15,977 research outputs found
A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs
Coordinated multi-point (CoMP) schemes have been widely studied in the recent
years to tackle the inter-cell interference. In practice, latency and
throughput constraints on the backhaul allow the organization of only small
clusters of base stations (BSs) where joint processing (JP) can be implemented.
In this work we focus on downlink CoMP-JP with multiple antenna user equipments
(UEs) and propose a novel dynamic clustering algorithm. The additional degrees
of freedom at the UE can be used to suppress the residual interference by using
an interference rejection combiner (IRC) and allow a multistream transmission.
In our proposal we first define a set of candidate clusters depending on
long-term channel conditions. Then, in each time block, we develop a resource
allocation scheme by jointly optimizing transmitter and receiver where: a)
within each candidate cluster a weighted sum rate is estimated and then b) a
set of clusters is scheduled in order to maximize the system weighted sum rate.
Numerical results show that much higher rates are achieved when UEs are
equipped with multiple antennas. Moreover, as this performance improvement is
mainly due to the IRC, the gain achieved by the proposed approach with respect
to the non-cooperative scheme decreases by increasing the number of UE
antennas.Comment: 27 pages, 8 figure
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
Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing
A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access
Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to
a central Virtual Base Station (VBS) pool. In particular, the key capabilities
of C-RANs in computing-resource sharing and real-time communication among the
VBSs are leveraged to design a joint dynamic radio clustering and cooperative
beamforming scheme that maximizes the downlink weighted sum-rate system utility
(WSRSU). Due to the combinatorial nature of the radio clustering process and
the non-convexity of the cooperative beamforming design, the underlying
optimization problem is NP-hard, and is extremely difficult to solve for a
large network. Our approach aims for a suboptimal solution by transforming the
original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP),
which can be solved efficiently using a proposed iterative algorithm. Numerical
simulation results show that our low-complexity algorithm provides
close-to-optimal performance in terms of WSRSU while significantly
outperforming conventional radio clustering and beamforming schemes.
Additionally, the results also demonstrate the significant improvement in
computing-resource utilization of C-RANs over traditional RANs with distributed
computing resources.Comment: 9 pages, 6 figures, accepted to IEEE MASS 201
Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems
In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA
system (named as secondary system) dynamically accessing a spectrum licensed to
a primary network, thereby improving the efficiency of spectrum usage. A
cluster-based relay-assisted architecture is proposed for the secondary system,
where relay stations are employed for minimizing the interference to the users
in the primary network and achieving fairness for cell-edge users. Based on
this architecture, an asymptotically optimal solution is derived for jointly
controlling data rates, transmission power, and subchannel allocation to
optimize the average weighted sum goodput where the proportional fair
scheduling (PFS) is included as a special case. This solution supports
decentralized implementation, requires small communication overhead, and is
robust against imperfect channel state information at the transmitter (CSIT)
and sensing measurement. The proposed solution achieves significant throughput
gains and better user-fairness compared with the existing designs. Finally, we
derived a simple and asymptotically optimal scheduling solution as well as the
associated closed-form performance under the proportional fair scheduling for a
large number of users. The system throughput is shown to be
, where is the
number of users in one cluster, is the number of subchannels and is
the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL
PROCESSIN
Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?
In order to meet the performance/privacy requirements of future
data-intensive mobile applications, e.g., self-driving cars, mobile data
analytics, and AR/VR, service providers are expected to draw on shared
storage/computation/connectivity resources at the network "edge". To be
cost-effective, a key functional requirement for such infrastructure is
enabling the sharing of heterogeneous resources amongst tenants/service
providers supporting spatially varying and dynamic user demands. This paper
proposes a resource allocation criterion, namely, Share Constrained Slicing
(SCS), for slices allocated predefined shares of the network's resources, which
extends the traditional alpha-fairness criterion, by striking a balance among
inter- and intra-slice fairness vs. overall efficiency. We show that SCS has
several desirable properties including slice-level protection, envyfreeness,
and load driven elasticity. In practice, mobile users' dynamics could make the
cost of implementing SCS high, so we discuss the feasibility of using a simpler
(dynamically) weighted max-min as a surrogate resource allocation scheme. For a
setting with stochastic loads and elastic user requirements, we establish a
sufficient condition for the stability of the associated coupled network
system. Finally, and perhaps surprisingly, we show via extensive simulations
that while SCS (and/or the surrogate weighted max-min allocation) provides
inter-slice protection, they can achieve improved job delay and/or perceived
throughput, as compared to other weighted max-min based allocation schemes
whose intra-slice weight allocation is not share-constrained, e.g., traditional
max-min or discriminatory processor sharing
Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness
This paper addresses coordinated downlink beamforming optimization in
multicell time-division duplex (TDD) systems where a small number of parameters
are exchanged between cells but with no data sharing. With the goal to reach
the point on the Pareto boundary with max-min rate fairness, we first develop a
two-step centralized optimization algorithm to design the joint beamforming
vectors. This algorithm can achieve a further sum-rate improvement over the
max-min optimal performance, and is shown to guarantee max-min Pareto
optimality for scenarios with two base stations (BSs) each serving a single
user. To realize a distributed solution with limited intercell communication,
we then propose an iterative algorithm by exploiting an approximate
uplink-downlink duality, in which only a small number of positive scalars are
shared between cells in each iteration. Simulation results show that the
proposed distributed solution achieves a fairness rate performance close to the
centralized algorithm while it has a better sum-rate performance, and
demonstrates a better tradeoff between sum-rate and fairness than the Nash
Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201
Optimizing egalitarian performance in the side-effects model of colocation for data center resource management
In data centers, up to dozens of tasks are colocated on a single physical
machine. Machines are used more efficiently, but tasks' performance
deteriorates, as colocated tasks compete for shared resources. As tasks are
heterogeneous, the resulting performance dependencies are complex. In our
previous work [18] we proposed a new combinatorial optimization model that uses
two parameters of a task - its size and its type - to characterize how a task
influences the performance of other tasks allocated to the same machine.
In this paper, we study the egalitarian optimization goal: maximizing the
worst-off performance. This problem generalizes the classic makespan
minimization on multiple processors (P||Cmax). We prove that
polynomially-solvable variants of multiprocessor scheduling are NP-hard and
hard to approximate when the number of types is not constant. For a constant
number of types, we propose a PTAS, a fast approximation algorithm, and a
series of heuristics. We simulate the algorithms on instances derived from a
trace of one of Google clusters. Algorithms aware of jobs' types lead to better
performance compared with algorithms solving P||Cmax.
The notion of type enables us to model degeneration of performance caused by
using standard combinatorial optimization methods. Types add a layer of
additional complexity. However, our results - approximation algorithms and good
average-case performance - show that types can be handled efficiently.Comment: Author's version of a paper published in Euro-Par 2017 Proceedings,
extends the published paper with addtional results and proof
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
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