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Implementation of the Multi-Level Adaptive Hierarchical Scheduling Framework

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Implementation of the Multi-Level Adaptive Hierarchical Scheduling Framework Implementation of the Multi-Level Adaptive Hierarchical Scheduling Framework Nima Moghaddami Khalilzad, Moris Behnam, Thomas Nolte MRTC/Malardalen¨ University P.O. Box 883, SE-721 23 Vaster¨ as,˚ Sweden [email protected] Abstract—We have presented a multi-level adaptive hierarchi- portions based on the worst case CPU demand of the tasks cal scheduling framework in our previous work. The framework is not an efficient design approach. Because even if the worst targets compositional real-time systems which are composed of case CPU demand is available, it will result in an unnecessary both hard and soft real-time systems. While static CPU portions CPU overallocation. Consequently, the CPU resource will be are reserved for hard real-time components, the CPU portions of wasted. soft real-time components are adjusted during run-time. In this paper, we present the implementation details of our framework To address this problem, we presented an adaptive frame- which is implemented as a Linux kernel loadable module. In work in [6] where for hard real-time systems we reserve the addition, we present a case-study to evaluate the performance CPU portions based on their worst case resource demand. and the overhead of our framework. On the other hand, soft real-time systems receive dynamic CPU portions based on their actual need at each point in I. INTRODUCTION time. For acquiring the resource demand of the soft real- time components, we monitor their behavior during run-time Hierarchical scheduling and resource reservation tech- and use the gathered information to adjust the component niques have been widely used for composing independent hard CPU reservations. While the design and evaluation of our real-time systems on a shared underlying hardware [1]. Using framework is presented in [6], in this paper we present the im- such techniques, the timing behavior of the individual systems plementation details of our Adaptive Hierarchical Scheduling (components) are studied in isolation, while the correctness (AdHierSched) framework1. In particular we present the of the entire systems is inferred from the correctness of the following contributions in this paper. individual components before the composition. This compo- sitional timing study is especially useful in open systems in • The data structures and the mechanisms that are used which components are added or removed during the system’s in the implementation of our framework as a Linux life time [2]. kernel loadable module. Hierarchical scheduling is often performed through CPU • The performance evaluation of AdHierSched with reservations. When dealing with hard real-time systems, based respect to timing requirements of the real-time com- on the worst case CPU demand of the individual components, ponents. a CPU portion is reserved for each component such that the component’s inner tasks are guaranteed to receive enough CPU • The overhead evaluation of the scheduler and the CPU resource time to complete their executions in time. reservation adapter component. While there exists a variety of techniques to handle the The rest of the paper is organized as follows. The related composition when composing hard real-time systems (e.g., [2], work is reviewed in Section II. In Section III we present [3], [4], [5]), the problem of composing soft and hard real- our system model. Section IV presents the data structures time systems together has not been deeply studied. A con- and implementation techniques used in AdHierSched. We siderable group of soft real-time systems have the following evaluate the performance of AdHierSched in Section V. attributes. First of all, they demonstrate a wide difference The overhead of AdHierSched is presented in Section VI. between their worst case and average case CPU demands. Finally, the paper is concluded in Section VII. Secondly, occasional timing violations can be tolerated in these type of systems. Last but not least, resource demand II. RELATED WORK analysis are rarely done for these systems. As a result, de- There exists an enormous number of papers that address signers do not have enough information about the resource the implementation of real-time schedulers (e.g, RTLinux [7] demand requirements of such systems. Note that the timing and RTAI [8]). However, in this paper we only review a part of requirements are often known a priori, whereas, the resource requirements are unknown. In dealing with real-time systems 1The source code is available at: that have the aforementioned attributes, reserving the CPU http://www.idt.mdh.se/∼adhiersched. them that focus either on hierarchical scheduling or on adaptive A. Server model scheduling. We use the periodic resource model [25] in our framework, In [9] hierarchical scheduling is done on top of the Vx- and we implement the periodic model using the periodic