Real-time workflows oriented online scheduling in uncertain cloud environment

Workflow scheduling has become one of the hottest topics in cloud environments, and efficient scheduling approaches show promising ways to maximize the profit of cloud providers via minimizing their cost, while guaranteeing the QoS for users’ applications. However, existing scheduling approaches are inadequate for dynamic workflows with uncertain task execution times running in cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. To cover the above issue, we introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertain factors on the workflow scheduling quality. Based on this architecture, we present a scheduling algorithm, incorporating both event-driven and periodic rolling strategies (EDPRS), for scheduling dynamic workflows. Lastly, we conduct extensive experiments to compare EDPRS with two typical baseline algorithms using real-world workflow traces. The experimental results show that EDPRS performs better than those algorithms.     

Data-Locality Aware Scientific Workflow Scheduling Methods in HPC Cloud Environments

Efficient data-aware methods in job scheduling, distributed storage management and data management platforms are necessary for successful execution of data-intensive applications. However, research about methods for data-intensive scientific applications are insufficient in large-scale distributed cloud and cluster computing environments and data-aware methods are becoming more complex. In this paper, we propose a Data-Locality Aware Workflow Scheduling (D-LAWS) technique and a locality-aware resource management method for data-intensive scientific workflows in HPC cloud environments. D-LAWS applies data-locality and data transfer time based on network bandwidth to scientific workflow task scheduling and balances resource utilization and parallelism of tasks at the node-level. Our method consolidates VMs and consider task parallelism by data flow during the planning of task executions of a data-intensive scientific workflow. We additionally consider more complex workflow models and data locality pertaining to the placement and transfer of data prior to task executions. We implement and validate the methods based on fairness in cloud environments. Experimental results show that, the proposed methods can improve performance and data-locality of data-intensive workflows in cloud environments.     

Science in the Cloud: Allocation and Execution of Data-Intensive Scientific Workflows

An important challenge for the adoption of cloud computing in the scientific community remains the efficient allocation and execution of data-intensive scientific workflows to reduce execution time and the size of transferred data. The transferred data overhead is becoming significant with emerging scientific workflows that have input/output files and intermediate data products ranging in the hundreds of gigabytes. The allocation of scientific workflows on public clouds can be described through a variety of perspectives and parameters, and has been proved to be NP-complete. This paper proposes an evolutionary approach for task allocation on public clouds considering data transfer and execution time. In our framework, a solution is represented using an allocation chromosome that encodes the allocation of tasks to nodes, and an ordering chromosome that defines the execution order according to the scientific workflow representation. We propose a multi-objective optimization that relies on a cloud cost model and employs tailored evolution operators. Starting from a population of possible solutions, we employ crossover and mutation operators on both chromosomes aiming at optimizing the data transferred between nodes as well as the total workflow runtime. The crossover operators combine parts of solutions to reduce data overhead, whereas the mutation operators swamp between parts of the same chromosome according to pre-defined rules. Our experimental study compares between the proposed approach and current state-of-the art approaches using synthetic and real-life workflows. Our algorithm performs similarly to existing heuristics for small workflows and shows up to 80 % improvements for larger synthetic workflows. To further validate our approach we compare between the allocation and scheduling obtained by our approach with that obtained by popular scientific workflow managers, when real workflows with hundreds of tasks are executed on a public cloud. The results show a 10 % improvement in runtime over existing schedulers, caused by a 80 % reduction in transferred data and optimized allocation and ordering of tasks. This improved data locality has greater impact as it can be employed to improve and study data provenance and facilitate data persistence for scientific workflows.     

