Network Working Group S. Randriamasy, Ed.
Internet-Draft Alcatel-Lucent Bell Labs
Intended status: Standards Track R. Yang
Expires: January 5, 2015 Yale University
Q. Wu
Huawei
L. Deng
China Mobile
N. Schwan
Thales Deutschland
July 4, 2014
ALTO Cost Calendar
draft-randriamasy-alto-cost-calendar-00
Abstract
The goal of Application-Layer Traffic Optimization (ALTO) is to
bridge the gap between network and applications by provisioning
network related information in order to allow applications to make
informed decisions. The present draft proposes to extend the cost
information provided by the ALTO protocol. The purpose is to broaden
the decision possibilities of applications to not only decide 'where'
to connect to, but also 'when'. This is useful to applications that
have a degree of freedom on when to schedule data transfers, such as
non- instantaneous data replication between data centers or service
provisioning to end systems with irregular connectivity. ALTO
guidance to schedule application traffic can also efficiently help
for load balancing and resources efficiency.
The draft specifies a new Cost Mode, "Calendar" Mode, that is
applicable to time-sensitive ALTO metrics and allows Applications to
carefully schedule their connections or data transfers. In the
Calendar Mode, an ALTO Server exposes ALTO Cost Values in JSON arrays
where each value corresponds to a given time interval. The time
intervals as well as other Calendar attributes are specified in the
IRD. Besides the functional time-shift enhancement the ALTO Cost
Calendar also allows to schedule the ALTO requests themselves and
thus save a number of ALTO transactions.
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 RFC 2119 [RFC2119].
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Status of This Memo
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This Internet-Draft will expire on January 5, 2015.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Motivating use cases for ALTO Cost Schedule . . . . . . . . . 4
2.1. Bulk Data Transfer scheduling . . . . . . . . . . . . . . 4
2.2. Endsystems with limited connectivity or access to
datacenters . . . . . . . . . . . . . . . . . . . . . . . 5
2.3. SDN Controller guided access to application endpoints . . 7
2.4. Large flow scheduling on extended ALTO topologies . . . . 8
2.5. Time-sensitve TE metrics Calendaring . . . . . . . . . . 9
3. Design considerations for an ALTO calendar . . . . . . . . . 10
4. ALTO extensions for a Cost Calendar . . . . . . . . . . . . . 12
4.1. ALTO Cost-Mode: Calendar . . . . . . . . . . . . . . . . 12
4.2. ALTO Calendar attributes in the IRD . . . . . . . . . . . 13
4.3. Example of calendared information resources in the IRD . 14
4.3.1. Example IRD with ALTO cost Calendars . . . . . . . . 15
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4.3.2. Example transaction for a routingcost Calendar to
face intermittent connectivity . . . . . . . . . . . 18
4.3.3. Example transaction for a bandwidth calendar . . . . 19
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 20
5.1. Information for IANA on proposed Cost Types . . . . . . . 21
5.2. Information for IANA on proposed Endpoint Propeeries . . 21
6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 21
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.1. Normative References . . . . . . . . . . . . . . . . . . 21
7.2. Informative References . . . . . . . . . . . . . . . . . 21
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 22
1. Introduction
IETF is currently standardizing the ALTO protocol which aims for
providing guidance to overlay applications, that need to select one
or several hosts from a set of candidates that are able to provide a
desired resource. This guidance is based on parameters that affect
performance and efficiency of the data transmission between the
hosts, e.g., the topological distance. The goal of ALTO is to
improve the Quality of Experience (QoE) in the application while
simultaneously optimizing resource usage in the underlying network
infrastructure.
The ALTO protocol therefore [ID-alto-protocol] specifies a Network
Map, which defines groupings of endpoints in a network region (called
a PID) as seen by the ALTO server. The Endpoint Cost Service and the
Endpoint (EP) Ranking Service then provide rankings for connections
between the specified network regions and thus incentives for
application clients to connect to ISP preferred endpoints, e.g. to
reduce costs imposed to the network provider. Thereby ALTO
intentionally avoids the provisioning of realtime information as
explained in the ALTO Problem Statement [RFC5693] and ALTO
Requirements [RFC5693]) drafts that write "Such information is better
suited to be transferred through an in-band technique at the
transport layer instead". Thus the current Cost Map and Endpoint
Cost Service are providing, for a given Cost Type, exactly one rating
per link between two PIDs or to an Endpoint. Applications are
expected to query one of these two services in order to retrieve the
currently valid cost values. They therefore need to plan their ALTO
information requests according to the estimated frequency of cost
value change. In case these value changes are predicable over a
certain period of time and the application does not require immediate
data transfer, it would save time to get the whole set of cost values
over the period in one ALTO response and using these values to
schedule data transfers would allow to optimise the network resources
usage and QoE.
