28,292 research outputs found
Multi-resource fairness: Objectives, algorithms and performance
Designing efficient and fair algorithms for sharing multiple resources
between heterogeneous demands is becoming increasingly important. Applications
include compute clusters shared by multi-task jobs and routers equipped with
middleboxes shared by flows of different types. We show that the currently
preferred objective of Dominant Resource Fairness has a significantly less
favorable efficiency-fairness tradeoff than alternatives like Proportional
Fairness and our proposal, Bottleneck Max Fairness. In addition to other
desirable properties, these objectives are equally strategyproof in any
realistic scenario with dynamic demand
An Application-Aware Spectrum Sharing Approach for Commercial Use of 3.5 GHz Spectrum
In this paper, we introduce an application-aware spectrum sharing approach
for sharing the Federal under-utilized 3.5 GHz spectrum with commercial users.
In our model, users are running elastic or inelastic traffic and each
application running on the user equipment (UE) is assigned a utility function
based on its type. Furthermore, each of the small cells users has a minimum
required target utility for its application. In order for users located under
the coverage area of the small cells' eNodeBs, with the 3.5 GHz band resources,
to meet their minimum required quality of experience (QoE), the network
operator makes a decision regarding the need for sharing the macro cell's
resources to obtain additional resources. Our objective is to provide each user
with a rate that satisfies its application's minimum required utility through
spectrum sharing approach and improve the overall QoE in the network. We
present an application-aware spectrum sharing algorithm that is based on
resource allocation with carrier aggregation to allocate macro cell permanent
resources and small cells' leased resources to UEs and allocate each user's
application an aggregated rate that can at minimum achieves the application's
minimum required utility. Finally, we present simulation results for the
performance of the proposed algorithm.Comment: Submitted to IEE
Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness
This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner
Enhanced Cluster Computing Performance Through Proportional Fairness
The performance of cluster computing depends on how concurrent jobs share
multiple data center resource types like CPU, RAM and disk storage. Recent
research has discussed efficiency and fairness requirements and identified a
number of desirable scheduling objectives including so-called dominant resource
fairness (DRF). We argue here that proportional fairness (PF), long recognized
as a desirable objective in sharing network bandwidth between ongoing flows, is
preferable to DRF. The superiority of PF is manifest under the realistic
modelling assumption that the population of jobs in progress is a stochastic
process. In random traffic the strategy-proof property of DRF proves
unimportant while PF is shown by analysis and simulation to offer a
significantly better efficiency-fairness tradeoff.Comment: Submitted to Performance 201
Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers
We study the multi-resource allocation problem in cloud computing systems
where the resource pool is constructed from a large number of heterogeneous
servers, representing different points in the configuration space of resources
such as processing, memory, and storage. We design a multi-resource allocation
mechanism, called DRFH, that generalizes the notion of Dominant Resource
Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH
provides a number of highly desirable properties. With DRFH, no user prefers
the allocation of another user; no one can improve its allocation without
decreasing that of the others; and more importantly, no user has an incentive
to lie about its resource demand. As a direct application, we design a simple
heuristic that implements DRFH in real-world systems. Large-scale simulations
driven by Google cluster traces show that DRFH significantly outperforms the
traditional slot-based scheduler, leading to much higher resource utilization
with substantially shorter job completion times
Weighted Max-Min Resource Allocation for Frequency Selective Channels
In this paper, we discuss the computation of weighted max-min rate allocation
using joint TDM/FDM strategies under a PSD mask constraint. We show that the
weighted max-min solution allocates the rates according to a predetermined rate
ratio defined by the weights, a fact that is very valuable for
telecommunication service providers. Furthermore, we show that the problem can
be efficiently solved using linear programming. We also discuss the resource
allocation problem in the mixed services scenario where certain users have a
required rate, while the others have flexible rate requirements. The solution
is relevant to many communication systems that are limited by a power spectral
density mask constraint such as WiMax, Wi-Fi and UWB
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