Luận án Nâng cao hiệu năng cân bằng tải trên điện toán đám mây

Nội dung text: Luận án Nâng cao hiệu năng cân bằng tải trên điện toán đám mây

1. 98 DANH MỤC CÁC CÔNG TRÌNH CÔNG BỐ Tran Cong Hung, Nguyen Khoi, Nguyen Xuan Phi (2013), “Survey traffic matrix for optimizing network performance”, Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in (CT1) Telecommunications (JSAT), October Edition, 2013 Volume 3, Issue 10, ISSN 1925-2676, pages 29-35, October 2013, Canada. Website: Nguyễn Xuân Phi, Trần Công Hùng (2015), “Giải Thuật Phòng Tránh Tình Trạng Quá Tải Trong Điện Toán Đám Mây”, Proceedings of The 2015 National (CT2) Conference on Electronics, Communications and Information Technology ECIT 2015, pages 66-70, ISBN: 978-604-67-0635-9, December, 10-11, 2015, Ho Chi Minh City, Viet Nam. Nguyen Xuan Phi, Tran Cong Hung (2016), “Study the Effect of Parameters to Load Balancing in Cloud Computing”, International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.3, May 2016.ISSN:0974- (CT3) 9322 [Online]; 0975-2293 [Print], DOI: 10.5121/ijcnc.2016.8303, pp.33- 45, SCOPUS, the Australian Research Council (ARC) Journal Ranking, Nguyen Xuan Phi, Tran Cong Hung (2017), “Load Balancing Algorithm to Improve Response time on Cloud Computing”, International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 7, No. 6, December (CT4) 2017, DOI: 10.5121/ijccsa.2017.7601, pp.1-12, Nguyen Xuan Phi, Cao Trung Tin, Luu Nguyen Ky Thu, Tran Cong Hung (CT5) (2018), “Proposed Load Balancing Algorithm to Reduce Response time and Processing time on Cloud Computing”, International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.3, May 2018, DOI:
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7. 110 kỳ của đại lượng X kéo dài hơn 1 năm. Để đánh giá thành phần này các giá trị của chuỗi thời gian được quan sát hàng năm. Ví dụ, Lượng dòng chảy đến hồ Trị An từ năm 1959 – 1985 - Thành phần bất thường (irregular component): Thành phần này dùng để chỉ sự thay đổi bất thường của các giá trị trong chuỗi thời gian. Sự thay đổi này không thể dự đoán bằng các số liệu kinh nghiệm trong quá khứ, về mặt bản chất thành phần này không có tính chu kỳ. 2. Tính dừng và tính mùa vụ a. Tính dừng của dữ liệu (Stationary) Nếu mỗi chuỗi thời gian gọi là dừng thì trung bình, phương sai, đồng phương sai (tại các độ trễ khác nhau) sẽ giữ nguyên không đổi dù cho chúng được xác định vào thời điểm nào đi nữa. Trung bình: E(Yt)=const (Kỳ vọng không đổi theo thời gian) Phương sai: Var(Yt)=const (Phương sai không đổi theo thời gian) Đồng phương sai: Covar(Yt,Yt-k)=gk (Đồng phương sai chỉ phụ thuộc khoảng cách của độ trễ mà không phụ thuộc thời điểm tính đồng phương sai đó, không phụ thuộc t) Để xem một chuỗi thời gian có dừng hay không, ta có thể sử dụng Đồ thị của Yt theo thời gian, Đồ thị tự tương quan mẫu (Sample Auto Correlation), hay kiểm định bước ngẫu nhiên (kiểm định Dickey-Fuller) Nếu chuỗi Yt không dừng, ta có thể lấy sai phân bậc 1. Khi đó chuỗi sai phân bậc 1 (Wt) sẽ có thể dừng. Sai phân bậc 1: Wt=Yt-Yt-1 Nếu chuỗi sai phân bậc 1 (Wt) không dừng, ta có thể lấy sai phân bậc 2. Khi đó chuỗi sai phân bậc 2 có thể dừng. Sai phân bậc 2: Vt=Wt-Wt-1 b. Hàm tự tương quan và hàm tự tương quan mẫu Hàm tự tương quan(ACF) ở độ trễ k được ký hiệu là ρk được định nghĩa như sau: Trong đó: - pk không có thứ nguyên - pk có giá trị từ -1 đến 1
8. 112 q: dựa vào SAC d: dựa vào số lần lấy sai phân để làm cho chuỗi dừng Mô hình ARIMA(1, 1, 1) : y(t) – y(t-1) = a0 + a1(y(t-1) – y(t-2) + e(t) + b1e(t-1)) Hoặc z(t) = a0 + a1z(t-1) + e(t) + b1e(t-1), Với z(t) = y(t) – y(t-1) ở sai phân đầu tiên : d = 1. Tương tự ARIMA(1,2,1) : h(t) = a0 + a1z(t-1) + e(t) + b1e(t-1), Với h(t) = z(t) – z(t-1) ở sai phân thứ hai : d = 2. (d lớn hơn 2 rất ít được sử dụng) 4. Kiểm tra chuần đoán mô hình Mô hình ARIMA tốt có RMSE nhỏ và sai số là nhiễu trắng: Sai số có phân phối chuẩn, và đồ thị SAC giảm nhanh về 0 Tìm kiếm mô hình ARIMA phù hợp là một quá trình thử và sai.
