IEEE Cloud 2012 poster presentation

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.
}
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