Search for: All records

When scheduling multi-mode real-time systems on multi-core platforms, a key question is how to dynamically adjust shared resources, such as cache and memory bandwidth, when resource demands change, without jeopardizing schedulability during mode changes. This paper presents Omni, a first end-to-end solution to this problem. Omni consists of a novel multi-mode resource allocation algorithm and a resource-aware schedulability test that supports general mode-change semantics as well as dynamic cache and bandwidth resource allocation. Omni's resource allocation leverages the platform's concurrency and the diversity of the tasks' demands to minimize overload during mode transitions; it does so by intelligently co-distributing tasks and resources across cores. Omni's schedulability test ensures predictable mode transitions, and it takes into account mode-change effects on the resource demands on different cores, so as to best match their dynamic needs using the available resources. We have implemented a prototype of Omni, and we have evaluated it using randomly generated multi-mode systems with several real-world benchmarks as the workload. Our results show that Omni has low overhead, and that it is substantially more effective in improving schedulability than the state of the art 

Modern latency-sensitive and real-time systems often use multi-core platforms; thus, tasks on different cores share certain hardware resources, such as the memory bus and certain cache levels. This has two undesirable consequences: (1) tasks can interfere with each other, causing high latency for the system as a whole, and (2) it becomes difficult to meet deadlines, since the worst-case timing of a given task depends on the worst task it might have to compete with. Static partitioning isolates tasks from each other by allocating a certain fraction of the resources to each; however, many tasks execute in different phases (e.g., memory-intensive and CPU-intensive) that have different requirements. Thus, system designers are left with a choice between overprovisioning, based on the most demanding phase, or suboptimal performance. In this paper, we propose a pair of techniques, called DNA and DADNA, to address the above challenge. DNA increases throughput and decreases latency, by building an execution profile of each task to identify the phases, and then dynamically allocating resources based on which task can benefit the most; DADNA further adds support for soft real-time workloads by taking deadlines into account. We have built a prototype of both techniques in the Xen hypervisor; our experimental results show that, compared to a state-of-the-art solution, DNA and DADNA can substantially improve schedulability, reduce job deadline miss ratios, and cut latencies by more than a factor of two even in extremely overloaded situations.