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In this draft we introduce use cases that describe applications that
have a degree of freedom on scheduling data transfers over a period
of time, thus they do not need to start a transfer instantaneously on
a retrieved request. For this kind of applications we propose to
extend the Cost Map and Endpoint Cost Services by adding a calendar
on the cost values, allowing applications to time-shift data
transfers.
In addition to this functional ALTO enhancement, we expect to further
gain by gathering multiple Cost Values for one cost type as firstly
one Cost Map reporting on N Cost Values is less bulky than N Cost
Maps containing one Cost value each and secondly, this reduces N ALTO
transactions to a single one. This is valuable for both the storage
of these ALTO maps and their transfer. Similar gains can be obtained
for the ALTO Endpoint Cost Service.
In this draft an "ALTO Calendar" is presented as a Cost Mode that is
applicable to time-sensitive ALTO metrics and allows applications
using such metrics to carefully schedule their connections or data
transfers. In the Calendar Mode, an ALTO Server exposes ALTO Cost
Values in JSON arrays where each value corresponds to a given time
interval. The time intervals as well as other Calendar attributes
(the ones suggested by Richard) are specified in the IRD and allow
the ALTO Client to interpret the received ALTO values. This draft
proposes a set of Calendar attributes to be added to the IRD, for
discussion I n the ALTO WG.
The remainder of this draft first provides a variety of use cases
that motivate the need for a 'calendar' cost mode. It then specifies
the needed extensions to the ALTO protocol and details some example
messages.
2. Motivating use cases for ALTO Cost Schedule
This section introduces use cases showing the benefits of providing
ALTO Cost values in 'calendar' mode. Most likely, the ALTO Cost
Calendar would be used for the Endpoint Cost Service, assuming that a
limited set of feasible Endpoints for a non-real time application is
already identified, that they do not need to be accessed immediately
and that their access can be scheduled within a given time period.
The Cost Map service, filtered or not, is also applicable as long as
the size of the Map is manageable.
2.1. Bulk Data Transfer scheduling
Some CDNs are prepopulating caches with content before it actually
gets available for the user and thus there is a degree of freedom on
when the content is transmitted from the origin server to the caching
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node. Other applications like Facebook or YouTube rely on data
replication across multiple sites for several reasons, such as
offloading the core network or increasing user experience through
short latency. Typically the usage pattern of these data centers or
caches follows a location dependent diurnal pattern.
In the examples above, data needs to be replicated across the various
locations of a CDN provider, leading to bulk data transfers between
datacenters. Scheduling these data transfers is a non-trivial task
as the transfer should not infer with the user peak demand to avoid
degradation of user experience and to decrease billing costs for the
datacenter operator by leveraging off-peak hours for the transfer.
This peak demand typically follows a diurnal pattern according to the
geographic region of the datacenter. One precondition to schedule
transfers however is to have a good knowledge about the demand and
link utilization patterns between the different datacenters and
networks.
While this usage data today already is gathered and also used for the
scheduling of data transfer, provisioning this data gets increasingly
complex with the number of CDN nodes and in particular the number of
datacenter operators that are involved. For example, privacy
concerns prevent that this kind of data is shared across
administrative domains. The ALTO Cost Calendar specified later in
this document avoids this problem by presenting an abstracted view of
time sensitive utilization maps through a dedicated ALTO service to
allow CDN operators a mutual scheduling of such data transfers across
administrative domains.
2.2. Endsystems with limited connectivity or access to datacenters
Another use case that benefits from the availability of multi-
timeframe cost information is based on applications that are limited
by their connectivity either in time or resources or both. For
example applications running on devices in remote locations or in
developing countries that need to synchronize their state with a data
center periodically, in particular if sometimes there is no
connection at all. Example applications is enterprise database
update, remote learning, remote computation distributed on several
data center endpoints.
Wireless connectivity has a variable quality or may even be
intermittent. On the other hand, the connectivity conditions are
often predicable. For non real time applications, it is thus
desirable to provide ALTO clients with routing costs to connection
nodes (i.e. Application Endpoints) over different time periods.
This would allow end systems using ALTO aware application clients to
schedule their connections to application endpoints.
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Another challenge arises with end systems using resources located in
datacenters and trading content and resources scattered around the
world. For non-real time applications, the interaction with
Endpoints can be scheduled at the time slots corresponding to the
best possible QoE. For instance, resource Ra downloaded from
Endpoint EPa at time t1, Resource Rb uploaded to EPb at time t2, some
batch computation involving Ra and Rb done on EPc at time t3 and
results R(A,B) downloaded to EPd and EPe at time t4. Example
applications are similar to the ones cited in the previous paragraph.
