Design of frameworks for self‐adaptive service‐oriented applications: A systematic analysis

Passini, William Filisbino; Lana, Cristiane Aparecida; Pfeifer, Vinicius; Affonso, Frank José

doi:10.1002/spe.3013

Cited by 6 publications

(4 citation statements)

References 66 publications

(97 reference statements)

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“…Several frameworks 32 have been proposed to endow service‐based applications (SBA) with autonomic capabilities and adapt them to the changes in execution environments. Examples include PAWS, 33 WS‐DIAMOND, 34 Meteor‐S, 35 and agent‐based systems 36,37 .…”

Section: Related Workmentioning

confidence: 99%

Autonomic computing and incremental learning for the management of big services

Ghedass

Charrada

2023

Softw Pract Exp

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Recent years have witnessed the emergence of big services, as a large‐scale big data‐centric service model, that resulted from the synergy between powerful computing paradigms (big data processing, service and cloud computing, Internet of Things, etc.). Big services are seen as a heterogeneous combination of physical and virtualized domain‐specific resources, with a huge volume of data and complex functionalities, all encapsulated and offered as services. This complexity of big services (composition units' heterogeneity, cross‐domain orientation, data massiveness), coupled with other environmental factors (cloud dynamicity, providers' policies, customer requirements) makes their management tasks beyond humans' capability. Therefore, endowing big service ecosystems with self‐adaptive behavior is a natural solution. To achieve this goal, this article models big services as autonomic computing systems, and structures their behavioral aspects (functional behavior, quality of service/data levels, management policies) as a multi‐view knowledge graph. To infer useful knowledge (e.g., conflicts between policies) for the autonomic big service's management tasks, we process the big service's knowledge graph (BSKG) via a graph neural network‐based graph embedding model. This latter is reinforced by an incremental learning method, that helps capturing the big services' frequent changes (e.g., QoS deviations, service failures, new policies), and drives autonomic managers to continuously update and enrich their knowledge w.r.t. the managed big service's current state. Finally, a flexible decision mechanism explores the BSKG structure and the latent knowledge, to locate and trigger the appropriate management policies, according to the big service's produced events.

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Section: Related Workmentioning

confidence: 99%

Autonomic computing and incremental learning for the management of big services

Ghedass

Charrada

2023

Softw Pract Exp

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Section: Introductionmentioning

confidence: 99%

“…There has been a growing concern among researchers about the lack of systematic evaluation for SAS and SHS [2][3][4]. A recent systematic mapping study found that only a small percentage of studies in this field focus on evaluating previously developed applications [3]. In addition, there are limited tools available to support evaluations based on runtime measures, with most studies focusing on evaluating the models used to design the system [4].…”

Section: Introductionmentioning

confidence: 99%

CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering

Malik,

Moonen

2023

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There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation methods for self-adaptive and self-healing systems. We proposed CHESS, a novel approach to address this gap by evaluating self-adaptive and self-healing systems through fault injection based on chaos engineering (CE).The artifact presented in this paper provides an extensive overview of the use of CHESS through two microservice-based case studies: a smart office case study and an existing demo application called Yelb. It comes with a managing system service, a self-monitoring service, as well as five fault injection scenarios covering infrastructure faults and functional faults. Each of these components can be easily extended or replaced to adopt the CHESS approach to a new case study, help explore its promises and limitations, and identify directions for future research.

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“…Laso et al 26 proposed a framework based on microservices which is helpful in integration of humans in the deployment of IoT devices. Filisbino Passini et al 27 comprehensive analysis on the design of self‐adaptive service oriented applications (Self‐Apps). The authors also highlight that there is a need to study the differences between both SOA and microservices architecture.…”

Section: Introductionmentioning

confidence: 99%

A service graph based extraction of microservices from monolith services of service‐oriented architecture

Raj

Sadam

2022

Softw Pract Exp

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Service-oriented architecture (SOA) has been widely used to design enterprise applications in the past two decades. The services in SOA are becoming complex with the increase in changing user requirements and SOA is still seen as monolithic from a deployment perspective. Monolithic services make the application complex, and it becomes difficult to maintain. With the evolution of microservices architecture, software architects started migrating legacy applications to microservices. However, existing migration approaches in the literature mostly focus on migrating monolithic applications to microservices. To the best of our knowledge, very few works have been done in migrating SOA applications to microservices. One of the major challenges in the migration process is the extraction of microservices from the existing legacy applications. To address this, we propose an approach to extract the candidate microservices using graph based algorithms. In particular, four algorithms are defined: (i) construction of service graph (SG), (ii) construction of task graph (TG) for each service of the a SOA application, (iii) extraction of candidate microservices using the SG of SOA application, and (iv) construction of a SG for a microservices application to retain the dependencies between the generated microservices. We chose a SOA-based web application to demonstrate the proposed microservices extraction approach and extracted the microservices. Additionally, we have evaluated the extracted microservices and compared them with SOA based services.

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Design of frameworks for self‐adaptive service‐oriented applications: A systematic analysis

Cited by 6 publications

References 66 publications

Autonomic computing and incremental learning for the management of big services

Autonomic computing and incremental learning for the management of big services

CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering

A service graph based extraction of microservices from monolith services of service‐oriented architecture

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