This document discusses requirements and technology issues for data centers of the future. It outlines a vision for modular, proximity-based data centers with mixed compute environments, redundancy, workload migration capabilities, and automation/orchestration. Current issues include a lack of standardization in orchestration/integration, limitations on linear scaling, and "flatness" challenges from multi-tiered network designs. The data center of the future aims to address these through software-defined networking, computing, and storage orchestrated in a secure, flat design. Companies that implement these technologies gain competitive advantages around utilization and rapid expansion.

1. CSC Proprietary and Confidential 1
July 22, 2022
Data Center Portfolio
Data Center of the Future
Christopher Whyte
General Manager
May 31, 2013

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DCotF Requirements
Modularity of capability – either through Utility Model with vendors as well as planned utilization and expansion goals
Modularity of facility – space expansion in the DC in increments relative to projected growth requirements
Proximity – DC “close” to both creation and consumption of data
Mixed compute environments – low workloads placed in cloud, high intensity workloads placed in bare metal systems, Hybrid
Cloud for short term peak workloads
Redundancy – the competitive mix of Disaster Recovery and Active/Active locations globally
X86 Migration – capability to move workloads safely and accurately from bare metal systems to Cloud environments
Automation – Management of workloads, by application, for the effective use of the environment
Orchestration – The human input necessary to initiate the automated deployment of workloads
Workload migration – capability to move workloads between data centers with minimal impact
Business Continuity – having a plan in place in the event of a significant event
CyberSecurity – offerings in the market that isolate and reduce the effects of physical, platform and network security events
Energy Efficiency – setting and attaining goals of PUE in relationship to the 1.0 ideal
Carbon Footprint – reducing and eliminating any materials in the Data Center extraneous to providing the delivery of
applications (examples include plastic facades and over-designed HVAC)

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Data Center Evolving Capabilities
Cloud
Offerings
Bare Metal
(Specific Workloads)
Utility Storage
Networks
Cybersecurity
Management
Service
APM
Hybrid Data
Center
Orchestration
Software
Defined
Compute
Software Defined
Networks
AVAILABLE TODAY UNDER DEVELOPMENT Software
Defined Data
Center (SDDC)
GBS DC
Assessment
GBS DC
Transformation
Multi-
tenancy
Tier 3
Data
Centers
Integration
APIs
Application
Development
Standard
SDDC
APIs
Applications for
SDDC
Standard
Abstraction
or
Virtual Layers
Scale Linearly: Data Center  Rack  Pod  Workload Financial: CapEx  OpEx
Flexibility: Managed  as-a-Service  Workload Demand Enabling: Applications  Analytics  Big Data

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CSC Proprietary and Confidential July 22, 2022
Current Technology Issues – Orchestration
and Integration
Orchestration – Automation between current generation systems is a complex mix of
automation and manual efforts
Proprietary Capabilities – Vendors introduce non-standardized solutions, often for
product differentiation, that limit multi-vendor response to an architecture
Software Defined – many of the APIs for software integration are introductory or yet to
be developed
Abstraction Layer – vendor abstraction layer(s) for integration have not reached a
state of maturity
Standardization – the market has multiple models for “definition by software”, which is
a precursor to Standards based Orchestration and Integration, and the market has yet
to define the standard

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Current Technology Issues – Linear Scale
There are two methods to create linear scale with respect to the workload
Pod – the unit mix of vendor equipment necessary to create the incremental
expansion of the data center. Examples today consist of V-Block, Flex-Pod and
OpenStack.
Utility Model – unit relative to consumption model. Examples today include Storage
as a Service.
The optimal Linear Scale would include all constituent components of the data center
workload deployment and management.
Today, many of these are separate. As an example, Cloud workload from Storage and
Network.
The complexity has as much to do with variability of the workload as to the time value
of the constituent resources in the Data Center. Data Centers typically have a very
long maintenance life whereas Network has an intermediate life and compute/disk
have a shorter lifespan.

