12,534 research outputs found
Scheduling of data-intensive workloads in a brokered virtualized environment
Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
Towards the fast and robust optimal design of Wireless Body Area Networks
Wireless body area networks are wireless sensor networks whose adoption has
recently emerged and spread in important healthcare applications, such as the
remote monitoring of health conditions of patients. A major issue associated
with the deployment of such networks is represented by energy consumption: in
general, the batteries of the sensors cannot be easily replaced and recharged,
so containing the usage of energy by a rational design of the network and of
the routing is crucial. Another issue is represented by traffic uncertainty:
body sensors may produce data at a variable rate that is not exactly known in
advance, for example because the generation of data is event-driven. Neglecting
traffic uncertainty may lead to wrong design and routing decisions, which may
compromise the functionality of the network and have very bad effects on the
health of the patients. In order to address these issues, in this work we
propose the first robust optimization model for jointly optimizing the topology
and the routing in body area networks under traffic uncertainty. Since the
problem may result challenging even for a state-of-the-art optimization solver,
we propose an original optimization algorithm that exploits suitable linear
relaxations to guide a randomized fixing of the variables, supported by an
exact large variable neighborhood search. Experiments on realistic instances
indicate that our algorithm performs better than a state-of-the-art solver,
fast producing solutions associated with improved optimality gaps.Comment: Authors' manuscript version of the paper that was published in
Applied Soft Computin
Solving Optimization Problems by the Public Goods Game
This document is the Accepted Manuscript version of the following article: Marco Alberto Javarone, ‘Solving optimization problems by the public goods game’, The European Physical Journal B, 90:17, September 2017. Under embargo. Embargo end date: 18 September 2018. The final, published version is available online at doi: https://doi.org/10.1140/epjb/e2017-80346-6. Published by Springer Berlin Heidelberg.We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.Peer reviewedFinal Accepted Versio
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