Internet DRAFT - draft-xiang-alto-exascale-network-optimization
draft-xiang-alto-exascale-network-optimization
ALTO WG Q. Xiang
Internet-Draft Tongji/Yale University
Intended status: Informational H. Newman
Expires: January 4, 2018 California Institute of Technology
G. Bernstein
Grotto Networking
H. Du
Tongji/Yale University
K. Gao
Tsinghua University
A. Mughal
J. Balcas
California Institute of Technology
J. Zhang
Tongji University
Y. Yang
Tongji/Yale University
July 3, 2017
Resource Orchestration for Multi-Domain Data Analytics
draft-xiang-alto-exascale-network-optimization-03.txt
Abstract
Data-intensive analytics is entering the era of multi-domain,
geographically-distributed, collaborative computing, where different
organizations contribute various resources to collaboratively
collect, share and analyze extremely large amounts of data. Examples
of this paradigm include the Compact Muon Solenoid (CMS) and A
Toroidal LHC ApparatuS (ATLAS) experiments of the Large Hadron
Collider (LHC) program. Massive datasets continue to be acquired,
simulated, processed and analyzed by globally distributed science
networks in these collaborations. Applications that manage and
analyze such massive data volumes can benefit substantially from the
information about networking, computing and storage resources from
each member's site, and more directly from network-resident services
that optimize and load balance resource usage among multiple data
transfers and analytics requests, and achieve a better utilization of
multiple resources in clusters.
The Application-Layer Traffic Optimization (ALTO) protocol can
provide via extensions the network information about different
clusters/sites, to both users and proactive network management
services where applicable, with the goal of improving both
application performance and network resource utilization. In this
document, we propose that it is feasible to use existing ALTO
services to provides not only network information, but also
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information about computation and storage resources in data analytics
networks. We introduce a uniform resource orchestration framework
(Unicorn), which achieves an efficient multi-resource allocation to
support low-latency dataset transfer and data intensive analytics for
collaborative computing. It collects cluster information from
multiple ALTO services utilizing topology extensions and leverages
emerging SDN control capabilities to orchestrate the resource
allocation for dataset transfers and analytics tasks, leading to
improved transfer and analytics latency as well as more efficient
utilization of multi-resources in sites.
Status of This Memo
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This Internet-Draft will expire on January 4, 2018.
Copyright Notice
Copyright (c) 2017 IETF Trust and the persons identified as the
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Requirements Language . . . . . . . . . . . . . . . . . . . . 5
3. Changes Since Version -02 . . . . . . . . . . . . . . . . . . 5
4. Problem Settings . . . . . . . . . . . . . . . . . . . . . . 6
4.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 6
4.2. Challenges . . . . . . . . . . . . . . . . . . . . . . . 6
5. Basic Idea . . . . . . . . . . . . . . . . . . . . . . . . . 7
5.1. Use ALTO services to provide multi-resource information . 7
5.2. Example 1: network information impacts data analytics
scheduling . . . . . . . . . . . . . . . . . . . . . . . 8
5.3. Example 2: encode multi-resources in abstract network
elements . . . . . . . . . . . . . . . . . . . . . . . . 9
6. Key Issues . . . . . . . . . . . . . . . . . . . . . . . . . 10
7. Unified Resource Orchestration Framework . . . . . . . . . . 11
7.1. Architecture . . . . . . . . . . . . . . . . . . . . . . 11
7.2. Workflow converter . . . . . . . . . . . . . . . . . . . 14
7.2.1. User API . . . . . . . . . . . . . . . . . . . . . . 14
7.3. Resource Demand Estimator . . . . . . . . . . . . . . . . 15
7.4. Entity Locator . . . . . . . . . . . . . . . . . . . . . 15
7.5. ALTO Client . . . . . . . . . . . . . . . . . . . . . . . 15
7.5.1. Query Mode . . . . . . . . . . . . . . . . . . . . . 16
7.6. ALTO Server . . . . . . . . . . . . . . . . . . . . . . . 16
7.7. Resource View Extractor . . . . . . . . . . . . . . . . . 16
7.8. Execution Agents . . . . . . . . . . . . . . . . . . . . 18
7.9. Multi-Resource Orchestrator . . . . . . . . . . . . . . . 18
7.9.1. Orchestration Algorithms . . . . . . . . . . . . . . 18
7.9.2. Online, Dynamic Orchestration . . . . . . . . . . . . 18
7.9.3. Example: A Max-Min Fairness Resource Allocation
Algorithm . . . . . . . . . . . . . . . . . . . . . . 19
8. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.1. Deployment . . . . . . . . . . . . . . . . . . . . . . . 20
8.2. Benefiting From ALTO Extension Services . . . . . . . . . 21
8.3. Limitations of the MFRA Algorithm . . . . . . . . . . . . 21
9. Security Considerations . . . . . . . . . . . . . . . . . . . 22
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 22
11. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 22
12. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
12.1. Normative References . . . . . . . . . . . . . . . . . . 22
12.2. Informative References . . . . . . . . . . . . . . . . . 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24
1. Introduction
As the data volume increases exponentially over time, data intensive
analytics is transiting from single-domain computing to multi-
organizational, geographically-distributed, collaborative computing,
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where different organizations contribute various resources, e.g.,
computation, storage and networking resources, to collaboratively
collect, share and analyze extremely large amounts of data. One
leading example is the Large Hadron Collider (LHC) high energy
physics (HEP) program, which aims to find new particles and
interactions in a previously inaccessible range of energies. The
scientific collaborations that have built and operate large HEP
experimental facilities at the LHC, such as the Compact Muon Solenoid
(CMS) and A Toroidal LHC ApparatuS (ATLAS), currently have more than
300 petabytes of data under management at hundreds of sites around
the world, and this volume is expected to grow to one exabyte by
approximately 2018.
