Low Priority Jobs Research Articles

An efficient two-stage optimization algorithm for a flexible job shop scheduling problem with worker shift arrangement

Due to the involvement of workers, scheduling production jobs necessitates consideration of worker shifts in most production activities. In this study, we address a flexible job shop scheduling problem with worker shift arrangement, considering constraints such as job priority, limited resources, and resource unavailability. To minimize the overdue days of low-priority jobs and ensure the timely delivery of high-priority jobs, we establish a mixed-integer programming model to allocate production resources, process sequencing, and schedule worker shifts. An improved differential evolution algorithm is proposed and designed such that overdue days and worker overtime of all jobs are calculated. Furthermore, we develop a two-stage intelligent optimization algorithm. First, we design a two-segment chromosome encoding and decoding method. Then, we propose generation strategies that follow the urgency of the priority rule to generate high-quality initial chromosomes. In adaptive worker shift adjustment, we prioritize high-priority jobs to align with delivery times. We conducted experiments to validate our model and algorithm by comparing them against four well-known intelligent optimization algorithms. Our improved algorithm proves to be highly beneficial in job and worker scheduling as it effectively minimizes overdue days and arranges worker overtime.

Ergodicity of Two Class Priority Queues with Preemptive Priority

Well-known results on the ergodicity of queues with preemptive priority were obtained under the assumption that jobs arrive according to the Poisson process. This assumption, however, does not always hold true in practice. In our work we find sufficient ergodicity conditions for queues with two priority classes with a single server, where interarrival times of high-priority jobs have either Erlang or hyperexponential distribution and interarrival times of low-priority jobs and service times of jobs of both classes have arbitrary continuous distributions. We present results in disciplines with loss of an interrupted job and with retrial of an interrupted job.

Coordinative Scheduling of Computation and Communication in Data-parallel Systems

For many data-parallel computing systems like Spark, a job usually consists of multiple computation stages and inter-stage communication (i.e., coflows). Many efforts have been done to schedule coflows and jobs independently. The simple combination of coflow scheduling and job scheduling, however, would prolong the average job completion time (JCT) due to the conflict. For this reason, we propose a new abstraction of scheduling unit, named coBranch, which takes the dependency between computation stages and coflows into consideration, to schedule coflows and jobs jointly. Besides, mainstream coflow schedulers are order-preserving, i.e., all coflows of a high-priority job are prioritized than those of a low-priority job. We observe that the order-preserving constraint incurs low inter-job parallelism. To overcome the problem, we employ an urgency-based mechanism to schedule coBranches, which aims to decrease the average JCT by enhancing the inter-job parallelism. We implement the urgency-based coBranch Scheduling (BS) method on Apache Spark, conduct prototype-based experiments, and evaluate the performance of our method against the shortest-job-first critical-path method and the FIFO method. Results show that our method achieves around 10 and 15 percent reduction in the average JCT, respectively. Large-scale simulations based on the Google trace show that our method performs better and reduces JCT by 23 and 35 percent, respectively.

Fairness in responsive parallelism

Research on parallel computing has historically revolved around compute-intensive applications drawn from traditional areas such as high-performance computing. With the growing availability and usage of multicore chips, applications of parallel computing now include interactive parallel applications that mix compute-intensive tasks with interaction, e.g., with the user or more generally with the external world. Recent theoretical work on responsive parallelism presents abstract cost models and type systems for ensuring and reasoning about responsiveness and throughput of such interactive parallel programs. In this paper, we extend prior work by considering a crucial metric: fairness. To express rich interactive parallel programs, we allow programmers to assign priorities to threads and instruct the scheduler to obey a notion of fairness. We then propose the fairly prompt scheduling principle for executing such programs; the principle specifies the schedule for multithreaded programs on multiple processors. For such schedules, we prove theoretical bounds on the execution and response times of jobs of various priorities. In particular, we bound the amount, i.e., stretch, by which a low-priority job can be delayed by higher-priority work. We also present an algorithm designed to approximate the fairly prompt scheduling principle on multicore computers, implement the algorithm by extending the Standard ML language, and present an empirical evaluation.

Cuckoo-inspired Job Scheduling Algorithm for Cloud Computing

Cloud computing offers a pool of virtual resources to users on demand. Among the main challenge facing cloud computing is job scheduling which is an NP-hard problem. Usually, scheduling should consider job priority, where high-priority jobs should be immediately allocated required resources, whereas low-priority jobs can wait. In this paper, we propose a scheduling algorithm that imitates the parasitic behaviour of the cuckoo bird. Cuckoos reproduce by exploiting the nests of other birds with eggs similar to their own. We have evaluated the proposed cuckoo scheduling algorithm under varying number of nodes and percentage of high priority jobs. The experimental results demonstrate the superiority of our proposed algorithm over the benchmarks in terms of average CPU utilization and average turnaround time for each type of job.

