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The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies' promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments. [ABSTRACT FROM AUTHOR]

The purpose of this investigation was to compare the effects of 2 different periodized resistance training programs on maximal strength, power, and muscle architecture, in trained individuals. Twenty-two resistance-trained men were randomly assigned to either a mixed session training group (MSP; n = 11; age = 23.7 ± 2.6 years; body mass = 80.5 ± 9.8 kg; height = 175.5 ± 6.1 cm) or a block periodization group (BP; n = 11; age = 25.7 ± 4.6 years; body mass = 81.1 ± 10.7 kg; height = 176.8 ± 8.4 cm). Both training programs were 10 weeks in duration and were equated in volume. Each training session of the MSP focused on power, maximal strength, and hypertrophy, whereas each mesocycle within the BP focused on one of these components. Subjects were assessed for body composition, muscle architecture, maximal strength, and power. In addition, perceived training load, and training volume were calculated. Subjects in MSP experienced greater improvements in fat free mass (p = 0.021), muscle thickness of the pectoralis and vastus lateralis (p < 0.05), and a greater improvement in 1RM bench press (p < 0.001; +8.6% in MSP and +2% in BP) than in BP. By contrast, BP resulted in greater improvements in vertical jump (p = 0.022; +7.2%) compared with MSP (+1.2%). No significant differences were noted between the groups for perceived training load (p = 0.362) nor training volume (p = 0.169). Results of this study indicated that in a 10-week training study, MSP may enhance muscle hypertrophy and maximal strength to a greater extent than BP, with the same training volume and perceived training load. However, BP may be more effective for vertical jump improvement. [ABSTRACT FROM AUTHOR]

Fog Computing caters to the immediate requirement of processing and storage to serve the application running on physical devices attached to the Internet of Things. However, edge devices are restricted by limited processing power and storage. Thus, the voluminous data and the velocity with which the devices produce this data must be judiciously distributed between the fog device and data centers in the cloud system. Such an arrangement ensures that the overall quality of service and quality of experience of end-users are maintained. The paper proposes an algorithm to allocate jobs that utilizes branch-bound technique and particle swarm optimization, a tradeoff between power consumption and task execution time over fog-cloud networks. The paper addresses the twin challenge of reducing the delay period or latency for communication and reducing the power consumption of the fog devices and machines deployed in the data centers. The paper aims to optimize workload allocation among various system components, including fog devices, machines in the data center, and the communication network between them. The graphical findings demonstrate both power consumption and delay have been reduced by employing the suggested algorithm in a fog-cloud scenario. [ABSTRACT FROM AUTHOR]

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs’ strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts. [ABSTRACT FROM AUTHOR]

Recently, edge computing is getting attention as a new computing paradigm that is expected to achieve short-delay and high-throughput task offloading for large scale Internet-of-Things (IoT) applications. In edge computing, workload distribution is one of the most critical issues that largely influences the delay and throughput performance of edge clouds, especially in distributed Function-as-a-Service (FaaS) over networked edge nodes. In this paper, we propose the Resource Allocation Control Engine with Reinforcement learning (RACER), which provides an efficient workload distribution strategy to reduce the task response slowdown with per-task response time Quality-of-Service (QoS). First, we present a novel problem formulation with the per-task QoS constraint derived from the well-known token bucket mechanism. Second, we employ a problem relaxation to reduce the overall computation complexity by compromising just a bit of optimality. Lastly, we take the deep reinforcement learning approach as an alternative solution to the workload distribution problem to cope with the uncertainty and dynamicity of underlying environments. Evaluation results show that RACER achieves a significant improvement in terms of per-task QoS violation ratio, average slowdown, and control efficiency, compared to AREA, a state-of-the-art workload distribution method.

Background: Clinical medical physics duties include routine tasks, special procedures, and development projects. It can be challenging to distribute the effort equitably across all team members, especially in large clinics or systems where physicists cover multiple sites. The purpose of this work is to study an equitable workload distribution system in radiotherapy physics that addresses the complex and dynamic nature of effort assignment. Methods:We formed a working group that defined all relevant clinical tasks and estimated the total time spent per task. Estimates used data from the oncology information system,a survey of physicists,and group consensus.We introduced a quantitative workload unit, "equivalent workday" (eWD), as a common unit for effort. The sum of all eWD values adjusted for each physicist's clinical full-time equivalent yields a "normalized total effort" (nTE) metric for each physicist, that is, the fraction of the total effort assigned to that physicist. We implemented this system in clinical operation. During a trial period of 9 months, we made adjustments to include tasks previously unaccounted for and refined the system. The workload distribution of eight physicists over 12 months was compared before and after implementation of the nTE system. Results: Prior to implementation, differences in workload of up to 50% existed between individual physicists (nTE range of 10.0%--15.0%). During the trial period, additional categories were added to account for leave and clinical projects that had previously been assigned informally. In the 1-year period after implementation, the individual workload differences were within 5% (nTE range of 12.3%--12.8%). Conclusion: We developed a system to equitably distribute workload and demonstrated improvements in the equity of workload.A quantitative approach to workload distribution improves both transparency and accountability. While the system was motivated by the complexities within an academic medical center, it may be generally applicable for other clinics. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicated coupled constraints among decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose a two-layer constrained RL algorithm, named Resource Allocation and Workload diStribution (RAWS) to solve them. Specifically, an outer layer first makes the resource allocation decision via an RL algorithm, and then an inner layer makes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks. [ABSTRACT FROM AUTHOR]

