356,230 research outputs found
Online Service with Delay
In this paper, we introduce the online service with delay problem. In this
problem, there are points in a metric space that issue service requests
over time, and a server that serves these requests. The goal is to minimize the
sum of distance traveled by the server and the total delay in serving the
requests. This problem models the fundamental tradeoff between batching
requests to improve locality and reducing delay to improve response time, that
has many applications in operations management, operating systems, logistics,
supply chain management, and scheduling.
Our main result is to show a poly-logarithmic competitive ratio for the
online service with delay problem. This result is obtained by an algorithm that
we call the preemptive service algorithm. The salient feature of this algorithm
is a process called preemptive service, which uses a novel combination of
(recursive) time forwarding and spatial exploration on a metric space. We hope
this technique will be useful for related problems such as reordering buffer
management, online TSP, vehicle routing, etc. We also generalize our results to
servers.Comment: 30 pages, 11 figures, Appeared in 49th ACM Symposium on Theory of
Computing (STOC), 201
Improved and Deterministic Online Service with Deadlines or Delay
We consider the problem of online service with delay on a general metric
space, first presented by Azar, Ganesh, Ge and Panigrahi (STOC 2017). The best
known randomized algorithm for this problem, by Azar and Touitou (FOCS 2019),
is -competitive, where is the number of points in the metric
space. This is also the best known result for the special case of online
service with deadlines, which is of independent interest.
In this paper, we present -competitive deterministic algorithms
for online service with deadlines or delay, improving upon the results from
FOCS 2019. Furthermore, our algorithms are the first deterministic algorithms
for online service with deadlines or delay which apply to general metric spaces
and have sub-polynomial competitiveness.Comment: Appears in STOC 202
On QoS-assured degraded provisioning in service-differentiated multi-layer elastic optical networks
The emergence of new network applications is driving network operators to not
only fulfill dynamic bandwidth requirements, but offer various grades of
service. Degraded provisioning provides an effective solution to flexibly
allocate resources in various dimensions to reduce blocking for differentiated
demands when network congestion occurs. In this work, we investigate the novel
problem of online degraded provisioning in service-differentiated multi-layer
networks with optical elasticity. Quality of Service (QoS) is assured by
service-holding-time prolongation and immediate access as soon as the service
arrives without set-up delay. We decompose the problem into degraded routing
and degraded resource allocation stages, and design polynomial-time algorithms
with the enhanced multi-layer architecture to increase the network flexibility
in temporal and spectral dimensions. Illustrative results verify that we can
achieve significant reduction of network service failures, especially for
requests with higher priorities. The results also indicate that degradation in
optical layer can increase the network capacity, while the degradation in
electric layer provides flexible time-bandwidth exchange.Comment: accepted by IEEE GLOBECOM 201
Information Technology Service Management with Cloud Computing Approach to Improve Administration System and Online Learning Performance
This work discusses the development of information technology service management using cloud computing approach to improve the performance of administration system and online learning at STMIK IBBI Medan, Indonesia. The network topology is modeled and simulated for system administration and online learning. The same network topology is developed in cloud computing using Amazon AWS architecture. The model is designed and modeled using Riverbed Academic Edition Modeler to obtain values of the parameters: delay, load, CPU utilization, and throughput. The simu- lation results are the following. For network topology 1, without cloud computing, the average delay is 54 ms, load 110 000 bits/s, CPU utilization 1.1%, and throughput 440 bits/s. With cloud computing, the average delay is 45 ms, load 2 800 bits/s, CPU utilization 0.03%, and throughput 540 bits/s. For network topology 2, without cloud computing, the average delay is 39 ms, load 3 500 bits/s, CPU utilization 0.02%, and throughput database server 1 400 bits/s. With cloud computing, the average delay is 26 ms, load 5 400 bits/s, CPU utilization email server 0.0001%, FTP server 0.001%, HTTP server 0.0002%, throughput email server 85 bits/s, FTP server 100 bits/sec, and HTTP server 95 bits/s. Thus, the delay, the load, and the CPU utilization decrease; but, the throughput increases. Information technology service management with cloud computing approach has better performance
HDAX: Historical symbolic modelling of delay time series in a communications network
There are certain performance parameters like packet delay, delay variation (jitter) and loss, which are decision factors for online quality of service (QoS) traffic routing. Although considerable efforts have been placed on the Internet to assure QoS, the dominant TCP/IP - like the best-effort communications policy - does not provide sufficient guarantee without abrupt change in the protocols. Estimation and forecasting end-to-end delay and its variations are essential tasks in network routing management for detecting anomalies. A large amount of research has been done to provide foreknowledge of network anomalies by characterizing and forecasting delay with numerical forecasting methods. However, the methods are time consuming and not efficient for real-time application when dealing with large online datasets. Application is more difficult when the data is missing or not available during online forecasting. Moreover, the time cost in statistical methods for trivial forecasting accuracy is prohibitive. Consequently, many researchers suggest a transition from computing with numbers to the manipulation of perceptions in the form of fuzzy linguistic variables. The current work addresses the issue of defining a delay approximation model for packet switching in communications networks. In particular, we focus on decision-making for smart routing management, which is based on the knowledge provided by data mining (informed) agents. We propose a historical symbolic delay approximation model (HDAX) for delay forecasting. Preliminary experiments with the model show good accuracy in forecasting the delay time-series as well as a reduction in the time cost of the forecasting method. HDAX compares favourably with the competing Autoregressive Moving Average (ARMA) algorithm in terms of execution time and accuracy. © 2009, Australian Computer Society, Inc
- …