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Current Technology Issues - Performance
• x86 is a generically programmable capability, but possibly shouldn’t be doing everything
• we can’t have both performance and price
Performance
Price
• in order to maintain network performance, specific silicon has been developed to handle the
data forwarding at a network level
• price vs performance increases dramatically when non-optimized general purpose
programmable devices (like those in hypervisors and virtual machines) are used to process
network data
L1 L2 L3 L4 – L7
In Silicon
Fastest forwarding X86 Workloads
Generic
Programmable
Largely in Software
20-25x
60-100x
• Using x86 to do L3 routing imposes a
20-35x price/performance loss today
• Using x86 to do L2 + L3 imposes a 60 –
100x price/performance loss today
• It consumes CPU that would normally be
sold to the customer (the thing the
customer is actually purchasing)
• This is part of the reasoning behind the
CBU utilizing top of rack (ToR) hardware
switching equipment
• Also part of the reason things like virus
checking should be done in the hypervisor
rather than on each individual virtual host

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Current Technology Issues – “flatness”
• Multi-tenancy really requires a secure, flat network.
• If the network is flat across all boundaries, no shadow exists.
• note: the connections between DC
1 and DC 2 allow the creation of a
full mesh
• there are proposed vendor and
implementation possibilities for this
today
• there is no longer a shadow of
capabilities in the DC
• multi-tenancy is provided by
tunneling the connectivity, edge
device to edge device
• As an example, services set up in a
multi-tier network cause a “shadow” of
capabilities on one part of the data center
environment, in this case, load balancing
and disk
• Adjacent computers (2) have no benefit
from the load balancers and diminished
value (due to latency) from the disk
• Computer in alternative data centers (3)
lose all value from the services
• Current generation L2 links between
Data Centers have been the root of large
scale outages
Data Center 1
Data Center 2
Core1 Core2
Dist1 Dist2
Acc1 Acc2
Core1 Core2
Dist1 Dist2
Acc1 Acc2
Dist1 Dist2
Acc1 Acc2
LB LB
1 2 3
Acc2
Acc4
Acc1
Acc3
Acc5
Data Center 1
Data Center 2
Acc2
Acc4
Acc1
Acc3
Acc5
1
2 3
LB
LB

8. CSC Proprietary and Confidential 8
July 22, 2022
Q&A

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Data Center Today
Facility
PUE (Measured) – Currently done today
+ Network
+ Application Load Balancing
+ Global Load Balancing
+ DNS
+ Firewall
+ Security (CyberSecurity)
+ Server
+ Virtual Machine (VMWare)
+ Operating System
+ Storage (EMC, Hitachi)
+ Security
+ Application Development
+ Monitoring
+ Management
+ Process

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CSC Proprietary and Confidential July 22, 2022
Data Center of the Future
Facility
Facility Architectural Definition (in process)
PUE (Measured) – Currently done today
+ Orchestration
+ Software Defined Networking (Network Orchestration)
+ Network
+ Application Load Balancing
+ Global Load Balancing
+ DNS
+ Firewall
+ Security (CyberSecurity)
+ Software Defined Compute (Computing Virtual Orchestration)
+ Server
+ Virtual Machine (VMWare)
+ Operating System
+ Storage (EMC, Hitachi)
+ Security
+ Application Development
+ Monitoring
+ Management
+ Process

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Data Center - Competitive Advantage
Companies that have implemented parts of this technology have a significant
competitive advantage. Most of the solutions are highly standardized. Gaps in
Orchestration capability were developed internally.
• Google developed many of these technologies independent of the current market
vendors
• The advantage to Google, near complete utilization of network, compute and
disk resources, where the industry averages are ~40%, ~30% and ~60%
• Facebook developed many of these technologies independent of the current
market vendors
• The advantage to Facebook allows rapid expansion and availability, with
application integration
• Amazon and Rackspace have limited, but extremely market useful, extensions of
this technology
• The advantage was rapid commoditization of an integrated solution
• Development of “hybrid-cloud” capabilities