Backfilling Algorithm Research Articles

Towards accommodating deadline driven jobs on high performance computing platforms in grid computing environment

Grid computing is a connected computing infrastructure that furnishes reliable, stable, ubiquitous, and economic access to high-end computational power. The dynamic nature of the grid brings several challenges to scheduling algorithms that operate in queuing-based scheduling approach. This approach typically performs scheduling based on a certain fixed priority which leads to increase the delay for the running applications. Thus, the overall performance will be deteriorated sharply. The main aim of this study is to minimize the delay in the scheduler for the dynamic jobs. Therefore, this paper tackles dynamic scheduling issues by proposing Swift Gap (SG) mechanism. SG comprises of two stages by applying two mechanisms: A Backfilling Mechanism and Metaheuristic Local Search Optimization Mechanism. In the first stage, the job is placed in the earliest gap available in the local resources’ schedules, while the second stage optimizes the performance by checking all available gaps among resources’ schedules to find a better gap to place the job in. To further improve the performance, the Completion Time Scheme (CTS) is developed. CTS reduces the delay by placing the job in the gap that guarantees the best start time for the job, and the fastest resource available. The integration between SG and CTS (SG-CTS) is achieved by applying best start time rule in the first stage only, whereas the second stage includes both rules.SG-CTS is evaluated through simulation by using real workloads that reflect a real grid system environment. The findings demonstrate that SG-CTS improves the slowdown by 27 %, bounded slowdown by 25 %, tardiness by 21 %, waiting time by 16 % and response time by 7% compared to Conservative backfilling mechanism followed by Gap Search (CONS-GS). Finally, SG-CTS is evaluated against Deadline-Based Backfilling (DBF) algorithm. The evaluation revealed that SG-CTS performs better than DBF for slowdown and waiting time in HPC2N workload.

Computation of workflow scheduling using backpropagation neural network in cloud computing: a virtual machine placement approach

For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment is achieved through the virtualization process. Based on system experience, this paper proposes the priority-based backfilling backpropagation neural network (PBF-NN) hybrid scheduling algorithm for measuring makespan and execution cost accurately. The backfill algorithm is used to schedule tasks to the available resources. The percentage of migration is reduced when this algorithm is used compared to the First Come First Serve algorithm. Then, the Berger model is used to measure the fairness of resource allocation. The system decides task reallocation based on the fairness value. The backpropagation neural network handles the virtual machine placement process with necessary training and testing. The proposed algorithm dynamically allocates the tasks and reduces the utilization of resources. We use an experimental study to illustrate how the proposed system enables higher efficiency in cost, makespan, and performance.

Log Analysis-Based Resource and Execution Time Improvement in HPC: A Case Study

High-performance computing (HPC) uses many distributed computing resources to solve large computational science problems through parallel computation. Such an approach can reduce overall job execution time and increase the capacity of solving large-scale and complex problems. In the supercomputer, the job scheduler, the HPC’s flagship tool, is responsible for distributing and managing the resources of large systems. In this paper, we analyze the execution log of the job scheduler for a certain period of time and propose an optimization approach to reduce the idle time of jobs. In our experiment, it has been found that the main root cause of delayed job is highly related to resource waiting. The execution time of the entire job is affected and significantly delayed due to the increase in idle resources that must be ready when submitting the large-scale job. The backfilling algorithm can optimize the inefficiency of these idle resources and help to reduce the execution time of the job. Therefore, we propose the backfilling algorithm, which can be applied to the supercomputer. This experimental result shows that the overall execution time is reduced.

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% - 29.3%), on minimum resources (3.9% - 25.2% less) while majority were meeting their deadlines (93.21% - 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

SummaryAn efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.

A Survey of Backfilling Algorithms in Cloud Resource Allocation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.

Deadline based task scheduling using multi-criteria decision-making in cloud environment

Abstract Task scheduling within the deadline is more challenging due to different task parameters: start time, execution time, deadline and number of virtual machines (VMs). The OpenNebula platform supports Backfilling algorithm to schedule deadline based tasks. If there are similar types of tasks, then the scheduling performance should be enhanced. In this work, we propose, implementation of Multi-criteria Decision Making (MCDM) using VIKOR method to the existing backfilling algorithm. The VIKOR method is implemented as a decision maker to resolve conflicts among similar tasks. The mechanism finds the best ideal task among the similar tasks and ranks them for scheduling. Therefore, a number of tasks are scheduled. The performance is evaluated by analyzing the number of tasks scheduled, resource utilization and task rejection, which is quite better in comparison to the existing algorithm.

An enhanced deadline constraint based task scheduling mechanism for cloud environment

In this work, we present a scheduling mechanism for deadline sensitive task or lease. It is very impelling and challenging to allocate on-demand resources within the deadline rather than rejecting the lease. The work studies the insight of existing backfilling algorithm that is used to schedule deadline sensitive lease in the Open Nebula cloud platform. In backfilling mechanism a lease is selected in First Come First Serve (FCFS) to be backfilled in which some ideal resources can be found out and allocated to other leases. Mainly, the scheduling performance of backfilling algorithm could be better when there are conflicts among the similar leases to backfill. Moreover, backfilling algorithm doesn’t allow to schedule a new deadline sensitive task during execution. The proposed work addresses such types of issues and improves the scheduling performances with respect to maximize the task acceptance ratio and reduce the task rejection ratio. Finally, the performances are compared with the existing mechanisms that are quite good.

