The data streaming paradigm and its use in Fog architectures

Vincenzo Gulisano
Vincenzo GulisanoAssociate Professor at Chalmers University of Technology
The	data	streaming	paradigm	
and	its	use	in	Fog	architectures
Vincenzo	Gulisano,	Ph.D.
EBSIS	Summer	School	2016
1
Agenda
• Introduction:	Fog	architectures
• Data	streaming	basics
• Trade-offs	and	challenges	in	Fog	streaming	analysis
• Conclusions
• Bibliography
2The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Agenda
• Introduction:	Fog	architectures
• Data	streaming	basics
• Trade-offs	and	challenges	in	Fog	streaming	analysis
• Conclusions
• Bibliography
3The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Introduction:	Fog	architectures
Advanced	Metering	Infrastructures	(AMIs)
[1,2,3,4]
Vehicular	Networks	(VNs)
[5,6]
4The	data	streaming	paradigm	and	its	use	in	Fog	architectures
AMIs VNs
• demand-response
• scheduling	 [7]
• micro-grids
• detection	of	medium	size	blackouts	[8]
• detection	of	non	technical	losses
• ...
5
• autonomous	 driving
• platooning
• accident	detection	[9]
• variable	tolls	[9]
• congestion	monitoring	 [10]
• ...
Access	to	their	data	à Increased	awareness,	security,	power-efficiency,	...
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
(some)	fundamental	questions
• where	do	we	process	data?
• how	do	we	process	data?
• how	do	we	enforce	security	and	privacy?
6The	data	streaming	paradigm	and	its	use	in	Fog	architectures
the	wrong incomplete	answer																		we	run	the	analysis	in	the	cloud!
7
1. does	the	infrastructure	allow	for	billions	of	
readings	per	day	to	be	transferred	continuously?
2. the	latency	incurred	while	transferring	data,	does	
that	undermine	the	utility	of	the	analysis?
3. is	it	secure	to	concentrate	all	the	data	in	a	single	
place?	[11]
4. is	it	smart	to	give	away	fine-grained	data?	[12]	
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
A	small	example	of	what	fine-grained	data	can	reveal...
8
source:	Private	Memoirs	of	a	Smart	Meter	[12]
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
a	better	answer																		we	leverage	the	entire	infrastructure!
9
Traditional	analysis	techniques	cannot	address	
all	the	challenges	in	these	setups	[13,14]
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Fog	architectures
10
source:	Fog	Computing	and	Its	Role	in	the	Internet	of	Things	[15]
Characteristics	[15]:
1. edge	location	/	location	
awareness	/	low	latency
2. geographical	distribution
3. large-scale
4. support	for	mobility
5. real-time	interactions
6. predominance	of	wireless
7. heterogeneous
8. interoperability	/	federation
9. interaction	with	the	cloud
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Fog	architectures,	applications	trade-offs	
and	challenges
11
Sensor/Edge	Devices Fog	Devices Cloud	Devices
Cost + ++ +++1
Computational	power + ++ +++
Inter-node	heterogeneity +++ ++ +
Intra-node heterogeneity + + +++
Size +++ ++ +
Communication wireless wireless/wired wired
Volume	of	data millions	of	small	streams2 thousands of	medium	aggregated	streams many	large	streams
Evolution	over	time +++ ++ +3
1. depends	on	who	owns	the	physical	hardware
2. with	exceptions	like	e.g.	Lidar	sensors	[16,17]
3. Virtual	environments	can	also	evolve	”behind	the	scenes”
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Agenda
• Introduction:	Fog	architectures
• Data	streaming	basics
• Trade-offs	and	challenges	in	Fog	streaming	analysis
• Conclusions
• Bibliography
12The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Motivation
Applications	in	sensor	networks,	cyber-physical	
systems:
• large	and	fluctuating	volumes	of	data	generated	
continuously
demand	for:
• Continuous	 processing	of	data	streams
• In	a	real-time	fashion
Store-then-process	is	not	feasible!!!
