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.
@cite_2 were the first to consider buffer management and scheduling in the context of network processors with heterogeneous processing requirements for the arriving traffic. They study both SRPT (shortest remaining processing time) and FIFO (first-in-first-out) schedulers with recycles, in both push-out and non-push-out buffer management cases, where a packet is recycled after processing according to the priority policy (FIFO or SRPT). They showed competitive algorithms and worst-case lower bounds for such settings. Although they considered a different architecture (FIFO with recycles) than the one we consider in this paper, they provided only a lower bound for the push-out FIFO case, and it remains unknown if it can be attained.
@cite_2
2141282318
We consider packet scheduling in a network providing differentiated services, where each packet is assigned a value. We study various queueing models for supporting QoS (Quality of Service). In the nonpreemptive model, packets accepted to the queue will be transmitted eventually and cannot be dropped. The FIFO preemptive model allows packets accepted to the queue to be preempted (dropped) prior to their departure, while ensuring that transmitted packets are sent in the order of arrival. In the bounded delay model, packets must be transmitted before a certain deadline, otherwise it is lost (while transmission ordering is allowed to be arbitrary). In all models the goal of the buffer policy is to maximize the total value of the accepted packets.Let α be the ratio between the maximal and minimal value. For the non-preemptive model we derive a Θ(log &alpha) competitive ratio, both exhibiting a buffer policy and a general lower bound. For the interesting case of two distinct values, we give an 2α--1 α competitive buffer policy, which exactly matches the lower bound. We also analyze a RED-like policy and derive its competitive ratio, which is approximately 2α--0.5 α for two values and Θ(log α) for multiple values. In addition we improve the previous known lower and upper bounds of the Fixed Partition and Flexible Partition policies.For the FIFO preemptive model, we improve the general lower bound and show a tight bound for the special case of queue size 2. We prove that the bounded delay model with uniform delay 2 is equivalent to a modified FIFO preemptive model with queue size 2. We then give improved upper and lower bounds on the 2-uniform bounded delay model. We also give lower bound for the 2-variable bounded delay model, which matches the previously known upper bound. We consider FIFO buffer management for switches providing differentiated services. In each time step, an arbitrary number of packets arrive, and only one packet can be sent. The buffer can store a limited number of packets, and, due to the FIFO property, the sequence of sent packets has to be a subsequence of the arriving packets. The differentiated service model is abstracted by attributing each packet with a value according to its service level. A buffer management strategy can drop packets. The goal is to maximize the sum of values of sent packets. For only two different packet values, we introduce the account strategy and prove that this strategy achieves an optimal competitive ratio of ≈ 1.282, if the buffer size tends to infinity, and an optimal competitive ratio of (√13-1) 2 ≈ 1.303, for arbitrary buffer sizes. For general packet values, the simple preemptive greedy strategy (PG) is studied. We show that PG achieves a competitive ratio of √3 ≈ 1.732 which is the best known upper bound on the competitive ratio of this problem. In addition, we give a lower bound of of 1+1 √2 ≈ 1.707 on the competitive ratio of PG which improves the previously known lower bound. As a consequence, the competitive ratio of PG cannot be further improved significantly. Abstract 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. Current network processors (NPs) increasingly deal with packets with heterogeneous processing times. In such an environment, packets that require many processing cycles delay low-latency traffic because the common approach in today's NPs is to employ run-to-completion processing. These difficulties have led to the emergence of the Multipass NP architecture, where after a processing cycle ends, all processed packets are recycled into the buffer and recompete for processing resources. In this paper, we provide a model that captures many of the characteristics of this architecture, and we consider several scheduling and buffer management algorithms that are specially designed to optimize the performance of multipass network processors. In particular, we provide analytical guarantees for the throughput performance of our algorithms. We further conduct a comprehensive simulation study, which validates our results.