SHAPE—an approach for self-healing and self-protection in complex distributed networks

Chopra, Inderpreet; Singh, Maninder

doi:10.1007/s11227-013-1019-3

Cited by 18 publications

(17 citation statements)

References 14 publications

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“…Further, lexical analyzer-based freeware tools such as MCat (windows) and MCELogs (Linux) 33 Further, lexical analyzer-based freeware tools such as MCat (windows) and MCELogs (Linux) 33 …”

Section: Self-configuringmentioning

confidence: 99%

“…Network fault can occur due to network breakage, physical damage, packet loss, and lack of scalability in distributed networks. 33 Whenever the node is added to the network, Hardware Hardening Agent (HHA) checks for its device drivers and performs the process of hardening. Software fault can occur due to unavailability of a required number of resources, storage space, resource contention (deadlock), and unhandled exception in high resource intensive workloads.…”

Section: Self-healingmentioning

confidence: 99%

“…33 Further, when the new node is added, a replica of the original drivers is created. 33 Further, when the new node is added, a replica of the original drivers is created.…”

Section: Self-healingmentioning

confidence: 99%

“…33 Once the hardening process is over, the status of nodes is forwarded to the monitor component (autonomic service manager) to prevent future faults. 33 Once the hardening process is over, the status of nodes is forwarded to the monitor component (autonomic service manager) to prevent future faults.…”

Section: Test Case 1: Fault Detection Rate -mentioning

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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“…Further, lexical analyzer-based freeware tools such as MCat (windows) and MCELogs (Linux) 33 Further, lexical analyzer-based freeware tools such as MCat (windows) and MCELogs (Linux) 33 …”

Section: Self-configuringmentioning

confidence: 99%

Section: Self-healingmentioning

confidence: 99%

“…33 Further, when the new node is added, a replica of the original drivers is created. 33 Further, when the new node is added, a replica of the original drivers is created.…”

Section: Self-healingmentioning

confidence: 99%

Section: Test Case 1: Fault Detection Rate -mentioning

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

Self Cite

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“…This is achieved though scanning untrusted activities and then reacting accordingly to these activities. This protection process is achieved without the user's awareness of it [11]. As discussed in [8], to build a self-protected, the following principles are required:…”

Section: Self-protectionmentioning

confidence: 99%

An Overview of Self-Protection and Self-Healing in Wireless Sensor Networks

Gaber

Hassanien

2014

Bio-Inspiring Cyber Security and Cloud Services: Trends and Innovations

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Wireless sensor networks (WSNs) are currently considered as one of the key technology enablers for the monitoring systems. Since WSNs are facing large amount of emerging network applications, techniques, protocols, and attacks, it is very recommended to minimize the intervention of human in the defense process which could be inefficient and error-prone. To build such realtime system using the WSN technologies while minimizing the human's intervention, it is required to provide autonomy, scalability, self-protection, and self-healing features in an WSN application. This chapter will give an overview of the autonomic computing paradigm which is the key concept of self-protection and self-healing. It then describes the need of these features for the WSN application. It also gives an overview of the self-protection and self-healing of WSNs.

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Designing a methodological framework for modeling and performance forecasting of self‐adaptive cloud systems

Sliem

Salmi

Ioualalen

2021

Concurrency and Computation

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Autonomic computing got an increasing attention during past years in the context of Cloud computing in order to optimize the Cloud systems performance and increase their cost-effectiveness. In that direction, a broad range of various self-adaptability strategies have been proposed to overcome uncertainties related to Cloud systems like workload variations, hardware failures and malicious attacks. A special attention in the literature has been paid to resource allocation for cost and performance optimization, and self-healing for better faut-tolerance. However, while a lot of progress has been done on these topics, there is still a lack of a standardized methodology, easily reproducible and allowing validation and comparison of a self-adaptability strategies.This article proposes a methodology allowing to forecast the autonomic Cloud systems performances. The idea is to compare different self-adaptability approaches at the design stage, and identify the most adequate and optimized configuration. This methodology is based on a new architecture description language and a stochastic Petri nets-based modeling and simulation approach. We illustrate our methodology in this article through a running example and a set of experiments.

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SHAPE—an approach for self-healing and self-protection in complex distributed networks

Cited by 18 publications

References 14 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

An Overview of Self-Protection and Self-Healing in Wireless Sensor Networks

Designing a methodological framework for modeling and performance forecasting of self‐adaptive cloud systems

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