern cloud computing environments.

The investigation casts light on several key research dimensions and design issues pertinent to the engineering of cloud monitoring tools. It underscores the growing significance of these tools in ensuring optimal efficiency, performance variation detection, SLA compliance, and management of resource operations within cloud infrastructures. These tools stand as pivotal allies for cloud providers and application developers striving to meet the demands of large, heterogeneous resource environments.

Cloud Monitoring Challenges and Research Dimensions

Within the cloud landscape, monitoring is primarily tasked with maintaining the health of software and hardware resources, ensuring compliance with QoS parameters articulated within SLAs, and serving critical functions such as resource planning, data center management, performance tracking, and troubleshooting. The paper identifies three primary challenges in cloud monitoring, namely: layer-specific application monitoring, interest-based information extraction during monitoring, and predictive status assessment of running applications.

The paper also proffers an evaluation framework comprised of multiple dimensions, including:

1. Monitoring Architectures: It compares the centralized and decentralized architectures, each with distinct advantages and limitations, particularly in areas of scalability, network costs, and resilience against failure.
2. Interoperability: This dimension focuses on the seamless operation of monitoring frameworks across cloud providers, emphasizing the need for cloud-agnostic systems that mitigate provider lock-in risks.
3. QoS Matrix: Differentiates between single and composite QoS parameter monitoring, highlighting the complexity involved in tracking multi-dimensional parameters that impact system performance.
4. Visibility: It examines the ability to monitor applications across different cloud layers, which is pivotal for comprehensive QoS tracking.
5. Programming Interfaces: Involves components such as APIs, command lines, and widgets, which facilitate unified monitoring across complex cloud systems.

Comparative Analysis of Cloud Monitoring Tools

The authors evaluate a plethora of commercial monitoring tools, detailing their architecture, interoperability, visibility, and programming capabilities. Tools like Monitis, RevealCloud, LogicMonitor, Nimsoft, and Nagios exemplify multi-layer and multi-cloud monitoring capabilities, while others like SPAE, CloudWatch, and OpenNebula present more limited functionalities with regard to cloud layer coverage or interoperability.

Implications and Speculations on Future Developments

This research beckons significant implications for cloud computing, where the rising complexity and scale necessitate sophisticated monitoring systems that can offer real-time insights whilst preserving the elasticity and dynamic nature of cloud environments. The decentralized approaches, particularly structured P2P networks, are poised to play a crucial role in overcoming architectural constraints seen in centralized systems. Further, the paper calls for the standardization in monitoring procedures and metrics which could propel future advancements.

In speculative foresight, the future of cloud monitoring tools may witness an increased incorporation of machine learning techniques to predict failures and optimize resource consumption dynamically. As cloud environments continue to evolve, enduring efforts towards interoperability, enhanced QoS monitoring, and cross-layer visibility are likely to shape groundbreaking advancements in the industry.

In conclusion, the research presented in this paper represents a critical analysis and mapping of current commercial cloud monitoring approaches. While significant strides have been made, the necessity for further research to refine and expand these tools cannot be overstated, particularly as cloud infrastructures burgeon in complexity and scale.