Serving legacy distributed applications by a self-configuring cloud processing platform

Panica, Silviu; Neagul, Marian; Crăciun, Ciprian; Petcu, Dana

doi:10.1109/idaacs.2011.6072727

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Maurer et al 16 investigated the impact of SLA violation on resource utilization in autonomic cloud computing systems. [15][16][17][18][19][20][21][22][23][24][25] However, the limited investigation has been done on the execution of heterogeneous workloads in the context of autonomic cloud computing. As a part of their work, a case-based reasoning approach is used to maintain the execution details of the workload in a centralized database.…”

Section: Self-configuringmentioning

confidence: 99%

“…There is a large body of research devoted to self-configuration of cloud resources for execution of homogenous cloud workloads only. [15][16][17][18][19][20][21][22][23][24][25] However, the limited investigation has been done on the execution of heterogeneous workloads in the context of autonomic cloud computing. Further, there is a need to execute workloads without the violation of SLA.…”

Section: 2mentioning

confidence: 99%

“…Similar work has been done by Etchevers et al, who used reconfigurable component‐based systems to design distributed applications to minimize execution time, but it increases their configuration interdependencies. Panica et al and Wolinsky et al examined the problem of execution of legacy distributed applications using self‐configuration of cloud infrastructure to maximize the availability and reliability of cloud services.…”

Section: Related Workmentioning

confidence: 99%

“…Their framework considers scheduling of single queued workload, whereas our model considers clustering of workloads based on their QoS requirements. There is a large body of research devoted to self‐configuration of cloud resources for execution of homogenous cloud workloads only . However, the limited investigation has been done on the execution of heterogeneous workloads in the context of autonomic cloud computing.…”

Section: Related Workmentioning

confidence: 99%

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RADAR: Self‐configuring and self‐healing in resource management for enhancing quality of cloud services

Gill

Chana

Singh

et al. 2018

Concurrency and Computation

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Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on a pay-per-use basis. However, existing mechanisms, frameworks, and techniques for management of resources are inadequate to manage these applications, environments, and the behavior of resources. There is a requirement of a Quality of Service (QoS) based autonomic resource management technique to execute workloads and deliver cost-efficient and reliable cloud services automatically. In this paper, we present an intelligent and autonomic resource management technique named RADAR. RADAR focuses on two properties of self-management: firstly, self-healing that handles unexpected failures and, secondly, self-configuration of resources and applications. The performance of RADAR is evaluated in the cloud simulation environment and the experimental results show that RADAR delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services. KEYWORDScloud computing, quality of service, resource provisioning, resource scheduling, self-configuring, self-healing, self-management, service level agreement INTRODUCTIONCloud computing offers various services like Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).However, providing dedicated cloud services that ensure various Quality of Service (QoS) requirements of a cloud user and avoid Service Level Agreement (SLA) violations is a difficult task. Based on the availability of cloud resources, dynamic services are provided without ensuring the required QoS. 1 To fulfill the QoS requirements of user applications, the cloud provider should change its ecosystem. 2 Self-management of cloud services is needed to provide required services and fulfill the QoS requirements of the user automatically.Autonomic management of resources manages the cloud service automatically as per the requirement of the environment, therefore maximizing resource utilization and cost-effectiveness while ensuring the maximum reliability and availability of the service. 3 Based on human guidance, a self-managed system keeps itself stable in uncertain situations and adapts rapidly to new environmental situations such as network, hardware, or software failures. 4 QoS based autonomic systems are inspired by biological systems, which can manage the challenges such as dynamism, uncertainty, and heterogeneity. IBM's autonomic model 3 based cloud computing system considers MAPE-k loop (Monitor, Analyze, Plan, and Execute) and its objective is to execute workloads within their budget and deadline by satisfying the QoS requirements of the cloud consumer. An autonomic system considers the following properties while managing cloud resources 1-3 :• Self-healing recognizes, analyzes, and recovers from the unexpected failures automatically.• Self-configuring adapts to the changes in the environment automatically.In this paper, we have developed a technique for self-configuRin...

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Section: Self-configuringmentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

RADAR: Self‐configuring and self‐healing in resource management for enhancing quality of cloud services

Gill

Chana

Singh

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

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“…An early study of migration from Grid to Cloud using mOSAIC concepts is provided in [95] for a platform intended for high education activities in Earth observation. The support for scientific legacy applications through self-configuration is exposed in [96]. For the particular case of engineering application, a comprehensive study can be found in [97].…”

Section: Scientific Applications Supportmentioning

confidence: 99%

Experiences in building a mOSAIC of clouds

Petcu

Martino

Venticinque

et al. 2013

J Cloud Comput Adv Syst Appl

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The diversity of Cloud computing services is challenging the application developers as various and non-standard interfaces are provided for these services. Few middleware solutions were developed until now to support the design, deployment and execution of service-independent applications as well as the management of resources from multiple Clouds. This paper focuses on one of these advanced middleware solutions, called mOSAIC. Written after the completion of its development, this paper presents an integrated overview of the mOSAIC approach and the use of its various software prototypes in a Cloud application development process. We are starting from the design concepts and arrive to various applications, as well as to the position versus similar initiatives.

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Cloud resource orchestration within an open‐source component‐based platform as a service

Petcu

Panica

Crăciun

et al. 2013

Concurrency and Computation

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SUMMARYIn the growing market of cloud computing dominated by proprietary solutions, the adoption of open-source and deployable middleware that hides the service heterogeneity and ensure code portability can provide important benefits for the fast development of new services. Therefore, this paper exposes the mechanisms for orchestrating cloud-enabled hardware and software resources supported by a recently developed open-source platform as a service.

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Serving legacy distributed applications by a self-configuring cloud processing platform

Cited by 11 publications

References 6 publications

RADAR: Self‐configuring and self‐healing in resource management for enhancing quality of cloud services

RADAR: Self‐configuring and self‐healing in resource management for enhancing quality of cloud services

Experiences in building a mOSAIC of clouds

Cloud resource orchestration within an open‐source component‐based platform as a service

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