Search or ask a question

What are the different types of load balancing algorithms used in fog computing?

Ant colony optimization algorithms

Computer science

Cloud computing

Latency (audio)

Load balancing (electrical power)

Best insight from top research papers

In fog computing, various types of load balancing algorithms are utilized to optimize resource utilization and enhance Quality of Service (QoS). These algorithms can be classified into approximate, exact, fundamental, and hybrid categories . One approach involves leveraging Genetic Algorithms for load balancing in cloud environments, which has shown improved response and processing times compared to other traditional methods like Round Robin and ESCE . Additionally, a novel model integrating Genetic and optimization algorithms has been proposed for load balancing on fog nodes, demonstrating enhanced task scheduling efficiency and QoS maintenance compared to existing algorithms like Round Robin, Hybrid Threshold based, and Hybrid Predictive Based models . Another strategy involves employing the Ant-colony optimization algorithm to balance load on fog nodes, resulting in reduced latency and improved response times compared to previous approaches .

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Journal Article•DOI Load Balancing Algorithms in Fog Computing Mostafa Haghi Kashani, Ebrahim Mahdipour - Show less +1 more 01 Mar 2023-IEEE Transactions on Services Computing 15 Citations	The different types of load balancing algorithms in fog computing are approximate, exact, fundamental, and hybrid algorithms, each with specific advantages and disadvantages.
Open access•Journal Article•DOI An Ant-colony Based Model for Load Balancing in Fog Environments 01 Mar 2023-Supercomputing frontiers and innovations	The paper focuses on an Ant-colony optimization algorithm for load balancing in fog environments, emphasizing latency reduction and efficient resource distribution.
Proceedings Article•DOI Using Genetic Algorithms for Load Balancing in Cloud Computing Lejla Hodzic, Sasa Mrdovic - Show less +1 more 11 Jun 2023	Not addressed in the paper.
Journal Article•DOI Load Balancing Algorithms in Fog Computing 01 Mar 2023-IEEE Transactions on Services Computing 13 Citations	The different types of load balancing algorithms in fog computing are approximate, exact, fundamental, and hybrid algorithms, each with specific characteristics and applications.
Journal Article•DOI Improved QoS with Fog computing based on Adaptive Load Balancing Algorithm Saurabh Saurabh, Rajesh Kumar Dhanaraj - Show less +1 more 17 May 2023-International Journal on Recent and Innovation Trends in Computing and Communication	The paper discusses various load balancing algorithms in fog computing, including Round Robin, Hybrid RR, Hybrid Threshold-based, and Hybrid Predictive-based models, with a novel Genetic algorithm-based approach for improved QoS.

My columns

Related Questions

What are the methods used in three phase load balancing systems?5 answersThree-phase load balancing systems employ various methods to ensure equitable distribution of loads among phases. These methods include manual load swapping based on load flow equations, capacity, and voltage constraints, automatic feeder reconfiguration and phase balancing using remote-controlled phase switch selector/controller, practical balancing algorithm for load reconfiguration at the unit level to achieve balance according to constraints, and an intelligent system utilizing AI-based micro-controllers to sense and distribute loads proportionately among phases in real-time. These techniques aim to optimize connections, minimize operational costs, enhance energy restoration, and reduce losses in electricity distribution systems.

What is the current state of the art in dynamic load balancing techniques for fog computing?5 answersThe current state of the art in dynamic load balancing techniques for fog computing involves a systematic exploration of various algorithms to address the challenge of workload distribution among fog nodes. These techniques aim to optimize resource utilization, throughput, response time, performance, and energy consumption in fog networks. Researchers have categorized load balancing algorithms into approximate, exact, fundamental, and hybrid types, each with its own set of advantages and disadvantages. By implementing load-aware strategies that monitor node loads and redistribute tasks accordingly, performance improvements can be achieved even in the presence of load imbalances among fog nodes. Despite the progress made in load balancing research, there are still open challenges and emerging trends that warrant further investigation to enhance the efficiency and effectiveness of fog computing systems.

What are the current challenges and open research directions in the field of dynamic load balancing for fog computing?5 answersCurrent challenges in dynamic load balancing for fog computing include addressing workload imbalance among nodes, enhancing Quality of Service (QoS) metrics like energy consumption and latency, and systematically consolidating existing load-balancing algorithms. Open research directions involve developing load-aware inference methods to improve performance despite load imbalances, exploring different load-balancing algorithms in fog computing, and considering factors like energy consumption, latency, and cost in resource allocation decisions within the fog-cloud continuum. These challenges and research directions highlight the importance of efficient resource management, improved system performance, and the need for further advancements in load-balancing techniques to optimize fog computing environments.

