Network Working Group Young Lee (Huawei)
Internet Draft Greg Bernstein (Grotto)
Intended status: Informational Ning So (University of Texas)
Tae Yeon Kim (ETRI)
Kohei Shiomoto (NTT)
Oscar Gonzalez de Dios (Telefonica)
March 3, 2011
Research Proposal for Cross Stratum Optimization (CSO) between Data
Centers and Networks
draft-lee-cross-stratum-optimization-datacenter-00.txt
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Abstract
Data Centers offer various application services to end-users such as
video gaming, cloud computing and others. Since the data centers used
to provide application services are distributed geographically around
a network, many decisions made in the control and management of
application services, such as where to instantiate another service
instance or to which data center out of several a new client is
assigned, can have a significant impact on the state of the network.
Conversely the capabilities and state of the network can have a major
impact on application performance.
Currently application decisions are made with very little or no
information concerning the underlying network used to deliver those
services. Hence such decisions may be sub-optimal from both
application and network resource utilization and quality of service
objectives. This document proposes a research program into cross
stratum application/network optimization focusing on the challenges
and opportunities presented by data center based applications and
carriers networks.
Table of Contents
1. Introduction..................................................2
2. Key Issues in Data Centers and Clouds..........................4
2.1. Some Obstacles of Cloud Computing.........................5
2.2. Changes in Network Access from Data Centers and Clouds....5
2.3. Virtual Machine Migration.................................6
2.4. Entities Involved.........................................6
2.5. Load Balancing............................................7
2.6. End-user capability and communication.....................7
3. Deployed Applications, Services, and Products..................8
4. Research Program...............................................9
4.1. Tentative Research Deliverables..........................10
5. References....................................................11
5.1. Informative References...................................11
Author's Addresses...............................................15
Intellectual Property Statement..................................15
Disclaimer of Validity...........................................16
1. Introduction
This document describes a research program on the automation of
certain interactions between data center based distributed
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applications and the supporting networking infrastructure. Data
center based applications are used to provide a wide variety of
services such as video gaming, cloud computing [Nurmi], grid
application [GFD-122] and others. High-bandwidth video applications
such as remote medical surgery, live concerts and sporting events are
also emerging. This document is mainly concerned with data center
applications that in aggregate or individually make substantial
bandwidth demands on the network. In addition these applications may
desire specific bounds on QoS related parameters such as latency and
jitter.
Figure 1 shows a network diagram of an example data center based
application. Data centers come in an extreme variety of sizes and
configurations but all contain compute servers, storage and
application control of some sort.
,-----. ---------------
---------- / App \ | DC 1 |
| End-user |. . .>( Control ) | o o o |
| | \ / | \|/ |
---------- `-----' | O |
| ----- --|------
| |
| |
| --------------------------|--
| / PE1 | \
| / ...................O \ --------------
| | . | | o o o DC 2 |
| | PE4 . PE2 | | \|/ |
----|---O.........................O---|---|---O |
| . | | |
| . PE3 | --------------
\ ..........O Carrier /
\ | Network /
---------------|-------------
|
--------|------
| O |
| /|\ |
| o o o |
| DC 3 |
---------------
Figure 1. Data center based application architecture example
This research is concerned with a subset of "cross stratum
optimization" (CSO) opportunities, e.g., combined optimization of
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resources in the application and network stratum. We use the term
stratum here to broadly differentiate the layers of most concern to
the application and to the network in general.
In the application stratum we are concerned with and limiting the
scope of this research to those distributed applications offered via
data centers. In particular, this project does not intend to cover
applications delivered in a strictly peer to peer manner. Application
resources can be roughly categorized into computing resources, i.e.,
servers of various types and granularities (VMs, memory, disk) and
content, e.g., video, audio, databases, large data sets, etc..
By the network stratum we mean the "network layer" (IP) and below,
e.g., MPLS, SDH, OTN, WDM. The network stratum has resources that
include routers, switches, and links. We are particularly interested
in further unleashing the potential presented by MPLS and GMPLS
control planes at the lower network layers in response to the high
aggregate or individual demands from the application layer.
The four main cross stratum optimization opportunities of this
research project are:
1. Resource optimization (application and network)
2. Responsiveness to quickly changing demands
3. Enhanced service resilience (via cooperative recovery
techniques between application and network)
4. Quality of application experience (QoE) enhancement (via better
use of existing network and application resources)
In the following document we first give a brief overview of data
center technology for network oriented readers, describe the current
state of application/network integration from the deployment, and
standards points of view, an then conclude with a more detailed
description of the research thrusts (optimization, resilience, QoE)
from the perspective of an IRTF project.
2. Key Issues in Data Centers and Clouds
This section provides some key issues related to data centers and
cloud computing that motivate the need for cross stratum optimization
between applications and networks.
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2.1. Some Obstacles of Cloud Computing
There are many drivers for the move towards data center based
application services. They include reducing maintenance costs, energy
costs, flexibility, scalability, etc...
