分散式物件導向環境已經成為平行分散運算與服務架構中主要的元件。在這些分散式物件軟體中，.NET Remoting為.NET提供了一個階層式的概念用來發展平行分散運算。此篇論文提供了一個使用網路處理器來實作的高效能與可程式化封包處理器的方法，其可支援.NET
Remoting在多重叢聚伺服器的環境中運作。

.NET Remoting中有三種不同的遠端呼叫模式，Single call,
singelton與client-activated。在這些呼叫模式中，我們必須維持續態呼叫的正確性。然而，在傳統的負載平衡機制缺乏了處理續態呼叫的能力，在這裡我們選用了網路處理器來解決這樣的問題，因為其具有可程式化的優點.另外，我捫也針對在格網運算的工作流程模型提出了一個排程策略。在我們排程策略的第一步先對在工作流程中的續態工作排程，隨著處理器分布的起始配置，我們在第二階段處理了乏態的排程，對續態工作的踰時限制也將在第二階段處理。這樣的機制不只提供給乏態負載平衡的需求也達到對於續態工作負載平衡的目標。

為了效能考量，我們將叢聚伺服器的閘道器實作在Intel的IXP1200網路處理器上。我們也利用Microsoft的NLB與我們的系統作傳輸量的比較實驗(可達55%的效能增進)。另外，我們也模擬比較了EFT與ETT還有Round-robin的效能，當續態工作量達到50%的時候，EFT的與ETT的效能比較在5%與21%之間,與RR的效能比較在8%與34%之間，我們的方案能有效的支援.NET Remoting在多重叢聚伺服器間的交換。

Distributed object-oriented environments have become important components for parallel and distributed computing and service frameworks. Among distributed object-oriented software, .NET Remoting provides a language layer of abstractions for performing parallel and distributed computing in .NET environments. In this thesis, we present our methodologies in supporting .NET Remoting over meta-clustered environments with network processors, a high-performance and programable packet processing machine.

In .NET Remoting, there are three types of Remoting invocation which are Single-Call, Singleton, and Client-Activated. Among those invocation types, the states of stateful sessions must be maintained. However, traditional load-balancing mechanism lacks the ability of handling stateful invocations. To deal with the demand of load-balancing mechanisms in distributed object-oriented environment, we take the advantage of the programmability of network processors to develop the content-based switch for distributing workloads generated from remote invocations in .NET. In addition, we also propose scheduling policy to incorporate work-flow models as the models are now incorporated in many of tools of grid architectures.

In the first step of our scheduling policy, we perform scheduling policy for stateful jobs in the work-flow models. With the initial placements of processor allocations, we then perform the scheduling policy for stateless applications in the second phase. Timeout constraints for stateful tasks are incorporated so that it might roll back processor assignments for stateful tasks during the second phase. This mechanism gives load-balancing for stateless tasks while also performs load-balancings of stateful tasks when the timing constraints are met.

For the efficiency, the implementations are based on the Intel IXP 1200 network processor which includes a high performance architecture to process packets through the cluster gateway. Experiments done at clusters with IXP 1200 network processors show that our scheme can significantly enhance the system throughput (up to 55%) compared to NLB method when the traffic is heavy. In addition, there is also a simulation result in our work-flow model showing that the improvement of EFT is from 5% to 21% when compared to ETT and is from 8% to 34% when compared to RR while the stateful task ratio is 50%. Our schemes are effective in supporting the switching of .NET Remoting computations over meta-cluster environments.

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