Challenges in Modern Data- Centers Management

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Challenges in Modern

Data-Centers Management

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Information provided in these slides is

for educational purposes only

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Welcome

• Hebrew - Shalom

• Arabic - Ahlan'wa sahla

• Bosnian - Dobrodošli

• Chinese (Cantonese) - (fòonying)

• Chinese (Mandarin) -

欢

迎

[simplified],

歡迎

[traditional] (huānyíng)

• Czech - Vítáme tĕ

• Danish - Velkommen

• Dutch - Welkom

• French - Bienvenue

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Outline

• Introduction

• Administrative and academic stuff

• Data centers

• History and facts

• Our course

• Lecture by lecture – what will be covered in each?

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Me (Edi)

• With Intel since 2011

• Formerly with IBM (almost 17 years)

• PhD in Computer Science from the Hebrew university

• Prof. Dror G. Feitelson

• Interested in anything related to “Systems”

• OS, Virtualization, Storage, etc.

• Distributed systems – resource management & job scheduling

• Performance evaluation and modeling

• Etc.

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Why the course?

• Data centers are

big

businesses

• 50 years of technological evolution

• Special skills required to operate them (experience, legacy)

• IT team in Intel (IDC-Haifa)

• Responsible for the data center facility, continuous operation, solutions development and deployment, users engagement, etc.

• Huge

experience (legacy….)

• Goal is to expose some of this experience in a structured way

1. Challenges we face

2. Solutions e.g., technologies, algorithms used to address them

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Administration

• Edi (me) – Main instructor and responsible for the course

• [email protected]

• Jalil (him) – Our super-talented teaching assistant

• Danny (not here) – Advisor and high-level supervisor

• Important dates

• Lectures: Wednesday’s 14:30-16:30 Taub 6

• Exams

• Moed A – 3/7/2015

• Moed B – 20/9/2015

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Academic

• Pre-requisites

• Basic knowledge on networking, computer and distributed systems e.g., clusters should be enough

• Requirements

1. Must attend 80% of the lectures

2. Must deliver homework assignments (30% of the grade)

• 4-5 assignments

3. Final exam (70% of the grade)

• 2-3 open questions + few closed ones (multiple-choice)

• In the spirit of the homework assignments

• Our site

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Schedule (tentative)

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Questions?

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Data centers

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Data center

• Facility used to house computer systems and associated components

• Telecommunications, storage systems, etc.

• “Production floor” of most modern companies, e.g.,

• Google – Information processing, etc.

• Amazon – Sales, Hosting (AWS), etc.

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History

• Started as a facility to house old complex computing systems

Challenges in Modern Data Centers Management, Spring 2015 14

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History cont.

• Big boost during the Dot-Com era (1997–2000)

• Companies emerged whose business solely surrounds the Web

• Requiring fast Internet connectivity and 24/7 non-stop operation

• Special facilities built to house such businesses

• Internet data centers (IDC)

• Leading to new technologies and practices

• Eventually migrated to the private data centers

• Grid-computing phenomenon

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History cont.

• Another big transformation as part of the Cloud era (2007+)

• New design and deployment philosophy

• Redundant (multiple copies), scalable (elasticity), high-availability (stateless)

• Technology makes “hosting” economically attractive

• Even for large-scale enterprises

• Environmental impact receives special attention

• Standard bodies specify requirements

• Huge effort to make data-centers appear “Green”

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Some facts

• Large data center can consume as much electricity as a small town

• In 2010 data centers accounted for 1.1%-1.5% of the global electricity use

• Electricity spends account for 25-30% of a data center TCO

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Some facts cont.

• Average life of a data center is 9 years

• Older than 7 years considered out-of-date (Green-computing)

• Minute of data-center downtime may cost tens-of-thousands of $$

• High-availability is critical component in the design

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Facebook

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IBM BlueGene/P

Originally posted to Flickr, CC BY-SA 2.0

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Our course

• Focuses on common management challenges

• Generic enough so they fit most usage models

• Impossible to cover all challenges

• Filtered the ones team has experience with

• Chose the ones we believe are most important

• The team

• Responsible for data centers facility, continuous

operation, solution development & deployment, etc.

• Domain experts

• Facility, networking, resource management, storage, business intelligence and analytics, security, etc.

