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Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine‐learning (ML) assisted scheduling optimization [J. Comput. Chem.2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load‐balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re‐normalized exciton model with time‐dependent density functional theory (REM‐TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML‐assisted coded calculations can further improve the load‐balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states. [ABSTRACT FROM AUTHOR]

Since Wireless Mesh Networks (WMNs) are commonly utilized for Internet access, the Internet Gateways (INGs) that link WMN to the wired network receive most of the traffic. Therefore, ING congestion is common, making traffic load balancing crucial. Despite significant research efforts focused on attaining load balancing among INGs and Mesh Router (MRs), more research is still required to address the inter-domain and intra-domain load balancing in WMN. In this paper, we have proposed a Tree-based Dual Load Balancing (TDLB) scheme that provides inter-domain and intra-domain load balancing and optimizes the number of INGs while maintaining QoS constraints in WMN. Our approach firstly covers the ING selection algorithm which achieves the objective of optimized gateway selection and places the ING to be close to each other. Then, the domain formulation algorithm assigns MRs to a domain according to some ordered priorities. We utilize the innovative ING reallocation algorithm, which accurately calculates the load ratio for each MR within the domain, facilitating the selection of the most suitable MR to be designated as the new ING. In order to accomplish load balancing, we perform inter-domain and intra-domain load balancing based on load ratio and cumulative load. The proposed TDLB scheme significantly enhances ING placement efficiency, network coverage, and traffic load balancing, leading to improved performance, scalability and a noteworthy contribution to the design and optimization of WMNs. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms in terms of fairness index, drop count and average path length in various scenarios. [ABSTRACT FROM AUTHOR]

With the advancement of ICT technology today, we can equip every single object with tiny and inexpensive radio modules; hence, they can interact and cooperate to perform complex tasks that have not been possible previously. During recent years several practical IoT-based applications have been proposed by deploying this improvement. With the expansion of IoT-based systems, many challenges began to emerge in this area. Energy optimization has been considered, as one of the most important problems in this domain, and data transmission has been identified as the primary accused. To tackle this problem, clustering has been suggested as a promising solution for reducing transmission distance and consequently energy consumption. The non-deterministic polynomial-time hard problem nature of clustering; alongside a variety of considerable parameters, limitations, and their contradiction, have made this problem more complex. As a result, various approaches have been proposed during recent years, each of which considered some parameters and real-world constraints. Here we present an efficient improvement on the existing energy-aware distributed unequal clustering protocol (EADUC). Our solution deploys a well-known swarm intelligence algorithm (SI), named Biogeography-based optimization (BBO) in a distributed manner to achieve heterogeneous load balancing. Our proposed work reflected a variety of real-world limitations such as energy, time, communication radius, and buffer size that have not been considered in many previous works simultaneously. Our simulations show approximately a 26% drop in the total number of dead nodes and a 1.59 % drop in energy consumption in comparison to the existing EADUC algorithm. [ABSTRACT FROM AUTHOR]

Software-defined vehicular networks (SDVN) is a promising technology for wireless data transmissions between vehicles. SDVN inherits software-defined networking principles and aims to improve the typical performance of safety and non-safety applications of vehicular adhoc networks. Consequently, enhancing the performance of Intelligent Transportation System (ITS). However, the performance of these ITS applications largely depends on the computational capability of the controller node, which involves creating or destroying a data path from the source vehicle to the destination vehicle and generating flow rules for the requests coming from the data plane elements. As a result, SDVN often suffers from the problems of overburdening the controller node with route requests under heavy traffic generation at vehicles and single-point controller failure. To counter these problems, solutions based on multiple controllers are proposed. In fact, the load-balancing problem remains an important issue. So, routing of data with multiple controllers and load-balancing, both topics in SDVN, go hand in hand. In this paper, we survey this state-of-the-art that discusses the above-mentioned challenges, starting with the SDVN preliminaries. We scrutinize the existing routing methodologies and also discuss load-balancing techniques. Furthermore, we provide real-time applications and services of SDVN, discuss trending research, potential future research directions, and the real-life applicability of SDVN that have not been addressed previously. [ABSTRACT FROM AUTHOR]