With such an increasing data volume, how to manage the storage and
analytics of these data in a globally distributed infrastructure has
become an increasingly challenging issue. Applications such as the
Production ANd Distributed Analysis system (PanDA) in ATLAS and the
Physics Experiment Data Export system (PhEDEX) in CMS have been
developed to manage the data transfers among different cluster sites.
Given a data transfer request, these applications make data transfer
decisions based on the availability of dataset replicas at different
sites and initiate retransmission from a different replica if the
original transmission fails or is excessively delayed. And HTCondor
[HTCondor] is deployed to achieve coarse-grained data analytics
parallelization across these sites. When a data analytics task is
submitted, HTCondor adopts a match-making process to assign the task
to a certain set of servers in one site, based on the coarse-grained
description of resource availability, such as the number of cores,
the size of memory, the size of hard disk, etc. However, neither
dataset transfers nor data analytics task parallelization takes fine-
grained information of cluster resources, such as data locality,
memory speed, network delay, network bandwidth, etc., into account,
leading to high data transfer and analytics latency and
underutilization of cluster resources.
The Application-Layer Traffic Optimization (ALTO) services defined in
[RFC7285] provide network information with the goal of improving the
network resource utilization while maintaining or improving
application performance. Though ALTO is not designed to provide
information about other resources, such as computing and storage
resources in cluster networks, in this document we propose that
exascale science networks can leverage existing ALTO services defined
in [RFC7285] and ALTO topology extension services defined in network
graph [DRAFT-NETGRAPH], path vector [DRAFT-PV], routing state
abstraction[DRAFT-RSA], multi-cost [DRAFT-MC] and cost-calendar
[DRAFT-CC] and etc. to encode information about multiple types of
resources in science networks, such as memory I/O speed, CPU
utilization, network bandwidth, and provide such information to
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orchestration applications to improve the performance of dataset
transfer and data analytics tasks, including throughput, latency,
etc.
This document introduces a unified resource orchestration framework
(Unicorn), which provides efficient multi-resource allocation to
support low-latency, multi-domain, geo-distributed data analytics.
Unicorn provides a set of simple APIs for authorized users to submit,
update and delete dataset transfer requests and data intensive
analytics requests. One important proposal we make in this document
is that it is feasible to use ALTO services to provide not only
network information, but also information on other resources in
multi-domain, geo-distributed analytics networks including computing
and storage.
A prototype of Unicorn with the dataset transfer scheduling component
has been implemented on a single-domain Caltech SDN development
testbed, where the ALTO OpenDaylight controller is used to collect
topology information. We are currently designing the resource
orchestration components to achieve low-latency data-intensive
analytics.
This document is organized as follows: Section 3 summarizes the
change of this document since version -01. Section 4 elaborates on
the motivation and challenges for coordinating storage, computing and
network resources in a globally distributed science network
infrastructure. Section 5 discusses the basic idea of encoding
multi-resource information into ALTO path vector and abstraction
services and gives an example. Section 6 lists several key issues to
address in order to realize the proposal of providing multi-resource
information by ALTO topology services. Section 7 gives the details
of Unicorn architecture for multi-domain, geo-distributed data
analytics. Section 8 discusses current development progress of
Unicorn and next steps.
2. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
3. Changes Since Version -02
o Add an example in Section 7 to show the importance of network
information in resource allocation for data analytics.
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o Update the architecture of Unicorn in Section 7, i.e., add the
entity locator and rename ANE aggregator to resource view
extractor.
o Add detailed description of how the entity locator and the
resource view extractor work.
o Minor changes in abstract and discussion sections.
4. Problem Settings
4.1. Motivation
Multi-domain, geo-distributed data analytics usually involves the
participation of countries and sites all over the world. Science
programs such as the CMS experiment and the ATLAS experiment at CERN
are typical examples. The site located at the LHC laboratory is
called a Tier-0 site, which processes the data selected and stored
locally by the online systems that select and record the data in
real-time as it comes off the particle detector, archives it and
transfers it to over 10 Tier-1 sites around the globe. Raw datasets
and processed datasets from Tier-1 sites are then transferred to over
160 Tier-2 sites around the world based on users' requests.
Different sites have different resources and belong to different
administration domains. With the exponentially increasing data
volume in the CMS experiment, the management of large data transfers
and data intensive analytics in such a global multi-domain science
network has become an increasingly challenging issue. Allocating
resources in different clusters to fulfill different users' dataset
transfer requests and data analytics requests require careful
orchestrating as different requests are competing for limited
storage, computation and network resources.
4.2. Challenges
Orchestrating exascale dataset transfers and analytics in a globally
distributed science network is non-trivial as it needs to cope with
two challenges.
o Different sites in this network belong to different administration
domains. Sharing raw site/cluster information would violate
sites' privacy constraints. Orchestrating data transfers and
analytics requests based on highly abstracted, non-real-time
network information may lead to suboptimal scheduling decisions.
Hence the orchestrating framework must be able to collect
sufficient resource information about different clusters/sites in
real-time as well as over the longer term, to allow reasonably
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optimized network resource utilization without violating sites'
privacy requirements.
o Different science programs tend to adopt different software
infrastructures for managing dataset transfers and analytics, and
may place different requirements. Hence the orchestrating
framework must be modular so that it can support different dataset
management systems and different orchestrating algorithms.
