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Scalable grid resource allocation for scientific workflows using hybrid metaheuristics
Grid infrastructure is a valuable tool for scientific users, but it is characterized by a high level of complexity which makes it difficult for them to quantify their requirements and allocate resources. In this paper, we show that resource trading is a viable and scalable approach for scientific users to consume resources. We propose the use of Grid resource bundles to specify supply and demand combined with a hybrid metaheuristic method to determine the allocation of resources in a market-based approach. We evaluate this through the application domain of scientific workflow execution on the Grid
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Distributed power allocation for D2D communications underlaying/overlaying OFDMA cellular networks
The implementation of device-to-device (D2D) underlaying or overlaying
pre-existing cellular networks has received much attention due to the potential
of enhancing the total cell throughput, reducing power consumption and
increasing the instantaneous data rate. In this paper we propose a distributed
power allocation scheme for D2D OFDMA communications and, in particular, we
consider the two operating modes amenable to a distributed implementation:
dedicated and reuse modes. The proposed schemes address the problem of
maximizing the users' sum rate subject to power constraints, which is known to
be nonconvex and, as such, extremely difficult to be solved exactly. We propose
here a fresh approach to this well-known problem, capitalizing on the fact that
the power allocation problem can be modeled as a potential game. Exploiting the
potential games property of converging under better response dynamics, we
propose two fully distributed iterative algorithms, one for each operation mode
considered, where each user updates sequentially and autonomously its power
allocation. Numerical results, computed for several different user scenarios,
show that the proposed methods, which converge to one of the local maxima of
the objective function, exhibit performance close to the maximum achievable
optimum and outperform other schemes presented in the literature
A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce
Nowadays many companies have available large amounts of raw, unstructured
data. Among Big Data enabling technologies, a central place is held by the
MapReduce framework and, in particular, by its open source implementation,
Apache Hadoop. For cost effectiveness considerations, a common approach entails
sharing server clusters among multiple users. The underlying infrastructure
should provide every user with a fair share of computational resources,
ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In
this paper we consider two mathematical programming problems that model the
optimal allocation of computational resources in a Hadoop 2.x cluster with the
aim to develop new capacity allocation techniques that guarantee better
performance in shared data centers. Our goal is to get a substantial reduction
of power consumption while respecting the deadlines stated in the SLAs and
avoiding penalties associated with job rejections. The core of this approach is
a distributed algorithm for runtime capacity allocation, based on Game Theory
models and techniques, that mimics the MapReduce dynamics by means of
interacting players, namely the central Resource Manager and Class Managers
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