A Precise Model for Google Cloud Platform

Challita, Stéphanie; Zalila, Faiez; Gourdin, Christophe; Merle, Philippe

doi:10.1109/ic2e.2018.00041

Cited by 32 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It connects a Compute instance to a Storage instance. 10 To summarize, during the OCCIware research and development project, the proposed OCCIware Metamodel was used to model various OCCI extensions: OCCI Infrastructure [20], OCCI Platform [22], OCCI SLA [23], OCCI Monitoring [24] and some additional proprietary extensions such as Docker [37], simulation [38,39], OMCRI [40], ModMaCAO [41], GCP [42], and Multi-Cloud 13 . Each OCCI extension is implemented as an Eclipse modeling project containing one extension model, which is an instance of OCCIware Metamodel.…”

Section: Typing Conceptsmentioning

confidence: 99%

“…• OCCI Extensions that represent the OCCIware-based modeling of concrete cloud domains such as OCCI Infrastructure [20], OCCI Platform [22], OCCI SLA [23], Docker [37], cloud mobile robotics [40], GCP [42], etc.…”

Section: Occiware Runtimementioning

confidence: 99%

“…6. GCP use case [42] represents the first use case that applies the OCCIware approach on a big cloud provider, aka GCP. The authors target to integrate both code generation and model interpretation strategies.…”

Section: Modmacao Use Casementioning

confidence: 99%

See 2 more Smart Citations

Model-driven cloud resource management with OCCIware

Zalila

Challita

Merle

2019

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Cloud computing has emerged as the main paradigm for hosting and delivering computing resources as services over Internet. It provides a delivery model for computing resources at infrastructure, platform, and software levels. However, there is a plethora of cloud providers offering different resource management interfaces. Due to this diversity, the consumption, provisioning, management, and supervision of cloud resources are subjected to four key issues, i.e., heterogeneity, interoperability, integration, and portability. To cope with these issues, Open Cloud Computing Interface (OCCI) is proposed as a community-based and open recommendation standard for managing any kind of cloud resources. Currently, only runtime implementations exist for OCCI, and each one targets a specific cloud service model such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS). Thus, OCCI lacks an approach to model and execute different OCCI artifacts. Our approach provides a generic modeling framework coupled with a generic runtime implementation. In this article, we propose the OCCIware approach, which represents the first approach to design, validate, generate, implement, deploy, execute, and supervise everything as a service with OCCI. This approach provides OCCIware Studio, the first model-driven tool chain for OCCI. It is built around OCCIware Metamodel, which defines the static semantics for the OCCI standard in Ecore and OCL. In addition, it proposes OCCIware Runtime, the first generic OCCI runtime implementation targeting all the cloud service models (IaaS, PaaS, and SaaS). OCCIware provides a unique and unified framework to manage OCCI artifacts and, at the same time, it represents a factory to build cloud domain-specific modeling frameworks where each framework targets a specific cloud domain. OCCIware has been applied in various cloud domains and use-cases, which validate its applicability.• Model-Driven Managing Everything as a Service with OC-CIware. OCCIware is a model-driven vision to manage XaaS.It allows one to model any type of resources. It provides OCCI users with facilities for designing, editing, validating, generating, implementing, deploying, executing, managing, and supervising XaaS with OCCI.• Generating Cloud Domain-Specific Modeling Frameworks with OCCIware. OCCIware is a factory of cloud domainspecific modeling frameworks. Each generated Cloud Domain-Specific Modeling Studio (CDSMS) is dedicated for a particular cloud domain. Each CDSMS can be used to design configurations which conform to its related domain and hides the generic concepts of OCCI.The OCCIware approach is composed of two main components: OCCIware Studio and OCCIware Runtime. OCCIware Studio is the first model-driven tool chain for OCCI [9]. OC-CIware Studio has been built around OCCIware Metamodel. This metamodel represents a precise definition for OCCI [10]. It defines rigourously the static semantics of the OCCI Core Model [7], the core specification of OCCI, by resolving several identified...

