Internet DRAFT - draft-jinxiang-network-management-ngi
draft-jinxiang-network-management-ngi
Network Working Group JinXiang,Zhang
Internet-Draft Jiahai,Yang
Jianping,Wu
Intended status: Best Current Practice Jilong,Wang
Practice Network research center,Tsinghua University
Expires: April 8, 2009 October 8, 2008
Retriving MIB Information based on NGI
draft-jinxiang-operations-and-management-ngi-01
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Abstract
An important task of network management is to collect and analyze
MIB information of various object combinations based on the
Simple Network Management Protocol (SNMP) with proper frequency.
The purpose of this document is to propose two algorithms to retrieve
MIB information for a large (up to exponential) number of managed
objects using SNMP in Next Generation Internet (NGI).
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Self-Adaptive Algorithm . . . . . . . . . . . . . . . . . 4
2.1. Description of problems. . . . . . . . . . . . . . . . . . 4
2.2. Policy on fault tolerance. . . . . . . . . . . . . . . . . 4
2.3. Policy on self-adaptivity . . . . . . . . . . . . . . . . 4
2.4. Policy on dynamical recognition status of object . . . . . 6
2.5. Description of SAA . . . . . . . . . . . . . . . . . . . . 7
3. Group-Prefetching Algorithm . . . . . . . . . . . . . . . . . 8
3.1. Description of problems . . . . . . . . . . . . . . . . . 8
3.2. Policy on group prefetching . . . . . . . . . . . . . . . 8
3.3. Policy on extending MO of group self-adaptively . . . . . 10
3.4. Policy on shrinking MO of group self-adaptively. . . . . . 11
3.5. Policy on dynamical recognition status of object . . . . . 11
3.6. Policy on fault tolerance. . . . . . . . . . . . . . . . . 11
3.7. Description of GPA . . . . . . . . . . . . . . . . . . . 11
4. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 13
5. Security Considerations . . . . . . . . . . . . . . . . . . . 13
6. References . . . . . . . . . . . . . . . . . . . . . . . . . 13
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1. Introduction
With rapid and wide deployment of IPV6, the network scale expands and
the resources provided by network becomes more heterogeneous, and the
traditional pattern of network management and information retrieval
scheme becomes more and more difficult to meet the needs of network
users based on IPv6. Furthermore, NGI(Next Generation Internet) based
on IPv6 has some distinct features. Firstly the link bandwidth can be
G bit or even T bit. Secondly, there are more applications, such as
IPTV, HDTV and VoD. Finally, the existing management methods in IPv4
are not suitable for IPv6 network. On the other hand, according to
the new Moore Law, the bandwidth and traffic capacity can be doubled
in every six months. The number of managed objects increased
exponentially. As the network management can be fulfilled through the
information exchange between manager and agent, the performance of
network management is affected by the scheme to collect MIB[1].
The traditional method of network management information retrieval is
SIFD(Sequence and Interval Fixed Discrete single access Algorithm).
But there are some weaknesses with this algorithm. Firstly, If there
are a lot of managed objects (MO) in the managed network, SIFD cannot
recognize which of the MOs are more important, Secondly, SIFD cannot
access many discrete MOs simultaneously, Thirdly, SIFD may poll a MO
although its agent is down. So SIFD may use up all network bandwidth
and then result in network congestion.
To resolve these problems, three enhanced information retrieval
algorithms were proposed used to access table objects[2]. The
first algorithm - Serial Algorithm retrieves the table objects using
Get-Next operation, and the second algorithm - Pipelined Algorithm
also accesses all the objects in a table by the repetitive retrieval
of each column each time, moreover, the algorithm uses multiple
threads technology, in which one thread accesses multiple rows in the
table using Get-Next operation. The third algorithm - Parallel
Algorithm adopts a multi-threaded approach, in which each thread
generates its own stream of Get-Next requests and processes the
resulting stream of responses. However, all the three algorithms are
only used for the retrieval of table objects and needed to alter SNMP
, so the algorithms are not suitable for any kinds of MOs.
