Job Prioritization Research Articles

A multi-objective optimization of resource management and minimum batch VM migration for prioritized task allocation in fog-edge-cloud computing

Network resources and traffic priorities can be utilized to distribute requested tasks across edge nodes at the edge layer. However, due to the variety of tasks, the edge nodes have an impact on data accessibility. Resource management approaches based on Virtual Machine (VM) migration, job prioritization, and other methods were used to overcome this problem. A Minimized Upgrading Batch VM Scheduling (MSBP) has recently been developed, which reduces the number of batches required to complete a system-scale upgrade and assigns bandwidth to VM migration matrices. However, due to poor resource sharing caused by suboptimal VM utilization, the MSBP was unable to effectively ensure the global best solutions. In order to distribute resources and schedule tasks optimally during VM migration, this paper proposes the MSBP with Multi-objective Optimization of Resource Allocation (MORA) method. The major goal of this proposed methodology is to take into account different objectives and solve the Pareto-front problem to enhance lifetime of the fog-edge network. First, it formulates an NP-hard challenge for MSBP by taking into account a variety of factors such as network sustainability, path contention, network delay, and cost-efficiency. The Multi-objective Krill Herd optimization (MoKH) algorithm is then used to address the NP-hard issue using the Pareto optimality rule and produce the best solution. First, it introduces an NP-hard challenge for MSBP by accounting in network sustainability, path contention, network latency, and cost-efficiency. The Pareto optimality rule is then implemented to overcome the NP-hard problem and provide the optimum solution employing the Multi-objective Krill Herd optimization (MoKH) algorithm. This increases network lifetime and improves resource allocation cost efficiency. Finally, the simulation results show that the MSBP-MORA distributes resources more efficiently and hence increases network lifetime when compared to other traditional algorithms.

Energy-Aware and Delay-Sensitive Management of a Drone Delivery System

Problem definition: This paper considers a drone delivery system that delivers packages to multiple locations using a model that captures three distinctive features of interest: first, a battery-operated drone that can fly a limited number of distance units on a full battery; second, a set of demand locations with different flight distances and service-level requirements; and third, an adjustable flight speed to trade off energy efficiency for faster deliveries. By leveraging drone speed, job sequencing, and admission control, the system operator strives to achieve two managerial objectives: (i) to minimize energy- and congestion-related costs and (ii) to maximize the use of the battery energy between consecutive battery replacements. Academic/practical relevance: E-commerce and shipping companies worldwide are testing and launching commercial drone delivery services to residences. According to a recent study, such services are expected to grow to $100 billion market by the end of the next decade. Our work focuses on improving drone delivery operations, which can reduce costs and improve user satisfaction. Methodology: Focusing on the first objective, we approximate system dynamics using diffusion processes and derive an optimal control policy for the approximating system. Results: Interpreting that policy in the context of the physical system while taking into account the second objective, we devise an implementable set of speed control, job sequencing, and admission strategies. The sequencing strategy, which selects a portfolio of jobs (termed as an activity) to be accomplished within a full battery cycle, results in a win-win scenario—it allows both objectives to be approximately optimized at the same time. Managerial implications: Using the activity-based prioritization scheme provides opportunities to improve the overall performance of the system. Additionally, to achieve the intended job prioritization, the system operator can rely on a small set of activities that are of equal size to the number of job classes.

Unsettled Employment, Reshuffled Priorities? Career Prioritization among College-Educated Workers Facing Employment Instability during COVID-19.

Millions of workers experienced job instability during the COVID-19 pandemic. A prevailing assumption is that such experiences of instability intensify economic rationality in workers' career decision-making as a matter of course. In contrast, the authors argue that pandemic-related employment instability may have "unsettled" workers' lives in ways that elevated non-financial priorities such as meaningful work. Using proportionally representative survey data (N=1628), the authors compare the priorities of US college-educated workers who were laid off or furloughed during the pandemic to those whose jobs remained stable. Counter to expectations of heightened economic rationality, job-unstable workers were not more likely than job-stable workers to emphasize job security or salary in beliefs about good work. But, they were more likely to prioritize passion for work. These findings challenge common assumptions about job prioritization in the wake of crisis-related job instability and have implications for how scholars and policymakers interpret labor force trends.

Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

We study a difficult problem of how to schedule complex workflows with precedence constraints under a limited budget in the cloud environment. We first formulate the scheduling problem as an integer programming problem, which can be optimized and used as the baseline of performance. We then consider the traditional approach of scheduling jobs in a prioritized order based on the upward-rank of each job. For those jobs with no precedence constraints among themselves, the plain upward-rank priority scheme assigns priorities in an arbitrary way. We propose a job prioritization scheme that uses Markovian chain stationary probabilities as a measure of importance of jobs. The scheme keeps the precedence order for the jobs that have precedence constraints between each other, and assigns priorities according to the jobs' importance for the jobs without precedence constraints. We finally design a uniform spare budget splitting strategy, which splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workflows, including FFT, Gaussian elimination, typical scientific workflows, randomly generated workflows and workflows from an in-production cluster of an online streaming service company. We compare our algorithms with the-state-of-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand in average for most cases, and the Markovian based prioritization further improves the workflow makespan.

