A Weibull distribution accrual failure detector for cloud computing

Liu, Jiaxi; Wu, Zhibo; Wu, Jin; Dong, Jian; Zhao, Yao; Ding, Wen

doi:10.1371/journal.pone.0173666

Cited by 19 publications

(12 citation statements)

References 18 publications

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“…Wang et al [15] proposed FDKeeper, which has a layer based clustered architecture and takes into account three key parameters namely speed, scalability and heterogeneity. Liu et al [16] proposed an accrual FD for cloud. Authors use heart beat inter arrival time to compute next timeout interval.…”

Section: Related Workmentioning

confidence: 99%

A Failure Detector for Crash Recovery Systems in Cloud

Sinha¹,

Singh²,

Saini³

2019

IJITCS

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Cloud computing has offered remarkable scalability and elasticity to distributed computing paradigm. It provides implicit fault tolerance through virtual machine (VM) migration. However, VM migration needs heavy replication and incurs storage overhead as well as loss of computation. In early cloud infrastructure, these factors were ignorable due to light load conditions; however, nowadays due to exploding task population, they trigger considerable performance degradation in cloud. Therefore, fault detection and recovery is gaining attention in cloud research community. The Failure Detectors (FDs) are modules employed at the nodes to perform fault detection. The paper proposes a failure detector to handle crash recoverable nodes and the system recovery is performed by a designated checkpoint in the event of failure. We use Machine Repairman model to estimate the recovery latency. The simulation experiments have been carried out using CloudSim plus.

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Section: Related Workmentioning

confidence: 99%

A Failure Detector for Crash Recovery Systems in Cloud

Sinha¹,

Singh²,

Saini³

2019

IJITCS

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“…Analogous to

ϕ

FD, Xiong et al [9] and Liu et al [11] assume that the interval of heartbeat responses follows exponential distribution or Weibull distribution and calculate the probability of time interval between the current time and the time of last heartbeat response. If the probability is lower than a threshold, the monitor suspects the monitored node.…”

Section: Related Workmentioning

confidence: 99%

“…Existing failure detection methods [5,6,7,8,9,10,11,12] are designed to detect completely unreachable nodes, e.g., died or disconnected. A commonly used failure detection approach in a cluster is that, monitors estimate the state of each node based on the

ϕ

FD each node in the cluster is monitored by a set of other nodes.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are two main drawbacks of this kind of implementation. First, the existing failure detectors [5,6,7,8,9,10,11,12] cannot correctly detect a partially working node, e.g., a node which randomly drops packets. In this case, different monitors may give out different detection results on this node.…”

Section: Introductionmentioning

confidence: 99%

“…As the

ϕ

FD and other failure detection mechanisms proposed in [5,6,7,8,9,10,11,12] are not suitable for the scenario of stochastic packet loss, we propose an algorithm that can estimate the severity of packet loss of a link between two nodes based on the statistical information of TCP protocol, the round-trip-time (RTT). Further, we propose a model that can sense a node’s healthy status from anywhere of the cluster without the limitation of any node to be always reachable, choose a unique leader without election process and make more reliable decisions about removing faulty nodes.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster

Fan

Wang

Yong

2018

Sensors

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Decentralized clustering of modern information technology is widely adopted in various fields these years. One of the main reason is the features of high availability and the failure-tolerance which can prevent the entire system form broking down by a failure of a single point. Recently, toolkits such as Akka are used by the public commonly to easily build such kind of cluster. However, clusters of such kind that use Gossip as their membership managing protocol and use link failure detecting mechanism to detect link failures cannot deal with the scenario that a node stochastically drops packets and corrupts the member status of the cluster. In this paper, we formulate the problem to be evaluating the link quality and finding a max clique (NP-Complete) in the connectivity graph. We then proposed an algorithm that consists of two models driven by data from application layer to respectively solving these two problems. Through simulations with statistical data and a real-world product, we demonstrate that our algorithm has a good performance.

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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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A Weibull distribution accrual failure detector for cloud computing

Cited by 19 publications

References 18 publications

A Failure Detector for Crash Recovery Systems in Cloud

A Failure Detector for Crash Recovery Systems in Cloud

Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster

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

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