In this document we propose a Self-Adaptive Algorithm (SAA), that can
be used to retrieve any kind of MOs in NGI. This algorithm can adjust
the number of retrieved objects and the interval of retrieval
self-adaptively based on self-adaptivity policy, fault tolerance
policy and dynamical recognition status of objects etc.. It can be
used to reduce network resource consumption without any modification
of SNMP.
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The other algorithm we propose in this document is Group-Prefetching
algorithm (GPA). With GPA, many small managed objects are gathered
into one prefetching group to be accessed using group prefetching
schemes. As a result, it can be used to decrease the retrieval
frequency and network resource consumption without any modification
of SNMP.
2. Self-Adaptive Algorithm
Let us now consider an algorithm for management information retrieval
with the SNMP in NGI. Self-Adaptive Algorithm can adjust the
number of retrieving objects and interval of retrieval self-
adaptively by using the policies of self-adaptivity, fault tolerance,
dynamical recognition status of objects etc., and thus can use fewer
network resources without alteration of SNMP.
2.1. Description on problems
The model of SAA is as follows. if there are n MOs in the charge
of m agents, where n > m, the algorithm will adjust the interval to
the next polling based on the polling value, status and weights
of current polling results of n MOs. Moreover, the greater the weight
value is, the more possibly the MO of Gauge Counter Type overflows.
The algorithm complies with the principles as follows. Firstly,
Computation power of Network Management Station (NMS) consumed by
the algorithm should be as little as possible, Secondly, the key
transactions on managed equipment should not be affected by the Get
and Get-Next operations on this equipment, Thirdly, the management
traffic between NMS and agent must be as little as possible, Finally
, the algorithm must be self-adaptive and robust.
2.2. Policy on Fault tolerance
Since such MIB value as interface status and accounting bytes changes
dynamically, the polling interval of these MOs must be reasonable.
For example, the maximum value of actByts in the IP-Account-Table is
32 power of 2 sub-1, notated as Value-max= power(2,32)-1. So the
algorithm should retrieve the actByts value before it overflows.
Generally, if the baud rate of an interface is V b/s, the least
overflowing time can be calculated as Tflow-min= (power(2,32)-1)
*8/V (s). Furthermore, the real value of a MO can be recovered from
Error.log in case it overflows.
2.3. Policy on Self-adaptivity
The algorithm adjusts retrieval interval self-adaptively, and this
adjustment is based on the accounting MO of Gauge Counter Type since
the actByts MIB may overflow. The policy is specified as follows.
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Let Value[i-1] be the (i-1)th value and Tinter[i-1] be the interval
between (i-2)th and (i-1)th. And the first retrieval time is defined
as Tflow-min. Then Tinter[i], the ith retrieval interval, can be
calculated as two situations.
(1)Tinter[i] = max { Tflow-min, delta* Tinter[i-1]* Value-max
/ Value[i-1]}, If actByts MIB does not overflow
and i >= 2
(2)Tinter[i] = Tflow-min, otherwise
where delta is a balance factor to decrease the overflowing
probability.
From the above expression, we can see that the interval to next
polling is determined by current value value[i-1].
Now let us illustrate how to determine the value of delta (to
minimize the overflowing probability) using the following example.
Consider a 100Mb/s interface.
The minimum overflowing time of the MO of Gauge Counter Type is.
Tflow-min = (power(2,32)-1)*8/(100*power(10,6))=343s*0.095h.
To minimize the overflowing probability of the MO of Gauge Counter
Type when deciding the optimal value of delta, we choose the
sampling hits in the intervals of MOs and evaluate the performance
when delta is 0.52, 0.54, 0.56, 0.58, 0.6, 0.618, 0.64, 0.66, 0.68
respectively.
Firstly, let delta=0.52 and Tinter[i-1] =8.8 *Tflow-min, 9 *Tflow-min
, 9.2 *Tflow-min, 9.4* Tflow-min, 9.6 *Tflow-min, 9.8* Tflow-min,
10 *Tflow-min respectively, we can calculate a group of Value[i].
Secondly, calculate Tinter[i] using the self-adaptivity policy.