13The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Main	Memory
Motivation
DBMS	vs.	DSMS
Disk
1 Data
Query	Processing
3 Query	
results
2 Query
Main	Memory
Query	Processing
Continuous
Query
Data
Query	
results
14The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Before	we	start...	about	data	streaming	and	Stream	Processing	Engines	(SPEs)
15
An	incomplete,	non-sorted	list	of	SPEs	(cf.	related	work	in	[18]):
time
Borealis
The Aurora Project
STanfordstREamdatAManager
NiagaraCQ
COUGAR
StreamCloud
Covering	all	of	them	/	discussing	which	use	cases	are	best	for	each	one	out	of	scope...	
will	focus	on	the	ones	I	work	with	and	on	example	from	vehicular	networks
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
data	stream:	unbounded	sequence	of	tuples	sharing	the	same	schema
16
Example:	vehicles’	speed	reports
time
Field Field
vehicle	id text
time	(secs) text
speed	(Km/h) double
X	coordinate double
Y	coordinate double
A 8:00 55.5 X1 Y1
Let’s	assume	each	source	
(e.g.,	vehicle)	produces	
and	delivers	a	timestamp	
sorted	stream
A 8:07 34.3 X3 Y3
A 8:03 70.3 X2 Y2
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
continuous	query	(or	simply	query):	Directed	Acyclic	Graph	(DAG)	of	
streams	and	operators
17
OP
OP
OP
OP OP
OP OP
source	op	
(1+	out	streams)
sink	op	
(1+	in	streams)
stream
op	
(1+	in,	1+	out	streams)
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
data	streaming	operators
Two	main	types:
• Stateless	operators
• do	not	maintain	any	state
• one-by-one	processing
• if	they	maintain	some	state,	such	state	does	not	evolve	depending	
on	the	tuples	being	processed
• Stateful	operators
• maintain	a	state	that	evolves	depending	on	the	tuples	being	
processed
• produce	output	tuples	that	depend	on	multiple	input	tuples
18
OP
OP
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
stateless	operators
19
Filter
...
Map
Union
...
Filter	/	route	tuples	based	on	one	(or	more)	conditions
Transform	each	tuple
Merge	multiple	streams	(with	the	same	schema)	into	one
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
stateless	operators
20
Filter
...
Map
Union
...
source:	http://storm.apache.org/releases/2.0.0-SNAPSHOT/Trident-tutorial.html
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
stateless	operators
21
Filter
...
Map
Union
...
source:	https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/streaming/index.html
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
stateless	operators
22
Filter
...
Map
Union
...
source:	http://spark.apache.org/docs/latest/streaming-programming-guide.html#transformations-on-dstreams
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
stateful	operators
23
Aggregate	information	from	multiple	tuples	
(e.g.,	max,	min,	sum,	...)
Join	tuples	coming	from	2	streams	given	a	certain	predicate
Aggregate
Join
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
stateful	operators
24
source:	http://storm.apache.org/releases/2.0.0-SNAPSHOT/Trident-tutorial.html
source:	http://spark.apache.org/docs/latest/streaming-programming-
guide.html#transformations-on-dstreams
source:	http://spark.apache.org/docs/latest/streaming-programming-
guide.html#transformations-on-dstreams
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Wait	a	moment!	
if	streams	are	unbounded,	how	can	we	aggregate	or	join?
25The	data	streaming	paradigm	and	its	use	in	Fog	architectures
windows and	stateful	analysis	[18]
Stateful	operations	are	done	over	windows:
• Time-based	(e.g.,	tuples	in	the	last	10	minutes)
• Tuple-based	(e.g.,	given	the	last	50	tuples)
26
time
[8:00,9:00)
[8:20,9:20)
[8:40,9:40)
Usually	applications	rely	on	time-based	sliding	windows
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
time-based	sliding	window	aggregation	(count)
27
Counter:	4
time
[8:00,9:00)
8:05 8:15 8:22 8:45 9:05
Output:	4
Counter:	1
Counter:	2
Counter:	3
Counter:	3
time
8:05 8:15 8:22 8:45 9:05
[8:20,9:20)
we	assumed	each	source	
produces	and	delivers	a	
timestamp	sorted	stream!
What	happens	if	this	is	not	
the	case?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
time-based	sliding	window	joining
28
t1
t2
t3
t4
t1
t2
t3
t4
R S
Sliding	
window Window	
size WS
WSWR
Predicate	P
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
29
windows and	stateful	analysis
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
30
basic	operators	and	user-defined	operators
Besides	a	set	of	basic	operators,	SPEs	usually	allow	the	user	to	define	
ad-hoc	operators	(e.g.,	when	existing	aggregation	are	not	enough)
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
sample	query
For	each	vehicle,	raise	an	alert	if	the	speed	of	the	latest	report	is	more	
than	2	times	higher	than	its	average	speed	in	the	last	30	days.
