A Framework for Self-Healing and Self-Adaptation of Cloud-Hosted Web-Based Applications

Magalhaes, Joao Paulo; Silva, Luís Moura

doi:10.1109/cloudcom.2013.80

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…SHMR works in three different steps: It (1) monitors the working of the system to identify the occurrence of faults, (2) finds out the properties of faults, and (3) recovers the fault using an undo strategy. Magalhaes and Silva proposed a Self‐healing Framework for Web‐based Applications (SFWA) to fulfill the user SLA and improve resource utilization simultaneously through self‐adaption of cloud infrastructure. Experimental results show that SFWA adjusts the infrastructure dynamically to detect anomalies, which reduces the delay during workload execution.…”

Section: Related Workmentioning

confidence: 75%

“…The main difference between these works and ours is that none of them considers autonomic fault prevention mechanism for self-healing with dynamic checkpoint intervals, which is presented in this paper. The existing self-healing frameworks [4][5][6][7][8][9][10][11][12] focus on monitoring of faults to maximize fault detection rate, whereas our work focuses on fault detection and prevention mechanisms.…”

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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: Related Workmentioning

confidence: 75%

mentioning

confidence: 99%

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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“…Magalhaes et al [3] propose a self-healing framework for Cloud-host web-based applications, called SHoWA. It allows Cloud customers to launch recovery actions when a performance degradation occurs.…”

Section: Related Workmentioning

confidence: 99%

“…However, monitoring events and analyzing their correlations across layers is very promising to improve recovery. On the other hand, existing related approaches that monitor all Cloud layers and consider the correlations between them [3,4] generate a huge volume of data and consume a lot of storage space. Thus, they typically are quite slow and consequently delay the recovery.…”

Section: Introductionmentioning

confidence: 99%

CEP4Cloud: Complex Event Processing for Self-Healing Clouds

Mdhaffar

Halima

Jmaïel

et al. 2014

2014 IEEE 23rd International WETICE Conference

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This paper presents a cross-layer self-healing approach for Cloud computing environments, based on the Complex Event Processing method. It analyzes monitored events to detect performance-related problems and performs action to fix them without human intervention. Our proposal makes use of novel analysis rules, derived from a comprehensive study of the relationships between monitored metrics across multiple Cloud layers. The results of our study are used to define and optimize the analysis rules and identify the causes of performance-related problems. The results of several experiments demonstrate the benefits of the proposed approach in terms of speeding up the analysis without affecting the quality of the diagnosis.

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“…Regarding selfadaptation capabilities at software development level, tools such as GreenPipe [13] consider energy as a parameter to be optimised, but it is provided by the user and tailored for a particular type of applications. The PaaS SAM is similar to the SHoWA frameworks recovery planner [12]. The recovery planner is likened to a disease database, with a set of rules on how to treat certain anomalies in performance.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficiency Support Through Intra-layer Cloud Stack Adaptation

Djemame

Kavanagh

Armstrong

et al. 2017

Economics of Grids, Clouds, Systems, and Services

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Abstract. Energy consumption is a key concern in cloud computing. The paper reports on a cloud architecture to support energy efficiency at service construction, deployment, and operation. This is achieved through SaaS, PaaS and IaaS intra-layer self-adaptation in isolation. The self-adaptation mechanisms are discussed, as well as their implementation and evaluation. The experimental results show that the overall architecture is capable of adapting to meet the energy goals of applications on a per layer basis.

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A Framework for Self-Healing and Self-Adaptation of Cloud-Hosted Web-Based Applications

Cited by 10 publications

References 20 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

CEP4Cloud: Complex Event Processing for Self-Healing Clouds

Energy Efficiency Support Through Intra-layer Cloud Stack Adaptation

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