How can load balancing be improved in distributed computing systems?5 answersLoad balancing in distributed computing systems can be improved through various methods. One approach is to use dynamic load balancing based on capacity evaluation. This method involves adjusting the task load of cluster nodes by collecting and managing load status information. Another approach is to adopt an adaptive load balancing scheme that provides a fair allocation of jobs to computing resources. This scheme aims to reduce the cost of executing jobs in the system while improving throughput and reducing response time. Additionally, incorporating improved deep learning algorithms can enhance load balancing in large-scale distributed computing systems. This approach involves offloading computing tasks, constructing energy consumption models, and designing load balancing methods based on deep learning. Reinforcement learning-based approaches can also be effective in achieving balanced load in cloud-based systems. These approaches utilize reinforcement learning frameworks to optimize resource utilization and task allocation.

What are the different dynamic load-balancing algorithms used in cloud computing?3 answersDynamic load-balancing algorithms used in cloud computing include particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), throttled load balancing, Cat Swarm Optimization (CSO), and Spider Monkey Optimization (SMO). These algorithms aim to distribute the workload of cloud services efficiently and optimize resource utilization. They consider factors such as response time, data center processing time, virtual machine costs, data transfer costs, and total cost for different workloads and user bases. The performance of these algorithms is evaluated using metrics such as optimized response time, makespan, throughput, and resource utilization. Various studies have been conducted to compare and analyze the capabilities of different load-balancing algorithms in different cloud computing environments. The goal is to enhance the management of load balancing solutions and improve system performance in cloud computing.

What are the advantages and disadvantages of using genetic algorithms for load balancing in cloud computing?4 answersGenetic algorithms have several advantages for load balancing in cloud computing. They can effectively distribute user requests across cloud resources, resulting in better response and processing time compared to other load balancing algorithms such as round robin, ESCE, and throttled load balancing algorithms. Genetic algorithms have also been shown to provide near-optimum solutions and can improve the overall quality of applications deployed on the cloud. Additionally, nature-inspired load balancing algorithms, including genetic algorithms, have shown promising results in improving the performance of distributed cloud systems. However, it is important to note that genetic algorithms also have limitations. They may require significant computational resources and time to find optimal solutions, especially for large-scale cloud environments. Furthermore, the effectiveness of genetic algorithms can be influenced by the choice of parameters and the specific characteristics of the workload and cloud infrastructure.

See what other people are reading

How has the integration of artificial intelligence into cybersecurity measures impacted job growth in the industry?

The integration of artificial intelligence (AI) into cybersecurity measures has significantly impacted job growth in the industry. AI has enabled organizations to systematize and enhance outdated safety procedures, providing more effective threat detection, response capabilities, and vulnerability management. Additionally, AI technologies like machine learning have led to new levels of data analysis and detecting unusual behavior across networks, rapidly increasing security response times. AI-based solutions offer robust cyber defense capabilities, identifying various cyber threats such as malware attacks, network intrusions, phishing, and data breaches. As AI continues to evolve and address complex cybersecurity challenges, the demand for skilled professionals in AI and cybersecurity fields is expected to grow, creating new job opportunities and roles within the industry.What is filter method in feature selection?

A filter method in feature selection is a technique used to identify the most relevant features in a dataset before building a prediction model. These methods aim to enhance the performance of learning algorithms by reducing the dimensionality of the problem and selecting informative subsets of features. Various filter methods have been proposed in research literature. For instance, one study introduces a filter method called Joint Mutual Information with Class relevance (JoMIC), which combines Joint Mutual Information (JMI) and Mutual Information (MI) to select features highly relevant to a given class but non-redundant with other selected features. Another study proposes a hybrid filter-based and genetic feature selection algorithm for sentiment analysis, which outperforms traditional approaches in terms of classification accuracy, particularly in Arabic sentiment analysis.What is the definition of an urban 'slum' household according to the UN Habitat?

According to the UN-Habitat, an urban 'slum' household is defined as a human settlement with inadequate access to safe water, sanitation, and infrastructure, poor housing quality, overcrowding, and insecure residential status. These settlements are typically characterized by substandard housing, squalor, and are often found in urban regions of developing and undeveloped parts of the world. Slums are a result of rapid urbanization, poverty, high unemployment, and other factors like poor planning and economic stagnation. The UN-Habitat estimates that around one billion people currently live in slums, a number expected to rise significantly in the coming years if current trends persist. These informal settlements are crucial policy concerns globally, with interventions aimed at addressing the challenges of urban poverty and inequality.What are the advantages and disadvantages of using symbolic expression in genetic programming compared to traditional methods?

Symbolic expression in genetic programming offers advantages such as interpretable machine learning, effective relationship discovery, and the ability to handle high-dimensional problems efficiently. However, traditional genetic programming methods often struggle with real-world high-dimensional problems due to their stochastic evolutionary nature and poor demonstration as the problem scale increases. To address this limitation, a differentiable approach called DGP has been proposed, which constructs GP trees in a continuous manner, allowing for gradient-based optimization and better performance in high-dimensional symbolic regression tasks. Additionally, Control Variable Genetic Programming (CVGP) introduces a customized experiment design approach to expedite symbolic expression discovery over many independent variables, outperforming several baselines in learning complex expressions involving multiple variables.Is precordial lead placement has the capability to detect coronary artery disease?

