TRANSCLOUD

Design Considerations for a High-performance Cloud Architecture

Across Multiple Administrative Domains

Andy C. Bavier, Marco Yuen

Princeton University, Princeton, NJ, U.S.A.

Jessica Blaine, Rick McGeer, Alvin Au Young

HP Labs, Palo Alto, CA, U.S.A.

Yvonne Coady, Chris Matthews, Chris Pearson

University of Victoria, Victoria, BC, Canada

Alex Snoeren

University of California, San Diego, U.S.A.

Joe Mambretti

Northwestern University, Evanston, Illinois, U.S.A.

Keywords: Distributed query infrastructures, Federation across cloud domains, Advanced network architecture.

Abstract: In this position paper, we consider architectures of distributed interconnected clouds across geographically

distributed, independently-administered storage and computation clusters. We consider two problems:

federation of access across heterogeneous administrative domains and computation jobs run over the wide

area and heterogeneous data sets. We argue that a single, flexible architecture, analogous to the TCP/IP

stack for networking, is sufficient to support these jobs, and outline its major elements. As with the

networking stack, many elements are in place today to build an initial version of this architecture over

existing facilities. With the sponsorship of the US National Science Foundation GENI project and the

cooperation of the EU FIRE project, we are building an initial implementation, the TransCloud. We

describe our initial results.

1 INTRODUCTION

The dramatic trend of the first decade of the 21

century in the information technology industry was

the emergence of society-scale systems: online

services such as Google, eBay, iTunes, Yahoo!,

Twitter, and Facebook that routinely served millions

of simultaneously-connected users. These systems

gave rise to entirely new programming models and

systems problems: management of the data center as

a single, unified, “warehouse-scale” computer, each

of which had more raw computing power than

existed on the planet as late as 1990; programming

models for loosely-coupled, data-intensive parallel

operations (“data-intensive supercomputing”), most

concretely realized in the MapReduce architecture

from Google and its open-source cousin, Hadoop.

vast, highly-efficient distributed data stores such as

PNUTS and Cassandra; the re-emergence of

virtualization of time, space, and computing, to

permit services to migrate instantly around the globe

and radically new notions in networking to support

120

C. Bavier A., Yuen M., Blaine J., McGeer R., Au Young A., Coady Y., Matthews C., Pearson C., Snoeren A. and Mambretti J..

TRANSCLOUD - Design Considerations for a High-performance Cloud Architecture Across Multiple Administrative Domains.

DOI: 10.5220/0003450301200126

In Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER-2011), pages 120-126

ISBN: 978-989-8425-52-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

the new programming and management models.

As revolutionary as the last decade has been, the

coming decade promises a far more profound

transformation: the twin emergence of the

Computation Cloud and the Internet of Things. For

all of its power and promise, the Cloud today is little

more than a massive, well-indexed repository of

text, videos, photos, and music, and a vast, universal

transaction engine. The Cloud has merely automated

and made vastly more efficient traditional human

communications and commerce. Over the next

decade, widespread availability of massive

computation – the Computation Cloud - will give to

everyone the ability not only to look up what

someone knows, but to discover things that no one

knows.

Paired with the Computation Cloud is the

Internet of Things – a world where every object

houses a computer, sensor or sensors, and a

connection to the network. The applications range

from the trivial to the profound; milk that senses

when it’s going bad and tells its owner to drink it up,

and buy more; automobiles that drive themselves

and automatically (in coordination with other

vehicles on the road, and a network of smart

highways) avoid traffic jams; homes that pre-cool

themselves when power is cheap and plentiful and

turn off air conditioning when it is dear; smoke

alarms that can tell the difference between a real fire

and a ruined dinner, and call the firefighters for the

former; intelligent buildings that vector people to a

safe escape route in the event of an emergency; fine-

grained climate sensors that can predict severe

weather and evacuate people before the storm hits;

and many more.

