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Orleans is a software framework for building reliable, scalable, and elastic cloud applications. Its programming model encourages the use of simple concurrency patterns that are easy to understand and employ correctly. It is based on distributed actor-like components called grains, which are isolated units of state and computation that communicate through asynchronous messages. Within a grain, promises are the mechanism for managing both asynchronous messages and local task-based concurrency. Isolated state and a constrained execution model allow Orleans to persist, migrate, replicate, and reconcile grain state. In addition, Orleans provides lightweight transactions that support a consistent view of state and provide a foundation for automatic error handling and failure recovery. We implemented several applications in Orleans, varying from a messaging-intensive social networking application to a data- and compute-intensive linear algebra computation. The programming model is a general one, as Orleans allows the communications to evolve dynamically at runtime. Orleans enables a developer to concentrate on application logic, while the Orleans runtime provides scalability, availability, and reliability.
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xmeteorite
Orleans is a software framework for building reliable, scalable, and elastic cloud applications. Its programming model encourages the use of simple concurrency patterns that are easy to understand and employ correctly. It is based on distributed actor-like components called grains, which are isolated units of state and computation that communicate through asynchronous messages. Within a grain, promises are the mechanism for managing both asynchronous messages and local task-based concurrency. Isolated state and a constrained execution model allow Orleans to persist, migrate, replicate, and reconcile grain state. In addition, Orleans provides lightweight transactions that support a consistent view of state and provide a foundation for automatic error handling and failure recovery. We implemented several applications in Orleans, varying from a messaging-intensive social networking application to a data- and compute-intensive linear algebra computation. The programming model is a general one, as Orleans allows the communications to evolve dynamically at runtime. Orleans enables a developer to concentrate on application logic, while the Orleans runtime provides scalability, availability, and reliability.
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[GDSC YCCE] Build with AI Online Presentation
[GDSC YCCE] Build with AI Online Presentation
Benefits and Challenges of Using Open Educational Resources
Benefits and Challenges of Using Open Educational Resources
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Apmac poster 3 collums
1.
Funded by
Economic Managing A Cloud for Multi-agent Systems on Ad-hoc Networks Development Subhajit Sidhanta , Supratik Mukhopadhyay Assistantship Department of Computer Science Louisiana State University Why APMAC? APMAC architecture Contributions Multi-agent systems are software frameworks where APMAC ARCHITECTURE PPPDOS ARCHITECTURE We present the architecture, design, and autonomous intelligent agents interact with each other implementation of an ad-hoc cloud computing to achieve a computational goal. Existing high framework that can be readily assembled on-the-fly performance computing infrastructures such as clusters by connecting together heterogeneous mobile or cloud data centers may not be accessible in remote devices and is suitable for running multi-agent areas with intermittent network connectivity such as reasoning engines. We have designed and in military battlefields, disaster scenarios, etc.. The implemented a peer-to-peer distributed microkernel- availability of a high performance computing based operating system (PPDOS) that manages an infrastructure is essential for the success of such APMAC environment by providing a single machine, systems. An agent running on APMAC has a strongly consistent view of the infrastructure with a predefined structure shown below. single file system while managing resources and public class ReactiveAgent implements facilitating automated deployment and execution of agents. The microkernel of PPDOS is itself a Deterministic collection of agents, making it possible to , React ive { dynamically modify it by adding/deleting / / Cont rol l ed Var iables : Declaration of control features/modules without any stoppage in service. d variabl esMonitored Var iables ; The APMAC design enables multi-agent systems running / / Dec l arat i on of Monitored var i abl es on it to maintain autonomy, reactivity, deliberativity and interaction. PPDOS provide the user with the / / I n t e r n a l Var iables ; view of a single machine with a single file system, a / / Dec l arat i on of I n t e r n a l var i abl es single programming model, and a Unix-like interface. void i n i t ( ) . . . . / / i n i t i a l i z e s c o n t r o l Agents (both user and microkernel) communicate l e d var i abl es peer-to-peer asynchronously through a distributed For a distributed key-value store, we use Scalaris . It is an open source distributed key-value store that key-value store (similar to a Linda’s tuple-space [6]) / / cons t ruc tor method supports transactions. We have implemented PPDOS in Java. The PPDOS microkernel consists of a set of which supports transactions. PPDOS’s group ReactiveAgent ( ) { “system” agents that provides operating systems services and manage the deployment and execution of user management module maintains a distributed strongly init(); agents. The PPDOS microkernel and the deployed user agents communicate asynchronously through the consistent state of the system in the presence of for each monitored v a r i a b l e x Scalaris distributed key-value store. joining/leaving of nodes. PPDOS allows hot-swapping of one agent with another at runtime without any SUBSCRIBE ( x , leaseX ) disruption of service. Since for each c o n t r o l l e d v a r i a b l e y Results PPDOS microkernel is a collection of agents; we can REGISTER( y ) } dynamically extend/modify the functionality of the Figure 1 Figure 2 Figure 3 system without disrupting its functioning. An agent / / Methods spec i f y i ng update / / of c o n t r o l l e d var i abl es can migrate from one node to another at runtime while preserving its local state. PPDOS allows void Run ( ) { interaction with the native operating systems of the while ( t rue ) { nodes. Users interact with PPDOS through a command i f ( migrateSignalPresent ) shell deploying tasks and accessing resources. Operating system services like file services, deploying prepareForMigrat ion ( ) ; and unloading agents, time, etc., are provided by a else set of agents. The microkernel agents ensure that a { for each monitored v a r i a b l e x snapshot of the state of the system is always stored <valX , seqX> = read ( x ) ; in distributed key-value store. for each c o n t r o l l e d v a r i a b l e y { y=update ( valX ) ; Future Work PUBLISH ( y ) } We are currently working on developing a monitor to maintain for each monitored v a r i a b l e x synchronization of the nodes running PPDOS. DELETE( x , state , seqX) ; } Need to work on a dynamic scheduling mechanism for PPSOS which is currently happening manually. }}} Currently working on automatic generation of APMAC agents from void prepareForMigrat ion ( ) { The verification effort was carried out on a network of 10 machines. The graphs above demonstrate PPDOS’s performance specification and running them on PPDOS. writeToKeyValueStore ( " ready f o r mi grat i on " ) ; on various scales. The figure 1 compares the performance with that of standard Operating Systems like Windows 7 and There is a scope of integration of the APMAC private cloud with public cloud like Amazon thus making it more flexible. while ( t rue ) Ubuntu . The figure 2 shows PPDOS’s performance with increase of number of agents loaded. The figure 3 demonstrates the performance of complex learning agents on PPDOS. } RESEARCH POSTER PRESENTATION DESIGN © 2011 www.PosterPresentations.com
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