Workload Allocation Research Articles

Fair resource allocation: Using welfare-based dominance constraints

In this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision is acceptably fair. In contrast to existing literature, we incorporate fairness in the form of welfare dominance, ensuring that the resultant distribution of benefits to beneficiaries is at least as good as some reference distribution with respect to a set of social welfare functions that satisfy commonly accepted efficiency and fairness related axioms. We introduce a practical means to parameterize the problem, which allows for excluding welfare functions that are deemed insufficiently or overly sensitive to inequality. This allows for analyzing the impact of changes in inequality aversion on efficiency, thus revealing the trade-off between efficiency and fairness. We develop tractable reformulations for the resulting non-linear multi-level optimization problems. We then extend this approach for cases where resources are allocated to groups of individuals with different sizes. We demonstrate the potential use of the suggested framework on two case studies: a workload allocation problem and a healthcare provisioning problem.

A Hybrid Meta-Heuristic for Optimal Load Balancing in Cloud Computing

Nowadays, a trending technology that provides a virtualized computer resources based on the internet is named as cloud computing, these clouds performance mostly depends on the various factors among the load balancing. The allocation of the dynamic workload in between the cloud systems and equally shares the resources so that no database server is overloaded or under loaded is technically referred to as load balancing (LB). Therefore, in cloud an active load balancing scheme can perhaps enhance the reliability, services and the utilization of resources as well. In this manuscript, the benefits are integrated for Harries Hawks Optimization and Pigeon inspired Optimization Algorithm to create efficient load balancing scheme, which ensures the optimal resources utilizations with tasks response time. The proposed approach is implemented in JAVA Net beans IDE incorporated in the cloudsim framework that is analyzed based on different number of task in order to assess the performance. However, the simulation outcomes demonstrate that the proposed Hawks Optimization and Pigeon inspired Optimization algorithm based load balancing scheme is significantly balance the load optimally amid the Virtual Machines within a shorter period of time than the existing algorithms. The efficiency of the proposed method is 97% compared to the other existing methods. The computational time, cost, throughput analysis, make span, latency, execution time are determined and gets analysed, compared with the Harries Hawks Optimization, Spider Monkey Algorithm, Ant Colony Optimization and Honey Bee Optimization.

Flat-Rate Pricing and Truthful Offloading Mechanism in Multi-Layer Edge Computing

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities' computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights.

Evaluation of Pharmacy Student’s Placements Experience at Community Pharmacy Settings in Riyadh, Saudi Arabia

Objective: To assess opinions of pharmacy students towards community pharmacy placements experience. Methods: A cross-sectional study of male pharmacy students were conducted at king Saud university college of pharmacy. Data was collected by self-administered questionnaire for the period of June 2020 to July 2020. Deceptive statistics was applied to describe variable study. Results: A total of 77 out of 120 pharmacy students were returned the survey. Less half of the students (46.8%) agreed that their pharmacy preceptors organized interesting learning activities (either agreed or strongly agreed). A minority of students agreed that workload allocation in their workplace training was carefully planned. About 60% of students reported that their community pharmacy placements were a waste of time. Additionally, 32.5% of students stated that no one at the community pharmacy placement staff was interested in their problems. Only 37.8 % of the students enjoyed working at their community pharmacy placements, and about 46% of the students stated that they would like to return to their pharmacy placements. Conclusion: Findings shows that the pharmacy students had relatively low engagement with their pharmacy experiential placements. Pharmacy students had experienced low levels of satisfaction during their community pharmacy experiential placements. Key words: Pharmacy students, Placements, Preceptors, Saudi Arabia, Community pharmacy, Learning, Experience.

Efficient Workload Allocation and User-Centric Utility Maximization for Task Scheduling in Collaborative Vehicular Edge Computing

By integrating Mobile Edge Computing (MEC) into vehicular networks, vehicular edge computing extends computing capability to the vehicular network edge and hosts services in close proximity of connected vehicles. Parked Vehicles (PVs) occupy a large portion of the global vehicle and have idle states and resources. They collaborate with the MEC servers for cooperative task processing. This gives rise to a new computing paradigm, called by Collaborative Vehicular Edge Computing (CVEC). In CVEC, we introduce an offloading service provider that deploys an MEC server and schedules PVs on demand to handle offloading tasks. Efficient workload allocation and user-centric utility maximization are studied to optimize the network-wide task scheduling. In dynamic environment, offloading destination of each task is determined in a probabilistic manner. When necessary, the offloading service provider represents an offloading user to design a contract based incentive mechanism for the PVs. Based on contract theory and prospect theory, we model the offloading user's subjective evaluations on the utility in computation offloading, and derive an optimal contract to maximize the subjective utility under information asymmetry. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.

Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning

In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.

Dynamic task allocation and scheduling with contention-awareness for Network-on-Chip based multicore systems

Abstract In recent years, Network-on-Chip (NoC) based multicore systems have become popular for executing real-time applications. Mapping and scheduling of these applications are critical for system performance. The complexity of the problem increases for dynamic scenarios with real-time applications where new applications unknown at design-time, are submitted by users at runtime. Most of the existing works in this domain either ignore the allocation of communication workload or use estimated network contention while neglecting the impact on timing performance of tasks. In this work, we present an improved algorithm for dynamic task assignment and scheduling with link contention-awareness, which jointly allocates the tasks on the processors of NoC based multicore platform and schedules the communications on on-chip links. It minimizes communication latency and helps to satisfy the deadline of the tasks present in the allocated real-time applications. Experimental results show that the proposed approach achieves 25.3% and 45.6% improvement in task deadline satisfaction and communication latency compared to recent dynamic task allocation approaches while exhibiting 31.2% reduced runtime overhead.

Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing

Because of the tremendous growth in the number of smart vehicular devices and 5G mobile technologies, the Internet of Things (IoT) has experienced rapid expansion. This has led to a considerable increase in the volume of sensory data produced from, but not limited to, monitoring devices, traffic congestion in cities, safety, and pollution control. Cloud computing can deal with the corresponding workload by providing virtually unlimited computational resources. But, given the importance of the quality of service and security in delay-sensitive requests, other solutions like fog computing have also been introduced to speed up processing and management of sensory data in real scenarios like smart grid and IoT. Processing workloads at the network edge reduces the delay in mobile edge computing, but it highly increases the consuming power. Therefore, there is an urgent need for the improvement of the energy model of fog devices at the network edge. This paper is an attempt to modify this model using the green energy concept and reduce both delay and power consumption in multi-sensorial frameworks in secure IoT systems. In the proposed method, a Genetic Algorithm (GA) is used for handling a large number of requests and the corresponding quality and security limitations. Simulation results show that the proposed method can simultaneously reduce the delay and the power consumption of edge devices compared to a baseline strategy.

A Key Factor for Psychosomatic Burden of Frontline Medical Staff: Occupational Pressure During the COVID-19 Pandemic in China.

The global outbreak of COVID-19 has severely affected the entire population, especially healthcare staff on the frontline, who bear heavy psychosomatic burdens. A cross-sectional study was conducted with 723 participants in China from April 26 to May 9, 2020. We evaluated the psychosomatic status, including depression, anxiety, quality of life, somatic symptoms, stress, sleep disturbances, and posttraumatic stress symptoms in different exposure groups. We explored the risk factors that affect psychosomatic burdens and analyzed the relationship between psychosomatic problems and medical occupations. We found that the psychosomatic burdens of medical staff were significantly greater than those of non-medical staff (p < 0.01) and were positively related with the number of COVID-19 patients they came in contact with. Occupational pressure was a key factor for healthcare staff's psychosomatic problems (p < 0.01 for quality of life, somatic symptoms, anxiety, depression, stress; p = 0.012 for sleep disturbances), and it had a strong canonical correlation (p < 0.01). Workload and time allocation (WTA), one of the subdimensional indicators of occupational pressure, was strongly correlated with psychosomatic indicators. We suggest that rationalization of WTA is a desirable approach for anti-epidemic medical employees to alleviate psychosomatic burdens. Public health interventions should be undertaken to reduce the occupational pressure on this special population, which is critical for mitigation. This study presents results regarding the psychosomatic burdens of the healthcare workforce related to occupational pressure and provides multilevel data with groups of different exposure risks for policymakers to protect medical personnel. These findings draw attention to the working environments of healthcare workers and provide applicable results for clinical practice.

