79,678 research outputs found
Self-Specifying Machines
We study the computational power of machines that specify their own
acceptance types, and show that they accept exactly the languages that
\manyonesharp-reduce to NP sets. A natural variant accepts exactly the
languages that \manyonesharp-reduce to P sets. We show that these two classes
coincide if and only if \psone = \psnnoplusbigohone, where the latter class
denotes the sets acceptable via at most one question to \sharpp followed by
at most a constant number of questions to \np.Comment: 15 pages, to appear in IJFC
Self-Replication and Self-Assembly for Manufacturing
It has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach
to very low-cost manufacturing. The research presented here is intended to
be a step towards this vision. We describe a computational simulation of
nanoscale machines floating in a virtual liquid. The machines can bond
together to form strands (chains) that self-replicate and self-assemble
into user-specified meshes. There are four types of machines and the
sequence of machine types in a strand determines the shape of the mesh
they will build. A strand may be in an unfolded state, in which the bonds
are straight, or in a folded state, in which the bond angles depend on the
types of machines. By choosing the sequence of machine types in a strand,
the user can specify a variety of polygonal shapes. A simulation typically
begins with an initial unfolded seed strand in a soup of unbonded machines.
The seed strand replicates by bonding with free machines in the soup. The
child strands fold into the encoded polygonal shape, and then the polygons
drift together and bond to form a mesh. We demonstrate that a variety of
polygonal meshes can be manufactured in the simulation, by simply changing
the sequence of machine types in the seed
A Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications
We propose a framework for deployment and subsequent autonomic management of
component-based distributed applications. An initial deployment goal is
specified using a declarative constraint language, expressing constraints over
aspects such as component-host mappings and component interconnection topology.
A constraint solver is used to find a configuration that satisfies the goal,
and the configuration is deployed automatically. The deployed application is
instrumented to allow subsequent autonomic management. If, during execution,
the manager detects that the original goal is no longer being met, the
satisfy/deploy process can be repeated automatically in order to generate a
revised deployment that does meet the goal.Comment: Submitted to ICAC-0
Bid-Centric Cloud Service Provisioning
Bid-centric service descriptions have the potential to offer a new cloud
service provisioning model that promotes portability, diversity of choice and
differentiation between providers. A bid matching model based on requirements
and capabilities is presented that provides the basis for such an approach. In
order to facilitate the bidding process, tenders should be specified as
abstractly as possible so that the solution space is not needlessly restricted.
To this end, we describe how partial TOSCA service descriptions allow for a
range of diverse solutions to be proposed by multiple providers in response to
tenders. Rather than adopting a lowest common denominator approach, true
portability should allow for the relative strengths and differentiating
features of cloud service providers to be applied to bids. With this in mind,
we describe how TOSCA service descriptions could be augmented with additional
information in order to facilitate heterogeneity in proposed solutions, such as
the use of coprocessors and provider-specific services
A Middleware Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications
We propose a middleware framework for deployment and subsequent autonomic
management of component-based distributed applications. An initial deployment
goal is specified using a declarative constraint language, expressing
constraints over aspects such as component-host mappings and component
interconnection topology. A constraint solver is used to find a configuration
that satisfies the goal, and the configuration is deployed automatically. The
deployed application is instrumented to allow subsequent autonomic management.
If, during execution, the manager detects that the original goal is no longer
being met, the satisfy/deploy process can be repeated automatically in order to
generate a revised deployment that does meet the goal.Comment: Submitted to Middleware 0
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
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