rpm -ql sysstat grep bin
us - Time spent in user space sy - Time spent in kernel space ni - Time spent running niced user processes (User defined priority) id - Time spent in idle operations wa - Time spent on waiting on IO peripherals (eg. disk) hi - Time spent handling hardware interrupt routines. (Whenever a peripheral unit want attention form the CPU, it literally pulls a line, to signal the CPU to service it) si - Time spent handling software interrupt routines. (a piece of code, calls an interrupt routine…) st - Time spent on involuntary waits by virtual cpu while hypervisor is servicing another processor (stolen from a virtual machine)
processes, memory, paging, block IO, traps, disks and cpu activity
cfs/rt
Burst feature
Creating and organizing cgroups
I’ll Do It Later: Softirqs, Tasklets, Bottom Halves, Task Queues, Work Queues and Timers
We are expecting xxx to be revealed
it seams like Motorola might be he first brand to offer a phone with this Soc.
Now it’s time to talk about the Green Business awards. I’m sure you’re all excited.
So, having examined how we tackled our first issue, I’d like to share
UniCast 单播 MultiCast 多播 BroadCast 广播 实际是 BGP Anycast 任意播或泛播 AnyCast IP拥有MultiCast和UniCast各自的部分特性: 主要靠BGP的路由协议,有多个相同的ip的server,路由协议找到“最近”的一台server
任播指一个IP可以应用到多个服务器上
A DNS zone is a portion of the DNS namespace that is managed by a specific organization or administrator. 一个DNS zone是DNS名字空间的一部分,它被特定组织和管理机构管理。
Deep Learning Is Creating A Boom In AI Chips AI Is Expanding From Vision To Language OpenAI’s GPT-3 Is The First AI That “Understands” Language
Deep Learning Could Create More Economic Value Than The Internet Did.
ARM, RISC-V, and graphics processing units (GPUs) New Architectures Re-Invent The Data Center Every Few Decades Intel Seems Frozen In Time ARM Could Power The Majority Of Developer PCs By 2030 ARM Could Become The New Standard In The Cloud ARM & RISC-V Could Become The New Processor Standards By 2030 By 2030, The Accelerator Should Replace The CPU As The Main Server Compute Engine ARK Believes That Server Processors Will Transform In The Next Decade.
Video Game Monetization Models Are Shifting To Virtual Goods ARK Believes The Monetization Of Gaming Will Increase Video Games Are Becoming “Third Places“ Away From Home And Work Augmented Reality (AR) Is Primed To Scale “Virtual Reality” Could Approach Reality By 2030 The Revenue From Virtual Worlds Could Approach $400 Billion By 2025.
Incubated In China, Mobile Payments Are 2.5x Its GDP In The US, Digital Wallet Users Are Surpassing The Number Of Deposit Account Holders At The Largest Financial Institutions Digital Wallets Can Acquire Customers For A Fraction Of Banks’ Customer Acquisition Costs Traditional Banks Are Facing Potentially Sizeable Risks At Maturity, Each Digital Wallet User Could Be Worth Roughly $20,000
As Support For Its Network Increased, Bitcoin’s Price Hit An All-time High In Late 2020 Bitcoin Could Play A Pivotal Role As Corporate Cash.
ARK Believes Bitcoin Deserves A Strategic Allocation In Institutional Portfolios Bitcoin Trading Volume Is Comparable To That Of A Large Cap Stock And Has Grown At An Exponential Rate Institutional Investors Can Access Bitcoin In Sophisticated Ways We Believe Bitcoin Has Earned An Allocation In Well-Diversified Portfolios
Electric Vehicle Sales Have Taken Share In Good Times And Tough Times Wright’s Law Has Modeled The Decline In Battery Costs Successfully In Addition To Cost, EVs Are Competing On Range And Performance
The US Economy Is At Automation Levels Similar To That Of US Manufacturing In The Early 1990s Increased Automation And Productivity Can Provide Many Economic Benefits Automation could add 5%, or $1.2 trillion, to US GDP during the next five years. ARK believes automation will boost US real GDP growth by 100 basis points on average per year to 3.4%.