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Using imbalance metrics to optimize task clustering in scientific workflow executions

Scientific workflows can be composed of many fine computational granularity tasks. The runtime of these tasks may be shorter than the duration of system overheads, for example, when using multiple resources of a cloud infrastructure. Task clustering is a runtime optimization technique that merges multiple short running tasks into a single job such that the scheduling overhead is reduced and the overall runtime performance is improved. However, existing task clustering strategies only provide a coarse-grained approach that relies on an over-simplified workflow model. In this work, we examine the reasons that cause Runtime Imbalance and Dependency Imbalance in task clustering. Then, we propose quantitative metrics to evaluate the severity of the two imbalance problems. Furthermore, we propose a series of task balancing methods (horizontal and vertical) to address the load balance problem when performing task clustering for five widely used scientific workflows. Finally, we analyze the relationship between these metric values and the performance of proposed task balancing methods. A trace-based simulation shows that our methods can significantly decrease the runtime of workflow applications when compared to a baseline execution. We also compare the performance of our methods with two algorithms described in the literature.     

A scheduling framework for large-scale,parallel, and topology-aware applications

Scheduling of large-scale, distributed topology-aware applications requires that not only the properties of the requested machines be considered, but also the properties of the machines’ interconnections. This requirement severely complicates the scheduling process, as even a matching between a single multi-processor task and available machines in a single time slot becomes an NP-complete problem with no polynomial approximation. In this paper we propose a complete scheduling framework for multi-cluster, heterogeneous environments that provides, in practice, an efficient solution for the scheduling of topology-aware applications. The proposed framework is very flexible as it is composed of pluggable components and can be easily configured to support a variety of scheduling policies. We also describe three novel scheduling and coallocation algorithms that were developed and plugged into the framework. The proposed scheduling framework was integrated into the QosCosGrid ¹ system, where it is used as the main decision-making module.     

虚拟计算环境中的多机群协同调度算法

基于虚拟计算环境的核心机理,提出由自主调度单元、域调度共同体、元调度执行体为核心的多机群协同系统框架.剖析多机群任务并发运行性能模型,设计了多机群协同调度算法框架,提出最大空闲节点优先、最小网络拥塞优先、最小异构因子优先与最小异构空闲节点优先4种启发式资源选择策略.实验验证了协同调度模型与算法在任务集完成时间与系统平均利用率的测度上的有效性.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds

A growing number of data- and compute-intensive experiments have been modeled as scientific workflows in the last decade. Meanwhile, clouds have emerged as a prominent environment to execute this type of workflows. In this scenario, the investigation of workflow scheduling strategies, aiming at reducing its execution times, became a top priority and a very popular research field. However, few work consider the problem of data file assignment when solving the task scheduling problem. Usually, a workflow is represented by a graph where nodes represent tasks and the scheduling problem consists in allocating tasks to machines to be executed at a predefined time aiming at reducing the makespan of the whole workflow. In this article, we show that the scheduling of scientific workflows can be improved when both task scheduling and the data file assignment problems are treated together. Thus, we propose a new workflow representation, where nodes of the workflow graph represent either tasks or data files, and define the Task Scheduling and Data Assignment Problem (TaSDAP), considering this new model. We formulated this problem as an integer programming problem. Moreover, a hybrid evolutionary algorithm for solving it, named HEA-TaSDAP, is also introduced. To evaluate our approach we conducted two types of experiments: theoretical and practical ones. At first, we compared HEA-TaSDAP with the solutions produced by the mathematical formulation and by other works from related literature. Then, we considered real executions in Amazon EC2 cloud using a real scientific workflow use case (SciPhy for phylogenetic analyses). In all experiments, HEA-TaSDAP outperformed the other classical approaches from the related literature, such as Min–Min and HEFT.     

An architecture for workflow scheduling under resource allocation constraints

Research on specification and scheduling of workflows has concentrated on temporal and causality constraints, which specify existence and order dependencies among tasks. However, another set of constraints that specify resource allocation is also equally important. The resources in a workflow environment are agents such as person, machine, software, etc. that execute the task. Execution of a task has a cost and this may vary depending on the resources allocated in order to execute that task. Resource allocation constraints define restrictions on how to allocate resources, and scheduling under resource allocation constraints provide proper resource allocation to tasks. In this work, we provide an architecture to specify and to schedule workflows under resource allocation constraints as well as under the temporal and causality constraints. A specification language with the ability to express resources and resource allocation constraints and a scheduler module that contains a constraint solver in order to find correct resource assignments are core and novel parts of this architecture.     