The orchestrating framework must support the interaction between the
multi-resource orchestration module, the dataset transfer module, and
the data analytics execution module. The key information to be
exchanged between modules includes dataset information, the resource
state of different clusters and sites, the transfer and analytic
requests in progress, as well as trends and network-segment and site
performance from the network point of view. Such interaction ensures
that (1) the various programs can adopt their own data transfer and
analytics systems to be multi-resource-aware, and more efficient in
achieving their goals; and (2) the various orchestrating algorithms
can achieve a reasonably optimized utilization on not only the
network resource but also the computing and storage resources.
5. Basic Idea
5.1. Use ALTO services to provide multi-resource information
The ALTO protocol is designed to provide network information to
applications so that applications can achieve better performance and
the network more efficient use of resources. Different ALTO topology
services including path vector, routing state abstraction, multi-
cost, cost calendar, etc., have been proposed to provide fine-grained
network information to applications. In this document, we propose
that not only can ALTO provide network information of different
cluster sites, it can also provide information of multiple resources,
including computing and storage resources. To this end, the basic
"one-big-switch" abstraction provided by the base ALTO protocol is
not sufficient. Several examples have already been given in
[DRAFT-PV] and [DRAFT-RSA] to demonstrate that. There has been a
similar proposal before about using ALTO to provide resource
information for data centers [DRAFT-DC]. However, that proposal
requires a new information model for clusters or data centers, which
may affect the compatibility of ALTO. The solution of this proposal
is simpler. Its basic idea is that each computer node and storage
node can be seen as an "abstract network element" defined in ALTO-
path-vector [DRAFT-PV]. In this way, Unicorn can fully reuse all
existing ALTO services by introducing only one cost-mode (pv) and two
cost-metrics (ne and ane), instead of introducing a new information
model.
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5.2. Example 1: network information impacts data analytics scheduling
.-----------. .-----------.
| .-----. | | .-----. |
| | eh1 | | | | eh3 | |
| '-----' | | '-----' |
| | | |
| | | |
| | | |
| | | |
| .-----. | | .-----. |
| | eh2 | | | | eh4 | |
| '-----' | | '-----' |
'-----------' '-----------'
Site 1 Site 2
distance(eh1, eh2) = 2 distance(eh1, eh2) = 2
Figure 1: An Example for Data Locality Information.
We first use the example in Figure 1 to show that only network
information itself has a huge impact on resource allocation for data
analytics. In this scenario, a MapReduce task needs to be executed.
The input data has two copies at end hosts eh1 and eh3, respectively.
And the end hosts eh2 and eh4 will be the computation nodes with the
same computation power, correspondingly. Using the common data
analytics resource management framework such as Hadoop it can be
revealed that both allocation options, i.e., eh1->eh2 and eh3->eh4,
have a storage-computation distance of 2, i.e., they have the same
data locality from Hadoop's view. As a result, it appears that both
options would provide the same performance for this task.
However, assume that the bandwidth between eh1 and eh2 is 100Mb/s
while that between eh3 and eh4 is 1Gb/s. These significant different
data accessing speeds decide that these options have very different
performances for the same task and the only optimal allocation option
is to allocate this task to eh3->eh4. This example demonstrates the
imperativeness of network information in making efficient resource
allocation decisions. Such information is not provided in Hadoop or
other similar or related projects such as Mesos. On the contrary, if
ALTO servers are deployed at these sites, applications can retrieve
such information via ALTO queries.
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5.3. Example 2: encode multi-resources in abstract network elements
We use the same dumbbell topology in [DRAFT-RSA] as an example to
show the feasibility of using ALTO topology service to provide multi-
resource information. In this topology, we assume the bandwidth of
each network cable is 1Gbps, including the cables connecting end
hosts to switches. Consider a dataset transfer request which needs
to schedule the traffic among a set of end host source-destination
pairs, say eh1 -> eh2, and eh3 -> eh4. Assume that the transfer
application receives information from the ALTO Cost Map service that
both eh1 -> eh2 and eh3 -> eh4 have bandwidth 1Gbps. In [DRAFT-RSA],
it is shown that whether each of the two traffic flows can receive
1Gbps bandwidth depends on whether the routes of two flows share a
bottleneck link. Path vector and routing state abstraction services
provide additional information about network state encoded in
abstract network elements. If the returned state is ane1 + ane2 <=
1Gbps, it means two flows cannot each get 1Gbps bandwidth at the same
time. If the returned state is ane1 <= 1Gbps and ane2 <= 1Gbps, it
means two flows each can get 1Gbps bandwidth.
+------+
| |
--+ sw6 +--
/ | | \
PID1 +-----+ / +------+ \ +-----+ PID2
eh1__| |_ / \ ____| |__eh2
| sw1 | \ +--+---+ +---+--+ / | sw2 |
+-----+ \ | | | |/ +-----+
\_| sw5 +---------+ sw7 |
PID3 +-----+ / | | | |\ +-----+ PID4
eh3__| |__/ +------+ +------+ \____| |__eh4
| sw3 | | sw4 |
+-----+ +-----+
Figure 2: A Dumbbell Network Topology
Other than network resource, assume in this topology eh1 and eh3 are
equipped with commodity hard disk drives (HDD) while eh2 and eh4 are
equipped with SSDs. Because the bandwidth of an HDD is typically
0.8Gbps and that of SSD is typically 3Gbps. Even if the returned
routing state is ane1 <= 1Gbps and ane2 <=1Gbps, the actual
bottleneck of each traffic flow is the storage I/O bandwidth at
source host. As a result, the total bandwidth of both traffic flows
can only reach 1.6Gbps.