Performance and energy are the two most important objectives for optimization on modern parallel platforms. In this article, we show that moving from single-objective optimization for performance or energy to their bi-objective optimization on heterogeneous processors results in a tremendous increase in the number of optimal solutions (workload distributions) even for the simple case of linear performance and energy profiles. We then study full performance and energy profiles of two real-life data-parallel applications and find that they exhibit shapes that are non-linear and complex enough to prevent good approximation of them as analytical functions for input to exact algorithms or optimization software for determining the Pareto front. We, therefore, propose a solution method solving the bi-objective optimization problem on heterogeneous processors. The method's novel component is an efficient and exact global optimization algorithm that takes as an input performance and energy profiles as arbitrary discrete functions of workload size, which accurately and realistically take into account resource contention and NUMA inherent in modern parallel platforms, and returns the Pareto-optimal solutions (generally speaking, load imbalanced). To construct the input discrete energy functions, the method employs a methodology that accurately models the energy consumption by a hybrid data-parallel application executing on a heterogeneous HPC platform containing different computing devices using system-level power measurements provided by power meters. We experimentally analyse the proposed solution method using three data-parallel applications, matrix multiplication, 2D fast Fourier transform (2D-FFT), and gene sequencing, on two connected heterogeneous servers consisting of multicore CPUs, GPUs, and Intel Xeon Phi. We show that it determines a superior Pareto front containing the best load balanced solutions and all the load imbalanced solutions that are ignored by load balancing methods. [ABSTRACT FROM AUTHOR]

Background: Interventions for acute ischaemic stroke require brain imaging. Computerised tomography (CT) scanning is the most common method used. In this study, the aim was to investigate the CT workload of acute stroke in an Irish level 3 hospital, seeing approximately 200 acute strokes per year. Method: A time frame for data collection: 17th of October 2017–17th of October 2018 was selected. Data were collected from ordering and viewing radiology systems and the Symphony Emergency Department (ED) system. Acute stroke CT brain scans were examined under numerous parameters including arrival time and time in CT scanner. Data were used to calculate 'time to CT' and to examine how this varied depending on the time of day. Scans were categorised into 5 time periods. All CT brains and other CT scans, after hours, in the same period were analysed. Results: Data were collected on 3739 CT Brain scans, 215 were acute stroke scans. One hundred twenty-four acute stroke scans were performed after hours. Acute stroke scans accounted for 9.4% of all out-of-hour CT scans, rising to 14.8% Monday to Friday. Median time to CT in acute stroke patients: period 1 00:30 mins, period 2 00:34 mins, period 3 00:49 mins, period 4 00:34 mins, and period 5 00:39 mins. Conclusion: Acute stroke imaging constitutes a relatively small portion of the out-of-hour CT workload. Due to the emergency status of these scans, providing an acute stroke radiology service requires radiology staff to operate with extremely short response times 24 h a day. [ABSTRACT FROM AUTHOR]

Summary: Energy efficiency is a contemporary and challenging issue in geographically distributed data centers. These data centers consume significantly high energy and cast a negative impact on the energy resources and environment. To minimize the energy cost and the environmental impacts, Internet service providers use different approaches such as geographical load balancing (GLB). GLB refers to the placement of data centers in diverse geolocations to exploit variations in electricity prices with the objective to minimize the total energy cost. GLB helps to minimize the overall energy cost, achieve quality of service, and maximize resource utilization in geo‐distributed data centers by employing optimal workload distribution and resource utilization in the real time. In this paper, we summarize various optimization‐based workload distribution strategies and optimization techniques proposed in recent research works based on commonly used optimization factors such as workload type, load balancer, availability of renewable energy, energy storage, and data center server specification in geographically distributed data centers. The survey presents a systemized and a novel taxonomy of workload distribution in data centers. Moreover, we also debate various challenges and open research issues along with their possible solutions. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been considerably increased. The processing resources of IoT nodes cannot cope with such huge workloads. Processing parts of the workload in clouds could solve this problem, but the quality of services for end-users will be decreased. Given the latency reduction for end-users, the concept of processing in the fog devices, which are at the edge of the network has been evolved. Optimizing the energy consumption of fog devices in comparison with cloud devices is a significant challenge. On the other hand, providing the expected-quality of service in processing the requested workloads is highly dependent on the propagation delay between fog devices and clouds, which due to the nature of the distribution of clouds with the different workloads, is highly variable. To date, none of the proposed solutions has solved the problem of workload allocation given the criteria of minimizing the energy and delay of fog devices and clouds, simultaneously. This paper presents a processing model for the problem in which a trade-off between energy consumption and delay in processing workloads in fog is formulated. This multi-objective model of the problem is solved using NSGAII algorithm. The numerical results show that by using the proposed algorithm for workload allocation in a fog-cloud scenario, both of energy-consumption and delay can be improved. Also, by allocating 25% of the IoT workloads to fog devices, the energy consumption and delay are both minimized. [ABSTRACT FROM AUTHOR]