Dynamic Scheduling of a Battery-Operated Queue

This paper considers a drone delivery system that delivers packages to multiple locations, using a model that captures three distinctive features of interest: first, a battery-operated drone that can fly a limited number of distance units on a fully charged battery; second, a set of demand locations with different flight distances and service-level requirements; third, an adjustable flight speed to trade off energy efficiency for faster deliveries. By leveraging drone speed, job sequencing and admission control, the system operator strives to achieve two managerial objectives: (i) to minimize the energy- and congested-related cost, (ii) and to maximize usage of battery energy over a discharge/charge cycle. E-commerce and shipping companies worldwide are testing and launching commercial drone delivery service to residences. According to a recent study, such service is expected to grow to $100 billion market by the end of next decade. Our work focuses on improving drone delivery operations, which can reduce costs and improve user satisfaction. Focusing on the first objective, we approximate system dynamics using diffusion processes and derive an optimal control policy for the approximate system. Interpreting that policy in the context of the physical system while taking into account the second objective, we devise an implementable set of speed control, job sequencing and admission strategies. The sequencing strategy, which selects a portfolio of jobs (termed as an activity) to be accomplished within a full battery cycle, results in a win-win scenario -- it allows both objectives to be optimized simultaneously. Using the activity-based prioritization method provides opportunities to improve the overall performance of the system. Additionally, to achieve the intended job prioritization, the system operator can rely on a small set of activities that is of equal size to the number of job classes.

Decentralized scalable fairshare scheduling

This work addresses Grid fairshare allocation policy enforcement and presents Aequus, a decentralized system for Grid-wide fairshare job prioritization. The main idea of fairshare scheduling is to prioritize users with regard to predefined resource allocation quotas. The presented system builds on three contributions: a flexible tree-based policy model that allows delegation of policy definition, a job prioritization algorithm based on local enforcement of distributed fairshare policies, and a decentralized architecture for non-intrusive integration with existing scheduling systems. The system supports organization of users in virtual organizations and divides usage policies into local and global policy components that are defined by resource owners and virtual organizations. The architecture realization is presented in detail along with an evaluation of the system behavior in an emulated environment. In the evaluation, convergence noise types (mechanisms counteracting policy allocation convergence) are characterized and quantified, and the system is demonstrated to meet scheduling objectives and perform scalably under realistic operating conditions.

Composite dispatching rule design for dynamic scheduling with customer‐oriented production priority control

AbstractIn practice, the criteria considered for production scheduling objectives are generally derived from some desired production performance factors of a manufacturing system. On the other hand, customer‐oriented production priority management focuses on the analysis of operational production management policies that cannot be readily measured in a quantitative manner. This paper presents an approach for multiobjective composite dispatching rule design, which simultaneously considers scheduling criteria and the customer‐oriented production priority management policies. The correctness of elementary dispatching rule selection is a critical aspect of composite dispatching rule design. Data envelopment analysis is adopted to select a set of corresponding elementary rules that appropriately convey the scheduling criteria. The selected elementary rules are composed with weighted aggregation to optimize the scheduling criteria of interest. Analytic hierarchy process is used for overall job prioritization by combining the different schedules derived from the composite dispatching rule and the customer‐oriented production priority rules. The implementation of the presented approach on single‐machine, multiobjective, dynamic scheduling is demonstrated by a case study. The production performance of composite dispatching rules that would be subsequently achieved by the presented approach is compared with some conventional dispatching rules, and the results are found to be satisfactory. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Job prioritization and fair share in the LHCb experiment

The high demanding computing needs of the LHCb experiment are fulfilled by an extensive use of the Grid resources. Although these are wide and growing, they still remain finite. This paper addresses how all LHCb users can fairly access these resources and execute their tasks in an order determined by identity, group, job type and accounting information.

A rule-based heuristic finite capacity scheduling system for semiconductor backend assembly

Finite capacity scheduling (FCS) has been widely implemented in manufacturing companies for detailed shop floor operation management. Most of the traditional and artificial intelligence-based FCS systems lack the ability to provide a satisfying and practical solution for the large-scale scheduling problem in a complex environment. This paper describes a rule-based FCS system for the daily production scheduling (DPS) of a semiconductor backend assembly company. DPS is the FCS module of the gintic scheduling system (GSS), which has been enhanced and implemented successfully in a semiconductor backend assembly company, which is a world market leader in semiconductor packaging technology. Since the focus of this paper is on the DPS module, the overall framework and the other core modules of GSS are briefly covered. The detailed design of the six DPS modules, namely data input/integration, model configuration, data pre-processing, job prioritization (JP) executive, machine selection (MS) executive and result output, are presented in detail. The inference engines of JP and MS executive modules are discussed, which perform a unique rule-based job prioritization and machine selection process of the work order list and the assignments on the machines to achieve the desired scheduling objectives specified by the end user. Group technology is incorporated into the system to categorise the resources and parts for set-up/changeover reduction consideration. The successful deployment of the system in the industry endorsed the fact that the implemented DPS system has the capability to solve large-scale scheduling problems and performs well in the complex industrial environment. Favourable feedbacks received from the company include reduction in changeover and set-up, improved on-time delivery, increased machine utilization, decreased cumulative cycle time of work order, etc.