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It has been verified in the CMS experiment, and also several studies
on commercial data centers that network resource are not always the
bottleneck of large dataset transfers and data analytics. Many have
reported that storage resources and computing resources become the
bottleneck in a fairly large percentage of dataset transfers and data
analytics tasks in science networks and commercial data centers.
In this example, if we look at the end hosts as abstract network
elements, the storage I/O bandwidth of each host can also be encoded
as an abstract element into the path-vector. And under the storage
and route settings above, the returned cluster state would be ane1
<=0.8Gbps and ane2 <=0.8Gbps, which provides a more accurate capacity
region for the requested traffic flows.
6. Key Issues
The last section described the basic idea of using ALTO topology
services to provide multi-resource information and gives an example
to demonstrate its feasibility. Next we list and discuss several key
issues to address in this proposal.
o Can ALTO topology services provide data locality information?
Existing ALTO topology services do not provide such information.
Many studies have pointed out that such information plays a vital
role in reducing the latency of data-intensive analytics. If ALTO
topology services can encode such information together with
information of other resources together, data-intensive
applications can benefit a great deal in terms of information
aggregation and communication overhead.
o How to quickly map applications' resource allocation decision on
abstract multi-resource view back to the physical multi-resource
view of clusters/sites? Fine-grained resource information can be
encoded into abstract network elements to reduce overhead and
provide certain privacy protection of clusters. Such information
can be highly compressed (see the dumbbell example used in this
document as well as in [DRAFT-PV] and [DRAFT-RSA]). In
preliminary evaluations on RSA, the network element compression
ratio can be as high as 80 percent. This ratio is expected to be
even higher in large-scale data center or cluster setting, e.g. a
fat-tree topology with k=48. Therefore a fast mapping from the
resource orchestration decisions based on the abstract view back
to the physical view is needed to satisfy the stringent latency
requirement of large dataset transfers and data-intensive
analytics.
o How much privacy, including key resource configurations, raw
topology, intra-cluster scheduling policy, etc., will be exposed?
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Compared with the "one-big-switch" abstraction, other ALTO
topology services such as path vector [DRAFT-PV] and routing state
abstraction [DRAFT-RSA] provide fine-grained resource information
to applications. Even if such information can be encoded into
abstract network elements, it still risks exposing private
information of different clusters/sites. Current internet drafts
of these services did not provide any formal privacy analysis or
performance measurement. This would be one of the key issues this
document plans to investigate in the future.
o How does current ALTO services such as path-vector and RSA scale
when they are used to provide abstract information concerning
multiple resources in clusters? Another issue along this line is
how to balance the liveness of fine-grained resource information
and the corresponding information delivery overhead? Although
encoding information of network elements into abstract network
elements can achieve a very competitive information compression
ratio, large dataset transfers and analytics applications always
involve many network elements in multiple clusters/sites and the
absolute number of involved network elements keep increasing as
the scale of clusters increase. In addition, when resource
information in a cluster changes, the ALTO services need to inform
all related applications. In either cases, delivering fine-
grained resource information would cause high communication
overhead. There still lacks of an analytics or experimental
understanding on the scalability of path-vector and RSA services.
7. Unified Resource Orchestration Framework
7.1. Architecture
This section describes the design details of key components of the
Unicorn framework: the workflow converter, the resource demand
estimator, the ALTO clients, the ALTO servers, the resource view
extractor, the multi-resource orchestrator and the execution agents.
Figure 3 shows the architecture of Unicorn. The overall process is
as follows.
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.---------.
| Users |
'---------'
| 1
.- - - - - - - - - - - - - - -|- - - - - - - - - - - - - - - -.
| .--------------------. |
| Unicorn | Workflow Converter | |
| '--------------------' |
| | 2 |
| .-----------------------------. |
| | Resource Demand Estimator | |
| '-----------------------------' |
| | 3 |
| .-----------------------------. |
| | Multi-Resource Orchestrator | |
| '-----------------------------' |
| / | | 10 | \ |
| 9 / | 4 | 4 | \ 9 |
| .-------------. .-------. | .-------. .-------------. |
| |Resource View| |Entity | | |Entity | |Resource View| |
| | Extractor | |Locator| | |Locator| | Extractor | |
| '-------------' '-------' | '-------' '-------------' |
| | 8 | 5 | 5 | | 8 |
| .----------------. .-----------. .----------------. |
| | ALTO Client(s) | .-| Execution | | ALTO Client(s) | |
| '----------------' | | Agents | '----------------' |
| | 6 | '-----------' | 6 |
| .----------------. '-----------' .----------------. |
| | ALTO Server(s) | / \ | ALTO Server(s) | |
| '----------------' / \ '----------------' |
| | 7 / 11 11 \ | 7 |
| .----------------. / \ .----------------. |
| | Site 1 | . . . | Site N | |
| '----------------' '----------------' |
'- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -'
Figure 3: Architecture of Unicorn.
o STEP 1 Authorized users submit high-level data analytics workflows
to Unicorn through a set of simple APIs.
o STEP 2 The workflow converter transforms the high-level data
analytics workflows into low-level task workflows, i.e., a set of
analytics tasks with precedence encoded in a directed acyclic
graph (DAG).