Fog-integrated Cloud has emerged as a novel computing paradigm that brings Cloud computing services to the network's edge in real-time, though with limited capabilities. Despite its advantages, there are several challenges including workload distribution, energy consumption, computational time, and network latency, that require attention. The workload of IoT applications can be distributed over the Fog or Cloud devices based on their priority, deadline, and latency restrictions. In this work, we introduce a novel population-based metaheuristic called MAYA, a modified variant of the JAYA algorithm, to address the Energy-Efficient Workload Distribution of Sensors (EEWDS) in the Fog-Cloud ecosystem. The workload distribution of IoT applications depends on several factors such as request deadlines, the energy consumed during transmission, and needed computation. The performance of the proposed model for the energy consumption, computation time, C O 2 emission, fairness index, and the convergence rate, is evaluated through simulation experiments. The results are compared in two scenarios: one concerning to methodology, where the performance is compared with JAYA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) techniques. The other scenario is based on the environment, where we examine Cloud-only, Fog-only, and Fog-Cloud integrated environments. Compared to JAYA, GA, PSO and ACO, the proposed MAYA technique demonstrates significant improvements, including reduction in energy consumption by 34.76%, 88.92%, 85.36% and 93.84%; decrease in computation time by 37.64%, 85.07%, 87.22%, and 91.08%; decrease in C O 2 emissions by 23.46%, 76.24%, 97.17%, and 99.02%; and increase in fairness index by 9.62%, 3.72%, 16.90%, and 15.26% respectively. • An IoT workload distribution method has been developed, which aims to minimize energy consumption in uploading, downloading, and computation. • A new technique MAYA, which is a modified version of JAYA metaheuristic, has been introduced. • In addition, C O 2 emission is also measured associated with the proposed MAYA technique in the Fog-integrated Cloud environment. • Furthermore, computation time, running time, convergence rate, and fairness index have also been studied. • A comparative study has been performed in two classic scenarios: Methodology-based and Environment-based. [ABSTRACT FROM AUTHOR]

Workload is the overall assignments to be completed by individuals in a given time. In academia, the number of instructional hours, credit hours, contact hours, class sizes, non-instructional schedules, student-teacher ratio, scholarly activities, and administrative and community services will determine the workload of faculty members in a semester. Any discrimination, favoritism, partiality, or managerial biases in the distribution of the workloads would lead to misconceptions among the academic staff that will affect the work culture of educational institutions. In this context, this paper examines the perceptions of 256 academic staff chosen by stratified random sampling from ten universities in Saudi Arabia by using a questionnaire on the general practices of universities for the allocation of the equitable workload in three variables such as teaching, research, and academic administration. Study results revealed that the academic staff positively responded to all the practices in the three variables. This paper answered the questions of what are the significant differences and significant correlations in the perceptions of academic staff concerning equitable workload distribution in the three variables. Firstly, it was found that the academic staff as groups by gender, nationality, type of university, and tenure has a significant perceptional difference in the three variables. In Saudi Arabia, foreigners work on contract basis, therefore, this study revealed that foreign staff members place more emphasis on teaching instead of research and administration. This result was found in college faculty members in science, arts, and other college faculty members. The study further found a relationship among three types of faculty departments/disciplines as far as teaching and administrative work is concerned, but there is no relation between research and administrative work. In conclusion, the authors have recommended, some practical suggestions for equitable workload among academic staff. [ABSTRACT FROM AUTHOR]

The current practice of cancer genetic counseling is undergoing widespread change and scrutiny. While there are clinical resources for genetic counselors (GCs) regarding the delivery of cancer genetic services, there is limited literature regarding effective management of a genetic counseling clinical program. We have developed administrative tools to manage a large team of GCs at a single academic medical center over a period of increasing demand for genetics services, with the initial aim of decreasing wait time for urgent genetic counseling visits. Here, we describe the three main elements of the clinical operations: Balancing patient volume between GCs, scheduling tracks for both routine and urgent appointments, and a team of triaging GCs to ensure appropriate patient referrals. For each of these elements, we describe how they have been modified over time and present data to support the utility of these strategies. The preliminary evidence offered here suggests that these tools allow for an equitable distribution of patient volume between team members, as well as the timely and accurate scheduling of urgent patients. As a result of the experiences presented here, other genetic counseling programs grappling with similar issues should be aware that it is possible to shift clinical operations to serve certain patient populations in a more timely fashion while keeping both providers and GC staff satisfied. [ABSTRACT FROM AUTHOR]

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in the context of VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap by providing an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures of equity, and point out important connections between their properties and the properties of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e., composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions.We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis remain valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research on equity in logistics. [ABSTRACT FROM AUTHOR]