+-----+ +-----+
| EPa | | EPb | <----- Rb
+-----+ +-----+ (t2=50)
| +-------+ |
Ra --------------> | EPc | |
(time t1=10) | | |
|t3=100 | <----------------- Rb
+-------+
| \
| \
R(Ra,Rb)
(t4=200)
| \
| -------------------.
V V
+-----+ +-----+
| EPd | | EPe |
+-----+ +-----+
Last, the ALTO Cost calendar is beneficial to optimizing ALTO
transactions themselves. Indeed, let us assume that an Application
Client is located in an end sytem with limited resources and/or has
an access to the network that is either intermittent or provides an
acceptable QoE in limited but predictable time periods. In that
case, it needs to both schedule its resources demanding networking
activities and its ALTO requests. Instead of having to figure out
when the cost values may change and having to carefully schedule
multiple ALTO requests, it could aviod this by relying on Cost
Shedule attributes that indicate the time granularity, the validity
and time scope of the cost information, together with the time
related cost values themselves.
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2.3. SDN Controller guided access to application endpoints
The Software Defined Networking (SDN), see [sdnrg], is a model that
attempts to manage and reconfigure networks in a more flexible way in
order to better cope with the traffic challenges posed by nowadays
resources greedy applications. To this end, one option is "moving
the control plane out of the network elements into "controllers", see
[SDN charter, http://www.1-4-5.net/~dmm/sdnrg/sdnrg.html], that
implements the network control and management. The SDN Controllers
are deemed to gather the network state information and provide it in
an abstracted form to SDN aware applications while gathering their
requirements in QoE and exchanging other application "management"
information and commands.
The relevance of ALTO to perform a number of SDN functions has been
recently highlighted. An ALTO Server can assist an SDN Controller by
hosting abstracted network information that can be provided to SDN
aware applications via an ALTO Client. It can also assist other SDN
Control operations using information in and outside the ALTO scope.
The SDN primitive "Get network resources" provides applications with
informations allowing them to evaluate the expected QoE. QoE related
information includes delay and bandwidth at the application endpoints
as well as on the network paths. Such information may be provided
via the ALTO Service by proposed extensions of the ALTO protocol that
define new ALTO Cost Types allowing to abstract and report QoE to
applications.
One key objective of an SDN controller is the ability to balance the
application traffic whenever possible. For non real time
applications, data and resources transfer can be time shifted,
resources availability may often be predicable and last, strong
incentives for applications to time shift their traffic may be given
by network operators appropriately setting routing cost values at
different time values, according to their policy to cope with network
occupation over time.
To achieve this objective, the SDN controller can:
1. get the network state history from its controlled network
elements through its southbound API
2. possibly derive an estimation or a prediction of these values
over given time frames
3. compute estimates and/or network provider preferences on end to
end paths and store their abstraction in an ALTO Server in the
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form of ALTO Cost Calendar values defined for different time
periods
4. deliver these values to the SDN applications via the ALTO
Endpoint Cost Service, as estimations covering the past and/or
the future and/or preferences.
This way:
o On one hand, the applications get the best possible QoE, as they
can pick the best time for them to access one or more Endpoints,
o One the other hand the SDN controller achieves load balancing as
it may guide the application traffic so as to better distribute
the traffic over time, and thus optimize its resources usage.
Note that we distinguish between "estimates" that we see as value
aggregations represented with units such as bytes, seconds,
percentage and "preferences" that we see as abstracted costs or
scores w.r.t. a metric or state such as 'routingcost',
'bandwidthscore', 'link quality'.
2.4. Large flow scheduling on extended ALTO topologies
[draft-yang-alto-topology-00] presents initial thinking on extending
ALTO for topology exposure services, that would provide flexible
abstractions based on the raw network topology. Among other
features, an ALTO topology may expose several paths between a source
(src) and destination (dst), or topology details may be provided on
restricted parts. This work was presented to the ALTO WG at IETF88.
The presentation slides [slides-88-alto-5-topology] on
[draft-yang-alto-topology-00] expose a use case entitled "Large Flow
Scheduling". This case includes a "daylife example" where a Google
Map service proposes multiple routes between 2 points A and B, each
calculated w.r.t. length and estimated time. For each of these
selected paths, the map service exposes a time-sensitive qualitative
value taking 4 values between Slow and Fast. A user of this
application may thus organize its transfer w.r.t. metrics, paths and
time, provided s/he does not have to commute immediately.