TWO-CLASS ROUTING WITH ADMISSION CONTROL AND STRICT PRIORITIES

We consider the problem of routing and admission control in a loss system featuring two classes of arriving jobs (high-priority and low-priority jobs) and two types of servers, in which decision-making for high-priority jobs is forced, and rewards influence the desirability of each of the four possible routing decisions. We seek a policy that maximizes expected long-run reward, under both the discounted reward and long-run average reward criteria, and formulate the problem as a Markov decision process. When the reward structure favors high-priority jobs, we demonstrate that there exists an optimal monotone switching curve policy with slope of at least −1. When the reward structure favors low-priority jobs, we demonstrate that the value function, in general, lacks structure, which complicates the search for structure in optimal policies. However, we identify conditions under which optimal policies can be characterized in greater detail. We also examine the performance of heuristic policies in a brief numerical study.

M/M/c Queue with Two Priority Classes

This paper provides the first exact analysis of a preemptive M/M/c queue with two priority classes having different service rates. To perform our analysis, we introduce a new technique to reduce the two-dimensionally infinite Markov chain (MC), representing the two class state space, into a one-dimensionally infinite MC, from which the generating function (GF) of the number of low-priority jobs can be derived in closed form. (The high-priority jobs form a simple M/M/c system and are thus easy to solve.) We demonstrate our methodology for the c = 1, 2 cases; when c > 2, the closed-form expression of the GF becomes cumbersome. We thus develop an exact algorithm to calculate the moments of the number of low-priority jobs for any c ≥ 2. Numerical examples demonstrate the accuracy of our algorithm and generate insights on (i) the relative effect of improving the service rate of either priority class on the mean sojourn time of low-priority jobs; (ii) the performance of a system having many slow servers compared with one having fewer fast servers; and (iii) the validity of the square root staffing rule in maintaining a fixed service level for the low-priority class. Finally, we demonstrate the potential of our methodology to solve other problems using the M/M/c queue with two priority classes, where the high-priority class is completely impatient.

Efficient analysis of the MMAP[K]/PH[K]/1 priority queue

In this paper we consider the MMAP/PH/1 priority queue, both the case of preemptive resume and the case of non-preemptive service. The main idea of the presented analysis procedure is that the sojourn time of the low priority jobs in the preemptive case (and the waiting time distribution in the non-preemptive case) can be represented by the duration of the busy period of a special Markovian fluid model. By making use of the recent results on the busy period analysis of Markovian fluid models it is possible to calculate several queueing performance measures in an efficient way including the sojourn time distribution (both in the time domain and in the Laplace transform domain), the moments of the sojourn time, the generating function of the queue length, the queue length moments and the queue length probabilities.

Optimal admission and preemption control in finite-source loss systems

We study the optimal control of both admission and preemption in a two-class finite source loss system, and prove that the optimal low priority (LP) job admission policy is a state dependent threshold based policy. We also provide the sufficient cost conditions for which the LP job admission and preemption are always optimal. Finally, we demonstrate that the optimal LP job preemption policy is a threshold based policy, using numerical results.

Fast contraband detection in large capacity disk drives

In recent years the capacity of digital storage devices has been increasing at a rate that has left digital forensic services struggling to cope. There is an acknowledgement that current forensic tools have failed to keep up. The workload is such that a form of ‘administrative triage’ takes place in many labs where perceived low priority jobs are delayed or dropped without reference to the data itself. In this paper we investigate the feasibility of first responders performing a fast initial scan of a device by sampling on the device itself. A Bloom filter is used to store the block hashes of large collections of contraband data. We show that by sampling disk clusters, we can achieve 99.9% accuracy scanning for contraband data in minutes. Even under the constraints imposed by low specification legacy equipment, it is possible to scan a device for contraband with a known and controllable margin of error in a reasonable time. We conclude that in this type of case it is feasible to boot the device into a forensically sound environment and do a pre-imaging scan to prioritise the device for further detailed investigation.

Dynamic Resource Allocation Scheme in Cloud Computing

Cloud Computing environment provisions the supply of computing resources on the basis of demand, as and when needed. It builds upon advances of virtualisation and distributed computing to support cost efficient usage of computing resources, emphasizing on resource scalability and on-demand services. It allows business outcomes to scale up and down their resources based on needs. Managing the customer demand creates the challenges of on-demand resource allocation. Virtual Machine (VM) technology has been employed for resource provisioning. It is expected that using virtualized environment will reduce the average job response time as well as executes the task according to the availability of resources. Hence VMs are allocated to the user based on characteristics of the job. Effective and dynamic utilization of the resources in cloud can help to balance the load and avoid situations like slow run of systems. This paper mainly focuses on allocation of VM to the user, based on analyzing the characteristics of the job. Main principle of this work is that low priority jobs (deadline of the job is high) should not delay the execution of high priority jobs (deadline of the job is low) and to dynamically allocate VM resources for a user job within deadline