31
time
A 8:00 55.5 X1 Y1 A 8:07 34.3 X3 Y3
A 8:03 70.3 X2 Y2
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
32
Remove	
unused	fields
Map
Field
vehicle	id
time	(secs)
speed	(Km/h)
X	coordinate
Y	coordinate
Field
vehicle	id
time	(secs)
speed	(Km/h)
Compute	average	
speed	for	each	
vehicle	during	the	
last	30	days
Aggregate
Field
vehicle	id
time	(secs)
avg	speed	(Km/h)
Join
Check	
condition
Filter
Field
vehicle	id
time	(secs)
speed	(Km/h)
Join	on	
vehicle	id
Field
vehicle	id
time	(secs)
avg	speed	(Km/h)
speed	(Km/h)
sample	query
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
33
M A J F
sample	query
Notice:
• the	same	semantics	can	be	defined	in	several	ways	(using	different	
operators	and	composing	them	in	different	ways)
• Using	many	basic	building	blocks	can	ease	the	task	of	distributing	
and	parallelizing	the	analysis	(more	in	the	following...)
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Agenda
• Introduction:	Fog	architectures
• Data	streaming	basics
• Trade-offs	and	challenges	in	Fog	streaming	analysis
• Conclusions
• Bibliography
34The	data	streaming	paradigm	and	its	use	in	Fog	architectures
1. Distributed	deployment
2. Parallel	deployment
3. Ordering	and	determinism
4. Shared-nothing	vs	shared-memory	parallelism
5. Load	balancing
6. Elasticity
7. Fault	tolerance
8. Data	sharing	(differential	privacy)
35
8 aspects	/	use	cases	to	discuss	challenges	(and opportunities)
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Before	we	start...	
36
Following	examples	are	from	vehicular	networks
Road-side	unit
RSU
Vehicle
Server
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
1	- Distributed	deployment	– where	to	place	a	given	operator?	[19]
37
M
1. What	hardware	is	available	at	each	possible	
deployment	location?
2. What	data	is	available	at	each	possible	
deployment	location,	depending	on
1. the	infrastructure?
2. its	security	regulations?
3. its	privacy	regulations?
?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
1	- Distributed	deployment	– where	to	place	a	given	operator?
38
M
1. Low	latency	(if	all	the	required	data	is	locally	
accessible)
2. Access	to	all	local	data	(parts	of	which	might	
not	be	reported	to	RSUs	/	Servers)	[4]
3. Embedded	devices	/	limited	computational	
power	[20]
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
1	- Distributed	deployment	– where	to	place	a	given	operator?
39
M
1. Access	to	multiple	vehicles	/	high	mobility
2. Access	to	smaller	sets	of	data	/	partial	view	of	
the	data	[22]
3. Embedded	devices	/	higher	computational	
power
4. Wireless	/	wired	communication
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
1	- Distributed	deployment	– where	to	place	a	given	operator?
40
1. Global	view	of	the	data	(when	possible	[22])
2. Access	to	aggregated	data
3. High	computational	power
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
M
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
41
If	a	parallel	operator	(e.g.,	M)	feeds	multiple	downstream	
parallel	operators	(e.g.,	A),	how	do	we	route	tuples?	[23,24]
M
M A
A
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
42
M
M A
A
A 8:00 55.5 X1 Y1
A 8:07 34.3 X3 Y3
A 8:03 70.3 X2 Y2
A 8:00 55.5 X1 Y1
A 8:07 34.3 X3 Y3
A 8:03 70.3 X2 Y2
When	computing	e.g.	
per-vehicle	statistics,	
we	need	to	make	sure	
that	all	the	tuples	
referring	to	the	same	
vehicles	are	sent	to	
the	same	aggregate	
instance
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
• Depending	on	the	stateful	operator	semantic:
• Partition	input	stream	into	buckets
• Each	bucket	is	processed	by	1	node	
• #	buckets	>>	#	nodes
43
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
• Depending	on	the	stateful	operator	semantic:
• Partition	input	stream	into	buckets
• Each	bucket	is	processed	by	1	node	
• #	buckets	>>	#	nodes
Keys
domain
Agg1 Agg2 Agg3
A
D
E
B
C F
44
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
• Depending	on	the	stateful	operator	semantic:
• Partition	input	stream	into	buckets
• Each	bucket	is	processed	by	1	node	
• #	buckets	>>	#	nodes
Keys
domain
Agg1 Agg2 Agg3
A
D
E
B
C F
45
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
46
How	can	we	parallelize	the	analysis	of	e.g.	an	aggregate	when	
the	operations	are	not	performed	 for	disjoint	partitions	of	a	
stream	(e.g.,	for	all	vehicles	rather	than	for	each	vehicle?)
