8615000
Fair scheduling of dynamic task systems on multiprocessors
In dynamic real-time task systems, tasks that are subject to deadlines are allowed to join and leave the system. In previous work, Stoica et al. and Baruah et al. presented conditions under which such joins and leaves may occur in fair-scheduled uniprocessor systems without causing missed deadlines. In this paper, we extend their work by considering fair-scheduled multiprocessors. We show that their conditions are sufficient on M processors, under any deadline-based Pfair scheduling algorithm, if the utilization of every subset of M-1 tasks is at most one. Further, for the general case in which task utilizations are not restricted in this way, we derive sufficient join/leave conditions for the PD^2 Pfair algorithm. We also show that, in general, these conditions cannot be improved upon without causing missed deadlines.
5149437
The case for fair multiprocessor scheduling
In this paper, we compare the PD/sup 2/ Pfair algorithm to the EDF-FF partitioning scheme, which uses "first fit" (FF) as a partitioning heuristic and the earliest-deadline-first (EDF) algorithm for per-processor scheduling. We present experimental results that show that PD/sup 2/ is competitive with, and in some cases outperforms, EDF-FF. These results suggest that Pfair scheduling is a viable alternative to partitioning. Furthermore, as discussed herein, Pfair scheduling provides many additional benefits, such as simple and efficient synchronization, temporal isolation, fault tolerance, and support for dynamic tasks.
18673248
Mixed Pfair/ERfair scheduling of asynchronous periodic tasks
Pfair scheduling was proposed by Baruah, Cohen, Plaxton, and Varvel as a non-work-conserving way of optimally and efficiently scheduling periodic tasks on a multiprocessor. In this paper, we introduce a work-conserving variant of Pfair scheduling called "early-release" fair (ERfair) scheduling. We also present a new scheduling algorithm called PD2 and show that it is optimal for scheduling any mix of early-release and non-early-release asynchronous, periodic tasks. In contrast, almost all prior work on Pfair scheduling has been limited to synchronous systems. PD2 is an optimization of an earlier deadline-based algorithm of Baruah, Gehrke, and Plaxton called PD; PD2 uses a simpler tie-breaking scheme than PD to dis ambiguate equal deadlines. We present a series of counterexamples that suggest that, in general, the PD2 tie-breaking mechanism cannot be simplified. In contrast to this, we show that no tie-breaking information is needed on two-processor systems.
45060983
Deadline-based scheduling of periodic task systems on multiprocessors
We consider the problem of scheduling periodic task systems on multiprocessors and present a deadline-based scheduling algorithm for solving this problem. We show that our algorithm successfully schedules on m processors any periodic task system with utilization at most m2/(2m - 1).
11919636
Quick-release fair scheduling
In prior work on multiprocessor fairness, efficient techniques with provable properties for reallocating spare processing capacity have been elusive. In this paper, we address this shortcoming by proposing a new notion of multiprocessor fairness, called quick-release fair (QRfair) scheduling. Under QRfair scheduling, each task is specified by giving both a minimum and a maximum weight (i.e., processor share). The goal is to schedule each task (as the spare capacity changes) at a rate that is (i) at least that implied by its minimum weight and (ii) at most that implied by its maximum weight. We present a quick-release variant of the PD/sup 2/ Pfair scheduling algorithm called PD/sup Q/ and prove that the allocations of PD/sup Q/ always satisfy (i) and (ii). Also, we present results from simulation experiments that show the efficacy of PD/sup Q/.
14106112
Mixed Pfair/ERfair scheduling of asynchronous periodic tasks
In this paper, we prove that a simplified variant of the PD Pfair algorithm, called PD/sup 2/, is optimal for scheduling any mix of early-release and non-early-release asynchronous tasks on a multiprocessor. This result breaks new ground by incorporating both early-release and non-early-release tasks under a common framework. In addition, all prior work on optimal multiprocessor Pfair scheduling algorithms has been limited to synchronous periodic task systems.
42237174
On the Complexity of Distributed Network Decomposition
In this paper, we improve the bounds for computing a network decomposition distributively and deterministically. Our algorithm computes an (n?(n),n?(n))-decomposition innO(?(n))time, whereformula. As a corollary we obtain improved deterministic bounds for distributively computing several graph structures such as maximal independent sets and ?-vertex colorings. We also show that the class of graphs G whose maximum degree isnO(?(n))where ?(n)=1/log lognis complete for the task of computing a near-optimal decomposition, i.e., a (logn, logn)-decomposition, in polylog(n) time. This is a corollary of a more general characterization, which pinpoints the weak points of existing network decomposition algorithms. Completeness is to be intended in the following sense: if we have an algorithmAthat computes a near-optimal decomposition in polylog(n) time for graphs inG, then we can compute a near-optimal decomposition in polylog(n) time for all graphs.
47213893
A Constant-Factor Approximation Algorithm for Packet Routing and Balancing Local vs. Global Criteria
We present the first constant-factor approximation algorithm for a fundamental problem: the store-and-forward packet routing problem on arbitrary networks. Furthermore, the queue sizes required at the edges are bounded by an absolute constant. Thus, this algorithm balances a global criterion (routing time) with a local criterion (maximum queue size) and shows how to get simultaneous good bounds for both. For this particular problem, approximating the routing time well, even without considering the queue sizes, was open. We then consider a class of such local vs. global problems in the context of covering integer programs and show how to improve the local criterion by a logarithmic factor by losing a constant factor in the global criterion.
28892549
Improved Monte Carlo Linear Solvers Through Non-diagonal Splitting
Fast, but approximate, solutions to linear algebra problems have many potential applications, such as in graph partitioning, preconditioning, information retrieval, etc. Monte Carlo techniques appear attractive for such needs. While Monte Carlo linear solvers have a long history, their application has been limited due to slow convergence. Despite the development of techniques to improve their accuracy, current methods suffer from the drawback that they are stochastic realizations of inherently poor iterative methods. The reason for such choices is the need for efficient Monte Carlo implementation, which has restricted the splittings that are considered. However, in this paper we demonstrate that such restrictions are not necessarily required, and that efficient Monte Carlo implementations are possible even with splittings that do not appear amenable to it.
5089320
Better approximation guarantees for job-shop scheduling
Job-shop scheduling is a classical NP-hard problem. Shmoys, Stein, and Wein presented the first polynomial-time approximation algorithm for this problem that has a good (polylogarithmic) approximation guarantee. We improve the approximation guarantee of their work and present further improvements for some important NP-hard special cases of this problem (e.g., in the preemptive case where machines can suspend work on operations and later resume). We also present NC algorithms with improved approximation guarantees for some NP-hard special cases.
83148
Latency tolerance through parallelization of time in scientific applications
Summary form only given. Distributed computing environments, such as the grid, promise enormous raw computational power, but involve high communication overheads. It is therefore considered that they are ideally suited for "embarrassingly parallel" applications, such as Monte Carlo, and for certain applications where the loosely-coupled nature of the science involved in the simulations leads to a coarse grained computation. In a typical application, this is not feasible. We discuss our solution strategy, based on scalable functional decomposition, which can be used to keep the computation coarse grained, even on a large number of processors. Such decomposition can be attempted through a variety of means. We discuss the use of time parallelization to achieve this. We demonstrate results with a model problem, and then discuss its implementation for an important problem in nanomaterials simulation. We also show that this technique can be extended to make it inherently fault-tolerant.