Priority scheduling with consolidation based backfilling algorithm in cloud

In cloud computing, scheduling plays an eminent role while processing enormous jobs. The paralle jobs utmost need parallel processing capabilities which leads to CPU underutilization mainly due to synchronization and communication among parallel processes. Researchers introduced several algorithms for scheduleing parallel jobs namely, Conservative Migration Consolidation supported Backfilling (CMCBF) and Aggressive Migration Consolidation supported Backfilling (AMCBF). The greatest challenge of a existing scheduling algorithm is to improve the data center utilization without affecting job responsiveness. Hence, this work proposes an Effective Multiphase Scheduling Approach (EMSA) to process the jobs. In EMSA, the jobs are initially preprocessed and batched together to avoid starvation and to mitigate unwanted delay. Later, an Associate Priority Method has been proposed which prioritizes the batch jobs to minimize the number of migrations. Finally, the prioritized jobs are scheduled using Priority Scheduling with BackFilling algorithm to utilize the intermediate idle nodes. Moreover, the virtualization technology partitions the computing capacity of the Virtual Machine (VM) into two-tier VM as foreground VM (FVM) and Background VM (BVM) to improve node utilization. Hence, Priority Scheduling with Consolidation based BackFilling algorithm has been deployed in a two-tier VM that processes the jobs by utilizing the VMs effectively. Experimental results show that the performance of the proposed work performs better than other existing algorithms by increasing the resource utilization by 8%.

Expansion slot backfill scheduling for concurrent workflows with deadline on heterogeneous resources

The cost of using a computational resource is measured from startup to shutdown, including the cost of time slots between tasks. Concurrent scheduling of multiple scientific workflows on heterogeneous resources can improve resources utilization and reduce the cost. To make full use of waste time slots between the tasks and improve the completion rate of workflows, Expansion Slot Backfill (ESB) algorithm is proposed in this paper for scheduling multiple deadline-constrained workflows on a fixed set of resources. All workflows are mapped to the resources with one by one strategy. Each new task tries to backfill the earliest time slot in turn. When the time slot is not enough to backfill, it can be expanded elastically by the slide of the earlier tasks. If these tasks slide cause some workflow to exceed the deadline constraint, such workflow with fewer time slots is discarded and the slide is withdrawn. Experiments with multiple parameter variations show that the algorithm get better performance in resource utilization, workflows throughput and time complexity.

A 2-Approximation Algorithm for Scheduling Parallel and Time-Sensitive Applications to Maximize Total Accrued Utility Value

For a time-sensitive application, the usefulness of its end results (also called the application's accrued utility value in the paper) depends on the time when the application is completed and its results are delivered. In this paper, we address the accrued utility value maximization problem for narrow parallel and time-sensitive applications. We first consider the problem in the context of a discrete time domain and present the Spatial-Temporal Interference Based (STIB) scheduling algorithm. We formally prove that the STIB algorithm is a 2-approximation algorithm. Second, we extend our work to a continuous time domain and present a heuristic scheduling algorithm, i.e., the Continuous Spatial-Temporal Interference Based (STIB-C) algorithm to maximize the system's total accrued utility value when the system operates in a continuous time domain. The extensive empirical evaluations reveal that: (1) in a discrete time domain, the systems’ total accrued utility values obtained through the STIB algorithm are consistent with the theoretic bound, i.e., they never go below 50 percent of the optimal value. In fact, on average, the STIB algorithm can achieve over 92.5 percent of the optimal value; (2) compared to other scheduling policies listed in the literature, the developed STIB and STIB-C algorithms have clear advantages in terms of the system's total accrued utility value and the profitable application ratio. In particular, in terms of the system's total accrued utility value, both the STIB and the STIB-C algorithms achieve as much as six times for both the First Come First Come Serve(FCFS) with backfilling algorithm and the Gang Earliest Deadline First (EDF) algorithm, and 4.5 times for the 0-1 Knapsack based scheduling algorithm. In terms of the profitable application ratio, both the STIB and the STIB-C algorithms obtain as much as four times for both the FCFS with backfilling algorithm and the Gang EDF algorithm, and two times for the 0-1 Knapsack based scheduling algorithm.

Deadline sensitive lease scheduling in cloud computing environment using AHP

The OpenNebula is an open source environment which provides cloud resources with the help of Haizea as a lease manager. The Haizea supports different types of leases from which deadline sensitive lease is one of them. In real time, most of the leases are deadline sensitive leases. These deadline sensitive leases are scheduled by using the backfilling algorithm. In the backfilling algorithm one of the lease is selected from the best effort queue which will provide the free resources to schedule the deadline sensitive lease. But in some scenario backfilling algorithm does not provide better scheduling if there is similar types of leases and must be in conjugative in sequence. This work aims to use AHP (Analytic Hierarchy Process) as a decision maker in the backfilling algorithm to choose the possible best lease from the given best effort queue in order to schedule the deadline sensitive lease. The proposed work improves the performance of the backfilling algorithm by scheduling more number of leases and minimizing the lease rejection using AHP.