The use case on Large flow scheduling on extended ALTO topologies in
the present section illustrates one modality of ALTO topology
service, that would expose several paths between end to end (src,
dst) pairs, computed w.r.t. one of more metrics, possibly under given
constraints. On top of this enriched topology service, non real-time
applications may also choose the time of data/resources transfer,
taking thus advantage of a richer set of decision variables.
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The use case "Large Flow Scheduling" of presentation
[slides-88-alto-5-topology]can thus be adapted as follows:
o Step1 - obtain the set T transfer tasks {(src, dest, data)}
o Step2 - identify one or more paths for each (src, dst): several
information sources exist among which:
* (a) ALTO CostMap with a "path" metric, // not specified here
* (b) an ALTO Topology Service providing a path computation hint
(e.g. w.r.t. routingcost and/or other metrics)
o Step 3 - while T not empty:
* 1 - query for example values for some metric 'available
bandwidth' on paths:
+ to this end, query the values in the ALTO 'calendar' Mode:
on the selected (src, dst) for a set of time intervals.
With this mode, the ALTO client will receive an array of
values, each applicable to a time slot .
* 2 - schedule data transfer at the time slots corresponding to
the preferred value.
2.5. Time-sensitve TE metrics Calendaring
Draft [draft-wu-alto-te-metrics] , proposes to extend the set of ALTO
metrics with 11 ALTO traffic engineering (TE) metrics to reflect
measurement on network delay, jitter, packet loss, hop count, and
bandwidth. ALTO TE metrics that are time-sensitive, either by nature
such as bandwidth and delay related metrics, or due to "normally"
changing network conditions or both.
The values of ALTO TE metrics are typically collected from routing
protocols and provided in a non-real time manner. In "normally"
changing network conditions, TE metric values remain uniformly
distributed over given time intervals and can be aggregated over
bigger time intervals of periodic patterns. For example, an ALTO
Server may collect values for e.g. delay from a routing protocol
produced by measurements done every second over a measurement period
of 30 seconds. The ALTO Server may then aggregate these values over
two measurement periods (i.e. 60 seconds) and repeat the operation as
it wishes. Then every hour, the ALTO Server provides these delay
values in 'calendar' mode, encoded as an array of 60 values, assumed
to estimate network performance statistics on each minute of this
hour.
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Another example is Bandwidth Calendaring. Bandwidth Calendaring
allows network operators to reserve resources in advance according to
agreements with their customers, enabling them to transmit data with
specified starting time and duration, for example, for a scheduled
bulk data replication between data centers. Traditionally, this can
be supported by a Network Management System operation such as path
pre-establishment and activation on the agreed starting time.
However, this does not provide efficient network usage since the
established paths exclude the possibility of being used by other
services even when they are not used for undertaking any service.
A Cost calendar provided by an ALTO server can support the scheduled
bulk data replication application with better efficiency since it can
alleviate the burden of processing on network elements. This
requires the ALTO server to maintain the calendared TE cost metrics
on the end to end paths associated to data transfer.
To support cost calendaring for these time-sensitive ALTO TE metrics,
the network topology and the dynamicity of the traffic need to be
considered. For example, a small topology with low density and low
capacity that carries inpredictable, heavy and bursty traffic has few
chances to exhibit stationary TE metric value patterns over large
periods and would benefit to use the ALTO Calendar over smaller time
slots. Some ALTO TE metric values, even aggregated over time may
need to be updated at a frequency that would require doing ALTO
request at a pace that would be overload both the ALTO Client and the
Server.
3. Design considerations for an ALTO calendar
This section enumerates a set of challenges in designing the
calendaring specifications, and will be updates upon discussions in
the ALTO WG.
An ALTO Cost calendar provided by the ALTO Server is an array of
values for a given metric, where each value corresponds to a time
interval which length is specified for this metric in the IRD,
together with other attributes describing the time scope of the
calendar. Most likely, the ALTO Cost Calendar would be used for the
Endpoint Cost Service, assuming that a limited set of feasible
Endpoints for a non-real time application is already identified, that
they do not need to be accessed immediately and that their access can
be scheduled within a given time period. The Cost Map service,
filtered or not, is also applicable as long as the size of the Map is
manageable.
A calendar is used to schedule transfers of application data or
services and has several characteristics:
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o the Calendar values are assumed to be stationary on each time
interval,
o the ALTO Server may provide values on past time periods that can
be interpreted as historical experience and used to anticipate
future cost values,
o the ALTO Server may provide stationary values on present or future
time periods that can be interpreted as predictions on cost
values,
o the ALTO Server may provide stationary values on time intervals
covering the past, and/or present and/or future.
o for metrics provided with units and claiming to be aggregated from
network measurements, the values can be interpreted as
estimations.
o For abstracted metrics provided with no units such as the
'routingcost' defined in the base ALTO protocol or abstracted
unitless scores on network performances such as some potential
'bandwidth score' or 'unreliability cost', the values can be
interpreted as network provider preferences.
