Flexible Rollback Recovery in Dynamic Heterogeneous Grid Computing

Abstract—Large applications executing on Grid or cluster architectures consisting of hundreds or thousands of computational nodes create problems with respect to reliability. The source of the problems are node failures and the need for dynamic configuration over extensive runtime. This paper presents two fault-tolerance mechanisms called Theft-Induced Checkpointing and Systematic Event Logging. These are transparent protocols capable of overcoming problems associated with both benign faults, i.e., crash faults, and node or subnet volatility. Specifically, the protocols base the state of the execution on a dataflow graph, allowing for efficient recovery in dynamic heterogeneous systems as well as multithreaded applications. By allowing recovery even under different numbers of processors, the approaches are especially suitable for applications with a need for adaptive or reactionary configuration control. The low-cost protocols offer the capability of controlling or bounding the overhead. A formal cost model is presented, followed by an experimental evaluation. It is shown that the overhead of the protocol is very small, and the maximum work lost by a crashed process is small and bounded. Index Terms—Grid computing, rollback recovery, checkpointing, event logging. Ç

Contributions of the PPC to online control of visually guided reaching movements assessed with fMRI-Guided TMS

The posterior parietal cortex (PPC) plays an important role in controlling voluntary movements by continuously integrating sensory information about body state and the environment. We tested which subregions of the PPC contribute to the processing of target- and body-related visual information while reaching for an object, using a reaching paradigm with 2 types of visual perturbation: displacement of the visual target and displacement of the visual feedback about the hand position. Initially, functional magnetic resonance imaging (fMRI) was used to localize putative target areas involved in online corrections of movements in response to perturbations. The causal contribution of these areas to online correction was tested in subsequent neuronavigated transcranial magnetic stimulation (TMS) experiments. Robust TMS effects occurred at distinct anatomical sites along the anterior intraparietal sulcus (aIPS) and the anterior part of the supramarginal gyrus for both perturbations. TMS over neighboring sites did not affect online control. Our results support the hypothesis that the aIPS is more generally involved in visually guided control of movements, independent of body effectors and nature of the visual information. Furthermore, they suggest that the human network of PPC subregions controlling goal-directed visuomotor processes extends more inferiorly than previously thought. Our results also point toward a good spatial specificity of the TMS effects. © 2010 The Author

Inherently stable priority list scheduling in an extended scheduling environment

The application of computers in safety-critical systems is expanding rapidly. With reliability specifications becoming increasingly stringent, formal verification methods are often required. The underlying algorithms must be provably correct and free from unexpected side effects. Many safety-critical systems in sensor-based control applications, such as robotics, process control and manufacturing systems, operate in hard real-time environments, where the missing of deadlines of specific tasks could have catastrophic results. Multiprocessor systems may be needed to meet deadlines under increasing computational workloads or for fault tolerance reasons. This dissertation addresses non-preemptive static priority list scheduling, a simple, low overhead approach to scheduling tasks on multiprocessors. This type of scheduling is subject to scheduling anomalies, one of which can cause deadlines to be missed by shortening the run-time of one or more tasks. Two restrictive methods exist to avoid this problem, involving preprocessing the precedence graph or strictly enforcing the priority list order. Whereas the first method may introduce many more precedence constraints, thus increasing run-time overhead, the second method is potentially more susceptible to poor utilization. A number of inherently stable run-time dispatching algorithms are presented, that overcome these disadvantages. They are targeted towards a new extended scheduling model which permits modeling processes and events external to the processors. The algorithms range from very simple fixed-overhead dispatchers to an optimal greedy dispatcher and are provably correct. The optimal dispatcher never leaves a processor idle if there exists a task that can be started safely. It serves only as a comparison tool for lower overhead algorithms, due to its high run-time complexity. Performance of the new dispatchers has been simulated using numerous representative task systems, including several real world applications and pathological workloads, comparing their relative performance. Simulations showed that even the simple dispatchers performed close to optimal for non-pathological workloads. This research presents practical solutions to the scheduling instability problem. It advocates non-preemptive list scheduling algorithms which can handle external events and are at once stable, efficient, and low in run-time overhead

Logics in Animal Cognition: Are They Important to Brain Computer Interfaces (BCI) And Aerospace Missions?

Conventional wisdom is that logic and language are tightly connected to logics in human cognition. However, recent studies have revealed that, in animal cognition, there exist logics that do not depend on languages. In other words, logical behavior is not human brain specific. At least four logics: perceptual logic, technical logic, social logic, and inference logic have been studied in animal cognition. Despite the obvious differences between animals and humans in using languages, recent studies confirm that both humans and animals utilize the socalled sensor brain maps for most sensory modalities: populations of neurons are selectively tuned to different stimulus features or feature combinations (Ewert 2005, Ma and Krings 2009). This commonality suggests that the studies of animal logics should also be insightful for understanding human logics. After briefly reviewing some of the recent advances in animal logics research, we turn to a more practical research field—the Brain Computer Interface (BCI) [also known as Brain Machine Interface (BMI)] in biomedicine. BCI promises to provide nonmuscular communication and control for people with severe motor disabilities. A fundamental goal of BCI is to translate thought or intent into action with brain activity only (Birbaumer 2006). If we recognize that logic is about the way of thinking and it is probably the most reliable and possibly most efficient way to understand thoughts, an interesting question could be: will the understanding of animal logics be very helpful for BCI research? The current BCI research is primarily targeted for rehabilitation applications. In this article, we also discuss the potential of using BCI techniques in aerospace systems and space explorations. One can imagine the potential that an astronaut operates a robot device by only thinking. Perhaps a revolutionary breakthrough from BCI technology can be the 'copiloting' of aerial vehicles by multiple pilots including some who stations at the ground. This copiloting not only reduces the stress (brain fatigue) of pilots, but also enhances the reliability and fault tolerance of aerial vehicles