463 research outputs found
Asymptotic Laws for Joint Content Replication and Delivery in Wireless Networks
We investigate on the scalability of multihop wireless communications, a
major concern in networking, for the case that users access content replicated
across the nodes. In contrast to the standard paradigm of randomly selected
communicating pairs, content replication is efficient for certain regimes of
file popularity, cache and network size. Our study begins with the detailed
joint content replication and delivery problem on a 2D square grid, a hard
combinatorial optimization. This is reduced to a simpler problem based on
replication density, whose performance is of the same order as the original.
Assuming a Zipf popularity law, and letting the size of content and network
both go to infinity, we identify the scaling laws and regimes of the required
link capacity, ranging from O(\sqrt{N}) down to O(1)
Wireless Network Stability in the SINR Model
We study the stability of wireless networks under stochastic arrival
processes of packets, and design efficient, distributed algorithms that achieve
stability in the SINR (Signal to Interference and Noise Ratio) interference
model.
Specifically, we make the following contributions. We give a distributed
algorithm that achieves -efficiency on all networks
(where is the number of links in the network), for all length monotone,
sub-linear power assignments. For the power control version of the problem, we
give a distributed algorithm with -efficiency (where is the length diversity of the link set).Comment: 10 pages, appeared in SIROCCO'1
Exploiting Caching and Multicast for 5G Wireless Networks
The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and industry in evolving traditional cellular networks, the enabling technology for 5G is still obscure. This paper puts forward a network paradigm toward next-generation cellular networks, targeting to satisfy the explosive demand for mobile data while minimizing energy expenditures. The paradigm builds on two principles; namely caching and multicast. On one hand, caching policies disperse popular content files at the wireless edge, e.g., pico-cells and femto-cells, hence shortening the distance between content and requester. On other hand, due to the broadcast nature of wireless medium, requests for identical files occurring at nearby times are aggregated and served through a common multicast stream. To better exploit the available cache space, caching policies are optimized based on multicast transmissions. We show that the multicast-aware caching problem is NP-hard and develop solutions with performance guarantees using randomized-rounding techniques. Trace-driven numerical results show that in the presence of massive demand for delay tolerant content, combining caching and multicast can indeed reduce energy costs. The gains over existing caching schemes are 19% when users tolerate delay of three minutes, increasing further with the steepness of content access pattern
Energy-Aware Base Stations: The Effect of Planning, Management, and Femto Layers
We compare the performance of three base station management schemes on three different network topologies. In addition, we explore the effect of offloading traffic to heterogeneous femtocell layer upon energy savings taking into account the increase of base station switch-off time intervals. Fairness between mobile operator and femtocell owners is maintained since current femtocell technologies present flat power consumption curves with respect to served traffic. We model two different user-to-femtocell association rules in order to capture realistic and maximum gains from the heterogeneous network. To provide accurate findings and a holistic overview of the techniques, we explore a real urban district where channel estimations and power control are modeled using deterministic algorithms. Finally, we explore energy efficiency metrics that capture savings in the mobile network operator, the required watts per user and watts per bitrate. It is found that the newly established pseudo distributed management scheme is the most preferable solution for practical implementations and together with the femotcell layer the network can handle dynamic load control that is regarded as the basic element of future demand response programs
Concave Switching in Single and Multihop Networks
Switched queueing networks model wireless networks, input queued switches and
numerous other networked communications systems. For single-hop networks, we
consider a {()-switch policy} which combines the MaxWeight policies
with bandwidth sharing networks -- a further well studied model of Internet
congestion. We prove the maximum stability property for this class of
randomized policies. Thus these policies have the same first order behavior as
the MaxWeight policies. However, for multihop networks some of these
generalized polices address a number of critical weakness of the
MaxWeight/BackPressure policies.
