Deadline-constrained Scheduling Research Articles

A deadline constrained scheduling algorithm for cloud computing system based on the driver of dynamic essential path.

To solve the problem of the deadline-constrained task scheduling in the cloud computing system, this paper proposes a deadline-constrained scheduling algorithm for cloud computing based on the driver of dynamic essential path (Deadline-DDEP). According to the changes of the dynamic essential path of each task node in the scheduling process, the dynamic sub-deadline strategy is proposed. The strategy assigns different sub-deadline values to every task node to meet the constraint relations among task nodes and the user’s defined deadline. The strategy fully considers the dynamic sub-deadline affected by the dynamic essential path of task node in the scheduling process. The paper proposed the quality assessment of optimization cost strategy to solve the problem of selecting server for each task node. Based on the sub-deadline urgency and the relative execution cost in the scheduling process, the strategy selects the server that not only meets the sub-deadline but also obtains much lower execution cost. In this way, the proposed algorithm will make the task graph complete within its deadline, and minimize its total execution cost. Finally, we demonstrate the proposed algorithm via the simulation experiments using Matlab tools. The experimental results show that, the proposed algorithm produces remarkable performance improvement rate on the total execution cost that ranges between 10.3% and 30.8% under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than IC-PCP, DCCP and CD-PCP.

Concurrent workflow budget- and deadline-constrained scheduling in heterogeneous distributed environments

In heterogeneous distributed environment, it is a great challenge to schedule multiple workflows submitted at different times. Particularly, scheduling of concurrent workflows with deadline and budget constraints makes the problem become more complex. Recent studies have proposed dynamic scheduling strategies for concurrent workflows which have limitations in inconsistent environments. Therefore, this paper presents a new dynamic scheduling algorithm for concurrent workflows. This algorithm proposes a uniform ranking that considers the time and costs for both workflows and workgroups to assign priorities for tasks. In the resource selection phase, it controls the resource selection range for each task based on an optimistic budget for the current task and selects resources for the current task according to a defined bi-factor. The experimental results show that our algorithm outperforms the existing algorithms in both consistent and inconsistent environments.

MapReduce Scheduling for Deadline-Constrained Jobs in Heterogeneous Cloud Computing Systems

MapReduce is a software framework for processing data-intensive applications with a parallel manner in cloud computing systems. Some MapReduce jobs have the deadline requirements for their job execution. The existing deadline-constrained MapReduce scheduling schemes do not consider the following two problems: various node performance and dynamical task execution time. In this paper, we utilize the Bipartite Graph modelling to propose a new MapReduce Scheduler called the BGMRS. The BGMRS can obtain the optimal solution of the deadline-constrained scheduling problem by transforming the problem into a well-known graph problem: minimum weighted bipartite matching. The BGMRS has the following features. It considers the heterogeneous cloud computing environment, such that the computing resources of some nodes cannot meet the deadlines of some jobs. In addition to meeting the deadline requirement, the BGMRS also takes the data locality into the computing resource allocation for shortening the data access time of a job. However, if the total available computing resources of the system cannot satisfy the deadline requirements of all jobs, the BGMRS can minimize the number of jobs with the deadline violation. Finally, both simulation and testbed experiments are performed to demonstrate the effectiveness of the BGMRS in the deadline-constrained scheduling.

ATSDS: adaptive two-stage deadline-constrained workflow scheduling considering run-time circumstances in cloud computing environments

A significant aspect of cloud computing is scheduling of a large number of real-time concurrent workflow instances. Most of the existing scheduling algorithms are designed for a single complex workflow instance. This study examined instance-intensive workflows bounded by SLA constraints, including user-defined deadlines.The scheduling method for these workflows with dynamic workloads should be able to handle changing conditions and maximize the utilization rate of the cloud resources. The study proposes an adaptive two-stage deadline-constrained scheduling (ATSDS) strategy that considers run-time circumstances of workflows in the cloud environment. The stages are workflow fragmentation and resource allocation.In the first stage, the workflows according to cloud run-time circumstances (number of Virtual Machines (VMs) and average available bandwidth) are dynamically fragmented. In the second stage, using the workflow deadline and the capacity of the VMs, the workflow fragments created are allocated to the VMs to be executed. The simulation results show improvements in terms of workflow completion time, number of messages exchanged, percentage of workflows that meet the deadline and VM usage cost compared to other approaches.

Efficient parallel evolutionary algorithms for deadline-constrained scheduling in project management

Deadline-constrained scheduling in project management is a NP-hard optimisation problem with major relevance in software engineering and other real-life situations dealing with the planning of activities that must be completed before specific dates. This article introduces efficient parallel versions for two evolutionary algorithms genetic algorithm and hybrid evolutionary algorithm, to solve the deadline-constrained scheduling problem in project management. The proposed algorithms have been engineered to compute accurate solutions in reduced execution times. Specific evolutionary operators, including a parallel local search operator in the hybrid evolutionary algorithm, are proposed for efficiently solving realistic problem instances, and both a master-slave parallel strategy and a distributed subpopulation model are applied to further improve the computational efficiency and the results quality. The experimental analysis performed on both a set of standard problem instances and new large problem instances demonstrate that accurate solutions are computed by the proposed techniques, especially for the distributed subpopulation version of the hybrid evolutionary algorithm. The comparative experimental evaluation demonstrates that the parallel evolutionary algorithms are able to outperform in reduced execution times the results computed using one of the best well-known deterministic techniques for the problem, in particular when solving instances with tight deadline constraints.

MUS: a novel deadline-constrained scheduling algorithm for Hadoop

Hadoop is a popular framework to process growing volumes of data across clusters of computers, and has achieved great success both in industry and academic researches. Although Hadoop has powerful batch processing capabilities, it can not support the real-time services, such as online payment or monitoring sensor data. These real-time services have strict deadlines in common, where service response after the deadline is considered useless. Current researches on time-constrained scheduling algorithms generally aim at shortening the completion time, rather than guaranteeing the specific latency for the real-time services. In this paper, we study the deadline-constrained scheduling problem on Hadoop, where service requests arrive randomly and no prior information is available. A maximum urgency scheduling MUS algorithm is proposed, and then implemented as a pluggable scheduler on Hadoop. This novel algorithm can be applied in heterogeneous environments with a low computation complexity. Experiments indicate that the MUS algorithm maximises the number of jobs meeting their deadlines while maintains the fairness among different types of jobs.