Works operating system. Hierarchical scheduling on top of the servers which work as follows. The servers are released period- FreeRTOS operating system is presented in [10]. ExSched [11] ically, providing their children with a predefined amount of the is a platform independent real-time scheduler which has a CPU time in each period. The periodic servers idle their CPU hierarchical scheduling plug-in. Hierarchical scheduling is also allocation if there is no active task/server inside them. Any implemented in µC/OS-II [12]. All of these works are two- server implementation compliant with the periodic resource level hierarchical schedulers and are designed for hard real- model can be used in our framework. A periodic server is rep- j j j j j time applications, i.e, they are not adaptive frameworks. resented with the following 4-tuple Si =< Pi ;Bi ; P ri ; ζi >, where P j, Bj, P rj and ζj represent period, budget, scheduling HLS [13] is a multi-level hierarchal scheduling imple- i i i i Sj mented in Windows 2000 which targets composing soft real- priority and importance of server i . The importance value time systems. In [14], Parmer and West presented a hierarchical represents the relative importance of the servers with respect scheme for managing CPU, memory and I/O. These frame- to their other sibling servers. This parameter is only used in an works are not adaptive in the sense that the resource demands overload situation where the total CPU demand is more than AdHierSched are not monitored and hence the resource reservations (if used) the available CPU. In the overload situations, are fixed during run-time. prioritizes the servers in the order of their importance. Note that the overload situation is only considered to happen in Hierarchical scheduling is also used for virtualization pur- soft real-time servers. The superscript in the server notation, poses. Recursive virtual machines are proposed in [15] where represents the parent server index. Based on the scheduling each virtual machine can directly access the microkernel. policy of Sj, a subset of the parameters in the server 4-tuple are A two-level hierarchical scheduler using L4/Fiasco as the used. For instance, when the scheduling is done according to et al. j j hypervisor is presented in [16]. Lee developed a vir- EDF, P ri is ignored. In the case of soft real-time servers, Bi is tualization platform using the Xen hypervisor [17]. A Virtual adapted during run-time. Thus, the budget is a function of time j CPU scheduling framework in the Quest operating system is Bi (t). Consequently, server’s children may receive a different developed by Danish et al. [18]. In [19], the CPU reservations share of the CPU in different server periods. The adaptation are used for scheduling virtual machines. The VirtualBox and is done through the budget controller component based on on- the KVM hypervisores are scheduled using CPU reservation line monitoring of the server’s workload. The budget controller techniques in [20]. adapts the budgets such that each server receives just enough The AQuoSA framework [21] is an adaptive framework im- CPU time at each server period. The details of the adaptation plemented in Linux which uses the CPU reservation techniques mechanism is presented in our previous work [6]. together with feedback loops to adjust the reservations during j Furthermore, server Si is composed of ni child servers and run-time. Our work is different than AQuoSA in the following j mi child tasks. Server Si schedules its children according to two aspects. First of all, we target multi-level hierarchical its local scheduling policy. Servers and tasks inherit the type systems while AQuoSA only targets flat systems, i.e the of their parents, e.g., if a server is a soft-real-time server its systems with one task per CPU reservation and without local children will also be treated as soft real-time servers/tasks. At schedulers. Secondly, we have implemented AdHierSched each point in time there is at most one server assigned to the as a kernel loadable module, whereas, AQuoSA requires kernel CPU which is called the “active server”. patching. Since our adaptation mechanism is designed for the pe- ACTORS [22] is an adaptive framework which targets riodic servers, we focus on the explanation of the periodic multicore systems. In this framework, the CPU reservations are servers in this paper. Nevertheless, the Constant Bandwidth used for providing isolation among real-time tasks, while the Server (CBS) [26] is implemented in AdHierSched, and it reservation sizes are being adjusted during run-time. ACTORS can be used inside the periodic servers for providing timing uses SCHED_DEADLINE [23] for implementing the CPU isolation among tasks and servers that reside inside the same reservations. Similar to the AQuoSA framework, ACTORS periodic server parent. addresses flat systems and not hierarchical systems. AIRS [24] is a framework designed to provide high quality B. Task model of service to interactive real-time applications.
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