Thirdly, retrieve Value[i], and calculate the average value
Value-avr[i]of the group. We repeat the three steps as above, and
calculate the average value Value-avr[i] for other groups. Then we
found the probabilitly that overflows in next interval decreases
with delta, and the sampling frequency increases with delta.
Contrarily, the more probable overflowing of next time is, the less
the sampling frequency is. And we have found that the overflowing
probability is minimum when delta is 0.618, the golden mean factor.
At the same time, in order to ensure that the key data would not
overflow, we decide whether to use self adaptivity policy according
to the weight value. We denote the predefined threshold as "alarm".
If the weight value exceeds alarm, we retrieve the MIB data
immediately, and the self-adaptivity policy will not be used until
the weight value is equal to or less than the threshold. So the
second part of our self-adaptivity policy is as follows:
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(1)Retrieve MIB data timely, if Weight>= Alarm
(2)Retrieve MIB data self adaptively, otherwise
Assume that the current time of NMS is T-sys, the next retrieval time
Taccess[i] can be calculated as Taccess[i]= T-sys + Tinter[i].
For example, with fixed interval Tinter[i-1]= Tflow-min, SIFD needs
to retrieve MOs 11(1/Tflow-min) times in an hour. However, if
Tinter[i-1]= Tflow-min =0.095h, Value[i-1]=1073741800, Tinter[i]=
delta* Tinter[i-1]* Value-max /Value[i-1] =0.618*0.095*4=0.235h,SAA
needs to retrieve MOs 5(1/1/ Tinter[i]) times in an hour. The
retrieving frequency of SAA is 50% less than that of SIFD.
In addition, if Tinter[i-1] is a random value, Our experiments shows
that the minimum value of Tinter[i] is Tflow-min and the
retrieving times of SAA is always less than that of SIFD.
If the maximum value of actByts is 64 power of 2 sub 1, notated as
Value-max=(power(2,64)-1), we have similar conclusions by SAA.
2.4. Policy on dynamical recognition status of object
We define two configuration tables. One is called Tab-equip-all and
it is used to store all equipment information, the other is called
Tab-equip-active and it is used to store active equipment information
. The active equipment is defined as follows.
Def. 1. An Active Equipment is one equipment that is operating
regularly. The active equipment table consists of all active
equipments in the network, and the active equipment table is notated
as Tab-equip-active. Each entry in Tab-equip-active is a quaternion
notated as Rg , and Rg ={O-addr,O-oid,O-weight,O-stus }, where
O-addr is IP address of the agent in charge of the MO, O-oid is the
identifier of MO, such as DN or OID, O-weight is the weight value of
MO,O-stus is the status of MO. Moreover, the retrieval interval can
also be included in the table if necessary.
The policy on dynamical recognition status of object is as follows.
The algorithm only retrieves the MOs in Tab-equip-active, which
ensures that the sleeping managed equipment cannot be polled.
Nevertheless, New equipments or the equipments whose status are
changed from sleeping to active should be added into Tab-equip-active
table by the Event process. Finally, a MO should be deleted from
Tab-equip-active table if the agent of the MO does respond three
times.
Moreover, as the storage space of MOs must be freed in certain time
, the clearing frequency can also be decided by the self-adaptivity
policy.
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2.5. Description of SAA
We define the data structure of SAA as follows.
typedef struct _ObjStruct {
char *name;
int type;
int oid_length;
ObjectID oid[MAX_SUBID_LEN];
} ObjStruct; /*data structure for MIB*/
typedef struct _InfoStruct {
char IpAddress[30]; /*IP address of Agent*/
ObjStruct *obj;
int CriticlValue; /*weight value*/
bool Status;
/*attribute status, initial value is 0, ie. sleeping*/
int Retrievalinterval;
/*current interval, initial value is Tflow-min */
int NextRetrievaltime;
/* next interval, initial value is Tflow-min */
} InfoStruct;
/*data struct for Tab-equip-all and Tab-equip-active */
SAA algorithm can be described as follows.
