181 research outputs found
Exploring the benefit of rerouting multi-period traffic to multi-site data centers
In cloud-like scenarios, demand is served at one of multiple possible data center (DC) destinations. Usually, the exact DC that is used can be freely chosen, which leads to an anycast routing problem. Furthermore, the demand volume is expected to change over time, e.g., following a diurnal pattern. Given that virtually all application domains today rely heavily on cloud-like services, it is important that the backbone networks connecting users to the DCs are resilient against failures. In this paper, we consider the problem of resiliently routing multi-period traffic: we need to find routes to both a primary DC and a backup DC (to be used in the case of failure of the primary one, or of the network connection to it), and also account for synchronization traffic between the primary and backup DCs. We formulate this as an optimization problem and adopt column generation, using a path formulation in two sub-problems: the (restricted) master problem selects "configurations" to use for each demand in each of the time epochs it lasts, while the pricing problem (PP) constructs a new "configuration" that can lead to lower overall costs (which we express as the number of network resources, i.e., bandwidth, required to serve the demand). Here, a "configuration" is defined by the network paths followed from the demand source to each of the two selected DCs, as well as that of the synchronization traffic in between the DCs. Our decomposition allows for PPs to be solved in parallel, for which we quantitatively explore the reduction in the time required to solve the overall routing problem. The key question that we address with our model is an exploration of the potential benefits of rerouting traffic from one time epoch to the next: we compare several (re) routing strategies, allowing traffic that spans multiple time periods to i) not be rerouted in different periods, ii) only change the backup DC and routes, or iii) freely change both primary and backup DC choices and the routes toward them
A Framework for Quality-Driven Delivery in Distributed Multimedia Systems
In this paper, we propose a framework for Quality-Driven Delivery (QDD) in distributed multimedia environments. Quality-driven delivery refers to the capacity of a system to deliver documents, or more generally objects, while considering the users expectations in terms of non-functional requirements. For this QDD framework, we propose a model-driven approach where we focus on QoS information modeling and transformation. QoS information models and meta-models are used during different QoS activities for mapping requirements to system constraints, for exchanging QoS information, for checking compatibility between QoS information and more generally for making QoS decisions. We also investigate which model transformation operators have to be implemented in order to support some QoS activities such as QoS mapping
Selecting the best locations for data centers in resilient optical grid/cloud dimensioning
For optical grid/cloud scenarios, the dimensioning problem comprises not only deciding on the network dimensions (i.e., link bandwidths), but also choosing appropriate locations to install server infrastructure (i.e., data centers), as well as determining the amount of required server resources (for storage and/or processing). Given that users of such grid/cloud systems in general do not care about the exact physical locations of the server resources, a degree of freedom arises in choosing for each of their requests the most appropriate server location. We will exploit this anycast routing principle (i.e., source of traffic is given, but destination can be chosen rather freely) also to provide resilience: traffic may be relocated to alternate destinations in case of network/server failures. In this study, we propose to jointly optimize the link dimensioning and the location of the servers in an optical grid/cloud, where the anycast principle is applied for resiliency against either link or server node failures. While the data center location problem has some resemblance with either the classical p-center or k-means location problems, the anycast principle makes it much more difficult due to the requirement of link disjoint paths for ensuring grid resiliency
Virtual-Mobile-Core Placement for Metro Network
Traditional highly-centralized mobile core networks (e.g., Evolved Packet
Core (EPC)) need to be constantly upgraded both in their network functions and
backhaul links, to meet increasing traffic demands. Network Function
Virtualization (NFV) is being investigated as a potential cost-effective
solution for this upgrade. A virtual mobile core (here, virtual EPC, vEPC)
provides deployment flexibility and scalability while reducing costs,
network-resource consumption and application delay. Moreover, a distributed
deployment of vEPC is essential for emerging paradigms like Multi-Access Edge
Computing (MEC). In this work, we show that significant reduction in
networkresource consumption can be achieved as a result of optimal placement of
vEPC functions in metro area. Further, we show that not all vEPC functions need
to be distributed. In our study, for the first time, we account for vEPC
interactions in both data and control planes (Non-Access Stratum (NAS)
signaling procedure Service Chains (SCs) with application latency requirements)
using a detailed mathematical model
Multi-Column Generation Model for the Locomotive Assignment Problem
We propose a new decomposition model and a multi-column generation algorithm for solving the Locomotive Assignment Problem (LAP). The decomposition scheme relies on consist configurations, where each configuration is made of a set of trains pulled by the same set of locomotives. We use the concept of conflict graphs in order to reduce the number of trains to be considered in each consist configuration generator problem: this contributes to significantly reduce the fraction of the computational times spent in generating new potential consists. In addition, we define a column generation problem for each set of variables, leading to a multi-column generation process, with different types of columns.