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o STEP 3 The resource demand estimator automatically finds the
optimal configuration (resource demand) of each task, i.e., the
number of CPUs, the size of memory and disk, I/O bandwidth, etc.
o STEP 4 The multi-resource orchestrator receives the resource
demand of a set of tasks and sends them to the entity locator at
each site.
o STEP 5 The entity locator at each site retrieves the entity
addresses of the end hosts that would be allocated for the tasks
to be scheduled, and passes these addresses to the ALTO clients,
and asks the ALTO clients to collect information about these end
hosts, i.e., properties of corresponding computing, storage and
networking resources.
o STEP 6 The ALTO clients issue ALTO queries defined in the base
ALTO protocol [RFC7285], e.g., EPS, ECS, Network Map, etc. and
ALTO extension services, e.g., routing state abstraction (RSA)
[DRAFT-RSA], path vector [DRAFT-PV], network graph
[DRAFT-NETGRAPH], multi-cost [DRAFT-MC], cost-calendar [DRAFT-CC]
and property map [DRAFT-PM], to collect resource information.
o STEP 7 The ALTO servers at each site accept the queries from the
ALTO client, collect resource information from the residing site
and send back to the ALTO clients.
o STEP 8 The ALTO clients send the response from ALTO servers to the
resource view extractor.
o STEP 9 The extractor uses a lightweight, optimal algorithm to
compress the raw resource information provided by ALTO servers
into a minimal, equivalent view of resources and sends back to the
multi-resource orchestrator.
o STEP 10 The orchestrator makes resource allocation decisions,
e.g., dataset transfer scheduling and analytics task placement,
based on the resource demand of analytics tasks and the resource
supply sent back from the resource view extractor. Decisions are
then sent to the execution agents deployed in corresponding sites.
o STEP 11 The execution agents receive and execute instructions from
the multi-resource orchestrator. They also monitor the status of
different tasks and send the updated status to the multi-resource
orchestrator.
Unicorn provides a unified, automatic solution for multi-domain, geo-
distributed data analytics. In particular, its benefits include:
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o On the resource demand side, it provides a set of simple APIs for
authorized users to submit and manage data analytics requests and
enables real-time requests' status monitoring. And it
automatically converts high-level analytics workflow into low-
level task workflows and finds the optimal configuration for each
task.
o On the resource supply side, it collects the resource information
of different sites through a common, REST based interface
specified by the ALTO protocol, encodes such information into
different entity abstractions and computes a minimal, yet accurate
view on resource supply dynamic.
o It provides a scalable multi-resource orchestrator that makes
efficient resource allocation decisions to achieve high resource
utilization and low-latency data analytics.
o The architecture of Unicorn is modular to support different
resource orchestration algorithms and the deployment of different
ALTO servers.
7.2. Workflow converter
The converter is the front end of Unicorn. It is responsible for
collecting high-level data analytics workflows from users and
transforming them into low-level task workflows, e.g., HTCondor
ClassAds. It provides a set of simple APIs for users to submit and
manage requests, and to track the status of requests in real-time.
7.2.1. User API
o submitReq(request, [options])
This API allows users to submit a request and specify
corresponding options. The request can be a data transfer request
or a data analytics request. Request options include priority,
delay, etc. It returns a request identifier reqID that allows
users to update, delete this request or track its status. The
additional options may or may not be approved, and the relative
priorities may be modified by the resource orchestrator depending
on the role of users (regular users or administrators at different
levels), the resource availability and the status of other ongoing
requests.
o updateReq(requestID, [options])
This API allows users to update the options of requests. It will
return a SUCCESS if the new options are received by the request
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parser. But these new options may or may not be approved, and may
be modified by the resource orchestrator depending on the role of
users (regular users or administrators), the resource availability
and the status of other ongoing requests.
o deleteReq(requestID)
This API allows users to delete a request by passing the
corresponding requestID. A completed request cannot be deleted.
An ongoing request will be stopped and the output data will be
deleted.
o getReqStatus(requestID)
This API allows users to query the status of a request by
specifying the corresponding requestID. The returned status
information includes whether the request has started, the assigned
priority, the percentage of finished sub-requests, transmission
statistics, the expected remaining time to finish, etc.
7.3. Resource Demand Estimator
The estimator leverages the fact that low-level tasks are typically
repetitive or have very high similarities. It uses reinforcement
learning to predict the optimal configuration for each task and
passes the resource demand to the multi-resource orchestrator for
further processing.
7.4. Entity Locator
The task configurations computed by the demand estimator has no
knowledge on the underlying structure of topology of each site, i.e.,
the addresses of end hosts and network devices. Hence they cannot be
directly used by the ALTO clients for querying resource information.
The entity locator retrieves the entity addresses of the end hosts
that would be allocated for the tasks to be scheduled, and passes
these addresses to the ALTO clients, and asks the ALTO clients to
collect information about these end hosts, i.e., properties of
corresponding computing, storage and networking resources.
7.5. ALTO Client
The ALTO client is in the back end of Unicorn and is responsible for
retrieving resource information through querying ALTO servers
deployed at different sites. The resource information needed in
Unicorn includes the topology, link bandwidth, computing node memory
I/O speed, computing node CPU utilization, etc. The base ALTO
protocol [RFC7285] provides an extreme single-node abstraction for
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this information, which only allows the multi-resource orchestrator
to make coarse-grained resource allocation decisions. To enable
fine-grained multi-resource orchestration for dataset transfer and
data analytics in cluster networks, ALTO topology extension services
such as routing state abstraction (RSA) [DRAFT-RSA], path vector
[DRAFT-PV], network graph [DRAFT-NETGRAPH], multi-cost [DRAFT-MC] and
cost-calendar [DRAFT-CC] are needed to provide fine-grained
information about different types of resources in clusters.