For multihop networks with fixed routing, we consider the Proportional
Scheduler (or (1,log)-policy). In this setting, the BackPressure policy is
maximum stable, but must maintain a queue for every route-destination, which
typically grows rapidly with a network's size. However, this proportionally
fair policy only needs to maintain a queue for each outgoing link, which is
typically bounded in number. As is common with Internet routing, by maintaining
per-link queueing each node only needs to know the next hop for each packet and
not its entire route. Further, in contrast to BackPressure, the Proportional
Scheduler does not compare downstream queue lengths to determine weights, only
local link information is required. This leads to greater potential for
decomposed implementations of the policy. Through a reduction argument and an
entropy argument, we demonstrate that, whilst maintaining substantially less
queueing overhead, the Proportional Scheduler achieves maximum throughput
stability.Comment: 28 page
Robust Routing in Networks of Mobile Radio Nodes
Mobile radio communication networks in the operational theater exhibit volatile network topology with rapid connectivity changes due to node mobility and the harsh conditions in the battlefield environment. Reliable packet transport becomes a challenging task in view of the constant connectivity changes. In this paper we present a systematic approach for reliable packet routing in mobile networks. A class of topology models that is broad enough to capture the topology changes encountered in a mobile network is introduced. The optimal routing policy is specified in terms of the Directed Acyclic shortest path Routing Graph (DARG). Two algorithms for computing the DARG are proposed. One of the two algorithms is iterative and amendable to distributed implementation. The DARG provided shortest path routing in random connectivity networks in analogy to the shortest path tree in fixed connectivity networks
Learning the optimal synchronization rates in distributed SDN control architectures
Since the early development of Software-DefinedNetwork (SDN) technology, researchers have been concernedwith the idea of physical distribution of the control plane to ad-dress scalability and reliability challenges of centralized designs.However, having multiple controllers managing the networkwhile maintaining a “logically-centralized” network view bringsadditional challenges. One such challenge is how to coordinatethe management decisions made by the controllers which isusually achieved by disseminating synchronization messages ina peer-to-peer manner. While there exist many architecturesand protocols to ensure synchronized network views and drivecoordination among controllers, there is no systematic method-ology for deciding the optimal frequency (or rate) of messagedissemination. In this paper, we fill this gap by introducingthe SDN synchronization problem: how often to synchronize thenetwork views for each controller pair. We consider two differentobjectives; first, the maximization of the number of controllerpairs that are synchronized, and second, the maximization of theperformance of applications of interest which may be affectedby the synchronization rate. Using techniques from knapsackoptimization and learning theory, we derive algorithms withprovable performance guarantees for each objective. Evaluationresults demonstrate significant benefits over baseline schemes thatsynchronize all controller pairs at equal rate
Robust solutions to constrained optimization problems by LSTM networks
Many technical issues for communications and computer infrastructures, including resource sharing, network management and distributed analytics, can be formulated as optimization problems. Gradient-based iterative algorithms have been widely utilized to solve these problems. Much research focuses on improving the iteration convergence. However, when system parameters change, it requires a new solution from the iterative methods. Therefore, it is helpful to develop machine-learning solution frameworks that can quickly produce solutions over a range of system parameters. We propose here a learning approach to solve non-convex, constrained optimization problems. Two coupled Long Short Term Memory (LSTM) networks are used to find the optimal solution. The advantages of this new framework include: (1) near optimal solution for a given problem instance can be obtained in very few iterations (time steps) during the inference process, (2) the learning approach allows selections of various hyper-parameters to achieve desirable tradeoffs between the training time and the solution quality, and (3) the coupled-LSTM networks can be trained using system parameters with distributions different from those used during inference to generate solutions, thus enhancing the robustness of the learning technique. Numerical experiments using a dataset from Alibaba reveal that the relative discrepancy between the generated solution and the optimum is less than 1% and 0.1% after 2 and 12 iterations, respectively
Novel Information Distribution Methods to Massive Mobile User Populations
Broadcast data delivery is encountered in many applications where there is a need to disseminate information to a large user community in a wireless asymmetric military communication environment. In this paper, we consider two types of broadcast data delivery systems, namely, push-based and pull-based, and provide a low-complexity near-optimal scheduling algorithms for both systems. By using a numerical study we also discuss the performance limit of a pull-based system. In addition, we identify the optimal memory management policy for the users in a push-based broadcast delivery system and propose implementable alternatives to the optimal policy
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