4,111 research outputs found
Using Negotiation to Reduce Redundant Autonomous Mobile Program Movements
Distributed load managers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of Autonomous Mobile Programs (AMPs) exhibit the same behaviour, identifying two types of redundant movement and terming them greedy effects. AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs. The paper explores the extent of greedy effects by simulation, and then proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation
A Lightweight Distributed Solution to Content Replication in Mobile Networks
Performance and reliability of content access in mobile networks is
conditioned by the number and location of content replicas deployed at the
network nodes. Facility location theory has been the traditional, centralized
approach to study content replication: computing the number and placement of
replicas in a network can be cast as an uncapacitated facility location
problem. The endeavour of this work is to design a distributed, lightweight
solution to the above joint optimization problem, while taking into account the
network dynamics. In particular, we devise a mechanism that lets nodes share
the burden of storing and providing content, so as to achieve load balancing,
and decide whether to replicate or drop the information so as to adapt to a
dynamic content demand and time-varying topology. We evaluate our mechanism
through simulation, by exploring a wide range of settings and studying
realistic content access mechanisms that go beyond the traditional
assumptionmatching demand points to their closest content replica. Results show
that our mechanism, which uses local measurements only, is: (i) extremely
precise in approximating an optimal solution to content placement and
replication; (ii) robust against network mobility; (iii) flexible in
accommodating various content access patterns, including variation in time and
space of the content demand.Comment: 12 page
Hierarchical Parallelisation of Functional Renormalisation Group Calculations -- hp-fRG
The functional renormalisation group (fRG) has evolved into a versatile tool
in condensed matter theory for studying important aspects of correlated
electron systems. Practical applications of the method often involve a high
numerical effort, motivating the question in how far High Performance Computing
(HPC) can leverage the approach. In this work we report on a multi-level
parallelisation of the underlying computational machinery and show that this
can speed up the code by several orders of magnitude. This in turn can extend
the applicability of the method to otherwise inaccessible cases. We exploit
three levels of parallelisation: Distributed computing by means of Message
Passing (MPI), shared-memory computing using OpenMP, and vectorisation by means
of SIMD units (single-instruction-multiple-data). Results are provided for two
distinct High Performance Computing (HPC) platforms, namely the IBM-based
BlueGene/Q system JUQUEEN and an Intel Sandy-Bridge-based development cluster.
We discuss how certain issues and obstacles were overcome in the course of
adapting the code. Most importantly, we conclude that this vast improvement can
actually be accomplished by introducing only moderate changes to the code, such
that this strategy may serve as a guideline for other researcher to likewise
improve the efficiency of their codes
Optimal provision of distributed reserves under dynamic energy service preferences
We propose and solve a stochastic dynamic programming (DP) problem addressing the optimal provision of regulation service reserves (RSR) by controlling dynamic demand preferences in smart buildings. A major contribution over past dynamic pricing work is that we pioneer the relaxation of static, uniformly distributed utility of demand. In this paper we model explicitly the dynamics of energy service preferences leading to a non-uniform and time varying probability distribution of demand utility. More explicitly, we model active and idle duty cycle appliances in a smart building as a closed queuing system with price-controlled arrival rates into the active appliance queue. Focusing on cooling appliances, we model the utility associated with the transition from idle to active as a non-uniform time varying function. We (i) derive an analytic characterization of the optimal policy and the differential cost function, and (ii) prove optimal policy monotonicity and value function convexity. These properties enable us to propose and implement a smart assisted value iteration (AVI) algorithm and an approximate DP (ADP) that exploits related functional approximations. Numerical results demonstrate the validity of the solution techniques and the computational advantage of the proposed ADP on realistic, large-state-space problems
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