30,561 research outputs found
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
Context-Aware Resource Allocation in Cellular Networks
We define and propose a resource allocation architecture for cellular
networks. The architecture combines content-aware, time-aware and
location-aware resource allocation for next generation broadband wireless
systems. The architecture ensures content-aware resource allocation by
prioritizing real-time applications users over delay-tolerant applications
users when allocating resources. It enables time-aware resource allocation via
traffic-dependent pricing that varies during different hours of day (e.g. peak
and off-peak traffic hours). Additionally, location-aware resource allocation
is integrable in this architecture by including carrier aggregation of various
frequency bands. The context-aware resource allocation is an optimal and
flexible architecture that can be easily implemented in practical cellular
networks. We highlight the advantages of the proposed network architecture with
a discussion on the future research directions for context-aware resource
allocation architecture. We also provide experimental results to illustrate a
general proof of concept for this new architecture.Comment: (c) 2015 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid
In this paper, the production decisions across multiple energy suppliers in
smart grid, powering cellular networks are investigated. The suppliers are
characterized by different offered prices and pollutant emissions levels. The
challenge is to decide the amount of energy provided by each supplier to each
of the operators such that their profitability is maximized while respecting
the maximum tolerated level of CO2 emissions. The cellular operators are
characterized by their offered quality of service (QoS) to the subscribers and
the number of users that determines their energy requirements. Stochastic
geometry is used to determine the average power needed to achieve the target
probability of coverage for each operator. The total average power requirements
of all networks are fed to an optimization framework to find the optimal amount
of energy to be provided from each supplier to the operators. The generalized
-fair utility function is used to avoid production bias among the
suppliers based on profitability of generation. Results illustrate the
production behavior of the energy suppliers versus QoS level, cost of energy,
capacity of generation, and level of fairness.Comment: 6 pages, 4 figure
A Delay-Optimal Packet Scheduler for M2M Uplink
In this paper, we present a delay-optimal packet scheduler for processing the
M2M uplink traffic at the M2M application server (AS). Due to the
delay-heterogeneity in uplink traffic, we classify it broadly into
delay-tolerant and delay-sensitive traffic. We then map the diverse delay
requirements of each class to sigmoidal functions of packet delay and formulate
a utility-maximization problem that results in a proportionally fair
delay-optimal scheduler. We note that solving this optimization problem is
equivalent to solving for the optimal fraction of time each class is served
with (preemptive) priority such that it maximizes the system utility. Using
Monte-Carlo simulations for the queuing process at AS, we verify the
correctness of the analytical result for optimal scheduler and show that it
outperforms other state-of-the-art packet schedulers such as weighted round
robin, max-weight scheduler, fair scheduler and priority scheduling. We also
note that at higher traffic arrival rate, the proposed scheduler results in a
near-minimal delay variance for the delay-sensitive traffic which is highly
desirable. This comes at the expense of somewhat higher delay variance for
delay-tolerant traffic which is usually acceptable due to its delay-tolerant
nature.Comment: Accepted for publication in IEEE MILCOM 2016 (6 pages, 7 figures
- …