Datacenters and Cloud Computing. Jia Rao Assistant Professor in CS

Datacenters and Cloud

Computing

Jia Rao

Assistant Professor in CS

What is Cloud Computing?

“A model for enabling ubiquitous, convenient, on-demand network access to a shared pool of

configurable computing resources.”

Trends

• Big players: Amazon, Google, Microsoft …

• 150+ billion dollar market by 2013 [Gartner, 2009]

• In 2012, 80% of new enterprise apps will be deployed on cloud platforms

[IDC, 2011]

Before...

• _{Small business was afraid of}

Now with the Cloud

• Individuals buy and

• Small business buy and

• Large business is happy to see

Access “services” delivered by data centers

Cloud Services

• Software as a Service (SaaS)

- The cloud provides a piece of software

• Platform as a Service (PaaS)

- The cloud provides a programming platform

• Infrastructure as a Service (IaaS)

- The cloud provides raw hardware

Simplicity

Why Clouds?

• Pay-as-you-go no upfront cost

• On-demand self-service convenient

Who Uses the Cloud?

• Individuals

‣ Use software online, augmented experience

• Small startups

‣ Can start small and expand later

• Big companies

‣ Outsource some of the IT management

• The government

Technologies behind the Cloud

‣ provide a high-density pool of computing power at low cost

• Server virtualization

‣ seamlessly splits hardware into pieces and provides

isolation, fault tolerance, and usage accounting

• High-speed network

‣ delivers services to users at low latency and high

Modern Data Centers

• Giant computing facility with more than 10,000s of computers

• Petabytes of storage

• 100,000 - 500,000 square feet footage

Why CC now, not then?

• Key enabler: server virtualization

‣ Server consolidation makes it possible to elastic

resource management, in response to

unpredictable traffic and resource demand

For many services, the peak load exceeds the average by factors of 2 to 10

Why CC now, not then?

• Emergence of new technologies in support of

“low-touch, low-margin, low-commitment” self-service

• Applications

‣ Back and storage, content delivery, e-commerce,

high-performance computing, search engine, video streaming, BigData analytics

• More economic

Server Virtualization

• The abstraction of hardware resources upon which

virtual servers run

• Benefits

resource multiplexing isolation, fault tolerance resource management convenience

Virtualization

CPU Memory Disk

Windows Linux

Other issues in the Cloud

• Data Lock-in

• Data transfer bottlenecks

The performance issue

• Degraded performance

‣ compared with running on dedicated systems

• Unpredictable performance

‣ performance varies significantly over time

The causes

• Lack of agility

‣ cloud resource: elasticity agility

! !

• Lack of guaranteed capacity

‣ multi-tenant interference

The Causes (cont’)

• Little understanding of APP + Cloud

‣ HPC@Cloud, BigData@Cloud, E-commerce@Cloud

• Little understanding of virtualization techniques

‣ Full, para, hybrid and hardware assisted virtualization

• Little understanding of emerging hardware

Research in UCCS

• Making apps run faster in the cloud

‣ Adapting apps to a cloud environment

‣ Cloud = multi-tenant interference + hardware heterogeneity ‣ Parallel programs, MapReduce, networking apps

‣ adjusting parallelism, task scheduling, data placement …

‣ Providing better app support in cloud infrastructure ‣ better CPU and disk scheduling

Research Threads

‣ interference-aware task scheduling

‣ exploiting an extra layer of locality

• Providing better VM scheduling to HPC apps (PPoPP’14)

‣ addressing LHP and vCPU stacking problem

‣ considering fairness and efficiency

• More accurate resource accounting (HPCA’13)

‣ a holistic approach for quantifying cache contention, MEM latency in

NUMA multicore systems

Methodology:

Course Details

Course Structure

• This is a research project-oriented course

‣ You must read research papers every week

‣ You must actively participate paper discussions ‣ You must write paper critiques every week

‣ You must present one paper to the class

‣ You must perform a research project related to

Course Structure

• Lectures on datacenters and cloud computing (until spring break)

‣ Datacenter fundamentals, Virtualization, MapReduce, brief

discussions about DC hardware, e.g., multicore processors, SSD, GPU and cloud management, e.g., Openstack, SDN

• Lectures on how to read and present a research paper

• Paper presentations and discussions (after spring break)

‣ Resource management, energy, reliability, and security

Course Requirements

• Research paper (2-member teams)

‣ no less than 2 pages project proposal ‣ no less than 5 pages final report

‣ in ACM or IEEE conference format

• Paper critiques (individuals)

‣ each student needs to submit 12 critiques ‣ Due at the corresponding presentations

Why This Course?

• No textbook needed

• No midterm or final exams

Distribution of Points

• In class discussion and attendance: 5%

• Paper critiques: 15%

• Paper presentation: 20%

• Programming assignment: 10%

• Research project: 50%

Q & A