諸如聲音、影像等的即時資料串流通常都不能忍受很高且不穩定的封包延遲。不幸的是，像WFQ (Weighted Fair Queueing)、SFQ (Start-time Fair Queueing)等為人所熟悉的演算法，在現實的網路環境當中都會遭遇由於封包到達時間不穩定所造成的封包延遲過大且不穩定的問題，最近幾年有些演算法試圖解決這個問題，例如LLQ (Low Latency Queueing)，但在LLQ演算法裡，只有高優先權的資料流有低延遲的保證，那些低優先權的資料流則有可能會有無法被服務的情形發生。LLEPS (Low Latency and Efficient Packet Scheduling)演算法也試圖解決這個問題，但是LLEPS需要很準確的時間間隔(time slot)支援，如果這個時間不準確，LLEPS仍會遭遇到不穩定的封包延遲問題，且如何正確地決定這個時間間隔在LLEPS這篇論文裡面並沒有提出，也仍是一個未解的問題，除此之外，即使LLEPS擁有準確的時間間隔，當封包以爆發(burst)形式出現時，LLEPS仍會遭遇封包延遲過大的問題。
因此在本篇論文中，我們利用累積信用的方式來保證即時資料串流有最低的延遲，並且透過模擬與之前所提出來的演算法做比較。

Real-time traffic flows, such as streaming audio and video data, can not endure high or unsteady packet latencies. Unfortunately, some well-known scheduling algorithms such as Weighted Fair Queueing (WFQ), Start-Time Fair Queueing (SFQ)...etc. in real networks will be subjected to high and unsteady latencies due to the unsteady queueing delay problem, `the buffer underrun problem'', that we will explain later in this paper. A few scheduling algorithms address this problem in recent years like Low Latency Queueing (LLQ) which may suffer from low priority traffic starvation problem. The QoS guarantee only satisfies the flow with highest priority. Another one that addresses this problem is Low Latency and Efficient Packet Scheduling algorithm (LLEPS) which requires additional parameter, time slot, support accurately. And how to determine the time slot value exactly to enable LLEPS to work efficaciously is another problem and is not mentioned in LLEPS. Even if we use accurate time slot value, the queueing delay of LLEPS is not stable enough when packets come in burst in the real networks.
Therefore, in this paper, we propose a packet scheduling algorithm, Credit-Based Low Latency Packet Scheduling (CBLLPS), using adaptive credit function to ensure low latency for streaming applications. Some simulation results are also presented.

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