C-RCE: an Approach for Constructing and Managing a Cloud Service Broker

Park, Joonseok; Kim, Ungsoo; Yun, Donggyu; Yeom, Keunhyuk

doi:10.1007/s10723-017-9422-2

Cited by 7 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This raises a question regarding the lack of expressiveness in current SLA specifications (e.g., WSLA, WS-Agreement) and their ability to cover big services' characteristics and to support customer requirements. For example, extensions to WSLA specification 43,44 has been limited to the management of contracts between cloud services actors (users, cloud brokers, service providers).…”

Section: Big Service Management Approachesmentioning

confidence: 99%

“…Although existing SLA meta‐models have covered various concepts, like in Reference 44 (e.g., contracting party, pricing, guarantee, compensation, governance, context, rule, quality metric, credit, penalty), the data level has been totally ignored. Some researchers have proposed SLA graph models for data services, 45 while others have mapped SLA components to data attributes (accessibility, integrity, cleanness, accuracy, heterogeneity, ownership, authenticity, volume, velocity rate, replication and staging, service dependencies) w.r.t.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Autonomic computing and incremental learning for the management of big services

Ghedass

Charrada

2023

Softw Pract Exp

View full text Add to dashboard Cite

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.

show abstract

Section: Big Service Management Approachesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Autonomic computing and incremental learning for the management of big services

Ghedass

Charrada

2023

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…[49] and a few are retrieved from the literature [45], [46], [47], [48]. The first task of the global manager is to group and position the service providers appropriately.…”

Section: Introductionmentioning

confidence: 99%

Global Manager – A Service Broker In An Integrated Cloud Computing, Edge Computing & IoT Environment

2022

KSII TIIS

View full text Add to dashboard Cite

The emergence of technologies like Big data analytics, Industrial Internet of Things, Internet of Things, and applicability of these technologies in various domains leads to increased demand in the underlying execution environment. The demand may be for compute, storage, and network resources. These demands cannot be effectively catered by the conventional cloud environment, which requires an integrated environment. The task of finding an appropriate service provider is tedious for a service consumer as the number of service providers drastically increases and the services provided are heterogeneous in the specification. A service broker is essential to find the service provider for varying service consumer requests. Also, the service broker should be smart enough to make the service providers best fit for consumer requests, ensuring that both service consumer and provider are mutually beneficial. A service broker in an integrated environment named Global Manager is proposed in the paper, which can find an appropriate service provider for every varying service consumer request. The proposed Global Manager is capable of identification of parameters for service negotiation with the service providers thereby making the providers the best fit to the maximum possible extent for every consumer request. The paper describes the architecture of the proposed Global Manager, workflow through the proposed algorithms followed by the pilot implementation with sample datasets retrieved from literature and synthetic data. The experimental results are presented with a few of the future work to be carried out to make the Manager more sustainable and serviceable.

show abstract