Dynamic Workload Allocation for Edge Computing

Artificial intelligence models implemented in power-efficient Internet-of-Things (IoT) devices have accuracy degradation due to limited power consumption. To mitigate the accuracy loss on IoT devices, an edge-server joint inference system is introduced. On the edge-server inference system, allocate more workloads to the server end can mitigate accuracy loss, but data transmission contributes to the power consumption of the edge device. Thus, in this article, we present a novel two-stage method to allocate workloads to the server or the edge to maximize inference accuracy under a power constraint. In the first stage, we present a clusterwise threshold-based method for estimating the trustworthiness of a prediction made at the edge. In the second stage, we further determine the workload allocation of a trustworthy image based on the probability of the top 1 prediction and the power constraint. In addition, we propose a fine-tuning process to the pretrained model at the edge for achieving better accuracy. In the experiments, we apply the proposed method to several well-known deep neural network models. The results show that the proposed method can improve inference accuracy up to 3.93% under a specific power constraint compared to previous methods.

Occupation, work-related stress, and personal characteristics among suicide deaths with occupation-related compensation claims in Korea.

ObjectivesThis study compared differences in age‐standardized suicide mortality rates, personal characteristics (demographics, employment conditions, and details of suicide), and work‐related stress by gender and occupation among workers who had committed suicide in Korea.MethodsData comprised 413 suicide death claims lodged with the Industrial Accident Compensation Insurance (IACI) from 2010 to 2018, which were coded. We calculated age‐standardized suicide mortality rates by gender and occupation. The chi‐square test, Fisher's exact test, and t‐test were conducted to examine gender differences. Frequency and percentage distribution by gender and occupation were calculated using descriptive statistics.ResultsRegardless of gender, age‐standardized suicide mortality rate was highest among “Managers.” Women who died by suicide were significantly younger and more likely to be unmarried, live alone, and have fewer years of continuous employment than men. “Managers,” “Professionals and Related Workers,” and “Clerks” experienced similar work‐related stresses, including “Difficult work to achieve,” “Fail to achieve allocation workload,” and “Change of job contents or workload.” “Skilled Agricultural, Forestry and Fishery Workers,” “Craft and Related Trades Workers,” and “Equipment, Machine Operating and Assembling Workers” had higher work‐related stress related to “Severe disease/injury” or “Causing a serious accident” compared with other workers.ConclusionsWork‐related stress related to suicide deaths differed by gender and occupation. The gender gap of labor market participation in Korea may affect gender differences in terms of demographics and employment conditions among workers who died by suicide. Our study suggests that gender‐ and occupation‐specific strategies and policies to reduce work‐related stress can prevent suicide among workers.

Design of a model based engineering deep learning scheduler in cloud computing environment using Industrial Internet of Things (IIOT)

The Industrial Internet of Things (IIoT) integrated with cloud computing resources offers effective advancements in the field of industrial automation with open connectivity and emergent computing. With such advancements, the supplementary services evolves in order to augment the automation process in manufacturing and production industries. However, the integration and transformation of cloud and IIoT environment poses serious limitation on reliability and computational performance. It is very necessary to develop a framework that handles and provides a bridge between the rapid input acquisitions from an IIoT device and rapid data processing or storage by the cloud. In this paper, model based engineering (MBE) model is used to control the workflow in cloud environment, which is integrated with a deep learning algorithm named recurrent neural network (RNN) algorithm. The RNN is utilized to control the future workload prediction and supply necessary resources to balance the workflow in cloud computing environment. The entire predictions are carried out based on the input data acquisition rate from the IIoT devices. The storage or processing of the input data is carried out by the Virtual Machines presents in cloud environment. The resources allocation and scheduling of workload is carried out in the control plane using RNN that optimizes the workflow. The RNN is resumed to solve iteratively the task of optimization. The experiments are conducted to test the efficacy of the model and the results are verified in terms of total energy consumption, task response time, and VM Utilization. The experiments are conducted with same/different types of IIoT devices. The simulation results reveal the improved efficacy of the MBE–RNN model than existing deep learning and MBE schedulers. The results show that the MBE–RNN acquires improved processing ability with reduced task response time and energy utilization.

Optimal Workload Allocation for Edge Computing Network Using Application Prediction

By deploying edge servers on the network edge, mobile edge computing network strengthens the real‐time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application’s requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short‐term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results.