9. 114 PHỤ LỤC 2. CÁC HÀM/PHƯƠNG THỨC ĐƯỢC CÀI ĐẶT BỔ SUNG VÀO CLOUDSIM public class VmExt extends Vm { private List LastRT; private double PredictedRT; public VmExt( int id, int userId, double mips, int numberOfPes, int ram, long bw, long size, String vmm, CloudletScheduler cloudletScheduler) { super(id, userId, mips, numberOfPes, ram, bw, size, vmm, cloudletScheduler); LastRT = new ArrayList (); } public double getPredictedRT() { List arraylist = getLastRT(); if(arraylist.size()>15){ double[] dataArray = new double[arraylist.size() - 1]; for (int i = 0; i < arraylist.size() - 1; i++) { dataArray[i] = arraylist.get(i); } ARIMA arima = new ARIMA(dataArray); int[] model = arima.getARIMAmodel(); this.PredictedRT = arima.aftDeal(arima.predictValue(model[0], model[1])); } else { this.PredictedRT =arraylist.get(arraylist.size()-1); } return this.PredictedRT;
10. 116 public double getResponseTime() { return this.getFinishTime() - this.getExecStartTime(); } } public class ArimaDatacenterBroker extends SimEntity { private static List LastRT; private double PredictedRT; public double getPredictedRT() { List arraylist = getLastRT(); if(arraylist.size()>50){ double[] dataArray = new double[arraylist.size() - 1]; for (int i = 0; i getLastRT() { return this.LastRT; } public void addLastestRT(Double RT) { LastRT.add(RT); if (LastRT.size() > 200) { LastRT.remove(0); }
11. 118 vm.addLastestRT(cloudlet.getResponseTime()); addLastestRT(cloudlet.getResponseTime()); getCloudletReceivedList().add(cloudlet); Log.printConcatLine(CloudSim.clock(), ": ", getName(), ": Cloudlet ", cloudlet.getCloudletId(), " received"); cloudletsSubmitted ; if (getCloudletList().size() == 0 && cloudletsSubmitted == 0) { // all cloudlets executed Log.printConcatLine(CloudSim.clock(), ": ", getName(), ": All Cloudlets executed. Finishing "); clearDatacenters(); finishExecution(); } else { // some cloudlets haven't finished yet if (getCloudletList().size() > 0 && cloudletsSubmitted == 0) { // all the cloudlets sent finished. It means that some bount // cloudlet is waiting its VM be created clearDatacenters(); createVmsInDatacenter(0); } } } protected void submitCloudlets() { int vmIndex = 0; List successfullySubmitted = new ArrayList (); for (CloudletExt cloudlet : getCloudletList()) { VmExt vm; // if user didn't bind this cloudlet and it has not been executed yet if (cloudlet.getVmId() == -1) { vm = getVmsCreatedList().get(vmIndex); } else { // submit to the specific vm vm = getReliableVm(); if (vm == null) { // vm was not created if (!Log.isDisabled()) {