![]() Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. Scheduling is a popular topic in operational management and computer science. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. Moreover, decentralized methods require a coordination mechanism with limited communications. ![]() Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. rc.top is stored directly after rc.left and this forms a POINT structĬlientToScreen(hWnd, reinterpret_cast(&rc.left)) ĬlientToScreen(hWnd, reinterpret_cast(&rc.One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Rectf.height = static_cast(rc.bottom - rc.top) Rectf.width = static_cast(rc.right - rc.left) PixelSpace Conversion Vector3f ConvertPixelSpace(HWND hWnd, float x, float y, PixelSpace curSpace, PixelSpace newSpace) Vector3f centre = ConvertPixelSpace(this->gfx.hWnd, centreX, centreY, ps, PixelSpace::Client) Return ConvertPixelSpace(this->hWnd, (float)pt.x, (float)pt.x, PixelSpace::Screen, PixelSpace::Client) ĭirect2D void DrawCircle(const Colour& c, float centreX, float centreY, float radius, PixelSpace ps) Convert from screen pixels to client pixels I want to know how I can fix my code so that it will draw the circle at the position given by GetCursorPos().ĭriver code Vector3f cursPos = input.GetCursorPos() ĭrawCircle(Colour::Green, cursPos.x, cursPos.y, 3/*radius*/) Here, dpiX and dpiY become 96 which I notice is also the constant that GetDpiForWindow() from the windows API returns when it is not dpi aware. ), "Failed to create D2D render target") This->factory->GetDesktopDpi(&dpiX, &dpiY) ĭ2D1_RENDER_TARGET_PROPERTIES rtDesc = D2D1::RenderTargetProperties(ĭ2D1::PixelFormat(DXGI_FORMAT_UNKNOWN, D2D1_ALPHA_MODE_PREMULTIPLIED),ĪssertHResult(this->factory->CreateDxgiSurfaceRenderTarget( ![]() This is the code for creating the D2D render target from the DXGI surface I obtained from the swapchain in my D3D11 code. I noticed changing the dpi for the D2D1_RENDER_TARGET_PROPERTIES affects this 'scaling' so I suspect the problem has something to do with dpi. The screen to client coordinates work perfectly but for some reason D2D draws the circle a small distance from the expected location (tens of pixels) as if the coordinate had been scaled by a small factor so that the error gets larger as the circle is drawn further from (0, 0). I have some code that uses GetCursorpos() from the windows API which is then converted to client coordinates and then draws a small circle at this position using D2D FillEllipse().
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