Reference [Armb] offers a very timely and readable review of cloud
computing practice and potential. Though here we do not differentiate
between cloud computing and medium and small data center based
computing that utilize modern virtualization techniques and possibly
other cloud computing techniques [Nurmi]. From their "top ten
obstacles and opportunities for cloud computing" we see that over
half have significantly involvement of the network.
1. Availability/Business Continuity
3. Data Confidentiality and Auditability
4. Data Transfer Bottlenecks
5. Performance Unpredictability
8. Scaling Quickly
9. Reputation Fate Sharing
2.2. Changes in Network Access from Data Centers and Clouds
At the high side of data center size we begin to see significant
changes in network access, e.g., from a drop-off of an optical metro
ring (a wavelength or two), to an end destination in a long haul DWDM
system (many wavelengths, multiple fibers). These changes have been
partly driven by the consolidation effort of existing smaller size
data centers into Super Data Centers in the government IT
infrastructure and carriers.
Another factor that contributes to high-speed network access is due
to emerging applications that require high bandwidth such as sporting
events, live converts, 3D video applications, remote medical surgery
and so on.
These changes provide still more motivation to enable the application
layer to take advantage of the dynamic networking features offered by
network capability such as MPLS/GMPLS.
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2.3. Virtual Machine Migration
A key enabler for data center cost savings, consolidation,
flexibility and application scalability has been the technology of
compute virtualization or Virtual Machines (VMs)[XEN]. A VM to the
software application looks like a dedicated processor with dedicated
memory and dedicated operating system. In modern data centers or
"computing clouds" the smallest unit of computing resource is the VM
[Nurmi]. In public data centers one can buy computing capacity in
terms of VMs for a particular amount of time. Though different VM
configurations may be offered that are optimized for different types
of processing (e.g., memory intensive, throughput intensive)[EC2].
VMs offer not only a unit of compute power but also as an
"application environment" that can be replicated, backed up and moved
[Clark]. Although VM migration started in the LAN, Wide area VM
migration has also been discussed in the literature, e.g., [Brad].
The impact of VM migration on the network and hence other services
has just recently been studied along with some mitigation approaches
[Stage].
Virtual machine migration has a variety of modes: (i) scheduled vs.
dynamic; (ii) bulk vs. sequential; (iii) point-to-point vs. point-to-
multi-point. Network capability can impact virtual machine migration
strategy. For certain mission critical applications, bandwidth
guarantee as well as performance guarantee must be provided by the
network. Make-before-break capability is also critical to support
seamless migration.
For certain applications such as disaster recovery, bulk migration is
required on the fly, which may necessitate concurrent computation and
path setup dynamically.
2.4. Entities Involved
We have the data center provider, a possibly separate application
provider, and the user (See Figure 2). Note that the data center
provider and the application provider may be potential competitors.
In addition network providers may also offer data center services,
making them potential competitors to an independent data center
provider. Hence, for cross stratum optimization, understanding of
various trust relationships is important when developing interfaces
application/network interfaces.
Figure 2 illustrates key entities involved.
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------------ ----------------------
| End-User |-----| Application Provider |-----
------------ ---------------------- |
| | |
| ---------------------- |
| | Data Center Provider | |
| ---------------------- |
| | |
| ---------------------- |
-----------| Network Provider |-----
----------------------
Figure 2: Key Entities involved in CSO
2.5. Load Balancing
As the application servers are distributed geographically across many
Data Centers for various reasons (e.g., load balancing), the decision
as which server to select for an application request from end-users
has many factors that can negatively affect the quality of experience
(QoE) of the users if not done correctly. One of the major drivers
for operating multiple Data Centers is allowing the application to be
closer to the end-users, so that the overall service performance and
the user experience can be enhanced.
Among the key factors to be considered in choosing the server for an
application or instantiating VM include:
. The utilization of the servers;
. The underlying network loading conditions within a data center
(LAN);
. The underlying network loading conditions between data centers
(MAN/WAN);
. The underlying network conditions between the end-user and data
center.
2.6. End-user capability and communication
As there are plethora of end-user terminal types (e.g., desktop
device, PDA, mobile phones, etc.), it is important for application to
capture end-user device capability and preference. For some
applications, the same user may have multiple devices. In such case,
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seamless device to device transition needs to be provided by
application providers to ensure acceptable QoE to the end-users.
For other applications, codec capability and/or terminal screen
dimension of end-user devices may also have impact on QoS and
bandwidth requirements.
Hence, the interface between end-user and application may need to be
enhanced to capture these aspects.