大规模短时间任务的低延迟集群调度框架

大规模数据分析环境中,经常存在一些持续时间较短、并行度较大的任务。如何调度这些低延迟要求的并发作业是目前研究的一个热点。现有的一些集群资源管理框架中,集中式调度器由于主节点的瓶颈无法达到低延迟的要求,而一些分布式调度器虽然达成了低延迟的任务调度,但在最优资源分配以及资源分配冲突方面存在一定的不足。从大规模实时作业的需求出发,设计和实现了一个分布式的集群资源调度框架,以满足大规模数据处理的低延迟要求。首先提出了两阶段调度框架以及优化后的两阶段多路调度框架;然后针对两阶段多路调度过程中存在的一些资源冲突问题,提出了基于负载平衡的任务转移机制,从而解决了各个计算节点的负载不平衡问题;最后使用实际负载以及一个模拟调度器对大规模集群中的任务调度框架进行了模拟和验证。对于实际负载,所提框架的调度延迟控制在理想调度的12%以内;在模拟环境下,该框架与集中式调度器相比在短时间任务的延迟上能够减少40%以上。     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.     

Online scheduling and placement of hardware tasks with multiple variants on dynamically reconfigurable field-programmable gate arrays

Hardware task scheduling and placement at runtime plays a crucial role in achieving better system performance by exploring dynamically reconfigurable Field-Programmable Gate Arrays (FPGAs). Although a number of online algorithms have been proposed in the literature, no strategy has been engaged in efficient usage of reconfigurable resources by orchestrating multiple hardware versions of tasks. By exploring this flexibility, on one hand, the algorithms can be potentially stronger in performance; however, on the other hand, they can suffer much more runtime overhead in selecting dynamically the best suitable variant on-the-fly based on its runtime conditions imposed by its runtime constraints. In this work, we propose a fast efficient online task scheduling and placement algorithm by incorporating multiple selectable hardware implementations for each hardware request; the selections reflect trade-offs between the required reconfigurable resources and the task runtime performance. Experimental studies conclusively reveal the superiority of the proposed algorithm in terms of not only scheduling and placement quality but also faster runtime decisions over rigid approaches.     

Proactive scheduling in distributed computing—A reinforcement learning approach

In distributed computing such as grid computing, online users submit their tasks anytime and anywhere to dynamic resources. Task arrival and execution processes are stochastic. How to adapt to the consequent uncertainties, as well as scheduling overhead and response time, are the main concern in dynamic scheduling. Based on the decision theory, scheduling is formulated as a Markov decision process (MDP). To address this problem, an approach from machine learning is used to learn task arrival and execution patterns online. The proposed algorithm can automatically acquire such knowledge without any aforehand modeling, and proactively allocate tasks on account of the forthcoming tasks and their execution dynamics. Under comparison with four classic algorithms such as Min–Min, Min–Max, Suffrage, and ECT, the proposed algorithm has much less scheduling overhead. The experiments over both synthetic and practical environments reveal that the proposed algorithm outperforms other algorithms in terms of the average response time. The smaller variance of average response time further validates the robustness of our algorithm.     

Distributed two-level cloud-based multimedia task scheduling

Recently, Multimedia cloud is emerging as a promising technology to effectively process multimedia services. A key problem in multimedia cloud is how to deal with task scheduling and load balancing to satisfy the quality of service demands of users. In this paper, we propose a two levels task scheduling mechanism for multimedia cloud to addresses the problem. The first level scheduling is from the users’ multimedia application to the data centers, and the second is from the data centers to servers. The data centers and virtual machines both are modeled as M/M/1 queuing systems. The algorithm proposed formulates the task-scheduling problem as cooperative game among data centers. Then we allocate the tasks received by a data center to servers using cooperative game again among servers. Various simulations are conducted to validate the efficiency of the proposed task scheduling approaches. The results showed that the proposed solutions provided better performance as compared to the existing approaches.     