Robotaxis Should Expand The Ride-Hailing Market Three Autonomous Strategies Are Evolving
Drones Should Reduce The Cost To Transport Goods And People Dramatically Autonomous Air Travel Has Become Possible And Affordable Drones Could Deliver A Substantial Share Of E-Commerce Shipments By 2030 Drones Should Accelerate The Shift To Food Delivery
Lower Satellite Launch Costs Could Enable Continuous Global Coverage With Low Latency
3D Printing Applications Vary By Industry, Volumes, And Complexity 3D Printing Enables Many Form Factors
The Genomic ‘Toolkit’ Is Expanding To Provide A Fuller, Richer, And More Accurate View Into Biology Historically, Researchers Had To Choose Between Accuracy With SRS Or Comprehensiveness With LRS As Costs For LRS Converge With SRS, Many Clinical Applications Could Shift To LRS
Diagnosed Early, Cancer Can Be Treated Successfully Based On A Single Blood Test, Multi-Cancer Screening Can Detect Dozens Of Early-Stage Cancers Thanks To Rapidly Declining Costs, Multi-Cancer Screening Is Approaching A Reimbursable Price Point Multi-Cancer Screening Could Prevent Roughly 66,000 Cancer Deaths Annually In The US Multi-Cancer Screening And Other Genomic Technologies Are Transforming Oncology
Cancer Therapies Are Shifting From Liquid To Solid Tumors Oncology Trials Are Shifting From Autologous To Allogeneic Cell Therapies Gene Therapies Could Shift From Ex Vivo To In Vivo Editing
embeding representation attention
Accurate prediction of protein structures and interactions using a three-track neural network
Borg, Omega, and Kubernetes
- Containers as the unit of management
An Overview of Openstack Architecture
Orchestration is the beginning, not the end
ReplicationController DaemonSet Job
Don’t make the container system manage port numbers Don’t just number containers: give them labels Be careful with ownership Don’t expose raw state
2.1 Computing Nova: Glance: It’s the Openstack Image service 2.2 Networking Neutron: 2.3 Storing Swift: Cinder: 2.4 Shared Services Keystone: Horizon: Ceilometer: 2.5 Supporting Services Database: Advanced Message Queue Protocol:
Large-scale cluster management at Google with Borg
hundreds of thousands of jobs, from many thousands of differ- ent applications, across a number of clusters each with up to tens of thousands of machines. (1) hides the details of resource management and failure handling so its users can focus on application development instead; (2) operates with very high reliability and availability, and supports applica- tions that do the same; (3) lets us run workloads across tens of thousands of machines effectively.
minimize fault-recovery time scheduling policies that reduce the probability of correlated failures
workload
MapReduce system [23], FlumeJava [18], Millwheel [3], and Pregel [59]
Constraints can be hard or soft; resource requirements
2.5 Priority, quota, and admission control 2.6 Naming and monitoring
3 Borg architecture 3.1 Borgmaster 3.2 Scheduling
resource manager and scheduling 3.3 Borglet 3.4 Scalability
4 Availability 5 Utilization
Omega: flexible, scalable schedulers for large compute clusters 调度器扩展
- 并行parallelism
- 共享状态 shared state (集群中的所有资源,相对于资源分组给不同的调度器)
- 无锁的乐观并行控制
monolithic cluster scheduler architectures. 整体的;巨石的,庞大的;完全统一的 集群的增大,使调度器更加复杂,也容易遇到调度器瓶颈 双层调度器,资源管理器保证并行的数据一致性, Mesos [13] and Hadoop-on-Demand [4]
Omega: shared state, using lock-free optimistic concurrency control 需求
混布(batch and low-latency jobs) 问题:增加了复杂度
types of jobs, flexibly support job-specific policies, scale to an ever-growing amount of scheduling work
pessimistic approach: a particular resource is only made available to one scheduler at a time optimistic approach: detects the (hopefully rare) conflicts, and undoes one or more of the conflicting claims. The optimistic approach increases parallelism, but potentially increases the amount of wasted scheduling work if conflicts occur too frequently.
Mesos [13] and Hadoop-on-Demand (HOD) YARN
Hawk uses task stealing as a run-time mechanism aimed at mitigating some of the delays caused by the occasion- ally suboptimal, distributed scheduling decisions.
Scheduling long jobs
Overall results on the Google trace
Omega
There is no central resource allocator in Omega
Each scheduler is given a private, local, frequently-updated copy of cell state that it uses for making scheduling decisions.