Design requirements for an ALTO calendar
o needs to convey dateless cyclic network provider preferences
expressed w.r.t. given ALTO metric values (e.g., hourly, daily,
weekly measurement/prediction)
o needs to convey dateless cyclic network status if the ALTO Server
claims to provide aggregated information on network status (e.g.,
hourly, daily, weekly measurement/prediction)
o needs to be able to convey the result of a particular instance of
time (e.g., to convey predicted network status during a
maintenance outage on July 4, 2014 from 5-7pm)
o needs at least the following attributes to report on cyclic
patterns:
* generic time zone,
* measurement estimation or preference value validity interval:
combining <nb-int-unit> and <interval-unit> to reflect for
example: 1hour, 2minutes, 1week, 1m onth
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* date range of the Calendar, e.g. number of intervals allowing
to derive the calendar time range in terms of: year, month,
week, day, hour, min, secs
o needs to expose validity period of the calendar: indicating when
the next ALTO Calendar for this date range should be fetched if
needed,
o needs to provide time stamps: last-update-time:
* last-update-time: the time that the metric values are computed
,
* next-update-time: when the client may fetch an update , that is
calendars
4. ALTO extensions for a Cost Calendar
The usage of a time-related ALTO Cost Calendar is rather proactive in
that it can be used like a "time table" to figure out the best time
to schedule data transfer and also anticipate predictable events
including predictable flash crowds. An ALTO Cost Calendar should be
viewed as a synthetic abstraction of real measurements that can be
historic or be a prediction for upcoming time periods.
Specifications on the cost "calendar" attributes are proposed here
and will be completed in further versions of this draft, upon
discussion with the ALTO WG.
4.1. ALTO Cost-Mode: Calendar
This draft introduces a new ALTO Cost Mode called "calendar". This
mode applies preferably to Costs that can be expressed in a single-
valued Cost Mode. In that sense, when the "numerical" mode is
available for a Cost-Type, the cost expressed in the "calendar" mode
is an extension of its expression from one value in the "numerical"
mode to an array of several values varying over time.
Types of Cost values such as JSONBool can also be expressed in the
"calendar" mode, as states may be "true" or "false" depending on
given time periods. They may be expressed as a single value which is
either "true" or "false" following a decision rule outside the ALTO
protocol.
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4.2. ALTO Calendar attributes in the IRD
To ensure that the application client understands the provided
information in the cost calendar in an unambiguous way, we specify
the Calendar attributes in the ALTO IRD "meta" information, that
defines the time scope of the "calendared" cost values. The
reference time zone for the provided values is UTC.
o interval-unit:
* expresses the unit in which the duration of an ALTO calendar
time interval duration is expressed. The time unit, ranges
from "second" to "year".
o nb-int-units:
* the number of time units per interval. For example: interval-
unit=minute and nb-int-units=5 means that each calendar value
is provided on a time interval that lasts 5 minutes.
o calendar-start-date:
* the date corresponding to the first value in the array,
expressed with a 2 to 4 digits sequence 'mn/hh/dd/mo/yyyy' to
be interpreted as minute/hour/day/month/year
o next-start-date:
* 'mn/hh/dd/mo/yyyy', to limit the number of provided values, in
case for instance of frequently changing values, and schedule
the next ALTO Calendar query, the starting date of the next
calendar,
o nb-intervals:
* the integer number of values of the cost calendar array, at
least equal to 1.
o calendar-time-zone:
* set to "UTC+hhmm", with hhmn quantifying the UTC time shift
where hh designates the hour and mn the minutes.
o
* next-calendar-update: the next date 'mn/hh/dd/mo/yyyy' at which
the ALTO Calendar values will be updated in the ALTO Server,
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o last-calendar-update:
* the last date 'mn/hh/dd/mo/yyyy' at which ALTO Calendar values
were updated in the ALTO Server.
Some remarks:
- another option to express the date format is tu use HTTP header
fields formats such as:
Date: Tue, 15 Nov 1994 08:12:31 GMT
- If the 'calendar-start-date' date is past, the application can also
use the information to compute statistics on values provided by ALTO
over time to guide applications. Besides some customized prediction
the ALTO Client may guess their reliability w.r.t. some measured QoE.
- The attributes 'last-calendar-update' and 'next-calendar-update'
reflect the update frequency and age of the ALTO Calendar
information. The difference between these two dates is not
necessarily constant. The ALTO Client should just consider that the
ALTO Server does not find it necessary to update the Calendar values
between these two dates. The ALTO Client may thus assume that the
ALTO Server considers the values as valid or stationary during this
period.