Online scheduling of moldable parallel tasks

In this paper, we study an online scheduling problem with moldable parallel tasks on m processors. Each moldable task can be processed simultaneously on any number of processors of a parallel computer, and the processing time of a moldable task depends on the number of processors allotted to it. Tasks arrive one by one. Upon arrival of each task, the scheduler has to determine both the number of processors and the starting time for the task. Moreover, these decisions cannot be changed in the future. The objective is to attain a schedule such that the longest completion time over all tasks, i.e., the makespan, is minimized. First, we provide a general framework to show that any \(\rho \)-bounded algorithm for scheduling of rigid parallel tasks (the number of processors for a task is fixed a prior) can be extended to yield an algorithm for scheduling of moldable tasks with a competitive ratio of \(4\rho \) if the ratio \(\rho \) is known beforehand. As a consequence, we achieve the first constant competitive ratio, 26.65, for the moldable parallel tasks scheduling problem. Next, we provide an improved algorithm with a competitive ratio of at most 16.74.     

民航信息交换任务调度方案

民航信息数据交换平台承担了大数据量的分布式传输任务,需要建立完善的任务调度模型和调度算法。基于民航信息交换平台的架构和需求,在分析比较现有的任务调度模型和调度算法基础上,提出适用于数据交换任务调度问题的解决方案,将点对多点数据传输网络映射为带约束的Steiner树模型,并用改进的遗传算法进行求解。实验仿真将此算法与最大带宽优先分配算法对比,结果充分验证了模型的正确性和可行性。     

Naval Mine Countermeasure Missions

Undersea operations using autonomous underwater vehicles (AUVs) provide a different and in some ways a more challenging problem than tasks for unmanned aerial vehicles and unmanned ground vehicles. In particular, in undersea operations, communication windows are restricted, and bandwidth is limited. Consequently, coordination among agents is correspondingly more difficult. In traditional approaches, a central planner initially assigns subtasks to a set of AUVs to achieve the team goal. However, those initial task assignments may become inefficient during real-time execution because of the real-world issues such as failures. Therefore, initial task allocations are usually subject to change if efficiency is a high concern. Reallocations are needed and should be performed in a distributed manner. To provide such flexibility, we propose a distributed auction-based cooperation framework, distributed and efficient multirobot-cooperation framework (DEMiR-CF), which is an online dynamic task allocation (reallocation) system that aims to achieve a team goal while using resources effectively. DEMiR-CF, with integrated task scheduling and execution capabilities, can also respond to and recover from real-time contingencies such as communication failures, delays, range limitations, and robot failures.     

Allocating fixed-priority periodic tasks on multiprocessor systems

In this paper, we study the problem of allocating a set of periodic tasks on a multiprocessor system such that tasks are scheduled to meet their deadlines on individual processors by the Rate-Monotonic scheduling algorithm. A new schedulability condition is developed for the Rate-Monotonic scheduling that allows us to develop more efficient on-line allocation algorithms. Two on-line allocation algorithms—RM-FF and RM-BF are presented, and shown that their worst-case performance, over the optimal allocation, is upper bounded by 2.33 and lower bounded by 2.28. Then RM-FF and RM-BF are further improved to form two new algorithms: Refined-RM-FF (RRM-FF) and Refined-RM-BF (RRM-BF), both of which have a worst-case performance bound of 2. We also show that when the maximum allowable utilization of a task is small, the worst-case performance of all the new algorithms can be significantly improved. The worst-case performance bounds of RRM-FF and RRM-BF are currently the best bounds in the class of on-line scheduling algorithms proposed to solve the same scheduling problem. Simulation studies show that the average-case performance of the newly proposed algorithms is significantly superior to those in the existing literature.