begin
copy all information in Tab-equip-all to Tab-equip-active, and
initialize a cyclic queue
initialize the retrieval time according to system time T-sys
L:sort IP addr in Tab-equip-active in ascending order of Taccess[i]
if empty_queue(Tab-equip-active) then
exit("all the MOs in the network cannot be accessed")
IPhead=Head_queue(Tab-equip-active), setup a Session between NMS and
agent whose IP is IPhead
if fail three times then
set status value to 0, delete the IPhead from the Tab-equip-active
table, goto L
retrieve all the relevant MIB information of the equipment
if TYPE of the MO is Gauge Counter then
begin
adjust Value[i-1], Tinter[i-1]and Weight
if IsFlow
then Tinter[i]= Tflow-min
else Tinter[i]= max{ Tflow-min, delta* Tinter[i-1]* Value-max
/ Value[i-1]}
Taccess[i]= T-sys + Tinter[i]
end
else if TYPE of the MO is TABLE then
free the store space using self-adaptivity policy
store the MIB information in database, goto L
end
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In the above description, empty_queue() is a BOOL function. The
function returns TRUE if the cyclic queue of Tab-equip-active is
empty, otherwise it returns FALSE. Head_queue() is a function used to
get the head of Tab-equip-active queue and return
the IP address of the first element. IsFlow is a BOOL variable, whose
initial value is FALSE, and its value is set to be TRUE when
overflowing.
3. Group-Prefetching Algorithm
Let us now consider another algorithm for management information
retrieval with the SNMP in NGI. with " Group-Prefetching Algorithm "
, many small managed objects are gathered into one prefetching group
to be accessed using group prefetching schemes, objects of group
changed self-adaptively, fault tolerance etc., and thus can decrease
the retrieval frequency and network resource consumption without
alteration of SNMP.
3.1. Description of problems
The model of GPA is as follows, Let us assume that there are n MOs
in the charge of m agents, where n > m. With this algorithm, we will
pick r small retrieving objects and put them into one prefetching
group to access according to the status attribute and time attribute,
where r<n. all objects of the same group were updated as a kind and
all the objects only need a kind identifier to identify time
attribute. Moreover, the algorithm adjusts the number of objects and
polling interval of next time based on the polling value, status and
weights of current polling results of n MOs. The status attribute
denotes the running status of the agent in charge of the object, and
its value is changed by the Event process, Time attribute represents
the time sensitivity of objects.
The algorithm complies with the principles as follows, Firstly,
the objects are classified into several Categories according to time
attribute relevance, and the objects with similar time attributes
are adjusted into one prefetching group to access, in addition, the
polling values of the objects are cached locally to decrease the
number of polling. Secondly, processing resources of NMS(Network
Management Station) occupied by the algorithm should be as few as
possible, thirdly, the management traffic between NMS and agent must
be as little as possible, lastly, the algorithm must be self-adaptive
and robust.
3.2. Policy of group prefetching
According to the time attributes of managed objects, we classify
managed objects into three kinds as follows[3].
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The first category is R_Data (Real-Time_Data). It requires that the
attributes value of such managed objects should be collected on time
to indicate the most recent status of them, One example in this
category is the attributes value of actPkts objects which are used to
account communications traffic in one network.
The second category is N_Data (Not-Real-Time_Data). The attributes
value of such managed objects is valid for a period after being
collected once, for example, the attributes value of a router
information table, and the time interval can be chosen as
TTL(Time To Live).
The third category is I_Data(Irrespective-Time_Data). The
attributes value of such managed objects need not be refreshed after
being collected once, for example, the attributes value of log or
system description of managed objects.
According to time attributes of managed objects, each managed object
needs an extra attribute to explain its time attribute when we define
the object using ASN.1 Macro. We use coding scheme for time attribute
to diminish the additional overhead caused by the definition of time
attribute and make system extended. For N kinds of time attribute
only Log2N bits are needed in every managed object for specifying the
time attribute. As we only adopt three kinds of objects, two bits in
a byte are enough to specify time attribute. That is.
0(00): R_Data, 1(01): N_Data, 2(10): I_Data.
The other six bits are reserved to be used in future, such as group
identifier.
The managed objects of R_Data are accessed by the discrete poll on
time, nevertheless, The managed objects of N_Data and I_Data are
suitable for group prefetching.
