We consider the problem of managing a bounded size First-In-First-Out (FIFO) queue buffer, where each incoming unit-sized packet requires several rounds of processing before it can be transmitted out. Our objective is to maximize the total number of successfully transmitted packets. We consider both push-out (when the policy is permitted to drop already admitted packets) and non-push-out cases. In particular, we provide analytical guarantees for the throughput performance of our algorithms. We further conduct a comprehensive simulation study which experimentally validates the predicted theoretical behaviour.
Various models have been proposed and studied, including, among others, QoS-oriented models where packets have weights @cite_25 @cite_11 @cite_21 @cite_27 and models where packets have dependencies @cite_4 @cite_3 . A related field that has received much attention in recent years focuses on various switch architectures and aims at designing competitive algorithms for such multi-queue scenarios; see, e.g., @cite_30 @cite_18 @cite_26 @cite_5 @cite_6 . Some other works also provide experimental studies of these algorithms and further validate their performance @cite_28 .
@cite_30 @cite_18 @cite_26 @cite_4 @cite_28 @cite_21 @cite_3 @cite_6 @cite_27 @cite_5 @cite_25 @cite_11
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Combined input and output queued (CIOQ) architectures with a moderate fabric speedupS> 1 have come to play a major role in the design of high performance switches. In this paper we study CIOQ switches with First-In-First-Out (FIFO) buffers providing Quality of Service (QoS) guarantees. The goal of the switch policy is to maximize the total value of packets sent out of the switch. We analyze the performance of a switch policy by means of competitive analysis, where a uniform performance guarantee is provided for all traffic patterns. Azar and Richter [8] proposed an algorithm β-PG(Preemptive Greedy with a preemption factor of β) that is 8-competitive for an arbitrary speedup value when β= 3. We improve upon their result by showing that this algorithm achieves a competitive ratio of 7.5 and 7.47 for β= 3 and β= 2.8, respectively. Basically, we demonstrate that β-PGis at most @math and at least @math -competitive. Motivated by the quality-of-service (QoS) buffer management problem, we consider online scheduling of packets with hard deadlines in a finite capacity queue. At any time, a queue can store at most b isin Z + packets. Packets arrive over time. Each packet is associated with a non-negative value and an integer deadline. In each time step, only one packet is allowed to be sent. Our objective is to maximize the total value gained by the packets sent by their deadlines in an online manner. Due to the Internet traffic's chaotic characteristics, no stochastic assumptions are made on the packet input sequences. This model is called a finite-queue model. We use competitive analysis to measure an online algorithm's performance versus an unrealizable optimal offline algorithm who constructs the worst possible input based on the knowledge of the online algorithm. For the finite-queue model, we first present a deterministic 3-competitive memoryless online algorithm. Then, we give a randomized (Phi 2 = (1+radic(5) 2) 2 ap 2.618)-competitive memoryless online algorithm. The algorithmic framework and its theoretical analysis include several interesting features. First, our algorithms use (possibly) modified characteristics of packets; these characteristics may not be same as those specified in the input sequence. Second, our analysis method is different from the classical potential function approach. We use a simple charging scheme, which depends on a clever modification (during the course of the algorithm) on the packets in the queue of the optimal offline algorithm. We then prove that a set of invariants holds at the end of each time step. Finally, we analyze the two proposed algorithm in a relaxed model, in which packets have no hard deadlines but an order. We conclude that both algorithms have the same competitive ratios in the relaxed model. We study the problem of buffer management in QoS-enabled network switches in the bounded delay model where each packet is associated with a weight and a deadline. We consider the more realistic situation where the network switch has a finite buffer size. A 9.82-competitive algorithm is known for the case of multiple buffers (Azar and Levy, SWAT'06). Recently, for the case of a single buffer, a 3-competitive deterministic algorithm and a 2.618-competitive randomized algorithm was known (Li, INFOCOM'09). In this paper we give a simple deterministic 2-competitive algorithm for the case of a single buffer. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.