Design Quality Metrics to Determine the Suitability and Cost-Effect of Self-* Capabilities for Autonomic Computing Systems

Jaleel, Abdul; Arshad, Shazia; Shoaib, Muhammad; Awais, Muhammad

doi:10.1109/access.2019.2944119

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…A best suite of design quality metrics for offering self‐management capabilities is discussed in Reference 49. The objective of this work is to include the self‐management capabilities in software systems.…”

Section: Related Workmentioning

confidence: 99%

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Agrawal

2021

Trans Emerging Tel Tech

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The advancements and rapid adoption of service oriented architecture, utility computing, virtualization, etc., have emerged in a new computing paradigm called cloud computing, in the last few years. Cloud computing enables on-demand delivery of data, software, and hardware in the form of services over the Internet. But the growing scale of cloud-users and type-of-services urge for an autonomous environment to automate the highly complex cloud environment in a suitable way to improve the overall management of the cloud systems. To counter these automation related issues, autonomic computing (AC) emerges as a potential solution to optimize the performance of cloud computing.The merge of these two prominent technologies gives rise to a new technological domain called autonomic cloud computing (ACC). In this survey article, the existing AC assisted solutions for cloud computing is broadly categorized into two groups, namely features based and parameters based. Features based solutions are further classified into management (of services, workload, and resources) and security aware solutions. Similarly, parameters based solutions are grouped into quality-of-service aware and performance management solutions. Finally, the comparative analysis of state-of-the-art ACC approaches with respect to the approach referred, performance metrics, outcome, and the derived inference is also presented to have an in-depth understanding of the existing literature. Overall, the work excites the research community to design the effective autonomous solutions for ACC resources and services. Additionally, several research gaps and challenges are identified and discussed in this article based on the study with a focus on the future research directions.

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Section: Related Workmentioning

confidence: 99%

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Agrawal

2021

Trans Emerging Tel Tech

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“…In a real-time system, the response time is always critical [63]. In the framework of MeDIC, we model the response time (T r ) as the time elapsed since a Subscriber agent originates a request to the reception of data in the required format.…”

Section: A the Evaluation Modelmentioning

confidence: 99%

Towards Medical Data Interoperability Through Collaboration of Healthcare Devices

et al. 2020

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In the era of smart devices and connected neighborhoods, the ubiquitous monitoring and care of patients are possible with the Internet of Medical Things (IoMT). Smart healthcare devices may serve their purpose well when they are able to share patient's data with each other. However, data formats vary widely across vendors, rendering these devices not interoperable. Recent solutions mostly rely on cloud services where a source device uploads the data, and the sink devices download it conforming to their own native formats. However, the quality of service is expected to deteriorate in a cloud processing regime with inherent network delays and traffic congestion, and the real-time data acquisition and manipulation is, therefore, not possible. This article presents MeDIC, a framework of Medical Data Interoperability through Collaboration of healthcare devices. MeDIC improves over a cloud-based IoMT by utilizing translation resources at the network edge, with its probing and translating agents. The probing agents maintain a capability list of MeDIC devices within a local network and enable one MeDIC device to request data conversion from another device when the former is not capable of this conversion by itself. The translating agent of the later then converts the data into the required format and returns it to the former. These novel agents allow IoMT devices to share their redundant computing resources for data translations in order to minimize cloud accesses. Legacy devices are supported through MeDIC-enabled, fog resource managers. We evaluate MeDIC in four use cases with rigorous simulations, which prove that this collaborative framework not only reduces the uplink traffic but also improves the response time, which is critical in real-time medical applications.

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“…Also, the role of complexity is recognized in [12] where the authors propose a set of design quality metrics to assess the design quality of self-management capabilities and where complexity and decision complexity are software quality estimators used in the proposed approach. Nevertheless, the decision complexity formula is computed and used in a different way that the complexity estimator suggested in this research.…”

Section: A Architectural Decision Networkmentioning

confidence: 99%

“…Content may change prior to final publication. [39] 2009, 2010, 2018 Source code Object-oriented complexity metrics [35] 2012 Graphs Estimation of dependency cycles and weights in nodes [37] 2015 Architectural structures Complexity and stability [26] [25] 2015, 2017 Design decision networks Meta-models relating qualities to estimate the evolution of design decisions [30] [36] 2017 Code Detect interconnections between elements [36] 2018 Graphs Architecture recovery techniques [40] 2018 Code Complexity analysis trend for predictive analysis [12] 2019 Graphs Estimation of decision complexity of selfmanagement capabilities and similarities between nodes last column of Table 2 to compute the number of nodes (i.e. the decisions) and their links between them.…”

Section: A Quality Decisions Modelmentioning

confidence: 99%

Estimating the Complexity of Architectural Design Decision Networks

2020

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The stability and longevity of software systems rely on the quality of design decisions over time. In modern software-intensive systems the number of design decisions taken, the dependencies between those decisions, and the number of design alternatives considered, complicate software maintenance and jeopardize the system's longevity. Despite the existence of complexity metrics applied to code, there is a lack of metrics for design decisions. As estimating the complexity of a set of design decisions is needed for understanding the difficulty of software evolution, this paper proposes and validates a new metric to estimate the complexity of decision networks. The metric is based on decision topologies and provides a way to understand the complexity of decision sets and reason about the maintenance difficulty. We validate our metric empirically in two different ways: (i) evaluating the complexity of two service-based platform systems, and (ii) analyzing the evolution of complexity in four open-source projects and compare how the evolution of complexity affects to the architecture in one of the open-source projects. Our results show that certain network topologies are more difficult to maintain, so we provide a set of tactics to reduce the complexity of design decision networks.

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Design Quality Metrics to Determine the Suitability and Cost-Effect of Self-* Capabilities for Autonomic Computing Systems

Cited by 9 publications

References 22 publications

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Towards Medical Data Interoperability Through Collaboration of Healthcare Devices

Estimating the Complexity of Architectural Design Decision Networks

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