Nascent signs of the Internet of Things are

already emerging. An American automobile has tens

of operator-visible sensors, and they already operate

the automobile in some ways better than the driver

can; anti-lock brakes are a classic example. The

RFID tags used to automatically pay tolls on many

American highway systems are used to trace traffic

speeds on major freeways. And the autodoc, long a

science fiction dream, is already emerging. This

year, the NHS rolled out a urine test kit that

transmitted results through a user’s cell phone.

The computation cloud is tightly coupled to the

Internet of things. The deployed sensors will range

in capacity and bandwidth from a few bytes

transmitted every few seconds to every few hours.

Together, they will generate vast quantities of

information; zettabytes and beyond. Extracting

information from all of that data is a formidable

computational task, well beyond current capabilities.

Only a vast new computational infrastructure will

suffice to process all that data. The challenges are

vast:

 Computational infrastructure. Reduction of the

data requires a flexible, universal programming

interface. Most data will need to be reduced at or

near the point of collection; the sheer volume of data

and real-time requirements ensure that. Therefore,

an open, standard, and sufficiently powerful

computational infrastructure – an arm of the

computational cloud – will need to be proximate to

any collection of sensors.

 Security. New, robust, and secure protocols will

be needed to access and manipulate the Internet of

Things. To date, the notorious lack of security in

cyberspace has been of limited consequence; while

there were large consequences in the real world, they

were indirect, typically requiring a separate physical

transaction for real-world effects. The Internet of

Things will enable direct manipulation of the

physical world.

 Data management. The Internet of Things

envisions a widely-distributed set of sensors, data

consumers, and fusion of information from many

disparate, distributed sources. In-situ reduction of

data at the source on a per-query basis, coordination

of widely distributed queries, and a wide variety of

data access mechanisms (“data blades” in the

parlance) will be required, including new distributed

computation mechanisms. Some early innovations

are already present: MapReduce and Hadoop in the

data center, and Sector and Sphere in the distributed

environment. But these are just the beginning. We

can anticipate the Internet of Things will required

the adaptation of many different data management

and query mechanisms, from distributed monitoring

systems (S3, Ganglia, PsePR) to sensor net systems

(TinyDB), to distributed information systems

(SWORD)

 Networking. Efficiently connecting the Internet

of Things and the Computational Cloud will require

new networking stacks and protocols, both wired

and wireless



 Networks must be adaptive in the face of

changing conditions, using one of a number of radio

frequencies and choosing routing on an adaptive

basis.

In this paper, we focus on three problems:

 Ensuring that Computation Cloud users can run

computation jobs wherever they have access, as

simply and transparently as they now download files

from multiple computers across the web



 Ensuring that execution of remote queries is

done efficiently and safely for both remote user and

data host



TRANSCLOUD - Design Considerations for a High-performance Cloud Architecture Across Multiple Administrative

Domains

121

 Designing a simple, efficient, network-aware

architecture for queries over geographically-

distributed heterogeneous data.



2 SCALABLE LIGHTWEIGHT

FEDERATION

The computation cloud offers individuals, small

companies, and researchers the ability to develop,

test and deploy Internet-scale services easily and at

low cost. However, many of these services require

the use of multiple facilities, or users wish to

transparently move their services across multiple

facilities. This gives rise to the desire to federate

facilities. In this section, we view facility federation

not as a set of agreements between federated

facilities, but rather as a set of services to developers

and facilities. This approach scales easily across

heterogeneous facilities, operating in different

environments.

We motivate our discussion by considering a

somewhat simpler analogue: electronic document

exchange. Of course there are many problems that a

truly reliable, fully-functional document exchange

system must have: there must be a rich permission

and security system so that documents can be shared

only with selected remote users; a rich variety of

formats and clients must be supported; documents

should be unforgeable, so an ironclad identification

system must be in place; and so on.

Of course we have a ubiquitous electronic

document exchange system: the World Wide Web,

and even a casual user will recognize that the web

has none of these features. Rather, the web has a

bare-bones protocol to send a document from one

computer to another, and a simple format that can be

easily rendered by a wide variety of clients. Other

features are layered by third-party software on a per-

site basis as needed.

