Impact of Size-Based Scheduling on Flow Level Performance in Wireless Downlink Data Channels

Aalto, Samuli; Lassila, Pasi

doi:10.1007/978-3-540-72990-7_94

Cited by 17 publications

(18 citation statements)

References 11 publications

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“…Size-based scheduling has received a lot of attention from the research community with applications to Web servers [15], Internet traffic [3,14,16] or 3G networks [2,10]. The key idea is to favor short flows at the expense of long ones because short flows are in general related to interactive applications like Email, Web browsing or DNS requests/responses; unlike long flows which represent background traffic.…”

Section: Introductionmentioning

confidence: 99%

EFD: An Efficient Low-Overhead Scheduler

2011

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Section: Introductionmentioning

confidence: 99%

EFD: An Efficient Low-Overhead Scheduler

2011

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“…Aalto et al [14] compared various scheduling algorithms from link delay and fairness aspects, and found that scheduling algorithms in the access network have their impact on the observed traffic in the core network.…”

Section: Model Creationmentioning

confidence: 99%

LTE core network testing using generated traffic based on models from real-life data

Varga

Olaszi

2013

2013 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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The aim of this paper is to describe a general methodology for testing the mobile network core with traffic generated from a simulated access-segment. A main element of this methodology is to derive load models and mobility models from real-life traffic patterns, and use these -as well as prerecorded protocol messages -in the process. The Evolved Packet Core (EPC) of Long Term Evolution (LTE) provides a good base for presenting practical elements of this methodology. This paper demonstrates the workflow of this methodology through a practical example of EPC load testing. This demonstration covers issues related to nodes and interfaces covered during such tests; dialogs used for session and mobility control; and crosscorrelating message flows that effect various elements across the architecture.

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“…Note that using knowledge of remaining flow sizes could still be exploited, cf. use of Shortest Remaining Processing Time (SRPT) as studied in [13].…”

Section: B Utility-based Policiesmentioning

confidence: 99%

“…We then experiment with ideas to minimize the flow-level delay. Assuming that the base station does not use instantaneous rate information, the system can be modeled as an M/G/1 queue and the benefits of size-based scheduling prove to be significant, see [13]. However, combining sizebased and opportunistic scheduling is difficult and no strong structural results on the optimal policy exist.…”

Section: Introductionmentioning

confidence: 99%

Flow-level stability and performance of channel-aware priority-based schedulers

Aalto

Lassila

2010

6th EURO-NGI Conference on Next Generation Internet

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Abstract-Channel-aware scheduling in modern wireless networks enables the system to exploit the random rate variations across different users to increase the performance of the system. We analyze channel-aware priority-based downlink scheduling policies at the so-called flow level with a stochastically varying number of users. The priority can be any monotonously increasing function of the instantaneous rate of the user, which generalizes the well-known linear weight-based policies. Also, ties are allowed within a user class, as well as between user classes. As the main result, we characterize when these priority-based policies are stable under an intuitive necessary condition, which holds for arbitrary tie breaking rules and is independent of the flow size distribution. Additionally, for the policies for which the necessary condition is not sufficient, a more stringent condition is derived in the case of two traffic classes. Finally, extensive simulations have been performed to compare the performance of different priority-based and utility-based policies. I. INTRODUCTIONOne key aspect in modern cellular systems affecting the performance is the scheduling of the radio resources among the users. In these systems, time is slotted and scheduling decisions can be made at the time scale of milliseconds. The fast scheduling and channel quality feedback enables the system to optimize the scheduling for data traffic by exploiting the fading phenomena causing random variations in the channel quality across the users. These channel-aware (also known as opportunistic) schedulers aim at increasing the throughput of the system by favoring those users having instantaneously good channels. A well-known example is the proportionally fair (PF) scheduler proposed for 1xEV-DO systems, see [1].Channel-aware schedulers can be broadly classified as utility/maxweight based and rate-based priority schedulers. Utility/maxweight based approaches use instantaneous rate information coupled with knowledge of the throughput/queue sizes/packet delays, see [2], [3], [4]. The rate-based approaches, such as the relatively best (RB) scheduler [5] and its generalizations to weight-based strategies [6], only use information about the rate (channel) statistics. Typically, these channel-aware schedulers have been analyzed at the time slot or the packet level with a static population of users, either assuming a saturated set of users, see [5], [4] or allowing packet level-dynamics and analyzing the system at the heavy traffic limit [2], [7]. The results have established that simple stochastic gradient policies of the utility and so-called maxweight policies (of either packet delays or queue lengths) have many desirable properties. However, in the above models the common aspect is that the number of users stays constant,

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Impact of Size-Based Scheduling on Flow Level Performance in Wireless Downlink Data Channels

Cited by 17 publications

References 11 publications

EFD: An Efficient Low-Overhead Scheduler

EFD: An Efficient Low-Overhead Scheduler

LTE core network testing using generated traffic based on models from real-life data

Flow-level stability and performance of channel-aware priority-based schedulers

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