A cost-effective strategy for splitting and allocating alerts' workloads during forensic investigations of very large IDS logs

The scarcity of suitable models that investigators can adopt to effectively manage and allocate resources during an outbreak of intrusions is gaining momentum in digital forensics. This paper proposes a cost-effective model that employs two measures of impurity to split and allocate alerts' workloads to two or more investigators of large intrusion logs. The premise is that the splitting and allocation of alerts' workloads to multiple investigators should be carried out such that the organisation will not incur marginal cost. The process must also guarantee timely delivery of forensic results whenever the intrusion detection systems (IDSs) trigger large quantity of alerts that exceed the capability of one investigator to analyse. This model uses large intrusion datasets, C++ language, clustering, entropy and Gini Index to demonstrate the profitable split-threshold that minimizes costs. The model pragmatically justifies the inclusion or exclusion of certain investigators of intrusion logs during critical moments at workplace.

Construction and application of reservoir development measure benefit evaluation system

The reservoir passes the peak of reserves.The reserves of new investment and development are decreasing year by year. The reserve resources are insufficient. Some of the main development units have entered the stage of secondary or tertiary oil recovery. The dependence of stable oil and gas production on measure production increases continuously. Under this background, the workload of oil well measures increases year by year. It is difficult to control the operation cost of measures. As a result, the overall economic benefits of oilfield enterprises have been declining year by year.The cost of ineffective measures has become an important factor restricting the economic development of oilfields. Through the construction of measures benefit evaluation system.Strengthen the controllability and predictability of each stage of implementation. Special attention should be paid to the pre control management of high cost wells and the transformation of production mode of low efficiency wells. Strictly follow the "ex ante argument, adjustment in matters, and post evaluation optimization" measures to run the management mode. Realize the reasonable allocation of workload and cost, and improve the effective rate of return of funds.

Joint Load Balancing and Offloading Optimization in Multiple Parked Vehicle‐Assisted Edge Computing

The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation‐intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle‐assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved.

Enhancing the labor division in the balancing of apparel assembly lines with parallel workstation through an improved ant colony algorithm

In order to overcome the inefficiency of labor division and management confusion in garment manufacturing assembly lines when introducing parallel workstations, an improved ant colony optimization (IACO) algorithm with multi-pheromones is proposed in this paper, which provides a new strategy to improve the match degree of operators and tasks in garment assembly line with parallel workstations. In proposed IACO, the first pheromone is set for ant to improve the efficiency of lines efficiency by balancing the workload allocation among workstations, and the second pheromone determines whether to create parallel workstations by valuing the complexity distribution of tasks. Existing real data of a garment manufacturing factory is used to verify the effective of IACO and the result shows that IACO contributes to a smooth work flow, and simultaneously reduce the efficiency loss of labor divisions (ELLD). Moreover, experiments are conduct to explore the trade-off relation between labor division and line arrangement efficiency when introducing parallel workstations. And based on the relation, strategies to create parallel workstations in the assembly line is provided for garment factories with different staff qualities for different apparel products.

Task aware optimized energy cost and carbon emission-based virtual machine placement in sustainable data centers

Management of IT services is rapidly adapting to the cloud computing environment due to optimized service delivery models. Geo distributed cloud data centers act as a backbone for providing fundamental infrastructure for cloud services delivery. Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption.

Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.

Energy‐efficient workload allocation in fog‐cloud based services of intelligent transportation systems using a learning classifier system

Nowadays, renewable energies have been considered as one of the important sources of energy supply in delay-sensitive fog computations in intelligent transportation systems due to their cheapness and availability. This study addresses the challenges of using renewable power supplies in delay-sensitive fogs and proposes an efficient workload allocation method based on a learning classifier system. The system dynamically learns the workload allocation policies between the cloud and the fog servers and then converges on the optimal allocation that fulfils the energy and delay requirements in the overall transportation system. Simulation results confirm that the proposed algorithm reduces the long-term costs of the system including service delay and operating costs. Also, compared to some other techniques, when the proposed method presents the most successful solution for reducing the average delay of the workloads and converging on the minimum value as well as retaining or even increasing the battery levels of fog nodes up to 100%. The lowest cost of the delay is 5 among other available methods, whereas in the proposed method, this value approaches 4.5.