Precordial lead placement plays a significant role in detecting coronary artery disease (CAD). Studies have shown that utilizing innovative techniques like the SafOne precordial lead ECG can aid in accurate CAD detection by reducing errors in lead placement and improving efficiency. Additionally, research suggests that the use of ultrashort one-lead ECG recordings, including noisy inputs from smartwatches, in combination with advanced algorithms like autoencoder neural networks and Support Vector Machines, can effectively detect CAD. Moreover, investigations focusing on the acoustic approach for CAD screening highlight the importance of ideal microphone placement on the chest to enhance accuracy in CAD detection, with specific positions above the apex of the heart showing promising results. Overall, proper precordial lead placement techniques are crucial for enhancing the detection of CAD, especially when combined with innovative technologies and analysis methods.What are the fundamental concepts and techniques used in machine learning?

Machine learning (ML) encompasses a variety of tools and structures to extract insights from data, covering both supervised and unsupervised learning techniques. ML involves learning from datasets, understanding core concepts like features, labels, algorithms, workflow, and model evaluation metrics. Fundamental concepts in ML include empirical risk minimization, regularization, probably approximately correct learning, and the maximum likelihood principle, which are crucial for controlling errors and understanding the learning process. Additionally, ML techniques are extensively applied in various domains due to their ability to handle complex problems and make data-driven decisions, with deep learning being a vibrant subfield that has gained significant attention. This comprehensive overview equips individuals with a broad understanding of ML's foundational principles and its diverse applications.What is the relationship between Platt scaling and isotonic regression in histology?

Platt scaling and isotonic regression are both methods used in different contexts. Platt scaling, as discussed in Context_1 and Context_2, is an online post-hoc calibration technique that combines logistic regression with Platt scaling to adapt between different settings and enhance calibration. On the other hand, isotonic regression, as described in Context_3, focuses on enhancing trend tests by imposing a monotonicity constraint on dose-response functions. In the context of histology, specifically in cutaneous melanoma, the study in Context_4 found that the presence of histological regression did not play a prognostic role in nodal involvement or survival. Therefore, while Platt scaling deals with calibration in predictive modeling, isotonic regression is more focused on enhancing trend tests in dose-response analysis, showing their distinct applications in different domains.What is the difference between framework and a model?

A framework is a structured outline or support system that provides a foundation for developing models, while a model is a representation of a system or process used to understand and predict its behavior. In the context of environmental systems, modelling frameworks offer pre-built functionality for creating models efficiently. On the other hand, models are specific representations that mimic real-world processes, such as flood forecasting models used to predict high water levels. Frameworks focus on alleviating limitations in representing different types of information within a system and optimizing hardware utilization for model development. In contrast, models are the actual simulations that utilize the framework to represent and analyze specific scenarios or phenomena within a system.How much scans are involved in drone mapping of canola seed production?

Drone mapping for canola seed production involves multiple scans at different growth stages. The process includes capturing ultrahigh resolution RGB images using a digital camera mounted on a UAV platform to assess plant growth traits. Additionally, UAV multi-spectral images are acquired at various heights to detect nutrient deficiencies and optimize fertilizer application. These scans are crucial for evaluating seedling stand count and seeding performance, with regression models based on shape features extracted from segmented plant objects. The use of drones in pest management for rapeseed fields also involves scans for pest imaging and identification, as well as precision pesticide spraying. Overall, drone technology offers a non-invasive and effective approach for monitoring canola crops through multiple scanning processes at different growth stages.What are the current trends in the digitization of accounting practices among small and medium-sized enterprises?

Current trends in the digitization of accounting practices among small and medium-sized enterprises (SMEs) reveal varying levels of readiness and challenges. Studies show that SMEs face obstacles such as limited resources, lack of understanding, and concerns about data security when adopting digital financial reporting systems. The emergence of digitalized accounting procedures involves a complex interplay of actors, regulatory frameworks, and technical standards, with national paths largely undocumented. Cloud computing is being applied to enhance accounting information management systems in SMEs, improving economic settlements and efficiency while necessitating robust network security measures. Research on SMEs in Sumedang district indicates a low level of digital readiness, with a focus needed on optimizing factors like knowledge about technology providers and collaboration to facilitate digital adoption. Studies emphasize the importance of digitalization in accounting information for SMEs, highlighting its positive impact on sustainable innovation ecosystems and public value generation.What is the function of architecture?

Architecture serves various functions depending on the context. In the realm of cellular metabolism, Architect, an automated pipeline, reconstructs metabolic models from protein sequences, enhancing precision and recall in enzyme annotations and model quality. Polymer architecture significantly influences the solution, rheological, and mechanical behavior of polymers, with hyperbranched polystyrenes exhibiting unique properties compared to linear polymers. Architectural design impacts the physical environment's effects on individuals, aiming to reduce stress and enhance productivity in settings like learning environments, hospitals, and operating rooms. Furthermore, the architectural organization of gap junction-connected cells, whether in tree or ring structures, influences their electrical properties, highlighting the importance of architecture in understanding cellular connectivity. Overall, architecture plays a crucial role in shaping functionality and experiences across various disciplines.