FUTURE WORK in .NET

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invocation of task T in the next 2 hours. Such annotations will bridge the gap between the end users understanding of the application requirements and their corresponding specification in the ATaG program. The challenge in defining this particular annotation is to devise a mechanism in the runtime which is capable of predicting the resource usage on the node (with some degree of confidence) based on activity observed on that node in the past.
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Resource management in the runtime system
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Two aspects of resource management are of interest in the context of extending the ATaG model. The first deals with the efficient management ofsensing resources and the packaging of sensing as a service provided by the runtime instead of a set of APIs to be learnt by the programmer and invoked by the application-level program. The second aspect deals with allowing the application developer to provide performance-related hints to the compiler. We now discuss each of these in more detail.
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Sensing as a service. Currently, there are three classes of APIs available to the ATaG programmer: (i) the g e t 0 and put 0 calls to the data pool for consuming and producing data items respectively, (ii) the network-awareness and spatial-awareness API (also offered by the runtime system) that allows a task instance to determine the composition of the neighborhood of its host node, and (iii) the API to the sensor interface. Since the task instance directly accesses the sensing interface, the runtime system is not aware of the access patterns and cannot optimize for cases where sensing resources might be used inefficiently. Consider a scenario where a periodic Task A is interested in sensor data not more than 10 minutes old, and Task B is interested in the same sensor data but with a tolerance of 30 minutes. In the current model, TaskA and Task B will be defined as abstract tasks with periodic firing rules with periods of 10minutes and 30 minutes, respectively. The tasks will read from the sensor at each invocation, although it is obvious that frequency of Task A s sampling is sufficient for Task B. A manual optimization in this case is to declare an abstract data item S produced locally by Task A and consumed locally by Task B, and to change the firing rule of Task B to any-data. Task A will now sample the sensing interface at every invocation but will produce an instance of S (containing the sensor reading) every third invocation. However, such manual optimization is not possible if Task A and Task B are part of different ATaG libraries being composed into a larger application.
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THE ABSTRACT TASK GRAPH
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Future work in this area involves the management of sensing (and actuation) resources through the ATaG runtime system. The ATaG model will be extended by defining a special class of read-only abstract data items (called sensor data items ) that can be consumed but not produced by user-defined abstract tasks. These data items will represent readings (scalar values, images, etc.) from the sensing interface(s). Task will access sensor data using the get ( 1 primitive, and the programmer will not be required to learn the details of accessing the variety of sensor interfaces. A set of annotations will be defined for the sensor data items. These annotations could indicate the type of sensing interface and other parameters such as spatial coverage and temporal coverage (frequency of sampling, freshness of data, etc.). This extension will allow the runtime a greater flexibility in task placement and resource management. More importantly, indirect access of sensor interfaces through the runtime system makes ATaG programs even more architecture-independent because the imperative part of the program (i.e., the task code) does not need to incorporate any code that is specific to a particular type of sensor or actuator. Nodes with diffeent sensors of the same type (i.e., producing the same type of sensor data item) can host instances of the same abstract task without the programmer being required to modify the code to adjust for the different sensor APIs.
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Application-level control of system performance. In almost all traditional parallel and distributed computing especially in scientific computing, all data were equal. The scheduling of tasks and handling of data was almost entirely influenced by end-to-end latency considerations. Hence, the many variants of the basic task graph (or other dependency graphs) did not support the concept of varying levels of importance that could be assigned to tasks or data. The nature of networked sensing is such that some data items and computation pathways could have greater importance than others, where importance could imply preferential processing in terms of immediate scheduling of the tasks involved or allocating more resources to ensure that some data items are routed with better quality (e.g., less latency) than others. For example, if the instance of the abstract data item represents the (possible) detection of a forest fire, the application developer would naturally want the runtime system to expedite the transmission of this data from the producer node to the designated supervisor node. Defining and supporting such annotations also requires a close integration with the network model, the architecture of the runtime system, and the availability of protocols that are capable of providing the required services.
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