7.5.1. Query Mode
The ALTO client should operate in different query modes depending on
the implementation of ALTO servers. If an ALTO server does not
support incremental updates using server-sent events (SSE)
[DRAFT-SSE], the ALTO client sends queries to this server
periodically to get the latest resource information. If the cluster
state changes after one query, the ALTO client will not be aware of
the change until next query. If an ALTO server supports SSE, the
ALTO client only sends one query to the ALTO server to get the
initial cluster information. When the resource state changes, the
ALTO client will be notified by the ALTO server through SSE.
7.6. ALTO Server
ALTO servers are deployed at different sites around the world, and at
strategic locations in the network itself, to provide information
about different types of resources in the cluster networks in
response to queries from the ALTO client. Such information include
topology, link bandwidth, memory I/O speed and CPU utilization at
computing nodes, storage constraints in storage nodes and etc. Each
ALTO server must provide basic information services as specified in
[RFC7285] such as network map, cost map, endpoint cost service (ECS),
etc. To support the fine-grained multi-resource allocation in
Unicorn, each ALTO server should also provide more fine-grained
information about different resources in clusters through ALTO
extension services such as the routing state abstraction [DRAFT-RSA],
path vector [DRAFT-PV], network graph [DRAFT-NETGRAPH], multi-cost
[DRAFT-MC] and cost-calendar [DRAFT-CC] services.
7.7. Resource View Extractor
In each site, the resource information collected by the ALTO clients
is not directly sent back to the orchestrator. Instead, we design a
resource view extractor to compress the resource information provided
by the ALTO servers into a minimal, equivalent view of all the
resources, i.e., computing, storage and networking resources, that
would be allocated to a set of tasks. The extractor works in the
following steps.
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o Resource-Task Matrix
Depending on specific services provided by the ALTO servers, the
responses collected by ALTO clients may include information of
different entities, e.g., endpoints, PIDs, ane, etc. Each entity
possesses a set of resources available for tasks to be scheduled.
For each entity property p in the responses, such as bandwidth,
delay, etc., the extractor composes an entity-task matrix M(p).
Each row of this M(p) represents an entity in the responses
provides information about property p and each column represents a
task to be scheduled. The element m(i, j) of M(p) is a variable
representing the amount of property p of entity i that task j can
get.
o Resource-Task Constraints
For each property p, the extractor composes a series of resource-
task constraints. The first set of constraints is sum m(i, j) =
r(p, j) for each task j. These constraints calculate r(p, j), the
total amount of property p provided to j by all the entities. The
exact rule of summation depends on the property p. For instance,
if p is latency, the summation is through common addition
operations, but if p is bandwidth, the summation is a minimum
function.
The second set of constraints is sum m(i, j) <= r(p, i) for each
entity i. These constraints represent the usage of resource i on
property p for all the tasks cannot exceed the capacity of
resource i on this property. The exact rule of summation also
depends on the property p. For instance, if p is bandwidth, the
summation is through common addition operations, but if p is
latency, the constraints become m(i,j) = r(p, i) for each each
entity i and each task j. This means that entity i provides the
same delay property for each task.
o Resource View Compression
The whole set of resource-task constraints are linear, and express
the view of resources that are available for the tasks to be
scheduled. The extractor uses a lightweight, optimal algorithm to
compress them into a minimal, equivalent view of resources, i.e.,
a minimal set of linear constraints that represent the same
feasible region as the original constraints. The basic idea of
this algorithm is described in [DRAFT-RSA].
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7.8. Execution Agents
Execution agents are deployed at each site and are responsible for
the following functions:
o Receive and process instructions from the multi-resource
orchestrator, e.g. dataset transfer scheduling, data analytics
task placement and execution, task update and abortion, etc.
o Monitor the status of data analytics tasks and send the updated
status to the multi-resource orchestrator.
Depending on the supporting data analytics frameworks, different
request execution agents may be deployed in each site. For instance,
in the CMS experiment at CERN, both MPI and Hadoop execution agents
are deployed.
7.9. Multi-Resource Orchestrator
The multi-resource orchestrator receives the resource demand
information, i.e., a set of low-level task workflows and their
configurations, from the resource demand estimator. It then asks the
entity locator in each site to get the addresses of end hosts that
would be allocated for the tasks to be scheduled and ALTO clients to
query properties related to these end hosts. When the resource view
extractor sends the response back, the orchestrator makes resource
allocation decisions, e.g., dataset transfer scheduling and analytics
task execution, based on both resource demand dynamic and resource
supply dynamic. The dataset transfer scheduling decisions include
dataset replica selection, path selection, and bandwidth allocation,
etc. The analytics task execution decisions include which cluster
should allocate how much resources to execute which tasks. These
decisions are sent to the execution agents at different sites for
execution.
7.9.1. Orchestration Algorithms
The modular design of Unicorn allows the adoption of different
orchestration algorithms and methodologies, depending on the specific
performance requirements. In Section 7.8.3, a max-min fairness
resource allocation algorithm for dataset transfer is described as an
example.
7.9.2. Online, Dynamic Orchestration
The multi-resource orchestrator should adjust the resource allocation
decisions based on the progress of ongoing requests, the utilization
and dynamics of cluster resources. In normal cases, the multi-
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resource orchestrator periodically collects such information and
executes the orchestration algorithm. When it is notified of events
such as request status update, cluster state update and etc., the
orchestrator will also execute the orchestration algorithm to adjust
resource allocations.
