APEX: adaptive disk scheduling framework with QoS support

APEX is an adaptive disk scheduling framework with Quality-of-Service (QoS) support designed for environments with highly varying disk bandwidth usage. APEX is based on a three-layer scheduling architecture: (1) the upper layer realizes different service classes using a set of queues; (2) the mid-layer distributes available disk bandwidth among these queues; and (3) the lower layer is handled by the disk itself, which does the final ordering of disk requests. We demonstrate the use of APEX in an example scenario, a Learning-on-Demand (LoD) application supported by a multimedia system, where students can search for and playback multimedia-based learning material. In this paper, we present the scheduling concepts of APEX which are based on an extended token bucket algorithm. The disk requests scheduled for service are assembled into batches in order to exploit the intelligence of modern disks. Combined with a specialized work-conservation scheme, this enables APEX to apply bandwidth where it is needed, without the loss of efficiency. We demonstrate, through simulations, that APEX provides both higher throughput and lower response times than other mixed-media disk schedulers while still avoiding deadline violations for real-time requests. We also show its robustness with respect to misaligned bandwidth allocation. The work was conducted while Ketil Lund was an employee at UniK – University Graduate Center, Kjeller, Norway.     

Building a retargetable local instruction scheduler

While high-performance architectures have included some Instruction-Level Parallelism (ILP) for at least 25 years, recent computer designs have exploited ILP to a significant degree. Although a local scheduler is not sufficient for generation of excellent ILP code, it is necessary as many global scheduling and software pipelining techniques rely on a local scheduler. Global scheduling techniques are well-documented, yet practical discussions of local schedulers are notable in their absence. This paper strives to remedy that disparity by describing a list scheduling framework and several important practical details that, taken together, allow implementation of an efficient local instruction scheduler that is easily retargetable for ILP architectures. The foundation of our machine-independent instruction scheduler is a timing model that allows easy retargetability to a wide range of architectures. In addition to describing how a general list-scheduler can be implemented within the framework of our timing model, experimental results indicate that lookahead scheduling can profoundly improve a scheduler's ability to produce a legal schedule. Further experimental data shows that deciding to schedule a data dependence DAG (DDD) in forward or reverse order depends significantly upon that target architecture, suggesting the possibility of scheduling in each direction and using the best of the two schedules. In contrast, experiments demonstrate little difference in code quality for schedules generated by either instruction-driven or operation-driven schedulers. Thus, the inherent flexibility of operation-driven methods suggests including that approach in a retargetable instruction scheduler. List scheduling is, of course, a heuristic scheduling method. A variety of scheduling heuristics are presented. In addition, the paper describes a method, using a genetic algorithm search, to ‘fine-tune’ the weights of twenty-four individual heuristics to form a DDD-node heuristic tuned to a specific architecture. © 1998 John Wiley & Sons, Ltd.     

Quantum-Based Earliest Deadline First Scheduling for Multiservices

Latency-rate (LR) schedulers have shown their ability in providing fair and weighted sharing of bandwidth with an upper bound on delivery latency of packets while earliest departure first (EDF) schedulers have shown their ability in providing LR-decoupled service whereby the delivery latency of packets is not bounded by the reserved rate. However, EDF schedulers require traffic shapers to ensure flow protection. We propose quantum-based earliest deadline first scheduling (QEDF), a quantum-based scheduler that provides flow protection, throughput guarantee and delay bound guarantee for flows that require LR-coupled and LR-decoupled types of reservations. It classifies flows into time-critical (TC), jitter-sensitive (JS), and rate-based (RB) classes and uses a quality-of-service forwarding rule to determine the next packet to be serviced by the scheduler. It provides nonpreemptive priority service to TC queues. This allows LR-decoupled reservation for flows that have a low rate and intolerable delay. Packets from JS queues can be delayed by other packets if forwarding the latter will not result in the former missing its deadline. As a quantum-based scheduler, the QEDF scheduler provides throughput guarantees for RB queues. We present both analytical and simulation results of QEDF, whereby we evaluated QEDF in its deployment as a single-class as well as a multiservice scheduler     

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。     

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers appropriate to each specific architecture implementation to achieve the best possible program speed. However, these heuristic schedulers are time-consuming and expensive to build. We present empirical results using both rollouts and reinforcement learning to construct heuristics for scheduling basic blocks. In simulation, the rollout scheduler outperformed a commercial scheduler on all benchmarks tested, and the reinforcement learning scheduler outperformed the commercial scheduler on several benchmarks and performed well on the others. The combined reinforcement learning and rollout approach was also very successful. We present results of running the schedules on Compaq Alpha machines and show that the results from the simulator correspond well to the actual run-time results.     

