利用混合式記憶體補貨策略於雲端儲存之記憶體配置

Abstract

雲端儲存乃雲端計算於基礎設施及服務的延伸運用。雲端儲存供應商將各處的資料儲存系統整合成一虛擬儲存池，並將其提供給消費者透過網路使用。隨著科技的發展與普及，全球對於資訊儲存空間的需求量日益攀升。同時，資訊儲存空間的需求量受到科技產品生命週期的影響，導致雲端儲存空間的需求量不僅大幅成長且呈現劇烈的波動。雲端儲存讓企業只需依其實際需求來租貸記憶體儲存空間，降低租貸企業的營運管理成本並可致力於其核心業務。可惜雲端儲存本身具有資訊安全疑慮、雲端供應商的可靠性、可用性和存取效能等問題。雲端供應商需要降低其記憶體維護和建構成本並同時避免沒有記憶體空間租給租貸商的情況發生。目前已有多篇論文從各種角度提出數種解決雲端儲存問題的方法，但仍沒有研究藉由存貨管理的方式來改善雲端儲存的記憶體利用率，因此需要找出一個適合雲端儲存環境的記憶體補貨策略。本研究利用市場資訊構築滾動式預測系統，並將其與限制理論的拉式補貨和緩衝管理結合，成為市場資訊混合式記憶體補貨策略。為了證明本研究的市場資訊混合式記憶體補貨策略能實際改善雲端儲存中的記憶體使用率，本研究再利用加權移動平均法和指數平滑法構築滾動式預測，並將其各自和限制理論的拉式補貨與緩衝管理結合以建構另兩個混合式記憶體補貨策略，接著比較此三種混合式補貨策略和限制理論本身在實際資料與模擬資料中的表現結果。最後證明市場資訊混合式記憶體補貨策略表現優於另外二個混合式記憶體補貨策略和限制理論本身，並且能夠在維持服務水準不變的情況下大幅改善雲端儲存的記憶體存貨水準。

In the technical industries, cloud storage is a segment with highly attention for these days. Cloud storage is an application of cloud computing under infrastructure as a service (IaaS). Cloud storage is a virtualized pool of data center which operated by host companies and people can store data into it through internet. The progress of technology makes demands of cloud storage become higher. Unstable life cycles of products will also cause high variation of demands in cloud storage. The maintenance and purchasing cost of memory space need to be considered while avoiding the possibility of running out of it. Many researches were proposed to solve issues in cloud storage, but there is no research can improve the utilizations of memory space via inventory management. As a result, it is worthy to develop a memory replenishment strategy which can adapt to the environment of cloud storage. This research offers a MI hybrid memory replenishment strategy, which uses market information (MI) to build rolling forecast and then combines it with the demand pull and buffer management of theory of constraints (TOC). Both practical and simulative data are used to verify the effectiveness and feasibility of MI hybrid memory replenishment strategy. Furthermore, weighted moving average (WMA) and exponential smoothing (ES) are also used to build rolling forecast and then combine with TOC’s demand pull and buffer management (DPBM) separately. In the end, it is proved that MI hybrid memory replenishment strategy performs better than WMA hybrid memory replenishment strategy, ES hybrid memory replenishment strategy and TOC. MI hybrid memory replenishment strategy can decrease a large amount of on hand memory without impacting service level in the environment of cloud storage.