578,461 research outputs found
Constraint-based protocols for distributed problem solving
AbstractDistributed Problem Solving (DPS) approaches decompose problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for solving problems characterized by many interdependencies among subproblems in the context of parallel and distributed architectures. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS where constraints define local problem solving and the exchange of information among agents declaratively. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different problems and we show how the framework applies to simple yet generalizable examples
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed
Computing (ICDCS 2017
A Generic Agent Organisation Framework For Autonomic Systems
Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems
Tarmo: A Framework for Parallelized Bounded Model Checking
This paper investigates approaches to parallelizing Bounded Model Checking
(BMC) for shared memory environments as well as for clusters of workstations.
We present a generic framework for parallelized BMC named Tarmo. Our framework
can be used with any incremental SAT encoding for BMC but for the results in
this paper we use only the current state-of-the-art encoding for full PLTL.
Using this encoding allows us to check both safety and liveness properties,
contrary to an earlier work on distributing BMC that is limited to safety
properties only.
Despite our focus on BMC after it has been translated to SAT, existing
distributed SAT solvers are not well suited for our application. This is
because solving a BMC problem is not solving a set of independent SAT instances
but rather involves solving multiple related SAT instances, encoded
incrementally, where the satisfiability of each instance corresponds to the
existence of a counterexample of a specific length. Our framework includes a
generic architecture for a shared clause database that allows easy clause
sharing between SAT solver threads solving various such instances.
We present extensive experimental results obtained with multiple variants of
our Tarmo implementation. Our shared memory variants have a significantly
better performance than conventional single threaded approaches, which is a
result that many users can benefit from as multi-core and multi-processor
technology is widely available. Furthermore we demonstrate that our framework
can be deployed in a typical cluster of workstations, where several multi-core
machines are connected by a network
DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework
In a Spiking Neural Networks (SNN), spike emissions are sparsely and
irregularly distributed both in time and in the network architecture. Since a
current feature of SNNs is a low average activity, efficient implementations of
SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand,
simulations of large scale neural networks can take advantage of distributing
the neurons on a set of processors (either workstation cluster or parallel
computer). This article presents DAMNED, a large scale SNN simulation framework
able to gather the benefits of EDS and parallel computing. Two levels of
parallelism are combined: Distributed mapping of the neural topology, at the
network level, and local multithreaded allocation of resources for simultaneous
processing of events, at the neuron level. Based on the causality of events, a
distributed solution is proposed for solving the complex problem of scheduling
without synchronization barrier.Comment: 6 page
Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks
We study the downlink linear precoder design problem in a multi-cell dense
heterogeneous network (HetNet). The problem is formulated as a general
sum-utility maximization (SUM) problem, which includes as special cases many
practical precoder design problems such as multi-cell coordinated linear
precoding, full and partial per-cell coordinated multi-point transmission,
zero-forcing precoding and joint BS clustering and beamforming/precoding. The
SUM problem is difficult due to its non-convexity and the tight coupling of the
users' precoders. In this paper we propose a novel convex approximation
technique to approximate the original problem by a series of convex
subproblems, each of which decomposes across all the cells. The convexity of
the subproblems allows for efficient computation, while their decomposability
leads to distributed implementation. {Our approach hinges upon the
identification of certain key convexity properties of the sum-utility
objective, which allows us to transform the problem into a form that can be
solved using a popular algorithmic framework called BSUM (Block Successive
Upper-Bound Minimization).} Simulation experiments show that the proposed
framework is effective for solving interference management problems in large
HetNet.Comment: Accepted by IEEE Transactions on Wireless Communicatio
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