In the realm of the Internet of Things (IoT), the significance of edge devices within multi-cluster communication systems is on the rise. As the quantity of clusters and devices associated with each cluster grows, challenges related to resource optimization emerge. To address these concerns and enhance resource utilization, it is imperative to devise efficient strategies for resource allocation to specific clusters. These strategies encompass the implementation of load-balancing algorithms, dynamic scheduling, and virtualization techniques that generate logical instances of resources within the clusters. Moreover, the implementation of data management techniques is essential to facilitate effective data sharing among clusters. These strategies collectively minimize resource waste, enabling the streamlined management of networking and data resources in a multi-cluster communication system. This paper introduces an energy-efficient resource allocation technique tailored for edge devices in such systems. The proposed strategy leverages a higher-level meta-cluster heuristic to construct an optimization model, aiming to enhance the resource utilization of individual edge nodes. Emphasizing energy consumption and resource optimization while meeting latency requirements, the model employs a graph-based node selection method to assign high-load nodes to optimal clusters. To ensure fairness, resource allocation collaborates with resource descriptions and Quality of Service (QoS) metrics to tailor resource distribution. Additionally, the proposed strategy dynamically updates its parameter settings to adapt to various scenarios. The simulations confirm the superiority of the proposed strategy in different aspects. [ABSTRACT FROM AUTHOR]

Abstract Increasing and heterogeneous service demands have led to traffic increase, and load imbalance challenges among network entities in the Internet of Things (IoT) environments. It can affect Quality of Service (QoS) parameters. By separating the network control layer from the data layer, Software-Defined Networking (SDN) has drawn the interest of many researchers. Efficient data flow management and better network performance can be reachable through load-balancing techniques in SDN and improve the quality of services in the IoT network. So, the combination of IoT and SDN, with conscious real-time traffic management and load control, plays an influential role in improving the QoS. To give a complete assessment of load-balancing strategies to enhance QoS parameters in SDN-based IoT networks (SD-IoT), a systematic review of recent research is presented here. In addition, the paper provides a comparative analysis of the relevant publications, trends, and future areas of study that are particularly useful for the community of researchers in the field.

Increasing and heterogeneous service demands have led to traffic increase, and load imbalance challenges among network entities in the Internet of Things (IoT) environments. It can affect Quality of Service (QoS) parameters. By separating the network control layer from the data layer, Software-Defined Networking (SDN) has drawn the interest of many researchers. Efficient data flow management and better network performance can be reachable through load-balancing techniques in SDN and improve the quality of services in the IoT network. So, the combination of IoT and SDN, with conscious real-time traffic management and load control, plays an influential role in improving the QoS. To give a complete assessment of load-balancing strategies to enhance QoS parameters in SDN-based IoT networks (SD-IoT), a systematic review of recent research is presented here. In addition, the paper provides a comparative analysis of the relevant publications, trends, and future areas of study that are particularly useful for the community of researchers in the field. [ABSTRACT FROM AUTHOR]

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications. Nevertheless, existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets, such as preservation and server infrastructure, in a limited number of large-scale worldwide data facilities. Optimizing the deployment of virtual machines (VMs) is crucial in this scenario to ensure system dependability, performance, and minimal latency. A significant barrier in the present scenario is the load distribution, particularly when striving for improved energy consumption in a hypothetical grid computing framework. This design employs load-balancing techniques to allocate different user workloads across several virtual machines. To address this challenge, we propose using the twin-fold moth flame technique, which serves as a very effective optimization technique. Developers intentionally designed the twin-fold moth flame method to consider various restrictions, including energy efficiency, lifespan analysis, and resource expenditures. It provides a thorough approach to evaluating total costs in the cloud computing environment. When assessing the efficacy of our suggested strategy, the study will analyze significant metrics such as energy efficiency, lifespan analysis, and resource expenditures. This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing. The proposed work demonstrates notable improvements of 12.15%, 10.68%, 8.70%, 13.29%, 18.46%, and 33.39% for 40 count data of nodes using the artificial bee colony-bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively. [ABSTRACT FROM AUTHOR]

The increasing wafer yield and shrinking size pose challenges for real-time defect inspection using a single computer. To address this, we propose enhancing real-time performance by increasing computing devices. However, uneven load distribution due to device performance or defects variations can reduce inspection efficiency. We introduce computer cluster load balancing to solve this, utilizing a load-balancing model and an objective function. We propose the adaptive discrete quantum particle swarm optimization algorithm (ADQPSO) for efficient load balancing and implement the adaptive dynamic smooth weighted round-robin algorithm based on ADQPSO. Experimental results demonstrate that our algorithm achieves the fastest execution speed and up to a 24% improvement in performance. Our approach significantly improves real-time performance and efficiency in wafer surface defect inspection. [ABSTRACT FROM AUTHOR]

COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load-balancing) of adult patients needing intensive care to hospitals with remaining capacity. Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges? The Minnesota MOCC was established with a focus on transfer of adult ICU and medical-surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey. From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID-19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical-surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request-acceptance differences. Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical-surgical patients was greatly decreased. [ABSTRACT FROM AUTHOR]

With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols. [ABSTRACT FROM AUTHOR]

Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models. [ABSTRACT FROM AUTHOR]

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines' affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible. [ABSTRACT FROM AUTHOR]

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers' side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA's methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node's resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates. [ABSTRACT FROM AUTHOR]

Abstract Big Data applications require extensive resources and environments to store, process and analyze this colossal collection of data in a distributed manner. Containerization with cloud computing provides a pertinent remedy to accommodate big data requirements, however requires a precise and appropriate load-balancing mechanism. The load on servers increases exponentially with increased resource usage thus making load balancing an essential requirement. Moreover, the adjustment of containers accurately and rapidly according to load as per services is one of the crucial aspects in big data applications. This study provides a review relating to containerized environments like Docker for big data applications with load balancing. A novel scheduling mechanism of containers for big data applications established on Docker Swarm and Microservice architecture is proposed. The concept of Docker Swarm is utilized to effectively handle big data applications' workload and service discovery. Results shows that increasing workloads with respect to big data applications can be effectively managed by utilizing microservices in containerized environments and load balancing is efficiently achieved using Docker Swarm. The implementation is done using a case study deployed on a single server and then scaled to four instances. Applications developed using containerized microservices reduces average deployment time and continuous integration.

Recently, the load imbalance of network resources in the Internet of Things (IoT) due to the diverse and heterogeneous workflow of data, has attracted the attention of many researchers. To make appropriate load-balancing decisions, the influential Quality of Service (QoS) parameters should be identified. Most load-balancing techniques have focused on optimizing the multi-objective QoS while, the QoS optimization from the user, service provider and infrastructure provider directions simultaneously is an important many-objective problem. Extracting the QoS parameters from various directions, this paper proposes a tolerable many-objective load-balancing technique to efficiently allocate computing resources to workflows. Cost, response time, energy consumption, and CPU utilization have been investigated as the user, service provider, and infrastructure’s QoS parameters solved by Grey Wolf Optimization (GWO) algorithm. Also, a load-tolerance concept has been introduced to improve the QoS-based load balancing and task acceptance rate together. The experiment results show that TMaLB has about 10% improvement compared to MaPSO and NSGA-III algorithms.

The emergence of two new technologies, namely software defined networking (SDN) and 5G networks, has greatly changed the development of network functions and network topologies. These two technologies provide cost benefits for mobile operators, a more flexible and scalable network, and a shorter time to market for new services and applications. Scalability and effectiveness are increased when 5G and SDN are used together. SDN increases the reliability of the 5G network by separating the control plane from the data plane. Incorrect load balancing, a lack of knowledge of network traffic, and other issues make it difficult to provide Quality of Service (QoS) with SDN. This research proposes a unique load-balancing method to resolve these concerns using Hierarchical Agglomerative Clustering (HAC) and Back Propagation Neural Network (BPNN) algorithms. The proposed method segments the network services into several groups after normalizing data requirements. It consists of two phases: in first phase, there is clustering of bandwidth for different services (e.g., social media, automated homes, and automated cars) inside the network for different data requirements. The agglomerative hierarchical clustering (single link technique) is implemented to make the clusters of bandwidth inside the SDN work based on minimum distance. After clustering, we allotted bandwidths to the respective clusters. In the second phase, the BPNN technique trains the network to choose the optimal path and check the error faults. The proposed approach evaluates the network delay, packet loss, throughput, latency rate, and bandwidth usage to evaluate the performance with Multiple Regression-based Searching (MRBS) and Software-defined Sensor Network Load Balancing (SDSNLB) algorithms. The experimental results of the proposed model are promising as the performance increased by 15%, 23%, 27%, 21%, and 30%, respectively, compared with existing approaches. In addition we compared the computational time complexity with increased the no of nodes and services, when the rate of nodes and services is varying, the proposed solution’s efficiency remains constant.

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi‐level optimization framework to optimize the computational sequences. Machine‐learning (ML) assisted static load‐balancing, and different dynamic load‐balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high‐throughput drug suit, solvent model, P38 protein, and SARS‐CoV‐2 systems. The results show that the usage rate of given computational resources for high throughput and large‐scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high‐performance computing (HPC) clusters. [ABSTRACT FROM AUTHOR]

A key challenge was optimizing the speed of the network while maintaining impartiality. Power monitoring solutions have been developed to reduce these load imbalances. These techniques frequently need unique hardware or software on the participants' end. To accomplish the cloud computing system of load balancing, this study created an intelligent virtual machine programming scheme using a machine learning technique. The researchers introduced two methods that identify optimal probabilistic solutions to the issue after conducting an in-depth investigation. A min-max load balancing solution is generated by the second method, while the first approach reduces the load on the network's crowded Access Points (APs). Let's focus on the mapping of links in particular because the mapping of nodes was established a priori. This study provides an innovative and effective Improved Lion Optimization (ILO) with Min-Max Algorithm for enabling VNE in real systems consisting, breaking new ground in the research to employ Genetic Operators for parallelization. Load balancing & power conservation were two crucial goals that must be taken into account, and also the findings reduce the cost of processing time, the proposed system outperforms the sequentially one in terms of both goals. In addition, the adaptive capacity of the proposed algorithm was assessed across various substrate structural configurations. The load balancing goal was achieved; the typical data center usage was higher than that of other methods, reaching nearly 80%; the largest amount of virtual machine migration was reduced by 94.5%; the data center's maximum energy consumption was reduced by 49.13%.

Big Data applications require extensive resources and environments to store, process and analyze this colossal collection of data in a distributed manner. Containerization with cloud computing provides a pertinent remedy to accommodate big data requirements, however requires a precise and appropriate load-balancing mechanism. The load on servers increases exponentially with increased resource usage thus making load balancing an essential requirement. Moreover, the adjustment of containers accurately and rapidly according to load as per services is one of the crucial aspects in big data applications. This study provides a review relating to containerized environments like Docker for big data applications with load balancing. A novel scheduling mechanism of containers for big data applications established on Docker Swarm and Microservice architecture is proposed. The concept of Docker Swarm is utilized to effectively handle big data applications' workload and service discovery. Results shows that increasing workloads with respect to big data applications can be effectively managed by utilizing microservices in containerized environments and load balancing is efficiently achieved using Docker Swarm. The implementation is done using a case study deployed on a single server and then scaled to four instances. Applications developed using containerized microservices reduces average deployment time and continuous integration. [ABSTRACT FROM AUTHOR]

This study investigated the problem of controlling a three-phase three-wire photovoltaic (PV)-type distribution static compensator (DSTATCOM). In order to model, simulate, and control the system, the MATLAB/SIMULINK tool was used. Different controllers were applied to create switching pulses for the IGBT-based voltage source converter (VSC) for the mitigation of various power quality issues in the PV-DSTATCOM. Traditional control algorithms guarantee faultless execution or outcomes only for a restricted range of operating situations due to their present design. Alternative regulators depend on more resilient neural network and fuzzy logic algorithms that may be programmed to operate in a variety of settings. In this study, an adaptive linear neural network (ADALINE) was proposed to solve the control problem more efficiently than the existing methods. The ADALINE method was simulated and the results were compared with the results of the synchronous reference frame theory (SRFT), improved linear sinusoidal tracer (ILST), and backpropagation (BP) algorithms. The simulation results showed that the proposed ADALINE method outperformed the compared algorithms. In addition, the total harmonic distortions (THDs) of the source current were estimated under ideal grid voltage conditions based on IEEE-929 and IEEE-519 guidelines. [ABSTRACT FROM AUTHOR]

Users nowadays in environments with fog computing require applications that respond quickly to their requests for everything they want to access and work quickly and require to increase in the Quality of Service metrics such as minimum energy consumption, bandwidth efficiency, and reduction latency in a fog network, resulting in an improvement in the system's performance, that is done by getting to know the workload on the network and how to deal with it. In this paper, the various fog computing workloads are described, along with where each one should be executed, in addition, discuss the load-balancing techniques and strategies count as a very important issue and one of the important challenges in the fog computing environment, that play a significant role in resource management like resource provisioning, task offloading, resource scheduling, and resource allocation this will be done based on reviewing previous research and discussing the most important concepts in it. [ABSTRACT FROM AUTHOR]