Let S(cur_g) denote the aggregate of current access group
whose time attribute is S(cur_g).Tatt . Let any_o be an object
belonging to the group, and its TTL is any_o.TTL. if we specify the
TTL of the group as S(cur_g).TTL, we have following result.
if S(cur_g).Tattr ==1 then
S(cur_g).TTL =min{ any_o.TTL, where any_o is in S(cur_g) };
otherwise, if S(cur_g).Tattr ==2 then
S(cur_g).TTL =any value;
Here, the S(cur_g).TTL of N_Data group is the minimum
value of all TTL of the managed objects, nevertheless, the
S(cur_g).TTL of I_Data group can be any value.
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Thereinto, each managed object of N_Data and I_Data needs another
attributes, Timestamp, to indicate the system time when the managed
objects in this group were accessed the latest time. We use
cur_systime to indicate the current system time, and S(cur_g)Tstamp
to indicate the Timestamp of the group. Then we prefetche information
of all managed objects in this group when any MO in this group is
requested.
Update value in cache and timestamp of MO;
if (cur_systime - S(cur_g).Tstamp ) >= S(cur_g).TTL.
return value in cache;
otherwise.
By grouping managed objects to cache and storing the attributes value
of the managed objects locally, we reduce the overhead of network
management.
For example, in a 100Mb/s interface network, the overhead of network
management, the packets include the packets used to obtain the IP
address of Sub_Manager/Agent and the packets of request/ response.
If there are 10 MOs that meet the needs of Group-Prefetching
condition, the packet numbers of SIFD is 22 while GPA is 14, the rate
is 0.64. the packet number of Policy of group prefetching of GPA is
fewer than that of SIFD, only 0.64 times.
3.3. Policy on extending MO of group self-adaptively
The algorithm can adjust the retrieval interval and the number of MO
in the group self-adaptively by using the self-adaptivity policy.
We use S(all_g) to denote all the MOs in a network, any_o
indicate any MO in the network, any_o is in S(all_g) but does belong
to any group. and its RTT(Round Trip Time)and time attribute are
any_o.RTT and any_o.Tatt respectively, S(cur_g).Count indicates the
number of MOs in S(cur_g).
If any_o is not in S(cur_g) and S(cur_g).Count < Max_gsize,then
If any_o.RTT <Alarm_Time and any_o.Tattr==S(cur_g).Tattr then
S(cur_g)= S(cur_g) + { any_o },
S(cur_g).TTL =min{ S(cur_g).TTL, any_o.TTL };
otherwise S(cur_g);
We compare the RTT of MO with predefined threshold -Alarm_Time, If
the Alarm_Time is larger than the value of RTT, we add the MO in the
group.
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Thereinto, Max_gsize is predefined maximum value of the number of MOs
in any group. Max_gsize is used to prevent group from being too big.
3.4. Policy on shrinking MO of group self-adaptively
If any_o is in S(cur_g),then
if any_o.RTT >= Alarm_Time then
S(cur_g)= S(cur_g)-{ any_o };
otherwise S(cur_g);
We compare the RTT of the accessed MO with the predefined threshold
-Alarm_Time, If the the value of RTT is larger than Alarm_Time, we
delete the MO from the group and access the MO discretely. And we
reuse GPA to access the MO when RTT reduce.
3.5. Policy on dynamical recognition status of object
We define two configuration tables, One is act_t,which is used to
store information of active equipments. The other is cur_t, which is
used to store equipment information of the MOs in current group .
With this algorithm, We only retrieves the MOs of group in act_t,
which ensures that the sleeping managed equipment can not be polled.
If any_o is in S(cur_g),then
if any_o.id is not in act_t then
S(cur_g)= S(cur_g)-{ any_o };
otherwise S(cur_g);
Thereinto, any_o.id is the id of Agent in charge of any_o , and IP
address of agent represents its id. Nevertheless, New equipments or
equipments whose status are changed from sleeping to active can be
added to act_t by the Event process. Finally, a MO should be deleted
from act_t if the agent of the MO does not respond three times.