- Attribute 'next-start-date' is different and reflects the duration
of the provided calendar. For example an ALTO Server may provide a
calendar for ALTO values changing every 5 minutes. Each calendar is
"1 hour" long and thus has 12 values. The ALTO Server may decide to
update the 24 hourly calendars day. Note also that this example 5
minutes interval may be the aggregation of real TE measurements done
every 30 seconds, but this latter aspect is outside the scope of this
draft as it is to be specified in the definition of the ALTO metric.
4.3. Example of calendared information resources in the IRD
This section describes an example IRD and related ALTO calendar
transaction in a scenario where an ALTO Server offers the Calendar
mode for several Cost Types that are either specified in the base
ALTO protocol, proposed in other drafts see
[draft-wu-alto-te-metrics] or suggested here as examples, like a cost
metric reporting on measured packet loss and called 'TEpktloss. The
provided example transactions are based on the use cases of section
2.
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These examples describe situations where a client has the choice of
trading content or resources with several Endpoints and needs to
decide with which Endpoint it will trade and at what time. For
instance, one may assume that the Endpoints are spread over different
time-zones, or have intermittent access. The ALTO Calendar mode
specified below allows these clients to retrieve Endpoint cost maps
valid for a certain timeframe (e.g. 24 hours), and get a set of
values, each applicable on a (e.g. hourly) slot. Thus the
application can optimize the needed data transfer according to this
information.
In the scenario of the present draft, the available Endpoint Costs
metrics are: "routingcost", "AShopcount", 'TEpktloss' and
'Availbandwidth'. "routingcost" and "AShopcount" are available in the
"numerical" Cost Mode and 'TEpktloss' , 'Availbandwidth' and
"routingcost" as well are available in the "calendar" Cost Mode.
Last, we suppose that the ALTO Client GETs the IRD on Tuesday July
1st 2014 at 13:15 .
o The Calendar for 'TEpktloss'': consists of 12 values provided each
on a time interval of 5 minutes, provided on a per hour basis, and
updated every day at midnight UTC+4. The calendar is updated
everyday at midnight.,
o The Calendar for 'Availbandwidth': consists of 12 values. It is
computed every day and updated at 0h00, for 24 hours, on time
intervals lasting 2 hours, with the first interval starting at
0h00. This information is then used to enable applications to see
which time intervals in a day are the most favorable to operate,
and which "busy " time intervals should be avoided.
o The Calendar for 'routingcost': consists of an array of 24 time
intervals lasting each 1 hour. The routingcost calendar covers a
1 day period, starting at midnight. It is updated every week on
sunday. An ALTO Client can thus store and use the needed
routingcost calendars for maximum 1 week. This may be applicable
for networks with poor or intermittent connectivity where the
operator may integrate monetary as well as network performance
metrics in the provided 'routingcost' values.
4.3.1. Example IRD with ALTO cost Calendars
The example IRD given in this section includes 2 particular URIs:
o "http://alto.example.com/endpointcost/lookup", in which the ALTO
Server offers the numerical mode for metrics "routingcost" and
"AShopcount".
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o "http://alto.example.com/endpointcost/calendar/lookup", in which
the ALTO Server provides "calendar" mode for metrics 'TEpktloss'
and 'Availbandwidth' and 'routingcost'.
GET /directory HTTP/1.1
Host: alto.example.com
Accept: application/alto-directory+json,application/alto-error+json
HTTP/1.1 200 OK
Content-Length: [TODO]
Content-Type: application/alto-directory+json
{
"meta" : {
"cost-types": {
"num-routingcost": {
"cost-mode" : "numerical",
"cost-metric" : "routingcost"
},
"num-AShopcount": {
"cost-mode" : "numerical",
"cost-metric" : "hopcount"
},
"calendar-TEpktloss": {
"cost-mode" : "calendar",
"cost-metric": "TEpktloss",
"description": {
"interval-unit" : "minute",
"nb-int-units" : 5,
"calendar-start-date" : 00/13/01/07/2014,
"nb-intervals" : 12,
"calendar-time-zone" : UTC+4,
"next-start-date" : 00/14/01/07/2014,
"last-calendar-update" : 00/00/01/07/2014,
"next-calendar-update" : 00/00/02/07/2014
}
},
"calendar-bw": {
"cost-mode" : "calendar",
"cost-metric": "Availbandwidth",
"description": {
"interval-unit" : "hour",
"nb-int-units" : 2,
"calendar-start-date" : 00/00/01/07/2014,
"nb-intervals" : 12,
"calendar-time-zone" : UTC+4,
"next-start-date" : 00/00/02/07/2014,
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"last-calendar-update" : 00/00/01/07/2014,
"next-calendar-update" : 00/00/02/07/2014
}
},
"calendar-routing": {
"cost-mode" : "calendar",
"cost-metric": "routingcost",
"description": {
"interval-unit" : "hour",
"nb-int-units" : 1,
"calendar-start-date" : 00/00/01/07/2014,
"nb-intervals" : 24,
"calendar-time-zone" : UTC+4,
"next-start-date" : 00/00/02/07/2014,
"last-calendar-update" : 00/00/29/06/2014,
"next-calendar-update" : 00/00/06/07/2014
}
... other meta ...