Cloud Resource Allocation as Preemptive Scheduling Approach

The increased degree of connectivity and the increased amount of data has led many providers to provide cloud services. Infrastructure as a Service (IaaS) is one of the Cloud Services it provides greater potential for a highly scalability of computing resources for demand in various applications like Parallel Data processing. The resources offered in the cloud are extremely dynamic and probably heterogeneous due to this dynamic load balancing, access balancing and scheduling of job is required. To achieve this many scheme are proposed, Nephele is one of the data processing framework which exploits the dynamic resource allocation offered by IaaS clouds for both task scheduling and execution. Specific tasks of processing a job can be allotted to different types of virtual machines which are automatically instantiated and terminated during the job execution. However the current algorithms are homogeneous and they do not consider the resource overload or underutilization during the job execution this increase task completion time. This paper introduces a new Approach for increasing the efficiency of the scheduling algorithm for the real time Cloud Computing services. Proposed method utilizes the Turnaround time Utility efficiently by discerning it into a gain function and a loss function for a single task based on their priorities. Algorithm has been executed on both preemptive and Non-preemptive methods. The experimental results show that it overtakes the existing utility based scheduling algorithms and also compare its performance with both preemptive and Non-preemptive scheduling approaches. Hence, Turnaround time utility scheduling approach which focuses on both high and the low priority jobs that arrives for scheduling is proposed.

PREEMPTIVE PRIORITY SCHEDULING WITH AGING TECHNIQUE FOR EFFECTIVE DISASTER MANAGEMENT IN CLOUD

Cloud computing has intern the attention of today’s leading IT industries offering huge potential for more flexible, readily scalable and cost effective IT operation. There is a significant anticipation for emergent innovation and expanded capabilities of a cloud-based environment as the cloud services are still in the infant stage. With all technology innovation, the biggest gain will be realized when the Cloud services are efficiently utilized. One of the prime contributions of Cloud is its capacity to handle a huge amount of data for either processing or storing. The inadequacy of this model is that it is prone to disaster. Some of the popular scheduling techniques applied by the researchers and leading IT industries are Round Robin, Preemptive scheduling etc. This research focuses on a novel approach for disaster management through efficient scheduling mechanism.This paper presents a Priority Preemptive scheduling (PPS) with aging of the low priority jobs in Cloud for disaster management. The implementation results show that the jobs at any instance of time are provided with the resources and henceforth preventing them to enter the starvation, which is one of the prime causes for disaster.

Flexible buffer management with thresholds and blocking for congestion control in multi-server computer systems

This paper is aimed at designing a congestion control system (decision maker) that scales gracefully with computer system capacity, providing high utilization, and fairness among users. The use of adequate buffer management scheme with thresholds and blocking are well-known techniques for computer systems traffic congestion control. This motivates the study of multi-server computer systems with two distinct priority classes (high and low priority traffics), partial buffer sharing scheme with thresholds and with blocking. The external traffic is modeled using the Poisson processes. The service times have been modeled using the exponential distribution. Adaptive buffer allocation algorithm (scheme) is designed to allow the input traffic to be portioned into different priority classes and based on the input traffic behavior it controls the thresholds dynamically. This algorithm allows input low priority jobs to enter into buffer if its occupancy level is less than the threshold value. Additionally, for low priority jobs a forward threshold and a reverse threshold are defined (hysteresis). Using an open Markovian queuing schema with blocking, and thresholds, a closed form cost-effective analytical solution for this model of computer system is obtained. The model of multi-server system is very accurate. It is derived directly from a state graph and a set of steady-state equations, followed by calculations of main measures of effectiveness. Based on numerical experiments and collected results we conclude that the proposed model with blocking, and thresholds can provide accurate performance estimates of multi-server systems.

Computing protection level policies for dynamic capacity allocation problems by using stochastic approximation methods

A dynamic capacity allocation problem is considered in this paper. A fixed amount of daily processing capacity is allowed. Jobs of different priorities arrive randomly over time and a decision is required on which jobs should be scheduled on which days. The jobs that are waiting to be processed incur a holding cost depending on their priority levels. The objective is to minimize the total expected cost over a planning horizon. In this paper the focus is on a class of policies that are characterized by a set of protection levels. The role of the protection levels is to “protect” a portion of the capacity from the lower priority jobs so as to make it available for the future higher priority jobs. A stochastic approximation method to find a good set of protection levels is developed and its convergence is proved. Computational experiments indicate that protection level policies perform especially well when the coefficient of variation for the job arrivals is high. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Technical appendix detailing the proofs of propositions.]