Linux I/O调度算法分析与研究

研究表明,好的磁盘调度算法可以明显优化磁盘的I/O性能,Linux提供四种调度算法可供选择。基于Linux 2.6.32内核源码,研究Noop、Deadline、Anticipatory、CFQ四种磁盘调度算法的基本原理和特点,分析每种算法的优缺点,这对系统管理员针对不同类型的应用场景来调整优化I/O调度算法有着重要意义。     

基于层次化的网格资源三层调度模型

在分析现有的资源调度方案及模型的基础上,提出了基于层次化的网格资源三层调度模型．它由主调度器、次级调度器和计算节点组成。主调度器根据任务的性质和需求,并参考下层次级调度器的执行情况,将部分任务分发到各次级调度器上,实现了主调度器与次级调度器之间的并行工作。基于该模型提出轮循任务分发策略。通过分析和模拟．该资源调度模型及任务分发策略在调度性能上明显优于集中式调度方案。     

Two schedulers to provide delay proportion and reduce queueing delay simultaneously

The proportional delay differentiation model provides controllable and predictable delay differentiation, that is, the packet delay proportion between two classes of services is consistent on any measured timescale. Previous studies have focused on improving the accuracy of the achieved delay proportion between classes, and have not considered reducing the packet queueing delay, since these proposed scheduling algorithms are independent of the packet service time, such that the mean queueing delay is invariant, as specified by the conservation law. This paper proposes maximum WTP (MWTP) and variance WTP (VWTP) schedulers, modified from the waiting-time priority (WTP) algorithm which is an excellent scheduler for performing proportional delay differentiation. All of the proposed schedulers account for the packet transmission time. Simulation results indicate that when the link utilization is moderate, the two schedulers not only yield more accurate delay proportions than the WTP scheduler, regardless of whether the timescale is long or short, but also reduce the mean queueing delay. The effects of load distribution, packet size, and coefficient of variation (CoV) of packet sizes, on the performance of all schedulers are also investigated. Our proposed schedulers always outperform WTP.     

Providing QoS with the Deficit Table Scheduler

A key component for networks with Quality of Service (QoS) support is the egress link scheduling algorithm. An ideal scheduling algorithm implemented in a high-performance network with QoS support should satisfy two main properties: good end-to-end delay and implementation simplicity. Table-based schedulers try to offer a simple implementation and good latency bounds. Some of the latest proposals of network technologies, like Advanced Switching and InfiniBand, include in their specifications one of these schedulers. However, these table-based schedulers do not work properly with variable packet sizes, as is usually the case in current network technologies. We have proposed a new table-based scheduler, which we have called Deficit Table (DTable) scheduler, that works properly with variable packet sizes. Moreover, we have proposed a methodology to configure this table-based scheduler in such a way that it permits us to decouple the bounding between the bandwidth and latency assignments. In this paper, we thoroughly review the provision of QoS with the DTable scheduler and our configuration methodology, and evaluate the performance of our proposals in a multimedia scenario. Simulation results show that our proposals are able to provide a similar latency performance than more complex scheduling algorithms. Moreover, we show the advantages of our decoupling configuration methodology over the usual ways of configuring this kind of table-based schedulers.     

面向5G边缘计算的Kubernetes资源调度策略

容器云是5G边缘计算的重要支撑技术,5G的大带宽、低时延和大连接三大特性给边缘计算带来较大的资源压力,容器云编排器Kubernetes仅采集Node剩余CPU和内存两大资源指标,并运用统一的权重值计算Node优先级作为调度依据,该机制无法适应边缘计算场景下精细化的资源调度需求.面向5G边缘计算的资源调度场景,通过扩展K...     