Our goal is to design a simple, ubiquitous system

which will permit users to easily run their programs

on a set of computers owned by a remote facility. As

with electronic document exchange, there are many

issues to be considered: acceptable use policies;

resource allocation; effects on other facility users

and, in a networked world, effects on the network;

global user ID’s and names; federated job control;

and, most importantly, agreements between various

hosting facilities as to what constitutes acceptable

use.

In the spirit of the web, we consider none of

these. Our goal is to design a system whereby users

can manage their jobs on facilities to which they

have independently obtained access. This involves

designing two central components.

1. An architecture and set of interfaces which

permit users to easily and rapidly upload, configure,

and run virtual machines

2. A service which manages a user’s access to and

use of facilities.

The first component is the analogue in our system to

a web server (more precisely, to the specification of

a webserver); the second, to a web browser.

The architecture we choose is the Slice-Based

Facility Architecture (SFA) (Peterson et al, 2007). It

is an open, standardized set of facilities to manage

individual VMs and networks of VMs. In order to

demonstrate its utility for this purpose, we have

added support for the SFA into the Eucalyptus

cluster management system. These efforts have

demonstrated that the functionality in the SFA is a

superset of the functionality supported by

Eucalyptus; in particular, the SFA offers the ability

control slices, or sets of virtual machines and the

topology of the network of virtual machines.

The key feature in the SFA required is the

delegate primitive, which permits one user (given by

a public ssh key) to assign its privileges to a

delegated ssh key.

The second component is easily added as a cloud

service which manages cloud services. The user

registers with the cloud service, and registers his

public key with the service itself, as well as the URI

of the services to which it is delegating permission.

The cloud service then uses the standard SFA calls

to instantiate slices, slivers, initialize and run VMs,

allocate resources, and control jobs.

3 SAFE EXECUTION OF

REMOTE JOBS AND QUERIES

At its most abstract level, a data management and

query infrastructure is an instantiation of a

distributed computing system of very wide

applicability: a wide-area distributed data

management, query, and computation system over

heterogeneous computing nodes and data types. By

“wide-area” here we mean nodes whose network

connectivity can be relatively low-bandwidth, in the

range of tens of kilobits/second and have internode

latencies in the range of tens of milliseconds and

beyond. Intermittent connectivity of some nodes is

the norm.

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Data types can range from small collections of

simple data records to very large media files. For the

former, consider RFID readings of vehicle positions,

used to determine average traffic speeds on

freeways. A record will consist of an RFID tag, a

time, and a position, perhaps tens of bytes, and there

will be at most a couple of records per second. For

the latter, consider weather video data of the sort

generated by the CASA experiment.

This general problem: large, heterogeneous data,

spread over a distributed computing infrastructure

with varying connectivity and no common

administrative interface – is ubiquitous through the

natural, social, and engineering sciences. We are

designing and implementing a computing

infrastructure which addresses the distributed data

management and query problem, and deploy it in a

live service.

The service we will deploy is the State of the

Internet service, deployed over the TransCloud

infrastructure.

The basis of the query engine is a sandboxed

environment which permits the user to run programs

safely and efficiently at remote sites, and is based on

two fundamental architectural building blocks:

1. Restricted Python (Repy), a sandboxed execution

environment originally used in the Seattle project

2. Google Native Client (NaCl), a sandboxed

native-code execution environment distributed with

the Firefox and Chrome browsers, with x86

implementations.

The two central elements work together to provide a

secure, efficient execution environment where side

effects are tightly controlled. NaCl offers an

efficient execution environment in a secure sandbox

for computation-intensive code; safety is guaranteed

by severely restricting access to system services.

However, any real job requires more system services

than NaCl provides. In particular, jobs in our context

require access to network connectivity and

resources. NaCl relies on a trusted service on the

client in order to provide these services. RePy is the

mechanism we choose: it has been widely deployed

on a number of platforms, and offers secure access

to a restricted but adequate set of system resources.

Optionally, NaClRePy can run inside a virtual

machine for added isolation and security. Our initial

deployment of NaClRePy inside the TransCloud

environment offers this.