show abstract

Section: Typing Conceptsmentioning

confidence: 99%

Section: Occiware Runtimementioning

confidence: 99%

See 1 more Smart Citation

Model-driven cloud resource management with OCCIware

Zalila

Challita

Merle

2019

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The P2P structure is more used for the construction of cloud platforms. (Challita et al, 2018) • Global online map service, similar to those provided by Google Maps or Bing Maps (Gorelick, et al 2017). On one hand, the original data collected by commercial data providers such as Teleatlas or Navteq can't be got or updated by the 3rd company.…”

Section: Related Workmentioning

confidence: 99%

Research and Practice on Spatio-Temporal Big Data Cloud Platform of the Belt and Road Initiative

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Spatio-temporal big data cloud platform is an important spatial information infrastructure that can provide different period spatial information data services, various spatial analysis services and flexible API services. Activities of policy coordination, facilities connectivity and unimpeded trade on the Belt and Road Initiative (B&R) will create huge demands to the spatial information infrastructure. This paper focuses on researching a distributed spatio-temporal big data engine and an extendable cloud platform framework suits for the B&R and some key technologies to implement them. A distributed spatio-temporal big data engine based on Cassandra&trade; and an extendable 4-tier architecture cloud platform framework is put forward according to the spirit of parallel computing and cloud service. Four key technologies are discussed: 1) a storage and indexing method for distributed spatio-temporal big data, 2) an automatically collecting, processing, mapping and updating method of authoritative spatio-temporal data for web mapping service, 3) a schema of services aggregation based on nodes registering and services invoking based on view extension, 4) a distributed deployment and extension method of the cloud platform. We developed a distributed spatio-temporal big data centersoftware and founded the main node platform portal with MapWorld&trade; map services and some thematic information services inChina and built some local platform portals for those countries in the B&R area. The management and analysis services for spatio-temporal big data were built in flexible styles on this platform. Practices show that we provide a flexible and efficient solution tobuild the distributed spatio-temporal big data center and cloud platform, more node portals can be aggregated to the main portal bypublishing their own web services and registering them in the aggregation schema. The data center and platform can support thestorage and management of massive data well and has higher fault tolerance and better scalability.</p>

show abstract

“…However, due to constraints such as processing power and battery lifetime of mobile devices, mobile users cannot efficiently receive services that require high computing power and low-latency. To meet these service requirements, a computing paradigm is widely used to deliver these services to mobile users using cloud computing, such as Google Cloud Platform (GCP) and Amazon Elastic Compute Cloud (EC2) [10,11]. With the computing paradigm of cloud computing and the development of many kinds of research, cloud computing could provide a well-established distribution model and application platform [12,13].…”

Section: Introductionmentioning

confidence: 99%

ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management

2019

View full text Add to dashboard Cite

Recently, new mobile applications and services have appeared thanks to the rapid development of mobile devices and mobile network technology. Cloud computing has played an important role over the past decades, providing powerful computing capabilities and high-capacity storage space to efficiently deliver these mobile services to mobile users. Nevertheless, existing cloud computing delegates computing to a cloud server located at a relatively long distance, resulting in significant delays due to additional time to return processing results from a cloud server. These unnecessary delays are inconvenient for mobile users because they are not suitable for applications that require a real-time service environment. To cope with these problems, a new computing concept called Multi-Access Edge Computing (MEC) has emerged. Instead of sending all requests to the central cloud to handle mobile users' requests, the MEC brings computing power and storage resources to the edge of the mobile network. It enables the mobile user device to run the real-time applications that are sensitive to latency to meet the strict requirements. However, there is a lack of research on the efficient utilization of computing resources and mobility support when mobile users move in the MEC environment. In this paper, we propose the MEC-based mobility management scheme that arranges MEC server (MECS) as the concept of Zone so that mobile users can continue to receive content and use server resources efficiently even when they move. The results show that the proposed scheme reduce the average service delay compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because mobile users can continuously receive services, even when they move frequently.

show abstract

A Precise Model for Google Cloud Platform

Cited by 32 publications

References 21 publications

Model-driven cloud resource management with OCCIware

Model-driven cloud resource management with OCCIware

Research and Practice on Spatio-Temporal Big Data Cloud Platform of the Belt and Road Initiative

ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management

Contact Info

Product

Resources

About