The need for cloud computing load balancing is a peculiar area of interest for researchers because it affects both the quality of service provided to users and resource utilisation on the part of cloud service providers. Due to the requirement to minimise processing costs, enhance throughput, improve resource efficiency, and optimise cloud node arrangement, existing cloud computing load balancing methods have been found to be restricted in their capacity to manage big-data cloud system load distribution. This research developed a novel Central-Regional Architecture Based Load Balancing Technique (CRLBT) different from the known central, distributive, and hierarchical cloud architectures. The proposed technique was developed by combining a formulated throughput maximisation algorithm with the algorithms; Throughput Maximised-Particle Swarm Optimisation (TM-PSO) and Throughput Maximised-Firefly optimisation (TM-Firefly). The developed technique was implemented using the MATLAB R2018 software package. The performance of the CRLBT in comparison to the already-in-use PSO and Firefly algorithms was evaluated using response time, throughput, job rejection ratio, and CPU utilisation rate. The significance of the improvement in load balancing brought about by the new approach was further assessed using a statistical T-Test. The results showed that the proposed CRLBT significantly outperformed the PSO and Firefly techniques regarding response time, throughput CPU utilisation rate, and task rejection ratio. Finally, significant improvements in response time, tax rejection ratio, CPU utilisation rate, and network throughput proved the ability of the proposed technique to handle task-resource distribution of big-data cloud centres superiorly. [ABSTRACT FROM AUTHOR]

In the era of beyond 5G technology, it is expected that more and more applications can use deep neural network (DNN) models for different purposes with minimum inference time which is highly important for providing a great user experience in the Internet of Things (IoT) use-cases. However, due to hardware limitations and low computational capabilities, it is almost impossible to execute complex DNN models on IoT devices. To provide a better user experience and mitigate these problems, we introduce split computing technology where the DNN model inference task is partially offloaded from an IoT device to a nearby dependable device called edge. While splitting the DNN between IoT-edge, it is unclear how much the DNN model needs to be partitioned according to varying network conditions to ensure a satisfactory user experience. To address these issues, we propose the following mechanisms that provide a trade-off between the computation and communication, namely: (1) dynamic split computation (DSC) mechanism: finds an optimal partitioning of the DNN model to be executed between IoT and edge to reduce the computation overhead (i.e., overall inference time); (2) reliable communication network switching (RCNS) mechanism: intelligently finds a suitable network to connect to either Wi-Fi/Cellular/Bluetooth to provide reliable data transport between IoT and edge. Further, as the edge devices perform DNN tasks extensively, it is possible that they run out of battery, or get interrupted by high-priority user applications executing on them due to which edge devices cannot complete the assigned DNN task in the desired time. To address these issues, we also propose a load-balancing mechanism between IoT and edge called task load balancing with prioritization (TLBP) model for single and multiple DNN task scenarios. We conduct extensive experiments with Raspberry Pi as an IoT device and Samsung Galaxy S20 smartphones as edge devices to test the proposed mechanism. The results show that the proposed algorithms substantially reduce the inference time of DNN models as compared to the on-device inference time and balance the tasks across the edge devices to minimize excessive battery drainage.

The Internet of Things (IoTs) is based on physical sensors that collect data and transfer it to remote cloud computing platforms for processing and storage. Healthcare applications are one of the very promising areas where IoTs are expected to achieve significant advances. Indeed, devices can be installed in patients' bodies to monitor vital signals and send the data to the cloud for storage, processing, and decision-making. In this work, we focus on the security aspect, particularly the confidentiality and access control of the data (for instance EMR) during transmission and later after the data is stored in the cloud. However, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is considered the most convenient for the accomplishment of fine access control. In fact, the current CP-ABE solutions are not efficient and suitable for use in the context of IoTs, as things that usually the data producers are very constrained and thus cannot perform the public-key cryptographic functions used by CP-ABE. In this paper, we propose to improve the standard CP-ABE to make it more convenient for constrained sensors by offloading the heavy encryption computation to multiple cooperative nodes. Ensuring a balanced distribution of workload between multiple sensors and multiple cooperative nodes. We based on the assistant nodes' capacity for the best workload distribution. We evaluated our proposal through a set of experiments. The obtained results show that our approach, compared to the existing outsourcing ABE solutions, improves computation time and energy consumption, ensures data confidentiality, and provides fine-grained access control. [ABSTRACT FROM AUTHOR]

