The Blacklisting Memory Scheduler: Balancing Performance, Fairness and Complexity

In a multicore system, applications running on different cores interfere at main memory. This inter-application interference degrades overall system performance and unfairly slows down applications. Prior works have developed application-aware memory schedulers to tackle this problem. State-of-the-art application-aware memory schedulers prioritize requests of applications that are vulnerable to interference, by ranking individual applications based on their memory access characteristics and enforcing a total rank order. In this paper, we observe that state-of-the-art application-aware memory schedulers have two major shortcomings. First, such schedulers trade off hardware complexity in order to achieve high performance or fairness, since ranking applications with a total order leads to high hardware complexity. Second, ranking can unfairly slow down applications that are at the bottom of the ranking stack. To overcome these shortcomings, we propose the Blacklisting Memory Scheduler (BLISS), which achieves high system performance and fairness while incurring low hardware complexity, based on two observations. First, we find that, to mitigate interference, it is sufficient to separate applications into only two groups. Second, we show that this grouping can be efficiently performed by simply counting the number of consecutive requests served from each application. We evaluate BLISS across a wide variety of workloads/system configurations and compare its performance and hardware complexity, with five state-of-the-art memory schedulers. Our evaluations show that BLISS achieves 5% better system performance and 25% better fairness than the best-performing previous scheduler while greatly reducing critical path latency and hardware area cost of the memory scheduler (by 79% and 43%, respectively), thereby achieving a good trade-off between performance, fairness and hardware complexity

Toward Contention Analysis for Parallel Executing Real-Time Tasks

In measurement-based probabilistic timing analysis, the execution conditions imposed to tasks as measurement scenarios, have a strong impact to the worst-case execution time estimates. The scenarios and their effects on the task execution behavior have to be deeply investigated. The aim has to be to identify and to guarantee the scenarios that lead to the maximum measurements, i.e. the worst-case scenarios, and use them to assure the worst-case execution time estimates. We propose a contention analysis in order to identify the worst contentions that a task can suffer from concurrent executions. The work focuses on the interferences on shared resources (cache memories and memory buses) from parallel executions in multi-core real-time systems. Our approach consists of searching for possible task contenders for parallel executions, modeling their contentiousness, and classifying the measurement scenarios accordingly. We identify the most contentious ones and their worst-case effects on task execution times. The measurement-based probabilistic timing analysis is then used to verify the analysis proposed, qualify the scenarios with contentiousness, and compare them. A parallel execution simulator for multi-core real-time system is developed and used for validating our framework. The framework applies heuristics and assumptions that simplify the system behavior. It represents a first step for developing a complete approach which would be able to guarantee the worst-case behavior