Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

Tawfeeg, Tawfeeg Mohmmed; Yousif, Adil; Hassan, Alzubair; Alqhtani, Samar M.; Hamza, Rafik; Bashir, Mohammed; Ali, A. B. M. Shawkat

doi:10.1109/access.2022.3188645

Cited by 8 publications

(5 citation statements)

References 88 publications

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“…The major shortcoming these methods are suffering from is when the number of short tasks exceed the number of long ones the allocation of resources is not optimal. Another category is the max-min which overcomes this shortage by scheduling first larger tasks but it penalizes short ones and increases their waiting time [4].…”

Section: Static Load Balancing Methodsmentioning

confidence: 99%

“…Whatever it is critical to first understand the used categorization. Indeed a classification of algorithms that is often encountered involves three main classes which are depicted as follows [4]:…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, while a first category to which we refer as being static acts only at reception of new tasks and decides on how assigning them according to a set of non-evolving information like tasks length and servers physical capabilities, a second category regroups dynamic approaches which are continuously operational over time incorporating information on current workload on each server, individual makespan of virtual machines and primitives like tasks or VMs migration to maintain an optimal utilization rate of all servers. A last category stands for regrouping all approaches that combine static and/ or dynamic approaches together or with other complementary mechanisms to enhance performance of load balancing and to mitigate common shortcomings [4] [5]. This is the way we build in our proposal, by integrating complementary algorithms for hybridizing load balancing and tasks scheduling.…”

Section: Introductionmentioning

confidence: 99%

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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: Static Load Balancing Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Elsakaan,

Amroun

2024

J Supercomput

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“…It is based on the usage of the gravitational emulation-based local search algorithms. The key parameters concentrated upon are mean absolute, and root mean square error and coverage properties [24].…”

Section: Optimization-based Clustering Approachesmentioning

confidence: 99%

Efficient Privacy Preserving Lightweight Cryptography for Multi-hop Clustering in Internet of Vehicles Network

Kumar,

Dubey,

kumar

et al. 2023

Preprint

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The Internet of Vehicles (IoV) is an element of the global connectivity infrastructure Connected vehicles are beneficial to the health of their users in a significant way. However, several drawbacks have been identified, creating the potential for compromised Internet of Vehicles devices. Moreover, the high mobility results in high energy consumption, which reduces the network's overall performance. The Efficient Privacy-preserving Lightweight-cryptography for Cluster-based Internet of Vehicles (EPLC-IoV) model that has been proposed is divided into five distinct sections. These include the perception layer, attack characteristics, a novel clustering strategy, energy-efficient multi-hop clustering along with adaptive link selection through an enhanced AODV protocol. Additionally, the proposed system performed identity authentication and protected users' privacy in some use cases, making V2V communication messages more trustworthy by allowing anonymous trust relationship authentication. Because, the proposed system allowed for anonymous authentication trust relationships, between vehicle nodes. The proposed model was evaluated alongside several established methodologies, including AODV, EPAM, and EMSUNET. The significant parameters focused in this work are packet delivery ratio (PDR), i.e., increased up to 82%, End to End delay (E2E_Delay) decreased up to above 50%, leading to the improved network lifetime.

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“…Virtual machine migration is a critical process in cloud computing environments, allowing for workload balancing, server consolidation, and maintenance operations without service interruptions. Despite its advantages, VM migration is a complex task that involves numerous challenges, such as load balancing, energy efficiency, and security levels [4,5,6]. Optimizing these aspects with use of User-Centric Interference-Aware Load Balancing (UCIALB) is crucial to maximizing the benefits of cloud computing and ensuring the reliability and performance of cloud services.…”

Section: Introductionmentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

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As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the Load Balancing and Energy-Efficient Migration Model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic Algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a Deep Reinforcement Learning-based Iterative-learning Contextual Side chaining Model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means Clustering and Fuzzy Logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ Bidirectional Long Short-Term Memory networks with Recurrent Graph Neural Network, for accurate workload prediction and informed migration decision Making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved by 2.5%, and the delay of computation was significantly reduced by 9.5%. Importantly, the integration of the Iterative-learning Contextual Side chaining Model significantly enhanced the security and quality of service (QoS) under attack scenarios, resulting in a 10.4% improvement in response speed, a 2.5% reduction in energy consumption during block mining, a 3.9% improvement in throughput, and an 8.5% reduction in storage costs. This Load Balancing and Energy-Efficient Migration Model represents a significant advancement in addressing the challenges of load balancing, energy efficiency, and security in VM migrations. Through the meticulous integration of bioinspired algorithms, advanced security measures, and machine learning techniques, our model provides a comprehensive and innovative solution that markedly improves system performance, reduces energy consumption, and fortifies security, thereby paving the way for a more efficient and secure cloud computing ecosystem.

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Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

Cited by 8 publications

References 88 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

Efficient Privacy Preserving Lightweight Cryptography for Multi-hop Clustering in Internet of Vehicles Network

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

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