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A Weighted Fair Queuing Algorithm for Charging Electric Vehicles on a Smart Grid
No full textDOI: http://dx.doi.org/10.1109/OnlineGreenCom.2013.6731041We are concerned with charging electric vehicles at home. The energy demand from electric vehicles can increase more rapidly than our ability to increase the generating capacity or the distribution facilities in the electric network. Our objective is to use a smart power distribution algorithm to reduce the inconvenience to electric vehicle owners. When there isn't sufficient capacity to charge all of the vehicles simultaneously, we will select different subsets of vehicles to charge in each 5 minute interval. The smart grid will control a switch on each charger. The order in which we charge the vehicles has a significant effect on the number of vehicles that are delayed when they would like to leave the charging station, and the amount of time that they are delayed. By reducing these measures, electric vehicles can be deployed more rapidly. We compare a weighted fair queuing algorithm for selecting the charging order and compare it with a first-come-first-served algorithm and a round robin charging rule. The weights are selected based on the battery level when vehicles arrive at their charging stations. We assume that there is a correlation between day-to-day driving distances, and charge vehicles that require more charge more rapidly. The three charging rules are evaluated using measured data on the power usage, commuting characteristics, and the distribution of commuting times.TRUEpu
DeepDive: Transparently Identifying and Managing Performance Interference in Virtualized Environments
Get PDFWe describe the design and implementation of DeepDive, a system for transparently identifying and managing performance interference between virtual machines(VMs) co-located on the same physical machine in Infrastructure-as-a-Service cloud environments.DeepDive successfully addresses several important challenges, including the lack of performance information from applications, and the large overhead of detailed interference analysis. We first show that it is possible to use easily-obtainable, low-level metrics to clearly discern when interference is occurring and what resource is causing it. Next, using realistic workloads, we show that DeepDive quickly learns about interference across co-located VMs. Finally, we show DeepDive’s ability to deal efficiently with interference when it is detected, by using a low-overhead approach to identifying a VM placement that alleviates interference.TRUEpu
Station Assignment with Applications to Sensing
Get PDFWe study an allocation problem that arises in various scenarios. For instance, a health monitoring system where ambulatory patients carry sensors that must periodically upload physiological data. Another example is participatory sensing, where communities of mobile device users upload periodically information about their environment. We assume that devices or sensors (generically called clients) join and leave the system continuously, and they must upload/download data to static devices (or base stations), via radio transmissions. The mobility of clients, the limited range of transmission, and the possibly ephemeral nature of the clients are modeled by characterizing each client with a life interval and a stations group, so that different clients may or may not coincide in time and/or stations to connect. The intrinsically shared nature of the access to base stations is modeled by introducing a maximum station bandwidth that is shared among its connected clients, a client laxity, which bounds the maximum time that an active client is not transmitting to some base station, and a client bandwidth, which bounds the minimum bandwidth that a client requires in each transmission. Under the model described, we study the problem of assigning clients to base stations so that every client transmits to some station in its group, limited by laxities and bandwidths. We call this problem the Station Assignment problem. We study the impact of the rate and burstiness of the arrival of clients on the solvability of Station Assignment. To carry out a worst-case analysis we use a typical adversarial methodology: we assume the presence of an adversary that controls the arrival and departure of clients. The adversary is limited by two parameters that model the rate and the burstiness of the stations load (hence, limitting the rate and burstiness of the client arrivals). Specifically, we show upper and lower bounds on the rate and burstiness of the arrival for various client arrival schedules and protocol classes. The problem has connections with Load Balancing and Scheduling, usually studied using competitive analysis. To the best of our knowledge, this is the first time that the Station Assignment problem is studied under adversarial arrivals.TRUEpu
Practical Challenges of IA in Frequency
Get PDFInterference Alignment (IA) is a promising technique at the physical layer which allows to increase the Degree-of-Freedom (DOF) of a communication by aligning all interfering signals into the same dimension, while the desired signal lies unaffected in an orthogonal dimension. IA has been widely studied in theory, but only limited practical work exists, since it poses significant challenges for real-world deployments. In this report, we study issues which are key to enable IA in practice.pu
Sentiment Analysis and Topic Detection of Spanish Tweets: A Comparative Study of NLP Techniques (Análisis de sentimientos y detección de asunto de tweets en español: un estudio comparativo de técnicas de PLN)
Get PDFA significant amount of effort is been invested in constructing effective solutions for sentiment analysis and topic detection, but mostly for English texts. Using a corpus of Spanish tweets, we present a comparative analysis of different approaches and classification techniques for these problems.pu
On the Effectiveness of Single and Multiple Base Station Sleep Modes in Cellular Networks
No full textIn this paper we study base station sleep modes that, by reducing power consumption in periods of low traffic, improve the energy efficiency of cellular access networks. We assume that when some base stations enter sleep mode, radio coverage and service provisioning are provided by the base stations that remain active, so as to guarantee that service is available over the whole area at all times. This may be an optimistic assumption in the case of the sparse base station layouts typical of rural areas, but is, on the contrary, a realistic hypothesis for the dense layouts of urban areas, which consume most of the network energy.
We consider the possibility of either just one sleep mode scheme per day (bringing the network from a high-power, fully-operational configuration, to a low-power reduced configuration), or several sleep mode schemes per day, with progressively fewer active base stations. For both contexts, we develop a simple analytical framework to identify optimal base station sleep times as a function of the daily traffic pattern.
We start by considering homogeneous networks, in which all cells carry the same amount of traffic and cover areas of equal size. Considering both synthetic traffic patterns and real traffic traces, collected from cells of an operational network, we show that the energy saving achieved with base station sleep modes can be quite significant, the actual value strongly depending on the traffic pattern. Our results also show that most of the energy saving is already achieved with one sleep mode scheme per day. Some additional saving can be achieved with multiple sleep mode schemes, at the price of a significant increase in complexity.
We then consider heterogeneous networks in which cells with different coverage areas and different amounts of traffic coexist. In particular, we focus on the common case in which some micro-cells provide additional capacity in a region covered by an umbrella macro-cell, and we prove that the optimal scheduling of micro-cell sleep times is in increasing order of load, from the least loaded to the most loaded. This provides a valuable guideline for the scheduling of sleep modes (i.e., of low-power configurations) in complex heterogeneous networks.pu
Systematic Software Testing Meets Networking
Get PDFNowadays users expect and demand highly dependable network connectivity and services. However, several recent episodes demonstrate that software errors and operator mistakes continue to cause undesired disruptions and outages. It is crucial to have reliable networks, and this requirement does not change with Software Defined Networking (SDN). Unfortunately, as the network programmability enhances and software plays a greater role in it, risks that buggy software may disrupt an entire network also increase. The centralized programming model, where a single controller program manages the network, seems to reduce the likelihood of bugs. However, the system is inherently distributed and asynchronous, with events happening at different switches and end hosts, and inevitable delays affecting communication with the controller.
This extended abstract presents an overview of efficient, systematic techniques for testing the SDN software stack at both its highest and lowest layer. That is, our testing techniques target at the top layer, the OpenFlow controller programs (Section 1) and, at the bottom layer, the OpenFlow agents (Section 2)—the software that each switch runs to enable remote programmatic access to its forwarding tables. The papers describing these tools have been published in [2] and [3]. Our goal here is to increase the awareness of the ever-increasing number of SDN adopters to our tools. In doing so, we hope to: (1) enable faster adoption of OpenFlow/SDN due to accelerated switch interoperability testing, and (2) decrease the chance of encountering bugs in the deployment of OpenFlow controller applications. Combined, our tools should increase the confidence in SDN as a whole.TRUEpu
Network Sharing: An Energy-Efficient Option for European Mobile Network Operators
No full textWe investigate the energy saving made possible by the network sharing approach, whereby all (or significant parts) of a network infrastructure are shared by different network operators. Our study reveals that in most European countries the amount of energy necessary to run mobile networks can be
reduced by 35 to 60%.FALSEpu