Subgraph enumeration is a heavy-computing procedure that lies at the core of Graph Pattern Mining (GPM) algorithms, whose goal is to extract subgraphs from larger graphs according to a given property. Scaling GPM algorithms for GPUs is challenging due to irregularity, high memory demand, and non-trivial choice of enumeration paradigms. In this work we propose a depth-first-search subgraph exploration strategy (DFS-wide) to improve the memory locality and access patterns across different enumeration paradigms. We design a warp-centric workflow to the problem that reduces divergences and ensures that accesses to graph data are coalesced. A weight-based dynamic workload redistribution is also proposed to mitigate load imbalance. We put together these strategies in a system called DuMato, allowing efficient implementations of several GPM algorithms via a common set of GPU primitives. Our experiments show that DuMato's optimizations are effective and that it enables exploring larger subgraphs when compared to state-of-the-art systems. [ABSTRACT FROM AUTHOR]

For the rapid growth computation requirements in big data and artificial intelligence area, CPU-GPU heterogeneous clusters can provide more powerful computing capacity compared to CPU clusters. The high parallel computing capabilities of GPUs greatly accelerate computation-intensive applications. And the number of GPUs on single computing node is scalable, which greatly improves the computing capacity of the cluster under the condition of limited cluster size. However, there is a lack of the effective load-balancing scheduling model in multi-GPU hardware environment. This article proposes AEML, an acceleration engine for multi-GPU load-balancing in distributed heterogeneous environment. AEML can effectively integrate GPUs into distributed processing framework and achieve great load-balance among multiple heterogeneous GPUs. We propose a heterogeneous task execution model based on multiple GPUs and multiple streams (MGMS), which can effectively balance the workload of multiple GPUs. MGMS model utilizes four core techniques: a fine-grained task mapping mechanism, a device resource unified management scheme, a novel resource-aware GPU task scheduling strategy, and a feedback-based streams adjustment scheme. The implementation of AEML system is based on Spark 3.0.0 and NVIDIA CUDA 10.0. We comprehensively evaluate the performance of AEML with multiple typical benchmarks. Experimental results show that AEML can fully exploit the computing power of GPUs and achieve great load-balance among multiple heterogeneous GPUs. [ABSTRACT FROM AUTHOR]

Abstract Today’s datacenter networks (DCNs) scale is rapidly increasing because of the wide deployment of cloud services and the rapid rise of edge computing. The bandwidth consumption and cost of a DCN are growing sharply with the extensions of network size. Thus, how to keep the traffic balanced is a key and challenging issue. However, the traditional load balancing algorithms such as Equal-Cost Multi-Path routing (ECMP) are not suitable for high dynamic traffic in cloud DCNs. In this paper, we propose a port-based forwarding load balancing scheduling (PFLBS) approach for Fat-tree based DCNs with some new features which can overcome the disadvantages of the existing load balancing methods in the following aspects. Firstly, we define a port-based source-routing addressing scheme, which decreases the switch complexity and makes the table-lookup operation unnecessary. Secondly, based on this addressing scheme, we proposed an effective routing mechanism which can obtain multiple available paths for flow scheduling based in Fat-tree. All the path information is saved in servers and each server only needs to maintain its own path information. Thirdly, we propose an efficient algorithm to implement large flows scheduling dynamically in terms of current link utilization ratio. This method is suitable for cloud DCNs and edge computing, which can reduce the complexity of the switches and the power consumption of the whole network. The experiment results indicate that the PFLBS approach has better performance compared with the ECMP, Hedera and MPTCP approaches, which decreases the flow completion time and improves the average throughput significantly. PFLBS is simple and can be implemented with a few signaling overheads.