7.9.3. Example: A Max-Min Fairness Resource Allocation Algorithm
In this section, we describe a max-min fair resource allocation
(MFRA) scheduling algorithm which aims to minimize the maximal time
to complete a dataset transfer subject to a set of constraints. To
make resource allocation decisions, MFRA requires sufficient network
information including topology, link bandwidth and recent historical
information in some cases. In a small-scale single-domain network,
an SDN controller can provide the raw complete topology information
for the MFRA algorithm. However, in a large-scale multi-domain
science network such as CMS, providing the raw network topology is
infeasible because (1) it would incur significant communication
overhead; and (2) it would violate the privacy constraints of some
sites. Several ALTO extension topology services including Abstract
Path Vector [DRAFT-PV], Network Graphs [DRAFT-NETGRAPH] and RSA
[DRAFT-RSA] can provide the fine-grained yet aggregated/abstract
topology information for MFRA to efficiently utilize bandwidth
resources in the network.
Ongoing pre-production deployment efforts of Unicorn in the CMS
network involve the implementation of the RSA service. Other than
topology information, the additional input of the MFRA algorithm is
the priority of each class of flows, expressed in terms of upper and
lower limits on the allocated bandwidth between the source and the
destination for each data transfer requests.
The basic idea of the MFRA algorithm is to iteratively maximize the
volume of data that can be transferred subject to the constraints.
It works in quantized time intervals such that it schedules network
paths and data volumes to be transferred in each time slot. When the
DTR scheduler is notified of events such as the cancellation of a
DTR, the completion of a DTR or network state changes, the MFRA
algorithm will also be invoked to make updated network path and
bandwidth allocation decisions.
In each execution cycle, MFRA first marks all transfers as
unsaturated. Then it solves a linear programming model to find the
common minimum transfer satisfaction rate (i.e., the ratio of
transferred data volume in a time interval over the whole data volume
of this request) that is satisfied by all transfer requests. With
this common rate found, MFRA then randomly selects an unsaturated
request in each iteration, increases its transfer rate as much as
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possible by finding residual paths available in the network, or by
increasing the allocated bandwidth along an existing path, until it
reaches its upper limit or can otherwise not be increased further, so
it is saturated. At each iteration, newly saturated requests are
removed from the subsequent process by fixing their corresponding
rate value, and completed transfers are removed from further
consideration. After all the data transfer rates are saturated in
the given time slot, then a feasible set of data transfer volumes
scheduled to be transferred in the slot across each link in the
network can be derived.
The MFRA algorithm yields a full utilization of limited network
resources such as bandwidth so that all DTR can be completed in a
timely manner. It allocates network resources fairly so that no DTR
suffers starvation. It also achieves load balance among the sites
and the network paths crossing a complex network topology so that no
site and no network link is oversubscribed. Moreover, MFRA can
handle the case where particular routing constraints are specified,
e.g., where all routes are fixed ahead of time, or where each
transfer request only uses one single path in each time slot, by
introducing an additional set of linear constraints.
8. Discussion
8.1. Deployment
The Unicorn framework is the first step towards a new class of
intelligent, SDN-driven global systems for multi-domain, geo-
distributed data analytics involving a worldwide ensemble of sites
and networks, such as CMS and ATLAS. Unicorn relies heavily on the
ALTO services for collecting and expressing abstract, real-time
resource information from different sites, and the SDN centralized
control capability to orchestrate data analytics workflows. It aims
to provide a new operational paradigm in which science programs can
use complex network and computing infrastructures with high
throughput, while allowing for coexistence with other network
traffic.
A prototype case study implementation of Unicorn has been
demonstrated on the Caltech/StarLight/Michigan/Fermilab SDN
development testbed. Because this testbed is a single-domain
network, the current Unicorn prototype leverages the ALTO
OpenDaylight controller, to collect topology information. The CMS
experiment is currently exploring pre-production deployments of
Unicorn, looking towards future widespread production use. To
achieve this goal, it is imperative to collect sufficient resource
information from the various sites in the multi-domain CMS network,
without causing any privacy leak. To this end, the ALTO RSA service
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[DRAFT-RSA] is under development. Furthermore, as will be discussed
next, other ALTO topology extension services can also substantially
improve the performance of Unicorn.
8.2. Benefiting From ALTO Extension Services
The current ALTO base protocol [RFC7285] exposes network topology and
endpoint properties using the extreme "my-Internet-view"
representation, which abstracts a whole network as a single node that
has a set of access ports, with each port connects to a set of end
hosts called endpoints. Such an extreme abstraction leads to
significant information loss on network topology [DRAFT-PV], which is
the key information for Unicorn to make dynamic scheduling and
resource allocation decisions. Though Unicorn can still allocate
resource for data transfer and analytics requests on this abstract
view, the resource allocation decisions are suboptimal.
Alternatively, feeding the raw, complete network topology of each
site to Unicorn is not desirable, either. First, this would violate
privacy constraints of different sites. Secondly, a raw network
topology would significantly increase the problem space and the
solution space of the orchestrating algorithm, leading to a long
computation time. Hence, Unicorn desires an ALTO topology service
that is able to provide only enough fine-grained topology
information.