3.6. Policy on fault tolerance
if the equipment or NMS of one MO in a group is down and can not
response, the values of MOs are not correct. There are several
possible solutions. We can delete the MO from the group if NMS is
down, otherwise,Or we can access the MO again if the packets are
lost owing to protocol. These events should be recorded in error.log
to recover the actual value of the MOs by NMS.
3.7. Description of GPA
At first, we define the data structure of GPA as follows.
typedef struct _ObjStruct {
char *name;
int type;
int oid_length;
ObjectID oid[MAX_SUBID_LEN];
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unsigned char Tattr; /*time attribute*/
int RTT; /*round trip time of access*/
int Group_label; /*initial value is 0*/
} ObjStruct; /* data structure for MIB */
typedef struct _InfoStruct {
ObjStruct *obj;
bool Status; /*status attribute*/
} InfoStruct; /*data structure for element in S(cur_g) */
GPA algorithm used to access a group of MOs can be described using
similar language as follows.
begin
if (cur_systime - S(cur_g).Tstamp )< S(cur_g).TTL
return the value in cache
else begin
initialize a cyclic queue with elements in S(cur_g)
reformed a request packet using all oid in S(cur_g)
setup a Session between Sub_Manager and Agent
if failure three times then
record the error information in Error.log,Exit
if S(cur_g).Tattr =1 then
S(cur_g).TTL =min{ any_o.TTL |any one of any_o in
S(cur_g) };
else if S(cur_g).Tattr =2 then
S(cur_g).TTL =any value;
if (cur_systime - S(cur_g).Tstamp ) >= S(cur_g).TTL
Update value in cache and timestamp of MO;
else return value in cache;
If any_o is not in S(cur_g) and S(cur_g).Count < Max_gsize,then
begin
If any_o.RTT <Alarm_Time and any_o.Tattr= S(cur_g).Tattr then
S(cur_g)= S(cur_g)+ { any_o },
S(cur_g).TTL =min{ S(cur_g).TTL, any_o.TTL };
else S(cur_g) = S(cur_g);
end
If any_o is in S(cur_g),then
if any_o.RTT >= Alarm_Time then
S(cur_g)= S(cur_g)-{ any_o };
else S(cur_g) = S(cur_g);
If any_o is in S(cur_g) then
if any_o.id is not in act_t then
S(cur_g)= S(cur_g)-{ any_o };
else S(cur_g) = S(cur_g);
end
write the information of MOs into database
end
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4. Acknowledgements
This research was supported by National Natural Science Foundation of
China "Research on Access Algorithm and storage schemes for
management information based on next generation Internet" under grant
Nos. 60473083. Some of the discussion about designing for IPV6
management came from private discussions with Changqin An, Hui Wang
, and ZhongHui Li.
5. Security Considerations
This document is informational and provides guidelines for
management information retrieval. It introduces no new
security concerns.
6. References
[1] Case, J., Fedor, M., Schoffstall, M., and J. Davin, Simple
Network Management Protocol (SNMP), RFC 1157, SNMP Research,
Performance Systems International, Performance Systems
International, MIT Laboratory for Computer Science, May 1990
[2] M. Rose, K. McCloghrie, and J. Davin, Bulk Table Retrieval with
the SNMP, RFC 1187, SNMP Research, Performance Systems
International, Performance Systems International, MIT Laboratory
for Computer Science October 1990
[3] Jayant, R., Haritsa, M.O., Nicholas, R., et al.MANDATE: Managing
networks using database technology. Journal on Selected Areas in
communications, 1993, 11(9):1361~1372.
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Internet-Draft Retrieving MIB Information based on NGI October 2008
Author's Address
JinXiang Zhang
Network research Center ,Tsinghua University P.R.China
No.1 QingHua Yuan, Haidian district Beijing
Phone: +86 10 62795818 ext 6128
Fax:
EMail: jxzhang@cernet.edu.cn
URI:
JinXiang,Zhang, et al. Expires April 8, 2009 [Page 14]
Internet-Draft Retrieving MIB Information based on NGI October 2008
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JinXiang,Zhang, et al. Expires April 8, 2009 [Page 15]
