A generic framework to integrate data caches in the WCET analysis of real-time systems

Worst-case execution time (WCET) analysis of systems with data caches is one of the key challenges in real-time systems. Caches exploit the inherent reuse properties of programs by temporarily storing certain memory contents near the processor, in order that further accesses to such contents do not require costly memory transfers. Current worst-case data cache analysis methods focus on specific cache organizations (set-associative LRU, locked, ACDC, etc.), most of the times adapting techniques designed to analyze instruction caches. On the other hand, there are methodologies to analyze the data reuse of a program, independently of the data cache. In this paper we propose a generic WCET analysis framework to analyze data caches taking profit of such reuse information. It includes the categorization of data references and their integration in an IPET model. We apply it to a conventional LRU cache, an ACDC, and other baseline systems, and compare them using the TACLeBench benchmark suite. Our results show that persistence-based LRU analyses dismiss essential information on data, and a reuse-based analysis improves the WCET bound around 17% in average. In general, the best WCET estimations are obtained with optimization level 2, where the ACDC cache performs 39% better than a set-associative LRU

Computing Same Block Relations for Relational Cache Analysis

In contrast to the classical cache analysis of Ferdinand, the relational cache analysis does not rely on precise address information. Instead, it uses same block relations between memory accesses to predict cache hits. The relational data cache analysis can thus also predict cache hits if fully unrolling a loop is not feasible during analysis, for example due to high memory consumption or long computation time. This paper proposes a static analysis based on abstract interpretation which is able to compute same block relations for relational cache analysis

Feedback Control of Cyber-Physical Systems with Multi Resource Dependencies and Model Uncertainties

The problem of modeling and controlling re- sources in a system with interaction between hardware and software is considered. A model encompassing both hardware and software dynamics is developed together with an on- line estimation scheme in order reduce dependence on a- priori information. A control structure is presented in order to control performance under constrained resource situations and to reduce effects of estimation errors and disturbances. The approach is applied to a conversational video case and evaluated through simulations

Capacity sharing and stealing in serverbased real-time systems

A dynamic scheduler that supports the coexistence of guaranteed and non-guaranteed bandwidth servers is proposed. Overloads are handled by an efficient reclaiming of residual capacities originated by early completions as well as by allowing reserved capacity stealing of non-guaranteed bandwidth servers. The proposed dynamic budget accounting mechanism ensures that at a particular time the currently executing server is using a residual capacity, its own capacity or is stealing some reserved capacity, eliminating the need of additional server states or unbounded queues. The server to which the budget accounting is going to be performed is dynamically determined at the time instant when a capacity is needed. This paper describes and evaluates the proposed scheduling algorithm, showing that it can efficiently reduce the mean tardiness of periodic jobs. The achieved results become even more significant when tasks’ computation times have a large variance

Optimal Time Utility Based Scheduling Policy Design for Cyber-Physical Systems

Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Energy-efficient thermal-aware multiprocessor scheduling for real-time tasks using TCPNs

We present an energy-effcient thermal-aware real-time global scheduler for a set of hard real-time (HRT) tasks running on a multiprocessor system. This global scheduler fulfills the thermal and temporal constraints by handling two independent variables, the task allocation time and the selection of clock frequency. To achieve its goal, the proposed scheduler is split into two stages. An off-line stage, based on a deadline partitioning scheme, computes the cycles that the HRT tasks must run per deadline interval at the minimum clock frequency to save energy while honoring the temporal and thermal constraints, and computes the maximum frequency at which the system can run below the maximum temperature. Then, an on-line, event-driven stage performs global task allocation applying a Fixed-Priority Zero-Laxity policy, reducing the overhead of quantum-based or interval-based global schedulers. The on-line stage embodies an adaptive scheduler that accepts or rejects soft RT aperiodic tasks throttling CPU frequency to the upper lowest available one to minimize power consumption while meeting time and thermal constraints. This approach leverages the best of two worlds: the off-line stage computes an ideal discrete HRT multiprocessor schedule, while the on-line stage manage soft real-time aperiodic tasks with minimum power consumption and maximum CPU utilization