3,561 research outputs found
Joint dimensioning of server and network infrastructure for resilient optical grids/clouds
We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful
Stochastic Online Shortest Path Routing: The Value of Feedback
This paper studies online shortest path routing over multi-hop networks. Link
costs or delays are time-varying and modeled by independent and identically
distributed random processes, whose parameters are initially unknown. The
parameters, and hence the optimal path, can only be estimated by routing
packets through the network and observing the realized delays. Our aim is to
find a routing policy that minimizes the regret (the cumulative difference of
expected delay) between the path chosen by the policy and the unknown optimal
path. We formulate the problem as a combinatorial bandit optimization problem
and consider several scenarios that differ in where routing decisions are made
and in the information available when making the decisions. For each scenario,
we derive a tight asymptotic lower bound on the regret that has to be satisfied
by any online routing policy. These bounds help us to understand the
performance improvements we can expect when (i) taking routing decisions at
each hop rather than at the source only, and (ii) observing per-link delays
rather than end-to-end path delays. In particular, we show that (i) is of no
use while (ii) can have a spectacular impact. Three algorithms, with a
trade-off between computational complexity and performance, are proposed. The
regret upper bounds of these algorithms improve over those of the existing
algorithms, and they significantly outperform state-of-the-art algorithms in
numerical experiments.Comment: 18 page
Timely Data Delivery in a Realistic Bus Network
Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I
An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System
Incorporating speed probability distribution to the computation of the route
planning in car navigation systems guarantees more accurate and precise
responses. In this paper, we propose a novel approach for dynamically selecting
the number of samples used for the Monte Carlo simulation to solve the
Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the
computation efficiency. The proposed method is used to determine in a proactive
manner the number of simulations to be done to extract the travel-time
estimation for each specific request while respecting an error threshold as
output quality level. The methodology requires a reduced effort on the
application development side. We adopted an aspect-oriented programming
language (LARA) together with a flexible dynamic autotuning library (mARGOt)
respectively to instrument the code and to take tuning decisions on the number
of samples improving the execution efficiency. Experimental results demonstrate
that the proposed adaptive approach saves a large fraction of simulations
(between 36% and 81%) with respect to a static approach while considering
different traffic situations, paths and error requirements. Given the
negligible runtime overhead of the proposed approach, it results in an
execution-time speedup between 1.5x and 5.1x. This speedup is reflected at
infrastructure-level in terms of a reduction of around 36% of the computing
resources needed to support the whole navigation pipeline
Dimensioning backbone networks for multi-site data centers: exploiting anycast routing for resilience
In the current era of big data, applications increasingly rely on powerful computing infrastructure residing in large data centers (DCs), often adopting cloud computing technology. Clearly, this necessitates efficient and resilient networking infrastructure to connect the users of these applications with the data centers hosting them. In this paper, we focus on backbone network infrastructure on large geographical scales (i.e., the so-called wide area networks), which typically adopts optical network technology. In particular, we study the problem of dimensioning such backbone networks: what bandwidth should each of the links provide for the traffic, originating at known sources, to reach the data centers? And possibly even: how many such DCs should we deploy, and at what locations? More concretely, we summarize our recent work that essentially addresses the following fundamental research questions: (1) Does the anycast routing strategy influence the amount of required network resources? (2) Can we exploit anycast routing for resilience purposes, i.e., relocate to a different DC under failure conditions, to reduce resource capacity requirements? (3) Is it advantageous to change anycast request destinations from one DC location to the other, from one time period to the next, if service requests vary over time
Routing in Mobile Ad-Hoc Networks using Social Tie Strengths and Mobility Plans
We consider the problem of routing in a mobile ad-hoc network (MANET) for
which the planned mobilities of the nodes are partially known a priori and the
nodes travel in groups. This situation arises commonly in military and
emergency response scenarios. Optimal routes are computed using the most
reliable path principle in which the negative logarithm of a node pair's
adjacency probability is used as a link weight metric. This probability is
estimated using the mobility plan as well as dynamic information captured by
table exchanges, including a measure of the social tie strength between nodes.
The latter information is useful when nodes deviate from their plans or when
the plans are inaccurate. We compare the proposed routing algorithm with the
commonly-used optimized link state routing (OLSR) protocol in ns-3 simulations.
As the OLSR protocol does not exploit the mobility plans, it relies on link
state determination which suffers with increasing mobility. Our simulations
show considerably better throughput performance with the proposed approach as
compared with OLSR at the expense of increased overhead. However, in the
high-throughput regime, the proposed approach outperforms OLSR in terms of both
throughput and overhead
Large scale probabilistic available bandwidth estimation
The common utilization-based definition of available bandwidth and many of
the existing tools to estimate it suffer from several important weaknesses: i)
most tools report a point estimate of average available bandwidth over a
measurement interval and do not provide a confidence interval; ii) the commonly
adopted models used to relate the available bandwidth metric to the measured
data are invalid in almost all practical scenarios; iii) existing tools do not
scale well and are not suited to the task of multi-path estimation in
large-scale networks; iv) almost all tools use ad-hoc techniques to address
measurement noise; and v) tools do not provide enough flexibility in terms of
accuracy, overhead, latency and reliability to adapt to the requirements of
various applications. In this paper we propose a new definition for available
bandwidth and a novel framework that addresses these issues. We define
probabilistic available bandwidth (PAB) as the largest input rate at which we
can send a traffic flow along a path while achieving, with specified
probability, an output rate that is almost as large as the input rate. PAB is
expressed directly in terms of the measurable output rate and includes
adjustable parameters that allow the user to adapt to different application
requirements. Our probabilistic framework to estimate network-wide
probabilistic available bandwidth is based on packet trains, Bayesian
inference, factor graphs and active sampling. We deploy our tool on the
PlanetLab network and our results show that we can obtain accurate estimates
with a much smaller measurement overhead compared to existing approaches.Comment: Submitted to Computer Network
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