Abstract not found

We study a basic problem in Multi-Queue switches. A switch connects m input ports to a single output port. Each input port is equipped with an incoming FIFO queue with bounded capacity B. A switch serves its input queues by transmitting packets arriving at these queues, one packet per time unit. Since the arrival rate can be higher than the transmission rate and each queue has limited capacity, packet loss may occur as a result of insufficient queue space. The goal is to maximize the number of transmitted packets. This general scenario models most current networks (e.g., IP networks) which only support a “best effort ” service in which all packet streams are treated equally. A 2-competitive algorithm for this problem was designed in [5] for arbitrary B. Recently, a 17 ≈ 1.89-competitive algorithm was presented for B> 1 in 9 [3]. Our main result in this paper shows that for B which is not too small our algorithm can do

We consider the problem of scheduling data in overloaded networks. We wish to maximize the total profit of data that is served.

Abstract The problem of maximizing the weighted throughput in various switching settings has beenintensively studied recently through competitive analysis. To date, the most general model that has been investigated is the standard CIOQ (Combined Input and Output Queued) switcharchitecture with internal fabric speedup S> = 1. CIOQ switches, that comprise the backbone ofpacket routing networks, are N * N switches controlled by a switching policy that incorporatestwo components: Admission control and scheduling. An admission control strategy is essential to determine the packets stored in the FIFO queues in input and output ports, while the schedulingpolicy conducts the transfer of packets through the internal fabric, from input ports to output ports. The online problem of maximizing the total weighted throughput of CIOQ switcheswas recently investigated by Kesselman and Ros'en in [15]. They presented two different online algorithms for the general problem that achieve non-constant competitive ratios (linear in eitherthe speedup or the number of distinct values, or logarithmic in the value range). We introduce the first constant-competitive algorithm for the general case of the problem, with arbitraryspeedup and packet values. Specifically, our algorithm is 8-competitive, and is also simple and easy to implement. 1 Introduction Overview: Recently, packet routing networks have become the dominant platform for data transfer. The backbone of such networks is composed of N * N switches, that accept packets through multiple incoming connections and route them through multiple outgoing connections. As network traffic continuously increases and traffic patterns constantly change, switches routinely have to efficiently cope with overloaded traffic, and are forced to discard packets due to insufficient buffer space, while attempting to forward the more valuable packets to their destinations.

We introduce the problem of managing a FIFO buffer of bounded space, where arriving packets have dependencies among them. Our model is motivated by the scenario where large data frames must be split into multiple packets, because maximum packet size is limited by data-link restrictions. A frame is considered useful only if sufficiently many of its constituent packets are delivered. The buffer management algorithm decides, in case of overflow, which packets to discard and which to keep in the buffer. The goal of the buffer management algorithm is to maximize throughput of useful frames. This problem has a variety of applications, e.g., Internet video streaming, where video frames are segmented and encapsulated in IP packets sent over the Internet. We study the complexity of the above problem in both the offline and online settings. We give upper and lower bounds on the performance of algorithms using competitive analysis. 1

Over the past decade, there has been great interest in the study of buffer management policies in the context of packet transmission for network switches. In a typical model, a switch receives packets on one or more input ports, with each packet having a designated output port through which it should be transmitted. An online policy must consider bandwidth limits on the rate of transmission, memory constraints impacting the buffering of packets within a switch, and variations in packet properties used to differentiate quality of service. With so many constraints, a switch may not be able to deliver all packets, in which case some will be dropped. In the online algorithms community, researchers have used competitive analysis to evaluate the quality of an online policy in maximizing the value of those packets it is able to transmit. In this article, we provide a detailed survey of the field, describing various models of the problem that have been studied, and summarizing the known results.

In recent years, there has been a lot of interest in Quality of Service (QoS) networks. In regular IP networks, packets are indistinguishable and in case of overload any packet may be dropped. In a commercial environment, it is much more preferable to allow better service to higher-paying customers or customers with critical requirements. The idea of Quality of Service guarantees is that packets are marked with values which indicate their importance. This naturally leads to decision problems at network switches when many packets arrive and overload occurs. In this paper, we give an overview of several models that have been studied in this area from an online perspective. These models differ by restrictions such as bounded delay, bounded size of queue etc. We first present results for a single buffer in Section 1 and then for multiple buffers in Section 2. This paper is not meant as a comprehensive survey of the work in this area. There are many more variations of these problems that have been studied, for instance, multiple output buffers [14]. Our goal was merely to give a taste of this problem area, and we hope you enjoy it. 1 Single buffer We consider a QoS buffering system that is able to hold B packets. Time is slotted. At the beginning of a time step a set of packets (possibly empty) arrives and at the end of the time step a single packet may

We consider online algorithms for scheduling weighted packets with deadlines in multiple sizebounded buffers. There are m ≥ 1 buffers B1, B2,..., Bm. At any time, a buffer Bi can store at most bi ∈ Z + packets. Packets arrive over time. Each packet is associated with a non-negative value, an integer deadline, and a target buffer that it can reside in. In each time step, only one pending packet is allowed to be sent. Our objective is to maximize the total value gained by delivering packets before their respective deadlines in an online manner. We call this model a single-buffer model (when m = 1) or a multi-buffer model (when m> 1). The single-buffer model generalizes the bounded-delay model (Hajek. CISS 2001. Kesselman et al. STOC 2001). Competitive analysis is employed to measure an online algorithm’s performance. For the single-buffer model, we first show that the lower bound of competitive ratios of a family of deterministic online algorithms is 2 — all previously known deterministic algorithms for the boundeddelay model fall in this category. Then we present a 3-competitive deterministic algorithm and a randomized 2.618-competitive algorithm. For the single-buffer model, no previously known algorithm has a competitive ratio better than 9.82 (Azar, Levy. SWAT 2006). The multi-buffer model has