Tightening the Bounds on Cache-Related Preemption Delay in Fixed Preemption Point Scheduling

Limited Preemptive Fixed Preemption Point scheduling (LP-FPP) has the ability to decrease and control the preemption-related overheads in the real-time task systems, compared to other limited or fully preemptive scheduling approaches. However, existing methods for computing the preemption overheads in LP-FPP systems rely on over-approximation of the evicting cache blocks (ECB) calculations, potentially leading to pessimistic schedulability analysis. In this paper, we propose a novel method for preemption cost calculation that exploits the benefits of the LP-FPP task model both at the scheduling and cache analysis level. The method identifies certain infeasible preemption combinations, based on analysis on the scheduling level, and combines it with cache analysis information into a constraint problem from which less pessimistic upper bounds on cache-related preemption delays (CRPD) can be derived. The evaluation results indicate that our proposed method has the potential to significantly reduce the upper bound on CRPD, by up to 50% in our experiments, compared to the existing over-approximating calculations of the eviction scenarios

A generic framework for context-sensitive analysis of modular programs

Context-sensitive analysis provides information which is potentially more accurate than that provided by context-free analysis. Such information can then be applied in order to validate/debug the program and/or to specialize the program obtaining important improvements. Unfortunately, context-sensitive analysis of modular programs poses important theoretical and practical problems. One solution, used in several proposals, is to resort to context-free analysis. Other proposals do address context-sensitive analysis, but are only applicable when the description domain used satisfies rather restrictive properties. In this paper, we argüe that a general framework for context-sensitive analysis of modular programs, Le., one that allows using all the domains which have proved useful in practice in the non-modular setting, is indeed feasible and very useful. Driven by our experience in the design and implementation of analysis and specialization techniques in the context of CiaoPP, the Ciao system preprocessor, in this paper we discuss a number of design goals for context-sensitive analysis of modular programs as well as the problems which arise in trying to meet these goals. We also provide a high-level description of a framework for analysis of modular programs which does substantially meet these objectives. This framework is generic in that it can be instantiated in different ways in order to adapt to different contexts. Finally, the behavior of the different instantiations w.r.t. the design goals that motivate our work is also discussed

A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

Approximating k-Forest with Resource Augmentation: A Primal-Dual Approach

In this paper, we study the $k$-forest problem in the model of resource augmentation. In the $k$-forest problem, given an edge-weighted graph $G(V,E)$, a parameter $k$, and a set of $m$ demand pairs $\subseteq V \times V$, the objective is to construct a minimum-cost subgraph that connects at least $k$ demands. The problem is hard to approximate---the best-known approximation ratio is $O(\min\{\sqrt{n}, \sqrt{k}\})$. Furthermore, $k$-forest is as hard to approximate as the notoriously-hard densest $k$-subgraph problem. While the $k$-forest problem is hard to approximate in the worst-case, we show that with the use of resource augmentation, we can efficiently approximate it up to a constant factor. First, we restate the problem in terms of the number of demands that are {\em not} connected. In particular, the objective of the $k$-forest problem can be viewed as to remove at most $m-k$ demands and find a minimum-cost subgraph that connects the remaining demands. We use this perspective of the problem to explain the performance of our algorithm (in terms of the augmentation) in a more intuitive way. Specifically, we present a polynomial-time algorithm for the $k$-forest problem that, for every $\epsilon>0$, removes at most $m-k$ demands and has cost no more than $O(1/\epsilon^{2})$ times the cost of an optimal algorithm that removes at most $(1-\epsilon)(m-k)$ demands