5. Introduction .. Cont,
• Publish / Subscribe Architectures
1- Lists at server:
Middleware is at the servers
2- Message broker:
Middleware is in a separate unit
3- BroadCast & filter at client:
Middleware is at the clients
6. Human Networks (HUMNETs)
• It’s a dynamic networks composed of
human-carried wireless devices.
• Applications in HUMNETs require contentbased networking services. (style of
communication that associates source
and destination pairs based on actual
content and interests, rather than letting
source nodes specify the destination)
7. Content-based
PublishSubscribe (CBPS)
• Content-based matching is the problem of finding all the
subscriptions that match a given notification.
• CBPS represents a compromise between the extremes
of publisher-side filtering of messages ( with event
directly transmitted to interested subscribers ) and
subscriber-side filtering of messages ( with events
broadcasted to all subscribers ).
• Event delivery is the task of delivering the notification to
the set of interested subscribers selected with contentbased matching.
8. Pub-sub service network
•
Two Approaches :
1. Filter-based approach:
Performs content-based filtering on
intermediate routing servers to dynamically
guide routing decisions.
2. Multicast-based approach:
Delivers events through a few high-quality
multicast groups that are pre-constructed to
approximately match user interests.
10. Pub-sub service network…Cont,
• In the filter-based approach, Routing decisions are made
via successive content-based filtering at all nodes from source
to destination: every pub-sub server along the way matches
the event with remote subscriptions from other servers and
then forwards it only toward directions that lead to matching
subscriptions
• In the multicast-based approach, A limited number of
multicast groups are computed before event transmission
begins. For each event the routing decision is made only once
at the publisher, mapping the event into the single appropriate
group. The event is then multicast to the group assuming IP
multicast or application-level multicast support. Because only
a limited number of multicast groups can be built, servers with
different interests may be clustered into same group and
events may be sent to uninterested servers as well.
11. Bloom filter-based pub-SUB “BSUB”
• It’s a content-based publish-subscribe
system.
• In B-SUB, messages are identified by
using strings that summarize their
contents. ( called keys )
13. Bloom filter-based pub-SUB “BSUB” …Cont,
• Advantages:
1- Frees users from addressing & routing tasks.
(reduces the overall overhead in the system)
2- Message producers & consumers are
separated.
3- Messages are forwarded only by brokers
(Perform content matching for the users)
15. B-SUB Components … Cont,
1- Broker Allocation:
•
Group of socially active nodes are selected to be brokers.
•
Normal users don’t participate in interest propagation & message
forwarding
•
Brokers are responsible for collecting subscriptions and forwarding
messages
•
A Broker stores a TCBF for propagating other users’ interests.
(which is called relay filters)
2- Pub – Sub forwarding
•
It’s separated into 2 parts: interests propagation and message
forwarding
16. Bloom Filters (BF)
• It’s a space-efficient data structure for representing sets
which supports probabilistic membership querying.
•
is a space-efficient probabilistic data structure that is
used to test whether an element is a member of a set
• BF maps a key through multiple hash functions into a bit
vector of a few bits being set. “ User’s interests are
represented as keys – Also messages are identified by
strings that summarize their contents are called Keys “
17. Bloom Filters (BF) … Cont,
• The locations of the set bits are determined by
the hash functions.
• A query of a key to a
BF checks if all the
hashed bits of the key
are set, which indicates
if the key is contained
in the BF
18. Bloom Filters (BF) … Cont,
• A BF for a set of keys is obtained by sequentially
inserting keys into the filter.
10
10
1
1
{K0}
20
+
10
10
1
1
{K1}
{k0,K1}
1
10
10
1
1
19. Bloom Filters (BF) … Cont,
• To merge multiple BFs we do a bit-wise
OR on them.
10
10
1
1
{K0}
10
M
10
10
1
1
{K1}
{k0,K1}
1
10
10
1
1
20. Bloom Filters (BF) … Cont,
• The basic BF doesn’t support deletions since we are
unable to trace the associated keys of set bits.
• The counting bloom filter (CBF) is proposed to provide
deletion.
• In a CBF each bit is associated with a counter, which
represent the number of keys that are associated with it.
• To delete a key from a CBF we decrement the counters
of the key’s hashed bits. A bit will be reset once its
counter reaches 0.
21. Bloom Filters (BF) … Cont,
• The sizes of messages in B-SUB are small
which are in order of hundreds of bytes. This
assumption is true in social networking
applications.
• Ex: twitter; a popular micro-blogging application,
requires a max size of 140 bytes for each post. If
a message is wrongly injected into the network,
the wasted bandwidth is acceptable)
22. How Bloom filters work?
“Message Forwarding”
• When a producer meets a consumer, the
consumer reports its interests in a BF to the
producer. The producer then queries all its
messages against the filter, and forwards all the
messages that match the filter, to the consumer.
• When a broker meets a producer, it forwards a
BF to the producer. The producer queries that
filter and determines the events that need to be
transmitted.
23. How Bloom filters work?
“Message Forwarding” … Cont,
• When a broker meets a consumer, the broker requests a
BF containing the consumer’s interests, then forwards
the matched messages to the consumer.
• Message are removed from brokers’ memory after being
forwarded. This is to prevent excessive copies in the
network.
•
Messages’ lifetime is controlled by their time-to-live
(TTL) values, which are identical to their maximum
tolerable delay. The TTL is counted since the message
has been created.
24. Temporal counting Bloom filter
(TCBF)
•
Extension to BF, proposed to perform content-based networking
tasks.
•
It doesn’t support direct deletion of elements. It only supports
temporal deletion, that is, A filter constantly decrements the
counter’s values of all its set bits, which is called Decaying
•
B-SUB uses TCBF to encode user’s interest & embed information
needed for brokers to make forwarding decisions.
•
B-SUB makes forwarding decisions through querying the TCBFs
( B-SUB can propagate interests by transmitting at most two TCBFs
of dozens of bytes)
•
The operations performed are only hashing and table lookup.
25. Problems with TCBF
• False positive (Spam) occur because a
key’s hashed bits are accidentally set by
other keys that have been put into the
TCBF.
• Because of false postivies, B-SUB may
falsely inject useless messages into the
network.
26. Decaying Factor (DF)
• It’s the key for adjusting B-SUB’s behaviors.
• If decaying is not used, the counters of the set
bits don’t change after being set, then no
interests will be removed.
• An obvious consequence is that a broker will
end up with carrying the interests from the users
that it meets rarely.
27. Decaying Factor (DF)…Cont,
• Suppose that each message has a delay limit of time T,
we should set the DF in such a way that an interest will
get removed after T since a consumer inserted the
interest once.
• If the broker contains the interest, then that means that
the broker has met a consumer that is interested in it
within T.
• If a message is forwarded by the broker it’s likely that the
message will be delivered within T.