Two-tier project and job scheduling for SaaS cloud service providers

This study addresses a two-tier job scheduling problem for SaaS cloud service providers which rely on resources leased from IaaS cloud providers to achieve elasticity to computational power. In our model, a project represents a user request which consists of multiple jobs; the SaaS is obligated to complete projects using multiple resources leased from IaaS or PaaS providers. The goals are to reduce the project turn-around time and to support priority scheduling by employing suitable scheduling algorithms. We propose a set of two-tier backfilling algorithms which extend the well-known conservative backfilling algorithm with project slack-time and priority concepts. Among the proposed algorithms, Two-Tier Strict Backfilling (2TSB) does not allow preemption in job waiting queues. On the other hand, preemption is allowable in Two-tier Flexible Backfilling which has two versions: 2TFB and 2TFB-SF (slack factor). In 2TFB, a new incoming project can preempt waiting jobs but not waiting projects, while 2TFB-SF permits preemption in both job and project waiting queues. Two-Tier Priority Backfilling (2TPB) algorithm takes priority into account such that only high-priority projects can preempt the low-priority ones. The experimental results indicate that, compared with 2TSB, 2TPB could reduce the mean turn-around time of high-priority projects by more than 25%.

Introducing New Backfill-based Scheduler for SLURM Resource Manager

Abstract The work proposes a design for a new external scheduler for SLURM (Simple Linux Utility for Resource Management). Schedulers, included in SLURM by default are good enough for many sites, but big supercomputers serving many users meet limitations of standard SLURM schedulers. In this work we discover methods to break these limitations via implementing new portable SLURM scheduler. We address the problem of maximizing the number of users, whose requests are processed in each given moment of time, and decrease start time of user's first task. Our approach is based on standard backfill algorithm and includes additional features, such as simplification of SLURM priority system, replacing slow SQL-based accounting checks by faster ACL checks and upgrading cluster administration convenience.

Квазипланировщик для использования простаивающих вычислительных модулей многопроцессорной вычислительной системы под управлением СУППЗ

The paper considers an approach to reduce an amount of multiprocessing computing system’s idle resources. The presented approach uses features of job scheduling mechanisms in modern batch systems, in particular, features of backfill algorithm. Authors developed the quasi scheduler, program that utilizes supercomputer idle resources under parallel job management system control (SUPPZ). Flowchart of the developed quasi scheduler is presented. Quasi scheduler’s users can submit tasks without waiting in queue, if there are enough idle resources for task execution. Computational experiment’s results presented in paper show effectiveness of supercomputer’s scheduling depends of the quasi scheduler usage.

Relaxed Parallel Scheduling Algorithms Investigation of Support Resource Reservation from Grid

In this paper, the grid computing environment resource reservation problem of using the parallel machine, proposed the relaxed time parallel scheduling models and algorithms support resource reservation. The simulation results of FCFS and EASY backfill algorithms in resource utilization, job bounded slowdown factor and the success rate of Advanced Reservation (AR) jobs were studied. Show that the relaxation mechanism, the average waiting time and the average bounded slowdown factor of non-reserved jobs down. EASY backfill algorithm which guarantees AR jobs quality of service at the same time, the better the performance improvement of non-AR jobs.

A multi-level scheduler for batch jobs on grids

This paper proposes a two-level scheduler for dynamically scheduling a continuous stream of sequential and multi-threaded batch jobs on grids, made up of interconnected clusters of heterogeneous single-processor and/or symmetric multiprocessor machines. The scheduler aims to schedule arriving jobs respecting their computational and deadline requirements, and optimizing the hardware and software resource usage. At the top of the hierarchy a lightweight meta-scheduler (MS) classifies incoming jobs according to their requirements, and schedules them among the underlying resources balancing the workload. At cluster level a Flexible Backfilling algorithm carries out the job machine associations by exploiting dynamic information about the environment. Scheduling decisions at both levels are based on job priorities computed by using different sets of heuristics. The different proposals have been compared through simulations. Performance figures show the feasibility of our approach.

Greedy scheduling with custom-made objectives

We present a methodology to automatically generate an online job scheduling method for a custom-made objective and real workloads. The scheduling problem comprises independent parallel jobs and parallel identical machines and occurs in Massively Parallel Processing systems and computational Grids. The system administrator defines the scheduling objective that may consider job properties and priorities of users or user groups. Our scheduling method combines a Greedy scheduling algorithm with the dynamic sorting of the waiting queue. This sorting algorithm uses a criterion that is modifiable by a set of parameters. Finding good parameter settings for the sorting criterion is viewed as a nonlinear optimization problem which is solved with the help of Evolution Strategies. We evaluate our scheduling method with real workload data and compare it to approximated optimal offline solutions and to the online results of the standard EASY backfill algorithm.