50–70% of MapRe- duce jobs can benefit from acceleration using opportunistic resources
Apache Mesos YARN [76] V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, B. Saha, C. Curino, O. O’Malley, S. Radia, B. Reed, and E. Baldeschwieler. Apache Hadoop YARN: Yet Another Resource Negotiator. In Proc. ACM Symp. on Cloud Computing (SoCC), Santa Clara, CA, USA, 2013. Facebook’s Tupperware Microsoft’s Apollo system [13] E. Boutin, J. Ekanayake, W. Lin, B. Shi, J. Zhou, Z. Qian, M. Wu, and L. Zhou. Apollo: scalable and coordinated scheduling for cloud-scale computing. In Proc. USENIX Symp. on Operating Systems Design and Implementation (OSDI), Oct. 2014.
Transformer GPT-3 (1750亿参数) OpenAI 万亿级的参数 稀疏化、 多模态 多模态预训练
Attention Is All You Need
Scaled Dot-Product Attention Multi-Head Attention
Embeddings and Softmax Positional Encoding
Why Self-Attention
Machine Translation Model Variations
Rearchitecting Kubernetes for the Edge
algorithms
dynamic placement for clustered web applications
travelling salesman problem(TSP) vehicle routing problem(VRP) theory of computational complexity 复杂性理论 combinatorial optimization NP-complete problem NP-hard problem optimal control problem 最佳控制问题 integer linear program 整数线性规划
Methodology and Contributions
define and evaluate intelligence in a way that enables comparisons between two systems
I Context and history I.1 Need for an actionable definition and measure of intelligence I.2 Definingintelligence:twodivergentvisions I.3 AI evaluation: from measuring skills to measuring broad abilities
A new perspective II.1 Criticalassessment II.2 Definingintelligence:aformalsynthesis II.3 Evaluatingintelligenceinthislight II.3.1 Fair comparisons between intelligent systems II.3.2 What to expect of an ideal intelligence benchmark
III A benchmark proposal: the ARC dataset III.1 Descriptionandgoals III.1.2 Core Knowledge priors III.2 Weaknesses and future refinements II.3 Evaluatingintelligenceinthislight
two types of systems
Akamai DNS
architecture, algorithms, design principles, and operation
Authoritative DNS Services(3)
DNS hosting for their domains, GTM for their datacenters, and CDN services for edge delivery of their content fetched from those datacenters.
Akamai DNS is architected to be resilient to both failures and attacks. Akamai DNS is architected for both scalability and performance. Akamai DNS is architected for rapid reconfigurability.(Unlike traditional authoritative DNS whose translations remain relatively static)
3.9M to 5.6M queries per second (qps)
SYSTEM ARCHITECTURE
- Cloudflare 1.1.1.1 Public Recursive Resolver
- Google Public DNS. (2019). Retrieved June 2019 from https://developers.google.com/speed/public-dns/
- Quad9 DNS Service. (2019). Retrieved June 2019 from https://www.quad9.net/
resolvers and the authoritative nameservers.
4 RESILIENCY
- saturate the available bandwidth
- send DNS queries directly to consume compute or bandwidth resource
- Random Subdomain 随机子域名查询,较低前面解析器的命中率,直接透传到权威服务器上
- Spoofed Source IP
- Spoofed Source IP & IP TTL
legitimate queries 合法的 suspicious queries 可疑的
5 DNS PERFORMANCE
Two-Tier can reduce resolution time in most situations over Akamai’s single-tier of toplevels.
6 RELATED WORK
7 CONCLUDING REMARKS
the key design principles that underlie the ar- chitecture of Akamai DNS
6.1 A Transport System for Application Acceleration
https://www.bmc.com/blogs/machine-learning-ai-frameworks/ https://blog.dominodatalab.com/choosing-the-right-machine-learning-framework/ https://blog.google/products/search/search-language-understanding-bert/ https://www.zhihu.com/question/68482809 https://mwhittaker.github.io/papers/html/burns2016borg.html https://www.nextplatform.com/2015/05/05/google-omega-to-become-part-of-borg-collective/ Online Service Placement and Request Scheduling in MEC Networks On the Measure of Intelligence NIPS 2021 What is Anycast? | How does Anycast work? Joint Heterogeneous Server Placement and Application Configuration in Edge Computing [10] Apollo: Scalable and Coordinated Scheduling for Cloud-Scale Computing This paper is included in the Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation. October 6–8, 2014 • Broomfield, CO 978-1-931971-16-4 OSDI 11th [11] Mesos- A Platform for Fine-Grained Resource Sharing in the Data Center [12] 2015atc_Hawk-Hybrid Datacenter Scheduling [13] 2020 Autopilot: workload autoscaling at Google [14] Borg: the Next Generation [15] A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges