雲端大型多人線上遊戲遊戲下基於負載預測之資源分配方法

Abstract

近年來大型多人線上遊戲(MMOG)已成為主流遊戲，其使用者超過數百萬人。對大型多人線上遊戲來說，值得研究的課題非常的多，其中又以資源分配為重要的研究課題之一。如何有效的進行資源分配使遊戲可以正常運作，又可使其滿足使用者之QoS，如讓使用者滿意之反應時間，是為產業與學界共同研究的課題。本論文利用支持向量機來進行資源預測並結合雲端的資源分配，以便有效地進行資源分配以提升使用者之QoS。支持向量機能運算大量的資料並具備快速計算的能力，使其被廣泛地運用在各種預測上。由於其預測準確率高於類神經網路，因此更能有效用於MMOG負載的預測。我們的方法是基於支持向量機於多伺服器架構下進行MMOG負載的預測，即利用每個地圖區域的虛擬機器之歷史負載量來預測該區域未來負載量的層級，從而進行有效的資源分配，以滿足使用者反應時間的要求。實驗結果顯示，我們基於支持向量機的預測方法在預測準確率上比基於類神經網路的預測方法高12.24%，且其在虛擬機器的使用數量上也減少8%。

In recent years, massively multiplayer online games (MMOGs) have hundreds to thousands of active concurrent players so as to have become a popular research topic in academia and industry. MMOGs consume huge resources due to a massive number of players. To utilize resources efficiently, we integrate MMOGs with cloud computing environments. An MMOG game world is composed of game regions. MMOG load management allocates resources (i.e. virtual machines, VMs) to each game region. In this paper, we propose an SVM (support vector machine)-based load prediction scheme to first forecast cloud resources needed based on the loading class of each game region in an MMOG cloud. Then we allocate resources needed to achieve reasonable response time with less resources used. Simulation results show that the proposed SVM-based load prediction is 12.24% better than neural network-based load prediction in term of prediction accuracy. In addition, the proposed SVM-based load prediction reduces 8% of the number of VMs used compared with the neural network-based load prediction.