Flying Ad Hoc NETworks (FANETs) are expected to have a significant impact in several use-cases, from smart agriculture and cities, to mission critical scenarios. The recent surge in the use of FANETs is motivated by their adaptable and flexible behaviours in different scenarios (e.g. disaster-hit locations) allowing the usage of services that require information from remote locations, such as for assessment of damages, checking for survivors, or providing onsite views to assist rescue teams. While FANETs have been developed to provide such critical services, disseminating data with proper performance faces challenges due to inherent properties of FANETs, namely frequent wireless disconnections, intermittent available nodes, and dynamic topologies, mostly when facing an increasing number of deployed unmanned aerial vehicles. Aiming to tackle these challenges, we propose a new Dynamic Clustering Mechanism with Load-Balancing able to support efficient dissemination of data packets in FANETs while ensuring good reliability and scalability factors. The proposed solution is based on the combination of a new meta-heuristic optimization scheme, known as Political Optimizer, used to perform clustering while addressing limitations caused by topology changes, and a new Shannon entropy function implemented to address cluster fault tolerance and traffic overloads. Simulation results show that by combining our proposed model with standard position-based routing protocols, a higher number of end-to-end transmissions are ensured, while supporting an average packet delivery ratio of 97%, an average end-to-end delay of 0.225 seconds, and an average power consumption 37% lower than other state-of-the-art clustering protocols.

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s. [ABSTRACT FROM AUTHOR]

Background: After the publication of a 2014 consensus statement regarding mass critical care during public health emergencies, much has been learned about surge responses and the care of overwhelming numbers of patients during the COVID-19 pandemic. Gaps in prior pandemic planning were identified and require modification in the midst of severe ongoing surges throughout the world.Research Question: A subcommittee from The Task Force for Mass Critical Care (TFMCC) investigated the most recent COVID-19 publications coupled with TFMCC members anecdotal experience in order to formulate operational strategies to optimize contingency level care, and prevent crisis care circumstances associated with increased mortality.Study Design and Methods: TFMCC adopted a modified version of established rapid guideline methodologies from the World Health Organization and the Guidelines International Network-McMaster Guideline Development Checklist. With a consensus development process incorporating expert opinion to define important questions and extract evidence, the TFMCC developed relevant pandemic surge suggestions in a structured manner, incorporating peer-reviewed literature, "gray" evidence from lay media sources, and anecdotal experiential evidence.Results: Ten suggestions were identified regarding staffing, load-balancing, communication, and technology. Staffing models are suggested with resilience strategies to support critical care staff. ICU surge strategies and strain indicators are suggested to enhance ICU prioritization tactics to maintain contingency level care and to avoid crisis triage, with early transfer strategies to further load-balance care. We suggest that intensivists and hospitalists be engaged with the incident command structure to ensure two-way communication, situational awareness, and the use of technology to support critical care delivery and families of patients in ICUs.Interpretation: A subcommittee from the TFMCC offers interim evidence-informed operational strategies to assist hospitals and communities to plan for and respond to surge capacity demands resulting from COVID-19. [ABSTRACT FROM AUTHOR]

Network-wide traffic measurement is of interest to network operators to uncover global network behavior for the management tasks of traffic accounting, debugging or troubleshooting, security, and traffic engineering. Increasingly, sophisticated network measurement tasks such as anomaly detection and security forensic analysis are requiring in-depth fine-grained flow-level measurements. However, performing in-depth per-flow measurements (e.g., detailed payload analysis) is often an expensive process. Given the fast-changing Internet traffic landscape and large traffic volume, a single monitor is not capable of accomplishing the measurement tasks for all applications of interest due to its resource constraint. Moreover, uncovering global network behavior requires network-wide traffic measurements at multiple monitors across the network since traffic measured at any single monitor only provides a partial view and may not be sufficient or accurate. These factors call for coordinated measurements among multiple distributed monitors. In this paper, we present a centralized optimization framework, LEISURE (Load- EqualIzed mea SUREment), for load-balancing network measurement workloads across distributed monitors. Specifically, we consider various load-balancing problems under different objectives and study their extensions to support both fixed and flexible monitor deployment scenarios. We formulate the latter flexible monitor deployment case as an MILP (Mixed Integer Linear Programming) problem and propose several heuristic algorithms to approximate the optimal solution and reduce the computation complexity. We evaluate LEISURE via detailed simulations on Abilene and GEANT network traces to show that LEISURE can achieve much better load-balanced performance (e.g., 4.75 × smaller peak workload and 70 × smaller variance in workloads) across all coordinated monitors in comparison to a naive solution (uniform assignment) to accomplish network-wide traffic measurement tasks under the fixed monitor deployment scenario. We also show that under the flexible monitor deployment setting, our heuristic solutions can achieve almost the same load-balancing performance as the optimal solution while reducing the computation times by a factor up to 22.5 × in Abilene and 800 × in GEANT.