Fault-Tolerance in the Scope of Cloud Computing

Rehman, A. U.; Aguiar, Rui L.; Barraca, João Paulo

doi:10.1109/access.2022.3182211

Cited by 19 publications

(9 citation statements)

References 99 publications

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“…(ii) Reactive: In this case means are concentrated on the methods that make it possible to recover quickly after the fault occurrence and try to bring back the system to the last known coherent state. A lot of known approaches like hardware-redundancy, jobs replication, checkpoint and restart and so on are utilized to ensure reliability, availability and integrity in cloud environment [11].…”

Section: Related Workmentioning

confidence: 99%

A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment

Elsakaan,

Amroun

2024

J Supercomput

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Today cloud computing is at the heart of all information technologies. This prodigious technological paradigm relies on a very simple concept defined as the ability to deliver hardware and software resources as service directly over internet. A set of mechanisms cooperate to maintain the cloud reliability and to allow continuous delivery of these services while guaranteeing the same quality of service (QoS) and respecting the service-level agreement (SLA) for each client. Load balancing is one of those mechanisms and it ensures a crucial service, it can be defined as the ability of the system to ensure fairness in the distribution of workload over all servers. The most recent load balancing techniques are hybrid methods involving in major cases the combination between static and dynamic approaches, in other cases it can go further by integrating other mechanisms in order to improve the overall efficiency of the systems. The performance can be evaluated by parameters which generally refers to the degree of compliance with SLA and QoS. In order to enhance load balancing and tasks scheduling in cloud environment we propose in this paper a different hybrid approach which allows the decomposition of the problem and to operate on two levels by going through two stages : (i) first clusters are built for each datacenter grouping together sub-sets of servers that have close utilization rates, (ii) then tasks scheduling and load balancing operate at the datacenter level to deal with distribution over clusters and at the cluster level to ensure fairness between servers of the same cluster. Our method allows hot-deployment in already operating cloud environments and an excellent scalability. It also offers decoupling of missions and strong interoperability between the different mechanisms. To prove its validity, we implemented it on the standard cloudSim plus simulator, before carrying out a comparative study which shows better results than existing approaches in terms of makespan, reduces reaction time, number of migrations required and SLA violations.

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Section: Related Workmentioning

confidence: 99%

A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment

Elsakaan,

Amroun

2024

J Supercomput

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“…Kumari and Kaur 13 presented a detailed review of only reactive and proactive fault tolerance approaches and discussed future research directions in cloud computing. Authors in Reference 14 offered a comprehensive overview of cloud computing, starting from the basics and progressing to more advanced topics. The focus then shifts to the key components of fault tolerance and system‐level metrics, and the requirements and uses of fault tolerance in cloud computing are discussed.…”

Section: Introductionmentioning

confidence: 99%

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Kirti,

Maurya,

Yadav

2024

Concurrency and Computation

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Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches.

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“…Rehman et al [11] substantial foundation on cloud computing supports readers in obtaining a thorough understanding of the problem from beginner to expert level. Before developing cloud computing fault-tolerance requirements and applications, focus on fault-tolerance components and system-level measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Proof. According to(10) and(11), the dynamic time 𝐷𝑇 𝑖,𝑘 reflects the comparative value amongst the earliest finish time 𝐸𝐹 𝑖,𝑘 𝑝 and the deadline 𝐷 𝑖 . If 𝐷𝑇 𝑖,𝑘 is less than zero, 𝐸𝐹 𝑖,𝑘 𝑝 will exceed the 𝐷 𝑖 deadline.…”

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confidence: 99%

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

Kalaskar,

Somasundaram

2024

IJEECS

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<span>Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.</span>

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Fault-Tolerance in the Scope of Cloud Computing

Cited by 19 publications

References 99 publications

A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment

A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

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