How many servers are best in a dual-priority [formula omitted] system?

We ask the question, “for minimizing mean response time (sojourn time), which is preferable: one fast server of speed 1, or k slow servers each of speed 1 / k ?” Our setting is the M / PH / k system with two priority classes of customers, high priority and low priority, where PH is a phase-type distribution. We find that multiple slow servers are often preferable, and we demonstrate exactly how many servers are preferable as a function of the load and service time distribution. In addition, we find that the optimal number of servers with respect to the high priority jobs may be very different from that preferred by low priority jobs, and we characterize these preferences. We also study the optimal number of servers with respect to overall mean response time, averaged over high and low priority jobs. Lastly, we ascertain the effect of the service demand variability of high priority jobs on low priority jobs.

A recursive analysis technique for multi-dimensionally infinite Markov chains

Performance analysis of multiserver systems with multiple classes of jobs often has a common source of difficulty: the state space needed to capture the system behavior grows infinitely in multiple dimensions. For example, consider two processors, each serving its own M/M/1 queue, where one of the processors (the "donor") can help the other processor (the "beneficiary") with its jobs, during times when the donor processor is idle [5, 16] or when some threshold conditions are met [14, 15]. Since the behavior of beneficiary jobs depends on the number of donor jobs in system, performance analysis of beneficiary jobs involves a two dimensionally infinite (2D-infinite) state space, where one dimension corresponds to the number of beneficiary jobs and the other dimension corresponds to the number of donor jobs. Another example is an M/M/2 queue with two priority classes, where high priority jobs have preemptive priority over low priority jobs (see for example [1, 3, 4, 8, 10, 11, 12, 17] and references therein). Since the behavior of low priority jobs depends on the number of high priority jobs in system, performance analysis of low priority jobs involves 2D-infinite state space, where each dimension corresponds to the number of each class of jobs in system. As we will see, when there are <i>m</i> priority classes, performance analysis of the lowest priority classes involves <i>m</i> dimensionally infinite state space.

Performance analysis of make-to-order manufacturing systems under different workload control regimes

This paper firstly discusses the make-to-order (MTO) manufacturing sector to show the different types of queuing network that may exist and need to be covered. The workload control (WLC) production planning method is modelled as a queuing network with limited buffer capacities in front of each workstation. Exact solutions for a general network with more than 3 or 4 workstations are not possible. An approximation algorithm, as an extension of earlier work on simple tandem queuing networks, has been developed to cope with any number of workstations and to allow flows forwards and backwards between the workstations. The essentials of the model and solution algorithm are briefly described. The second half of the paper presents the results of using the model and algorithm to analyse four issues in WLC in MTO. The first set of experiments examines the relative value of two WLC mechanisms for controlling manufacturing lead times, job release and order acceptance. The second set of experiments is to gain insight into how increased complexity in production layouts and the product variety impact on manufacturing performance measures. The third set of experiments examines the differential effects of extra buffer capacity at earlier or later workstations in the main path flow; whilst the final set of experiments examines the impact of having groups of high- and low-priority jobs.

Analysis of the M/G/1 queue under a combined preemptive/nonpreemptive priority discipline

The authors propose a combined preemptive/nonpreemptive priority discipline. When a high-priority job arrives at the system while a low-priority job is being in service, the high-priority job will be served immediately and the low-priority job will go back to the head of the queue of its class, if a discretion rule for preemption is satisfied. Otherwise, the high-priority job waits in queue until the completion of the low-priority job service. As the discretion rule for preemption, three schemes are considered, each based on the parameter of the low-priority job: the elapsed service time, the ratio of elapsed to total service time, and the remaining service time. Using the busy-period analysis technique, an M/G/1 queueing system with multiple priority classes of jobs is analyzed. Considered preemptive rules are the preemptive-resume and preemptive-repeat-identical policies. As results, the Laplace transforms associated with waiting time and response time, and the z-transform for the number of jobs in the system as well as their expectations are presented. >

Modeling a Multiprocessor System with Preemptive Priorities

Consider a system with N processors and two job types with one having preemptive priority over the other. Arrivals are Poisson and service times are exponential. We present two approaches for modeling the system. The difference between the two lies in the order with which we list the numbers of jobs of each type in the system in the state definition. Although both approaches yield matrix-geometric solutions, their implications for computation are significantly different. The first approach has attractive structural properties and an easily solvable rate matrix, but the system of equations at the boundary is most often prohibitively large. The second approach, while not amenable to any easy computation of its rate matrix, renders itself to an efficient solution at the boundary, and provides a basis for waiting time analysis for low priority jobs. In the paper, we present an efficient implementation of state reduction for solving the stationary probabilities associated with the boundary states. We give a numerical example to highlight various issues in the computer solution.