Several ALTO topology extension services including Path Vector
[DRAFT-PV], Network Graphs [DRAFT-NETGRAPH] and RSA [DRAFT-RSA],
[DRAFT-PM] are potential candidates for providing fine-grained
abstract network formation to Unicorn. In addition, we propose that
these services can also be used to provide information about
computing and storage resources of different cluster/sites by viewing
each computing node and storage node as a network element or abstract
network element. For instance, the path vector service supports the
capacity region query, which accepts multiple concurrent data flows
as the input and returns the information of bottleneck resources,
which could be a set of links, computing devices or storage devices,
for the given set of concurrent flows. This information can be
interpreted as a set of linear constraints for the multi-resource
orchestrator, which can help data transfer and analytics requests
better utilize multiple types of resources in different clusters.
8.3. Limitations of the MFRA Algorithm
The first limitation of the MFRA algorithm is computation overhead.
The execution of MFRA involves solving linear programming problems
repeatedly at every time slot. The overhead of computation time is
acceptable for small sets of dataset transfer requests, but may
increase significantly when handling large sets of requests, e.g.,
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hundreds of transfer requests. Current efforts towards addressing
this issue include exploring the feasibility of incremental
computation of scheduling policies, and reducing the problem scale by
finding the minimal equivalent set of constraints of the linear
programming model. The latter approach can benefit substantially
from the ALTO RSA service [DRAFT-RSA].
The second limitation is that the current version of MFRA does not
involve dataset replica selection. Simply denoting the replica
selection as a set of binary constraint will significantly increases
the computation complexity of the scheduling process. Current
efforts focus on finding efficient algorithms to make dataset replica
selection.
9. Security Considerations
This document does not introduce any privacy or security issue not
already present in the ALTO protocol.
10. IANA Considerations
This document does not define any new media type or introduce any new
IANA consideration.
11. Acknowledgments
The authors thank discussions with Shenshen Chen, Linghe Kong, Xiao
Lin and Xin Wang.
12. References
12.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>.
12.2. Informative References
[DRAFT-CC]
Randriamasy, S., Yang, R., Wu, Q., Deng, L., and N.
Schwan, "ALTO Cost Calendar", 2017,
<https://datatracker.ietf.org/doc/draft-ietf-alto-cost-
calendar>.
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[DRAFT-DC]
Lee, Y., Bernstein, G., Dhody, D., and T. Choi, "ALTO
Extensions for Collecting Data Center Resource
Information", 2014, <https://datatracker.ietf.org/doc/
draft-lee-alto-ext-dc-resource/>.
[DRAFT-MC]
Randriamasy, S., Roome, W., and N. Schwan, "Multi-Cost
ALTO", 2017, <https://datatracker.ietf.org/doc/draft-ietf-
alto-multi-cost/>.
[DRAFT-NETGRAPH]
Bernstein, G., Lee, Y., Roome, W., Scharf, M., and Y.
Yang, "ALTO Topology Extensions: Node-Link Graphs", 2015,
<https://tools.ietf.org/html/draft-yang-alto-topology-06>.
[DRAFT-PM]
Roome, W. and Y. Yang, "Extensible Property Maps for the
ALTO Protocol", 2015, <https://datatracker.ietf.org/doc/
draft-roome-alto-unified-props-new/>.
[DRAFT-PV]
Bernstein, G., Lee, Y., Roome, W., Scharf, M., and Y.
Yang, "ALTO Extension: Abstract Path Vector as a Cost
Mode", 2015, <https://tools.ietf.org/html/draft-yang-alto-
path-vector-01>.
[DRAFT-RSA]
Gao, K., Wang, X., Yang, Y., and G. Chen, "ALTO Extension:
A Routing State Abstraction Service Using Declarative
Equivalence", 2015, <https://datatracker.ietf.org/doc/
draft-gao-alto-routing-state-abstraction/>.
[DRAFT-SSE]
Roome, W. and Y. Yang, "ALTO Incremental Updates Using
Server-Sent Events (SSE)", 2015,
<https://datatracker.ietf.org/doc/draft-ietf-alto-incr-
update-sse/>.
[HTCondor]
Thain, D., Tannenbaum, T., and M. Livny, "Distributed
computing in practice: the Condor experience", 2005,
<http://dl.acm.org/citation.cfm?id=1064336>.
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[RFC7285] Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
"Application-Layer Traffic Optimization (ALTO) Protocol",
RFC 7285, DOI 10.17487/RFC7285, September 2014,
<http://www.rfc-editor.org/info/rfc7285>.
Authors' Addresses
Qiao Xiang
Tongji/Yale University
51 Prospect Street
New Haven, CT
USA
Email: qiao.xiang@cs.yale.edu
Harvey Newman
California Institute of Technology
1200 California Blvd.
Pasadena, CA
USA
Email: newman@hep.caltech.edu
Greg Bernstein
Grotto Networking
Fremont, CA
USA
Email: gregb@grotto-networking.com
Haizhou Du
Tongji/Yale University
51 Prospect Street
New Haven, CT
USA
Email: duhaizhou@gmail.com
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Kai Gao
Tsinghua University
30 Shuangqinglu Street
Beijing
China
Email: gaok12@mails.tsinghua.edu.cn
Azher Mughal
California Institute of Technology
1200 California Blvd.
Pasadena, CA
USA
Email: azher@hep.caltech.edu
Justas Balcas
California Institute of Technology
1200 California Blvd.
Pasadena, CA
USA
Email: justas.balcas@cern.ch
Jingxuan Jensen Zhang
Tongji University
4800 Cao'an Hwy
Shanghai 201804
China
Email: jingxuan.n.zhang@gmail.com
Y. Richard Yang
Tongji/Yale University
51 Prospect Street
New Haven, CT
USA
Email: yry@cs.yale.edu
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