Introduction Facility basics Networks RM Part I RM Part II RM Part III Data access Business Intelligence Predictive Analytics DC visit Security Part I Security Part II Summary

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Facility basics

• Building a data center is expensive

• Single rack location construction can cost up to $80K

• Total spend can reach hundreds-of-millions of $$

• Four main elements of the facility

• Power, Cooling, Space, Networks

• Total Cost of Ownership (TCO)

• Initial capital (CapEx)

• Long-term operational expenditures (OpEx)

• Power Usage Effectiveness (PUE)

• Power efficiency performance indicator

Facility basics

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Facility – challenges

• Optimizing cooling

• Hot Isle, Hot/cold air containment, Free cooling

• Optimizing power feeding

• Redundancy dilemma

• AC vs. DC

• Optimizing refresh rate

• 4-year optimal lifespan

Facility basics

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Networks

• Veins and arteries of the data center

• Play key role in its performance and high-availability

• Ensuring adequate availability

• Redundancy at layer 2 (data-link)

• Spanning Tree Protocol (SPT) & RSPT

• Per VLAN spanning tree (PVST)

• Multi-Switch Link aggregation (M-LAG)

• Redundancy at layer 3 (IP)

• Virtual Router Redundancy Protocol (VRRP)

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Resource management I – III

• 5% resource waste in 10K-server data center can cost $3K/day ($1M /year)

• It is critical to utilize resources efficiently as possible

• Resource management system (RMS) / Scheduler

1. Accepts requests from the users (millions per-day)

• VMs (Amazon), Map-reduce (Hadoop), Chip simulations (Intel), etc.

2. Queues and prioritizes them (decides which job to execute next)

• Subject to constraints, e.g., ensuring shares, deadlines, etc.

3. Allocates resources and launches the jobs on selected resources

• Various heuristics

RM Part I-III RM Parts I-III

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Resource management I

• Proportional-share scheduling

• Very common scheduling heuristics used in data centers

• Every entity (VO, project, user) should get its promised share of the resources

• Challenges

1. How to measure resource consumption ?

• 1-core X 4GB vs. 3-cores X 1GB

2. How to ensure fast ramp-up

• Limits, logical and physical buffers

3. Considering history

• Is this really important?

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Resource management II

• Matching the jobs with available resources

• Best-fit, worst-fit, first-fit, random, mix-fit, dynamic-programming, etc.

• Challenges

1. Optimizing resource matching

• Single vs. multiple dimensions

• One job at a time vs. multiple jobs (look-ahead)

2. Dealing with jobs that cannot be scheduled

• Reservation (backfilling)

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Resource management III

• Going global (meta-scheduling)

• Ensuring QoS, Load balancing

• Practical considerations

• Scalability, Robustness, Usability

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Data Access

• Jobs (VMs, map-reduce, simulations) use data

• Huge burden on the storage (DoS attacks)

• Challenges

1. Avoiding DoS within a data center

• Storage-side: Scale-out storage, Parallel NFS, etc.

• Client-side: cacheFS, CaMA (RO)

2. Enabling remote data access (going global)

• Synchronous and asynchronous replications

• Site-level caching, etc.

• Continuous Integration (CI) use case

• Know your workload…

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Our course cont.

Introduction_{Facility basics} Networks RM Part I RM Part II RM Part III Data access Business Intelligence Predictive Analytics DC visit Security Part I

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Business Intelligence (BI)

• Goal is to provide insights on the data center to help optimize its operation

• E.g., statistics on resource usage to help deiced which equipment to buy

• Involves collecting, preparing, storing,

analyzing

, and accessing the data

• Challenges in each layer e.g., impact on source system, responsiveness, etc.

• Focusing on

data analysis

• Optimizing data queries (SQL)

• Join-sort-aggregate implementations

• How to assemble them optimally using time and space considerations

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Predictive analytics

• One of the important usages for BI in the data center

• Help systems e.g., job scheduler, take data-driven actions in real time

• Deep dive into one such use-case

• Predicting jobs resource usage for optimizing resource allocation

• Data-Stream Mining (DSM)

• Continuous (endless) rapid incoming data

• Machine-learning must be applied online

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Predictive analytics – challenges

• Performance (real-time)

• Impossible to store all data (train) – each sample must be processed once

• Adaptability

• Non-stationary data – model must be adaptable (sliding windows)

• Quality, availability

• Perform at least as good as “no-stream” models

• Prediction must be provided continuously

• Cover well known algorithms

• Regression trees, Decision trees, etc.

• Multiple Sliding Windows (MSW)

• Mimran & Even, 2013

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Security

• Securing the data center is complex

• Security breach can cause real money, reputation, IP loss and legal actions

• Security control model helps organize things

• Divide the data center into layers

• For each layer describe its attack vectors, vulnerabilities and controls

• Challenges in two layers

1. Applications

• Web applications

• Code injections, e.g., SQL injection

• Web manipulations e.g., ClickJacking

• Web services

2. Identities Access Management (IAM)

• Managing multiple identities (SAML)

• Authentication

• Knowledge factors: Passwords, Kerberos

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Visit to IDC data center (3/6)

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Summary

• Course is unique

• Covers actual challenges encountered in real environments

• Delivered by domain experts with

huge

experience in designing and deploying solutions

• Interaction is important

• Don’t hesitate to ask (tough) questions