Tight bounds on the competitive ratio on accomodating sequences for the seat reservation problem

The unit price seat reservation problem is investigated. The seat reservation problem is the problem of assigning seat numbers on-line to requests for reservations in a train traveling through $k$ stations. We are considering the version where all tickets have the same price and where requests are treated fairly, i.e., a request which can be fulfilled must be granted. For fair deterministic algorithms, we provide an asymptotically matching upper bound to the existing lower bound which states that all fair algorithms for this problem are frac{1{2-competitive on accommodating sequences, when there are at least three seats. Additionally, we give an asymptotic upper bound of frac{7{9 for fair randomized algorithms against oblivious adversaries. We also examine concrete on-line algorithms, First-Fit and Random, for the special case of two seats. Tight analyses of their performance are given

Truthful Online Scheduling with Commitments

We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cloud scheduling, but there are strong lower bounds even for the algorithmic problem without incentive constraints. However, these lower bounds can be circumvented under the natural assumption of deadline slackness, i.e., that there is a guaranteed lower bound $s > 1$ on the ratio between a job's size and the time window in which it can be executed. In this paper, we construct a truthful scheduling mechanism with a constant competitive ratio, given slackness $s > 1$. Furthermore, we show that if $s$ is large enough then we can construct a mechanism that also satisfies a commitment property: it can be determined whether or not a job will finish, and the requisite payment if so, well in advance of each job's deadline. This is notable because, in practice, users with strict deadlines may find it unacceptable to discover only very close to their deadline that their job has been rejected

A combinatorial flow-based formulation for temporal bin packing problems

We consider two neighboring generalizations of the classical bin packing problem: the temporal bin packing problem (TBPP) and the temporal bin packing problem with fire-ups (TBPP-FU). In both cases, the task is to arrange a set of given jobs, characterized by a resource consumption and an activity window, on homogeneous servers of limited capacity. To keep operational costs but also energy consumption low, TBPP is concerned with minimizing the number of servers in use, whereas TBPP-FU additionally takes into account the switch-on processes required for their operation. Either way, challenging integer optimization problems are obtained, which can differ significantly from each other despite the seemingly only marginal variation of the problems. In the literature, a branch-and-price method enriched with many preprocessing steps (for TBPP) and compact formulations (for TBPP-FU), benefiting from numerous reduction methods, have emerged as, currently, the most promising solution methods. In this paper, we introduce, in a sense, a unified solution framework for both problems (and, in fact, a wide variety of further interval scheduling applications) based on graph theory. Any scientific contributions in this direction failed so far because of the exponential size of the associated networks. The approach we present in this article does not change the theoretical exponentiality itself, but it can make it controllable by clever construction of the resulting graphs. In particular, for the first time all classical benchmark instances (and even larger ones) for the two problems can be solved – in times that significantly improve those of the previous approaches

Tians scheduling: Using partial processing in best-effort applications

Abstract-To service requests with high quality, interactive services such as web search, on-demand video and online gaming keep average server utilization low. As servers become busy, queuing delays increase, and requests miss their deadlines, resulting in degraded quality of service with poor user experience and potential revenue loss. In this paper, we propose Tians scheduling, a group of scheduling algorithms for interactive services that can produce partial answers during overload. A Tians scheduler allocates processing time to each request based on system load with the objective of maximizing overall quality of responses. We propose three Tians scheduling algorithms -offline, online clairvoyant and online nonclairvoyant. For interactive applications with concave quality profile, we prove that the offline algorithm is optimal. We show the effectiveness of the online algorithms by conducting a simulation study modeling important applications -a web search engine and video-ondemand (VOD) system. Simulation results show a significant improvement of Tians over traditional server models: average response quality improves and the variance of responses decreases. Keywords-interactive services, best-effort applications, offline, online clairvoyant, online nonclairvoyant, partial results, quality profile, scheduling, VOD bandwidth allocation, web search engine

Dynamic bandwidth management to maximize user satisfaction degree on multiple MPLS paths

Master'sMASTER OF ENGINEERIN

Analysis of algorithms for online routing and scheduling in networks

We study situations in which an algorithm must make decisions about how to best route and schedule data transfer requests in a communication network before each transfer leaves its source. For some situations, such as those requiring quality of service guarantees, this is essential. For other situations, doing work in advance can simplify decisions in transit and increase the speed of the network. In order to reflect realistic scenarios, we require that our algorithms be online, or make their decisions without knowing future requests. We measure the efficiency of an online algorithm by its competitive ratio, which is the maximum ratio, over all request sequences, of the cost of the online algorithm\u27s solution to that of an optimal solution constructed by knowing all the requests in advance.;We identify and study two distinct variations of this general problem. In the first, data transfer requests are permanent virtual circuit requests in a circuit-switched network and the goal is to minimize the network congestion caused by the route assignment. In the second variation, data transfer requests are packets in a packet-switched network and the goal is to minimize the makespan of the schedule, or the time that the last packet reaches its destination. We present new lower bounds on the competitive ratio of any online algorithm with respect to both network congestion and makespan.;We consider two greedy online algorithms for permanent virtual circuit routing on arbitrary networks with unit capacity links, and prove both lower and upper bounds on their competitive ratios. While these greedy algorithms are not optimal, they can be expected to perform well in many circumstances and require less time to make a decision, when compared to a previously discovered asymptotically optimal online algorithm. For the online packet routing and scheduling problem, we consider an algorithm which simply assigns to each packet a priority based upon its arrival time. No packet is delayed by another packet with a lower priority. We analyze the competitive ratio of this algorithm on linear array, tree, and ring networks

Metodologie di ottimizzazione orientate alla QoS e applicate alle reti NGN, Multiservizio e multidominio

La presente tesi tratta lo studio di tecniche di ottimizzazione della rete con un approccio orientato sia al traffico che alle risorse di rete. Un’attenta analisi dello scenario e delle tipologie di traffico, ha consentito la definizione e la scelta dei parametri di qualità, i quali permettono di agire attivamente sull'efficienza della rete e sulla possibilità di implementare una politica di gestione della QoS. Inoltre, sono stati analizzati vari approcci architetturali relativi alla rete di trasporto che hanno condotto alla definizione di alcune linee guida utili, sin nella fase di progettazione, per predisporre al meglio la fruibilità di servizi diversificati e di alta qualità. La soluzione architetturale che meglio sposa gli obiettivi di performance è data dall’integrazione di MPLS e Diffserv con applicazione del Traffic Engineering (DS-TE), in cui, all’approccio di differenziazione del trattamento del traffico sulla base dell’appartenenza a classi di servizio (CoS), si affianca il meccanismo dell’MPLS, che offre garanzie di qualità end-to-end, per la costruzione dei percorsi, Label Switch Path (LSP), su cui inoltrare gli aggregati di traffico; infine, il TE permette una gestione efficiente delle risorse. Relativamente alla rete di backbone DS-TE, il focus è andato principalmente sulle problematiche di ottimizzazione del routing degli LSP. In particolare, si definiscono delle metodologie che consentono un miglior bilanciamento del carico sulla rete, ma soprattutto, che permettono agli aggregati classificati con una maggiore priorità di essere sempre serviti. Oltre all’approccio di riottimizzazione globale degli LSP sulla rete, è stata proposta una procedura che, ad ogni richiesta di un nuovo LSP, individua un percorso ottimo di instradamento,ricorrendo, ove necessario, ad una tecnica di preemption ottimizzata per rimuove LSP a priorità inferiore, e nel contempo, cerca per questi un percorso alternativo, riducendo il service disruption. Per quanto riguarda l’ottimizzazione della rete a garanzia della qualità per alcune tipologie di servizio specifiche, è stato preso in esame il caso delle applicazioni video-streaming che riscuotono, oggigiorno, grande interesse. In particolare, lo scenario considerato è quello di una rete di tipo wireless che, per la sua natura, produce un throughput di tipo bursty e, quindi, rende molto difficile l’alta qualità dello streaming video. L’algoritmo proposto è di tipo window-based e prevede un Source Rate Control che modifica il rate di sorgente compensando i periodi altalenanti tra basso ed alto throughput in modo tale da evitare il tipico “saw” effect. Il rate viene modificato periodicamente all’inizio di ogni finestra temporale, il suo valore si ottiene imponendo che il valore della probabilità di starvation, del buffer nell’end-system sia inferiore ad una certa soglia per tutta la durata della finestra. Tale scopo è perseguito facendo una previsione a breve-termine del ritardo che il traffico subirà nella rete per la durata della finestra, a partire dalle informazioni raccolte nelle finestre precedenti

Bandwidth Allocation with Preemption

Bandwidth allocation is a fundamental problem in the design of networks where bandwidth has to be reserved for connections in advance. The problem is intensified when the overall requested bandwidth exceeds the capacity and not all requests can be served. Furthermore, acceptance/rejection decisions regarding connections have to be made online, without knowledge of future requests. We show that the ability to preempt (i.e., abort) connections while in service in order to schedule "more valuable" connections substantially improves the throughput of some networks. We present bandwidth allocation strategies that use preemption and show that they achieve constant competitiveness with respect to the throughput, given that any single call requests at most a constant fraction of the bandwidth. Our results should be contrasted with recent works showing that non-preemptive strategies have at most inverse logarithmic competitiveness

A Study of Dynamic Bandwidth Allocation With Preemption and Degradation For Prioritized Requests

Bandwidth allocation is a fundamental problem in communication networks where bandwidth needs to be reserved for requests (connections) to guarantee a certain quality of service (QoS) for the request. Guaranteeing QoS to the request means that the user can explicitly speclfy certain requirements for a request such as bandwidth. The problem of bandwidth allocation is further intensified when the requested bandwidth exceeds the available unused bandwidth and so not all requests can be completely served. This research examines on-line bandwidth allocation, where the decision for acceptance or rejection of the request has to be made when future requests and their arrival statistics are not known. A request can be defined as a flow of information fiom a source to a destination with a certain amount of bandwidth, a priority level, a utility fbnction that is based on the bandwidth received, and a worth that is based on the utility function and the priority level. The goal of the research is to develop a scheduling heuristic for an overloaded system that attempts to schedule the requests such that the sum of the worths of the requests satisfied in a fixed interval of time is the maximum. The scheduling heuristic can preempt or degrade already scheduled requests. Three different types of utility functions, step, linear, and concave are examined. Other parameters being considered include network loading and the relative weights of the different priority levels