},
"resources" : {
... usual ALTO resources such as Network Map, Cost Maps ...
"endpoint-cost" : {
"uri" : "http://alto.example.com/endpointcost/lookup",
"media-types" : [ "application/alto-endpointcost+json" ],
"accepts" : [ "application/alto-endpointcostparams+json" ],
"capabilities" : {
"cost-constraints" : true,
"cost-type-names" : [ "num-routingcost", "num-AShopcount"]
}
},
"endpoint-cost-calendar-map" : {
"uri" : "http://alto.example.com/endpointcost/calendar/lookup",
"media-types" : [ "application/alto-endpointcost+json" ],
"accepts" : [ "application/alto-endpointcostparams+json" ],
"capabilities" : {
"cost-constraints" : true,
"cost-type-names" : [ "calendar-routingcost",
"calendar-TEpktloss",
"calendar-bw"]
}
}
}
}
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4.3.2. Example transaction for a routingcost Calendar to face
intermittent connectivity
Let us assume an Application Client located in an end sytem with
limited resources and having an access to the network that is either
intermittent or provides an acceptable quality in limited but
possibly predictable time periods. Therefore, it needs to both
schedule its resources demanding networking activities and minimize
its ALTO transactions.
The Application Client has the choice to trade content or resources
with a set of Endpoints of moderate 'routingcost', and needs to
decide with which Endpoint it will trade at what time. For instance,
one may assume that the Endpoints are spread on different time-zones,
or have intermittent access. In this example, the 'routingcost' is
assumed constant for the scheduling period and the time sentitive
decision metric is the path bandwidth reflected by a Cost type called
'pathoccupationcost'.
The ALTO Client embedded in the Application Client queries ALTO
information on 'pathoccupationcost' for the 24 hours following
(implicitely) the date of "lastupdate", as this resource is listed in
the IRD.
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POST endpointcost/calendar/lookup HTTP/1.1
Host: alto.example.com
Content-Length: [TODO]
Content-Type: application/alto-endpointcostparams+json
Accept: application/alto-endpointcost+json,application/alto-error+json
{
"cost-type" : {"cost-mode" : "calendar", "cost-metric" : "routingcost"},
"endpoints" : {
"srcs": [ "ipv4:192.0.2.2" ],
"dsts": [
"ipv4:192.0.2.89",
"ipv4:198.51.100.34",
"ipv4:203.0.113.45"
]
}
}
HTTP/1.1 200 OK
Content-Length: [TODO]
Content-Type: application/alto-endpointcost+json
{
"meta" : {},
"cost-type" : {"cost-mode" : "calendar", "cost-metric" : "routingcost"},
"endpoint-cost-calendar-map" : {
"ipv4:192.0.2.2": {
"ipv4:192.0.2.89" : [7, ... 24 values],
"ipv4:198.51.100.34" : [4, ... 24 values],
"ipv4:203.0.113.45" : [2, ... 24 values]
}
}
}
4.3.3. Example transaction for a bandwidth calendar
One example of non-real time information that can be provisioned in a
'calendar' is the expected path bandwidth. While the transmission
rate can be measured in real time by end systems, the operator of a
data center is in the position of formulating preferences for given
paths, at given time periods of given time scales, for example to
avoid hotspots due to diurnal usage patterns. In this example, we
assume that an ALTO Client requests a bandwidth calendar as specified
in the IRD to shedule its bulk data transfers as described in the use
cases of sections 2.1 and 2.5.