Numerical results, with different numbers of locomotives, are presented on adapted data sets coming from Canada Pacific Railway (CPR). They show that the newly proposed algorithm is able to solve exactly realistic data instances for a timeline spanning up to 6 weeks, in very reasonable computational times
Service Chain (SC) Mapping with Multiple SC Instances in a Wide Area Network
Network Function Virtualization (NFV) aims to simplify deployment of network
services by running Virtual Network Functions (VNFs) on commercial
off-the-shelf servers. Service deployment involves placement of VNFs and
in-sequence routing of traffic flows through VNFs comprising a Service Chain
(SC). The joint VNF placement and traffic routing is usually referred as SC
mapping. In a Wide Area Network (WAN), a situation may arise where several
traffic flows, generated by many distributed node pairs, require the same SC,
one single instance (or occurrence) of that SC might not be enough. SC mapping
with multiple SC instances for the same SC turns out to be a very complex
problem, since the sequential traversal of VNFs has to be maintained while
accounting for traffic flows in various directions. Our study is the first to
deal with SC mapping with multiple SC instances to minimize network resource
consumption. Exact mathematical modeling of this problem results in a quadratic
formulation. We propose a two-phase column-generation-based model and solution
in order to get results over large network topologies within reasonable
computational times. Using such an approach, we observe that an appropriate
choice of only a small set of SC instances can lead to solution very close to
the minimum bandwidth consumption
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
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Road accidents are an important issue of our modern societies, responsible
for millions of deaths and injuries every year in the world. In Quebec only, in
2018, road accidents are responsible for 359 deaths and 33 thousands of
injuries. In this paper, we show how one can leverage open datasets of a city
like Montreal, Canada, to create high-resolution accident prediction models,
using big data analytics. Compared to other studies in road accident
prediction, we have a much higher prediction resolution, i.e., our models
predict the occurrence of an accident within an hour, on road segments defined
by intersections. Such models could be used in the context of road accident
prevention, but also to identify key factors that can lead to a road accident,
and consequently, help elaborate new policies.
We tested various machine learning methods to deal with the severe class
imbalance inherent to accident prediction problems. In particular, we
implemented the Balanced Random Forest algorithm, a variant of the Random
Forest machine learning algorithm in Apache Spark. Interestingly, we found that
in our case, Balanced Random Forest does not perform significantly better than
Random Forest.
Experimental results show that 85% of road vehicle collisions are detected by
our model with a false positive rate of 13%. The examples identified as
positive are likely to correspond to high-risk situations. In addition, we
identify the most important predictors of vehicle collisions for the area of
Montreal: the count of accidents on the same road segment during previous
years, the temperature, the day of the year, the hour and the visibility
A Scalable Approach for Service Chain (SC) Mapping with Multiple SC Instances in a Wide-Area Network
Network Function Virtualization (NFV) aims to simplify deployment of network
services by running Virtual Network Functions (VNFs) on commercial
off-the-shelf servers. Service deployment involves placement of VNFs and
in-sequence routing of traffic flows through VNFs comprising a Service Chain
(SC). The joint VNF placement and traffic routing is called SC mapping. In a
Wide-Area Network (WAN), a situation may arise where several traffic flows,
generated by many distributed node pairs, require the same SC; then, a single
instance (or occurrence) of that SC might not be enough. SC mapping with
multiple SC instances for the same SC turns out to be a very complex problem,
since the sequential traversal of VNFs has to be maintained while accounting
for traffic flows in various directions. Our study is the first to deal with
the problem of SC mapping with multiple SC instances to minimize network
resource consumption. We first propose an Integer Linear Program (ILP) to solve
this problem. Since ILP does not scale to large networks, we develop a
column-generation-based ILP (CG-ILP) model. However, we find that exact
mathematical modeling of the problem results in quadratic constraints in our
CG-ILP. The quadratic constraints are made linear but even the scalability of
CG-ILP is limited. Hence, we also propose a two-phase column-generation-based
approach to get results over large network topologies within reasonable
computational times. Using such an approach, we observe that an appropriate
choice of only a small set of SC instances can lead to a solution very close to
the minimum bandwidth consumption. Further, this approach also helps us to
analyze the effects of number of VNF replicas and number of NFV nodes on
bandwidth consumption when deploying these minimum number of SC instances.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0671
A QoS mapping rule builder
Although many QoS management architectures have been recently introduced with a lot of advanced features, they have never been widely used in the existing applications due to the lack of interoperation between providers and users, or between network operators. One of the main issues is the heterogeneity of QoS information coming from different sources: clients, communication networks, servers, data .etc. In the context of Quality-Driven Delivery (QDD) referring to the ability of a system to deliver data objects while considering the end-users expectations, all components of a distributed multimedia system have to contribute to satisfy users requirements. The mapping activity is therefore essential for dealing with the variety of QoS information of these components. In this paper, we propose an approach aimed at creating QoS mapping rules using statistical data analysis and data mining techniques combined with monitoring tools. The automatic generation of QoS mapping rules allows adapting the QoS management architectures to different environments as well as different classes of users
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