4 A WIDE-AREA QUERY

INFRASTRUCTURE

NaClRePy merely offers a safe execution

environment. Above that, we require a distributed

query/data reduction environment that is network-

aware, processes and reduces data optimally with

consideration of latency, bandwidth, and available

processing capacity. Such an environment must

support common data types, and must be extensible

to new data types on an on-demand basis.

There are two central themes of the data

processing environment. The first is a distribution

mechanism, and the second an extensible data

extraction and processing mechanism. For the first,

we turn to early attempts to provide services of this

form, notably Astrolabe, Hadoop, and Hadoop’s

wide-area cousins: Sector and Sphere. Hadoop has

achieved widespread use and popularity in a cluster

environment. However, extension to the wide area is

still an unresolved issue. There are two major issues

to be addressed:

1. Use of addressable subnets, easy in a data center

environment but challenging in the wide area

2. Restrictions on bandwidth and large latencies in

the wide area.

Sector, Sphere, and Astrolabe have addressed some

of these issues. We will develop a hybrid approach

and report on it as a deliverable from this research.

Extensible data mechanisms have been primarily

addressed with Data Blade (Brown, 2001) in a SQL

database context, and in object-oriented databases.

We will extend the Data Blades mechanism, using

an XML-based metadata template system to guide

optimized queries.

5 PRIVATE NETWORKING

In this paper, we have largely considered

computation over distributed, separately-

administered clusters. However, connectivity

between distributed, jointly-administered clusters

must be considered.

The fundamental issue in inter-site networking

over the Internet is that the basic Internet protocols

are designed to ensure fairness and decentralized,

orderly traffic management in the absence of global

information. The price is performance. In particular,

the TCP/IP protocol suite involves strong caps on

performance based on latency and loss. These can be

avoided (Bavier et al, 2006) (Brassil et al, 2009), but

only with information obtained from the network. In

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the absence of such information, guaranteed

performance can only be obtained by significant

overprovisioning (McGeer et al, 2009). Indeed,

vanilla TCP/IP (Linux 2.6 kernel, default

parameters) can achieve a maximum throughput of

3.5 Mbit/sec on a coast-to-coast US (100 msec RTT)

link.

One solution is private point-to-point

connections between cluster instances. These private

networks offer significant freedom to cluster

operators. In particular, much higher performance

than the default TCP/IP performance is possible.

Indeed, the CHART system (Bavier et al, 2006;

Brassil et al, 2009) demonstrated line rate

performance over arbitrary latencies given explicit

line rate information. This can be obtained with

special-purpose equipment at each endpoint; it is

guaranteed on a private network.

Since mid-2010 we have been operating a cloud

over a private network between Northwestern

University, HP Labs in Palo Alto, CA, and the

University of California, San Diego using the CAVE

Wave on the National Lambda Rail and the Global

Lambda Integrated Facility. In directed throughput

tests we have measured direct TCP/IP connections

of over 5 Gb/sec using off-the-shelf endpoint and

switching equipment (Lee et al, 2010). We intend to

move this capability into production use in late

2011.

6 AN ARCHITECTURAL STACK

In this position paper, we have enumerated a number

of issues and our solutions to them. Some (the

Slice-based Facility Architecture, delegation, TCP

acceleration over private networks) we have brought

into being in our experimental cloud application.

The others, including our distributed query

architecture and secure, bare-metal execution

environment, are under intense development.

We have largely built our prototype system from

existing components: cluster managers such as

Eucalyptus and PlanetLab, distributed programming

environments (Hadoop, Sector/Sphere) and

distributed query interfaces (Pig, Astrolabe). This is

deliberate, both for ease of implementation and, far

more important, ease of adoption.

The Computation Cloud must be ubiquitous; in

order for it to be ubiquitous, it must be based on

standards, de jure if possible, de facto by necessity.

Every successful infrastructure has grown by

standardizing and formalizing existing practice. The

Web threw a hypertext skin over ftp; PlanetLab

canonized virtual machines on Linux; SMTP/POP

standardized sendmail. Our stack is no more than

codification of common practice:

 Virtualization: Xen VMs running over and under

Linux



 Cluster and VM Management: Eucalyptus

augmented by the Slice Facility Architecture toolset



 Safe, high-performance programming

environment: Google NaCl using Restricted Python

(RePy)



 Wide-area programming environments and query

systems: Hadoop, Pig, Sector, Sphere, and Astrolabe



 Delegation as a border primitive.