Composing high-performance schedulers: a case study from real-time simulation

Dynamic, high-performance or real-time applications require scheduling latencies and throughput not typically offered by current kernel or user-level threads schedulers. Moreover, it is widely accepted that it is important to be able to specialize scheduling policies for specific target applications and their execution environments. This paper presents one solution to the construction of such high-performance, application-specific thread schedulers. Specifically, scheduler implementations are composed from modular components, where individual scheduler modules may be specialized to underlying hardware characteristics or implement precisely the mechanisms and policies desired by application programs. The resulting user-level schedulers' implementations can provide resource guarantees by interaction with kernel-level facilities which provide means of resource reservation. This paper demonstrates the concept of composable schedulers by construction of several compositions for highly dynamic target applications, where low scheduling latencies are critical to application performance. Claims about the importance and effectiveness of scheduler composition are validated experimentally on a shared-memory multiprocessor. Scheduler compositions are optimized to take advantage of different low-level hardware attributes and of knowledge about application requirements specific to certain applications, including a Time Warp-based real-time discrete event simulator. Experimental evaluations are based on synthetic workloads, on a real-time simulation blending simulated with implemented control system components, and on a dynamic robot control program. Measurements indicate that schedulers can be composed and specialized to offer performance similar to that of dedicated scheduling co-processors. Copyright © 1999 John Wiley & Sons, Ltd.     

Application‐specific thread schedulers for distributed applications

This paper describes our work to improve the performance of distributed applications. We aim at certain application characteristics such as balancing load, allowing separately written applications to work better together, allowing a distributed application to adapt its behavior in more flexible ways, and so on. Our approach is to write application‐specific schedulers, which can access the global state of the application in making scheduling decisions. To achieve this goal, we extended our earlier work on CATAPULTS ( C reating A nd T esting AP plication‐specific U ser L evel T hread S chedulers), a domain‐specific language for creating and testing application‐specific user‐level thread schedulers, to distributed applications by adding ‘master schedulers’ for dealing with the distributed parts of applications. This paper presents our design of, experimentation with, and implementation of distributed CATAPULTS. This paper presents several realistic examples to measure the feasibility of this approach, specifically: a website application, an embedded application, and load balancing. Each example has a scheduling goal for which we developed a customized scheduler. We measured the performance with and without the customized scheduler. The customized scheduler for each example was fairly straightforward to develop and each achieved its scheduling goal. Copyright © 2011 John Wiley & Sons, Ltd.     

Garbage collection-aware utility accrual scheduling

The convenience and robustness of automatic memory management have long been exploited by modern systems that use type-safe programming languages such as Java. The timeliness requirements of real-time systems, however, impose specific demands on the operational parameters of the garbage collector. The memory requirements of real-time tasks must be accommodated with a predictable impact on the time-line and under the purview of the scheduler. Utility Accrual is a method of dynamic overload scheduling that is designed to respond to overload conditions by producing a schedule that heuristically maximizes a pre-defined metric of utility. Traditionally, UA schedulers have focused primarily on CPU overload. We explore memory overload conditions in which the memory demands exceed the system’s available memory bandwidth. This paper presents a utility accrual algorithm for uniprocessor CPU and garbage collection scheduling that addresses such memory overload conditions. By tightly linking CPU and memory allocation, the scheduler can appropriately respond to overload along both dimensions. This scheduler is the first of its kind to enable the use of automatic memory management in a utility accrual system. Experimental results based on actual Java application profiles indicate the benefits of our model when compared to memory-unaware scheduling.     

Scheduling for Responsive Grids

Grids are facing the challenge of seamless integration of the Grid power into everyday use. One critical component for this integration is responsiveness, the capacity to support on-demand computing and interactivity. Grid sched uling is involved at two levels in order to provide responsiveness: the policy level and the implementation level. The main contributions of this paper are as follows. First, we present a detailed analysis of the performance of the EGEE Grid with respect to responsiveness. Second, we examine two user-level schedulers located between the general scheduling layer and the application layer. These are the DIANE (distributed analysis environment) framework, a general-purpose overlay system, and a specialized, embedded scheduler for gPTM3D, an interactive medical image analysis application. Finally, we define and demonstrate a virtualization scheme, which achieves guaranteed turnaround time, schedulability analysis, and provides the basis for differentiated services. Both methods target a brokering-based system organized as a federation of batch-scheduled clusters, and an EGEE implementation is described.     

Libra: a computational economy‐based job scheduling system for clusters

Clusters of computers have emerged as mainstream parallel and distributed platforms for high‐performance, high‐throughput and high‐availability computing. To enable effective resource management on clusters, numerous cluster management systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users' quality of service requirements. It is intended to work as an add‐on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the Portable Batch System. The scheduler offers market‐based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user‐perceived value (utility), determined by their budget and deadline rather than system performance considerations. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared with system‐centric scheduling strategies. Copyright © 2004 John Wiley & Sons, Ltd.     