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POST endpointcost/calendar/lookup HTTP/1.1
Host: alto.example.com
Content-Length: [TODO]
Content-Type: application/alto-endpointcostparams+json
Accept: application/alto-endpointcost+json,application/alto-error+json
{
"cost-type" : {"cost-mode" : "calendar", "cost-metric" : "Availbandwidth"},
"endpoints" : {
"srcs": [ "ipv4:192.0.2.2" ],
"dsts": [
"ipv4:192.0.2.89",
"ipv4:198.51.100.34",
"ipv4:203.0.113.45"
]
}
}
HTTP/1.1 200 OK
Content-Length: [TODO]
Content-Type: application/alto-endpointcost+json
{
"meta" : {},
"cost-type" : {"cost-mode" : "calendar", "cost-metric" : "Availbandwidth"},
"endpoint-cost-calendar-map" : {
"ipv4:192.0.2.2": {
"ipv4:192.0.2.89" : [7, ... 12 values],
"ipv4:198.51.100.34" : [4, ... 12 values],
"ipv4:203.0.113.45" : [2, ... 12 values]
}
}
}
5. IANA Considerations
Information for the ALTO Endpoint property registry maintained by the
IANA and related to the new Endpoints supported by the acting ALTO
server. These definitions will be formulated according to the syntax
defined in Section on "ALTO Endpoint Property Registry" of
[ID-alto-protocol],
Information for the ALTO Cost Type Registry maintained by the IANA
and related to the new Cost Types supported by the acting ALTO
server. These definitions will be formulated according to the syntax
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defined in Section on "ALTO Cost Type Registry" of
[ID-alto-protocol],
5.1. Information for IANA on proposed Cost Types
When a new ALTO Cost Type is defined, accepted by the ALTO working
group and requests for IANA registration MUST include the following
information, detailed in Section 11.2: Identifier, Intended
Semantics, Security Considerations.
5.2. Information for IANA on proposed Endpoint Propeeries
Likewise, an ALTO Endpoint Property Registry could serve the same
purposes as the ALTO Cost Type registry. Application to IANA
registration for Endpoint Properties would follow a similar process.
6. Acknowledgements
Thank you to D. Lopez, H. Peng and the ALTO WG for fruitful
discussions.
7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[RFC5693] Seedorf, J. and E. Burger, "Application-Layer Traffic
Optimization (ALTO) Problem Statement", RFC 5693, October
2009.
7.2. Informative References
[ID-alto-protocol]
R.Alimi, R. Penno, Y. Yang, Eds., "ALTO Protocol, draft-
ietf-alto-protocol-27.txt", March 2014.
[article-gslh-alto-sdn]
V. Gurbani, M. Scharf, T.Lakshman, and V. Hilt, ,
"Abstracting network state in Software Defined Networks
(SDN) for rendezvous services, IEEE International
Conference on Communications (ICC) Workshop on Software
Defined Networks (SDN)", June 2012.
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[draft-jenkins-alto-cdn-use-cases-01]
B. Niven-Jenkins (Ed.), G. Watson, N. Bitar, J. Medved, S.
Previdi, , "Use Cases for ALTO within CDNs, draft-jenkins-
alto-cdn-use-cases-01", June 2011.
[draft-randriamasy-multi-cost-alto]
S. Randriamasy, Ed., W. Roome, N. Schwan, , "Multi-Cost
ALTO (work in progress), draft-randriamasy-alto-multi-
cost-07", October 2012.
[draft-wu-alto-te-metrics]
Q. Wu, Y. Yang, Y. Lee, D. Dhody, S. Randriamasy, , "ALTO
Traffic Engineering Cost Metrics (work in progress)", June
2014.
[draft-xie-alto-sdn]
H. Xie, T. Tsou, D. Lopez, H. Yin, , "Use Cases for ALTO
with Software Defined Networks (work in progress), draft-
xie-alto-sdn-extension-use-cases-01", January 2013.
[draft-yang-alto-topology-00]
Y. Yang, , "ALTO Topology Considerations (work in
progress)", July 2013.
[sdnrg] "Software Defined Network Research Group,
http://trac.tools.ietf.org/group/irtf/trac/wiki/sdnrg", .
[slides-88-alto-5-topology]
G. Bernstein, Y. Lee, Y. Yang, , , "ALTO Topology Service:
Use Cases, Requirements and Framework (presentation slides
IETF88 ALTO WG session),
http://tools.ietf.org/agenda/88/slides/
slides-88-alto-5.pdf", November 2013.
Authors' Addresses
Sabine Randriamasy (editor)
Alcatel-Lucent Bell Labs
Route de Villejust
NOZAY 91460
FRANCE
Email: Sabine.Randriamasy@alcatel-lucent.com
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Richard Yang
Yale University
51 Prospect st
New Haven, CT 06520
USA
Email: yry@cs.yale.edu
Qin Wu
Huawei
101 Software Avenue, Yuhua District
Nanjing, Jiangsu 210012
China
Email: sunseawq@huawei.com
Lingli Deng
China Mobile
China
Email: denglingli@chinamobile.com
Nico Schwan
Thales Deutschland
Email: ietf@nico-schwan.de
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