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

Resilience options for provisioning anycast cloud services with virtual optical networks

Optical networks are crucial to support increasingly demanding cloud services. Delivering the requested quality of services (in particular latency) is key to successfully provisioning end-to-end services in clouds. Therefore, as for traditional optical network services, it is of utter importance to guarantee that clouds are resilient to any failure of either network infrastructure (links and/or nodes) or data centers. A crucial concept in establishing cloud services is that of network virtualization: the physical infrastructure is logically partitioned in separate virtual networks. To guarantee end-to-end resilience for cloud services in such a set-up, we need to simultaneously route the services and map the virtual network, in such a way that an alternate routing in case of physical resource failures is always available. Note that combined control of the network and data center resources is exploited, and the anycast routing concept applies: we can choose the data center to provide server resources requested by the customer to optimize resource usage and/or resiliency. This paper investigates the design of scalable optimization models to perform the virtual network mapping resiliently. We compare various resilience options, and analyze their compromise between bandwidth requirements and resiliency quality

Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Intelligent distributed multimedia collection: content aggregation and integration

People's multimedia content is spread around their home network and content services on the Internet, such as YouTube, Flickr, Facebook. In this paper we present a system that aggregates all the multimedia content of the end user and integrates it into a unified collection for the user's convenience. The system provides location transparency of multimedia content, content filtering on player compatibility and metadata completion to aid in improved usability. This effectively enables the user to rediscover his multimedia collection without any technical knowledge. A proof-of-concept implementation known as Intelligent Distributed Multimedia Collection (IDMC) has been made that is able to detect and browse UPnP MediaServer devices as well as collect information from YouTube. This implementation also contains a media player and is able to control UPnP MediaRenderer devices remotely. Furthermore, performance has been measured to assess different ways of iterating through a multimedia collection

Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning

Demand response (DR) becomes critical to manage the charging load of a growing electric vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but models are largely uncertain for the EV scenario (e.g., customer behavior). Model-free approaches, based on reinforcement learning (RL), are an attractive alternative. We propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of charging stations. State-of-the-art algorithms either focus on a single EV, or control an aggregate of EVs in multiple steps (e.g., 1) make aggregate load decisions and 2) translate the aggregate decision to individual EVs). In contrast, our RL approach jointly controls the whole set of EVs at once. We contribute a new MDP formulation with a scalable state representation independent of the number of charging stations. Using a batch RL algorithm, fitted $Q$ -iteration, we learn an optimal charging policy. With simulations using real-world data, we: 1) differentiate settings in training the RL policy (e.g., the time span covered by training data); 2) compare its performance to an oracle all-knowing benchmark (providing an upper performance bound); 3) analyze performance fluctuations throughout a full year; and 4) demonstrate generalization capacity to larger sets of charging stations

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 simulator for the control network of smart grid architectures

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