Though different in application, this resembles the

classic wedding cake of the TCP/IP stack. We show

the notional architecture in figure 1.

Figure 1: Notional Architecture.

In the figure, it’s easy to see the layered services

provided, from virtual machine/bare metal allocation

to safe programming (alternately provided by

virtualization and safe/restricted environments) to

distributed query environments. Though our

implementation is not the ideal stack by any means,

exploration of this and similar stacks will yield a

distributed, usable cloud computing environment

analogous to the world wide web environment for

data transfer.

Not shown here, but implicit, is the delegation

primitive over the Slice Federation Architecture.

One may picture this as intercepting the pictured

stack at any level: it forms a ubiquitous

authorization primitive at each level of the stack.

7 RELATED WORK

We have clearly built on a large number of existing

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pieces of work, from Xen to Eucalyptus and

PlanetLab, to NaCl and RePy, to Hadoop, Sector,

Sphere, and Pig. These are less related work than

inspirations and building blocks for TransCloud.

Most closely related to our research is the Orca

control framework of GENI, led by Jeff Chase and

Ilia Baldine of Duke University and the Renaissance

Computing Institute (RENCI). Orca similarly

incorporates a unified SFA/Eucalyptus architecture

and private networking over high-performance

networks. The Emulab/ProtoGENI project under the

late Jay LePreau and currently led by Robert Ricci

has led the way to allocation of virtual networks.

8 CONCLUSIONS

In this paper, we’ve discussed the requirements for

federated, transcontinental cloud architectures over

networks of varying connectivity, with edge nodes

and clusters of varying capability. We’ve designed a

prototype services and protocol stack for such

Clouds, and are constructing an initial three-site

implementation under the auspices of the GENI

project.

We stress this represents only a first attempt at

an infrastructure. Further, this field continues under

rapid development. Eucalyptus was simply our first

cloud manager of choice. Other recent entrants

include Tashi (Kosuch et al., 2009), Nimbus

(Keahey, 2008), and OpenStack (http://

www.openstack.org). These have different strengths

and weaknesses, and are optimized for different

environments. For example, Tashi is tuned for big

data. Cloud programming and query infrastructures

are also undergoing rapid development, and we

expect a plethora of programming environments in

the coming years.

Our interest is not in presenting the notional

architecture in figure 1 as the optimum, but in

identifying common layers across multiple

architectures and abstracting the interfaces as

common APIs. In this we take inspiration from the

network community, which has enjoyed enormous

success from standardizing protocol specifications

while retaining implementation freedom. We seek

the minimum required APIs to permit easy

interoperation. Our current standard is the Slice

Facility Architecture, with a delegate primitive. We

will continue to build on this and develop it in the

coming years, and expand the TransCloud facility.

ACKNOWLEDGEMENTS

We are indebted to our colleagues on the

GENI/FIRE projects for their inspiration,

conversations, and constant encouragement. We

have already mentioned Jeff Chase and Ilia Baldine,

whose parallel project has provided a constant

source of inspiration; Chip Elliot, Aaron Falk, Mark

Berman, Heidi Dempsey and Vic Thomas of the

GENI Project Office, for inspiration and support.

Justin Cappos of the University of Washington

developed Seattle and has been a constant source of

advice and counsel. Paul Muller of the University of

Kaiserslautern and the G-Lab project was invaluable

in bringing in early experimenters, and together with

Michael Zink of the University of Massachusetts

provided the data stores used in our pilot query

project. The SFA-based Eucalytpus was first used

for a Cloud transcoding service suggested and

inspired by Ericsson research. We are indebted to

Jim Chen and Fei Yeh of Northwestern, Eric Wu

and Narayan Krishnan of HP Research Engineering

and System Support for IT support, Dejan Milojicic

of the Open Cirrus project, and Tony Mackey of the

HP Strategic Innovation Office. This research has

been supported by the GENI Project office under

contract 1779B

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