基于多核感知的应用服务并行调度平台

摘　要 在云计算环境下,PaaS和IaaS协同工作是发展的趋势,针对大数据的处理及应用服务越来越复杂和多样化的需求,利用应用虚拟化技术在PaaS层和IaaS层之间搭建一个应用服务层,研究在管理平台中基于多核感知的并行调度模型。通过应用服务管理平台,实现按业务逻辑需求用不同粒度的应用服务组装形成系统新功能并以平台中基于多核感知的并行调度模型来处理/响应用户的作业请求。一方面提高系统的灵活性和行业适应性,另一方面也能实现对计算资源进行细粒度的分配。从实验结果可知,应用服务管理平台能有效按业务需求完成应用服务组装,通过并行调度提高了系统对基础架构资源的利用率,并有效地提高管理平台的吞吐量。     

An interactive QoS framework for fixed WiMAX networks

The IEEE 802.16 standard defines several scheduling classes at MAC layer for preferential treatment of service flows depending on QoS requirements specific to a service flow. In this paper, a new framework has been proposed to solve and address QoS issues for fixed point to multipoint (PMP) 802.16 systems. The proposed framework consists of a uplink scheduler and Call Admission Control (CAC) module. The proposed CAC module interact with the uplink scheduler status and makes its decision based on the scheduler’s queues status. Extensive OPNET simulation demonstrates the effectiveness of the proposed framework.     

Assignment of independent tasks to minimize completion time

The task of scheduling dynamic applications that consist of single process tasks on a non-shared memory multicomputer is examined in this paper. Each task of the application is assumed to (1) require execution on a single processor, (2) have an estimate of its maximum execution time, and (3) not wait on communications with other tasks. The objective of the studied schedulers is to map an application's tasks onto the underlying hardware in such a way that the application's completion time is minimized. Experimental evaluation of the schedulers indicate that in many situations, a more sophisticated scheduler fails to outperform simpler schedulers.     

Pragmatic integrated scheduling for clustered VLIW architectures

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Scheduling for clustered architectures involves spatial concerns (where to schedule) as well as temporal concerns (when to schedule). Various clustered VLIW configurations, connectivity types, and inter‐cluster communication models present different performance trade‐offs to a scheduler. The scheduler is responsible for resolving the conflicting requirements of exploiting the parallelism offered by the hardware and limiting the communication among clusters to achieve better performance. In this paper, we describe our experience with developing a pragmatic scheme and also a generic graph‐matching‐based framework for cluster scheduling based on a generic and realistic clustered machine model. The proposed scheme effectively utilizes the exact knowledge of available communication slots, functional units, and load on different clusters as well as future resource and communication requirements known only at schedule time. The proposed graph‐matching‐based framework for cluster scheduling resolves the phase‐ordering and fixed‐ordering problem associated with earlier schemes for scheduling clustered VLIW architectures. The experimental evaluation in the context of a state‐of‐art commercial clustered architecture (using real‐world benchmark programs) reveals a significant performance improvement over the earlier proposals, which were mostly evaluated using compiled simulation of hypothetical clustered architectures. Our results clearly highlight the importance of considering the peculiarities of commercial clustered architectures and the hard‐nosed performance measurement. Copyright © 2007 John Wiley & Sons, Ltd.     

Power-aware opportunistic downlink scheduling in IEEE 802.16 wireless networks

The IEEE 802.16 is a standard for fixed and mobile Broadband Wireless Access (BWA). In this paper, we deal with two key challenges of 802.16-based networks. First, terminals close to cell edge experience poor channel quality, due to severe path-loss and high interference from concurrent transmissions in nearby cells. To address this issue, we propose a framework based on a static partitioning of bandwidth into chunks with different transmission power levels. Terminals with impaired channel conditions can then benefit from being allocated a higher amount of transmission power than the others. Secondly, transmissions should be scheduled according to Quality of Service (QoS) requirements to keep users with real-time video or voice calls satisfied, while best-effort connections should fairly share the remaining capacity. To this aim, we propose a scheduling algorithm, called Power-aware Opportunistic Downlink Scheduling (PODS), that aims at meeting both the QoS and fairness requirements, while taking into account the different power levels of the bandwidth chunks. The performance of the proposed scheduler is assessed through detailed packet-level simulation in realistic scenarios and compared with well-known scheduling algorithms. Results confirm that PODS is able to exploit power boosting to provide real-time connections with the desired level of QoS, irrespectively of their MSs’ channel quality.