3. Deployed Applications, Services, and Products
Most current methods are associated with IP networks. For instance,
Akamai and other content distribution networks (CDN) carriers, have
used some IP network knowledge to optimize their application overlay
network usage. When selecting the surrogate (cache or mirror)
location from the client location, many CDN providers use network
latency via a probing technique or proximity based on static
configuration to determine the optimal surrogate location. These
overlays are not closely integrated with carrier's network real load
condition such as link bandwidth utilization and availability. For
many current and emerging applications that require stringent QoS and
bandwidth guarantee, current CDN infrastructure is not well suited
for meeting such service need.
The IETF ALTO WG has focused on overlay optimization among peers by
utilizing information about topological proximity and appropriate
geographical locations of the underlay networks. With this method,
the optimization generally occurs in selecting peer location which
will help reduce IP traffic unnecessarily traversing IP service
providers. Current scope of this work does not address general
problems this document has been discussing such as the selection of
application servers based on resource availability and usage of the
underlying networks.
In some cases, application controllers can estimate network load
based on ping latency, and network topology based on trace routes in
the Internet, based on the assumption that the underlying transport
network is an IP network, and the routing is based on simple IP
forwarding.
In regards to load balancing, DNS redirect technique is currently
used to redirect end-user request to certain servers that host end-
user application.
In the current Intra-Data Center network, the server selection for an
application/VM is done by load-balancer. The load balancer is aware
of a certain level of server usage data (e.g., the number
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simultaneous instances of the application usage) and distributes the
application requests based on that data.
However, the current load balancing technology is insufficient in
providing an optimal decision across multiple VLANs and multiple Data
Centers. This capability is often referred to as global load
balancing.
First of all, there is no good mechanism for the communication
exchange among load balancers located in different Data Centers. This
implies that load balancers from different vendors cannot communicate
to each other.
Secondly, load balancers know little about the underlying network
conditions listed in the previous section. Nor is it user condition
aware.
When migrating existing VMs/applications from one data center to
another, the underlying network load condition in LAN/MAN/WAN can be
constraining factors. Migration of VMs/applications, for instance,
typically requires a high-speed data transfer across LAN/MAN/WAN to
minimize service impact. Application controllers responsible for this
operation is not aware of LAN/MAN/WAN network conditions.
4. Research Program
In the previous sections we have looked at key issues in Data Center
and Cloud Computing and some commercial service deployments on a
variety of cross layer optimization problems.
A common theme to the previous work was that sharing information
between the application and network stratums can lead to more optimal
solutions to the challenges facing distributed applications. In
addition to sharing information, both the application layer and
network may possess capabilities that are can very useful to each
other if appropriate access can be arranged, e.g., the dynamic high
bandwidth services that are enabled by MPLS/GMPLS.
Hence this research project is focused on the interfaces and services
that could be used between the application and network stratum to
address the four main problem thrusts of:
1. Joint application/network Resource optimization (global load
balancing)
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2. Responsiveness to quickly changing demands from/to application
to/from network
3. Enhanced service resilience (via cooperative recovery
techniques between application and network)
4. Quality of application experience (QoE) enhancement (via better
use of existing network and application resources)
Even though algorithms play a big part of optimization, in thrust (1)
we are concerned with the information that could be shared to promote
optimization and various optimization criteria rather than specific
algorithms. Note that this is similar to the approach taken with
MPLS-TE, GMPLS and PCE where specific algorithms are not
standardized.
4.1. Tentative Research Deliverables
a) Baseline network/application model - general enough to
include most cases of interest but no more.
b) Survey the various "trust or lack of" in the relationships
between various key players in both the application and
network stratum. Include a survey of various "summarization",
"abstraction", or other techniques that can reduce the level
of "trust" needed at an interface.
c) Survey of the data center/cloud based applications -
investigate the commonality and differences with respect to
their impact on network infrastructure.
d) Define key interfaces and their functionality and relate
these to current standards and potential future standards.
e) Investigation and report on the role of TE based network
infrastructure (MPLS, GMPLS) in providing support to dynamic
application loads, scaling and QoE enhancement.
f) Report on mechanisms for application level support for
network recovery and network support for application recovery.
g) Investigate the time frames and responsiveness of interest
to application/network interaction. For example what do
various applications need, what can the network provide, can
other techniques such as time based "load shifting" be
utilized.
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5. Security Considerations
TBD
6. IANA Considerations
This informational document does not make any requests for IANA
action.
7. References
7.1. Informative References
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Author's Addresses
Young Lee (Editor)
Huawei Technologies
1700 Alma Drive, Suite 500
Plano, TX 75075
USA
Phone: (972) 509-5599
Email: ylee@huawei.com
Greg M. Bernstein (Editor)
Grotto Networking
Fremont California, USA
Phone: (510) 573-2237
Email: gregb@grotto-networking.com
Ning So (Editor)
Univerity of Texas at Dallas
Email: ningso@yahoo.com
Tae Yeon Kim
ETRI
tykim@etri.or.kr
Kohei Shiomoto
NTT
Email : shiomoto.kohei@lab.ntt.co.jp
Oscar Gonzalez de Dios
Telefonica
Email : ogondio@tid.es
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