Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/09: Flow control and load shedding ??? Vasiliki Kalavri | Boston University 2020 Keeping up with the producers • Producers (back-pressure, flow control) 2 ??? Vasiliki Kalavri | Boston University 2020 Load management approaches 3 ! Load shedder (a) Load shedding (b) Back-pressure (c) Elasticity Selectively drop records: Suitable for transient load increase. Scale resource allocation: • Addresses the case of increased load and additionally ensures no resources are left idle when the input load decreases. ??? Vasiliki

been the general trend. How to manage so many clusters ,resources and businesses How to ensure load balancing of cluster nodes 1 2 Improper resource requests 3 Multi-tenant resource preemption How Business N … How to ensure load balancing of cluster nodes ？ Dynamic-Scheduler Node1 Node2 Kube-scheduler Pod Request Load Level Request Load Level Real Load Level Real Load Level Assigned to Node2 but high load, while some nodes have high resource requests but low load. Dynamic-Scheduler Node1 Node2 Kube-scheduler Pod Request Load Level Request Load Level Real Load Level Real Load Level

Discovery ................................................................................ 6 1.3.7 Load Balancing........................................................................................ service for an Application .....................................................26 3.3 Load Balancing using Rancher Load Balancing services ............................................27 3.4 Service Discovery one needs robust cluster management capabilities that can handle scheduling, service discovery, load balancing, resource monitoring and isolation, and more. For years, Google has used a cluster manager

AWS LOAD BALANCER OPERATOR 24.1. AWS LOAD BALANCER OPERATOR 发行注记 24.2. OPENSHIFT CONTAINER PLATFORM 中的 AWS LOAD BALANCER OPERATOR 24.3. 了解 AWS LOAD BALANCER OPERATOR 24.4. 在安全令牌服务集群中安装 AWS LOAD BALANCER BALANCER OPERATOR 24.5. 创建 AWS LOAD BALANCER CONTROLLER 实例 24.6. 创建多个入口 24.7. 添加 TLS 终止 24.8. 配置集群范围代理 136 139 139 139 140 140 142 142 167 170 184 184 186 188 188 190 190 193 193 194 195 Container Platform API 的请求 中提供 OAuth 访问令牌或 X.509 客户端证书来进行身份验证。 AWS Load Balancer Operator AWS Load Balancer (ALB) Operator 部署和管理 aws-load-balancer-controller 的实例。 Cluster Network Operator Cluster Network

reloaded at any time. Keras 41 config = layer_1.get_config() from_config Load the layer from the configuration object of the layer. config = layer_1.get_config() reload_layer Serialize the model Keras provides methods to serialize the model into object as well as json and load it again later. They are as follows:  get_config(): Returns the model as an object. config 10,000 test images. Below code can be used to load the dataset: from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() where  Line 1 imports minst from the

samples are provided to bridge the theory and practice gap. We have prepared a few helper functions: load_image(), show_image(), transform() and transform_and_show(), which will be used to transform the images preprocessing.image import ImageDataGenerator from urllib.request import urlopen IMG_SIZE = 224 def load_image(url): with urlopen(url) as request: img_array = np.asarray(bytearray(request.read()), dtype=np transform_opts) def transform_and_show(image_path, **transform_opts): # Load the image data # The data is formatted as (H, W, C) image = load_image(image_path) # Transformed Image transformed_image = transform(image

的实例： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM 进行对话的示例： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM 5-7B-Chat-AWQ ： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat-AWQ", # the quantized

Istio End of 2021 100% services migrated to Istio 8 Features currently used: ● HTTP/2 Load-balancing ● Traffic Shifting ● mTLS Features under investigation: ● Retries ● Circuit breaking Istio ● Moving HTTP/2 load-balancing from client-side to Envoy ● Label selector updates for app and version labels ● Istio default retry policy ● Istio proxy performance and load testing ● Abstracting Istio features 44 Moving HTTP/2 load-balancing from client-side to Envoy Adopting Istio ● We use gRPC heavily in our microservices ● But Kubernetes is pretty bad at load-balancing it ● So we solved it

AZ ○ Shared-Nothing Architecture ■ Hosts services catering to the AZ, e.g., AZ IPAM, Network Load-balancers, etc. ■ Full isolation by confining service failures to AZ boundary AZ 1 AZ 2 AZ n Control Plane Global Control Plane Region Rn Delegate #IstioCon Load balancing & Traffic Flow ● Two tiers of hardware Load-Balancers (LB) ● Application-Tier LB ○ K8s service realized on Application-Tier DNS lookup Application-Tier Load-Balancer Web-Tier Load-Balancer Application-Tier Load-Balancer Web-Tier Load-Balancer Application-Tier Load-Balancer Web-Tier Load-Balancer Pods Pods Pods AZ

the ball at the least full bin: • when d=2, the maximum load is ln ln n / ln 2 + O(1), with high probability • when d>2, the maximum load keeps decreasing, but only by a constant factor 10 • Consider the maximum load is Θ(ln n/ln ln n), with high probability ??? Vasiliki Kalavri | Boston University 2020 Dynamic resource allocation • Choose one among n workers • check the load of each worker worker and send the item to the least loaded one • load checking for every item can be expensive • Choose two workers at random and send the item to the least loaded of those two • the system uses two