M
M A
A
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
47
2	- Parallel	deployment	– how	do	we	parallelize	the	analysis?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
48
M
M A
A
A 8:00 55.5 X1 Y1
A 8:07 34.3 X3 Y3
A 8:03 70.3 X2 Y2
A 8:00 55.5 X1 Y1
A 8:07 34.3 X3 Y3
A 8:03 70.3 X2 Y2
What	if	tuple	with	
timestamp	8:00	
arrives	after	tuple	
with	timestamp	8:07?
3	– Ordering	and	determinism
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
49
3	– Ordering	and	determinism
if	tuples	that	aggregated	together	are	arbitrarily	interleaved,	
how	to	we	provide	correct	results?
time
[8:00,9:00)
8:05 8:15 8:22 8:45 9:05
Output:	4
time
[8:00,9:00)
8:05 8:15 8:22 8:45 9:058:55
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
50
3	– Ordering	and	determinism	– merge-sorting	[25]	&	buffering	[26,27]
addTuple(tuple,sourceID)
allows a tuple from sourceID to be merged by ScaleGate in the
resulting timestamp-sorted stream of ready tuples.
getNextReadyTuple(readerID)
provides to readerID the next earliest ready tuple that has not been
yet consumed by the former.
https://github.com/dcs-chalmers/ScaleGate_Java
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
4	– shared-nothing	vs.	shared-memory	parallelism	[28]
51
M
M
...
A
A
J
...
How	to	take	advantage	of	multi-core	
architectures?
How	to	boost	inter-node	parallelism	
and	intra-node	parallelism?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Shared-nothing	parallel	stream	join
Prod	
R
Prod	
S
PU1
PU2
PUN…
Cons
Add	tuple	to	PUi S
Add	tuple	to	PUi R
Consume	results
Pick	the	next	ready	tuple	t:
1. compare	t	with	all	tuples	in	opposite	window	given	P
2. add	t	to	its	window
3. remove	stale	tuples	from	t’s	window
Chose	a	PU
Chose	a	PU
Take	the	next	
ready	output	 tuple
Scalability
High	
throughput
Low	latency
Disjoint	
parallelism
Skew	
resilience
Determinism
52
Merge
4	– shared-nothing	vs.	shared-memory	parallelism
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Prod	
R
Prod	
S
PU1
PU2
PUN
…
53
enqueue()
dequeue()
ConsMerge
Shared-nothing	parallel	stream	join
4	– shared-nothing	vs.	shared-memory	parallelism
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Prod	
R
Prod	
S
PU1
PU2
PUN
…
Cons
Add	tuple	SGin
Add	tuple	SGin
Get	next	ready	
output	tuple
from	SGout
Get	next	ready	input	tuple	from	SGin
1. compare	t	with	all	tuples	in	opposite	window	given	P
2. add	t	to	its	window	in	a	round-robin	fashion
3. remove	stale	tuples	from	t’s	window
54
SGin SGout
Steps	for	PU
Shared-memory	parallel	stream	join
4	– shared-nothing	vs.	shared-memory	parallelism
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
55
t1
t2
R S
WR
t3
t4
R S
t4
t1
WR
R S
t4
t2
WR
R S
t4
WR
t3
Sequential	stream	join:
ScaleJoin	with	3	PUs:
Shared-memory	parallel	stream	join
4	– shared-nothing	vs.	shared-memory	parallelism
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Prod	
R
Prod	
S
PU1
PU2
PUN
…
Cons
Add	tuple	SGin
Add	tuple	SGin
Get	next	ready	
output	tuple
from	SGout
56
SGin SGout
Scalability
High	
throughput
Low	latency
Disjoint	
parallelism
Skew	
resilience
Determinism
Prod	
S
Prod	
S
Prod	
R
Get	next	ready	input	tuple	from	SGin
1. compare	t	with	all	tuples	in	opposite	window	given	P
2. add	t	to	its	window	in	a	round	robin	fashion
3. remove	stale	tuples	from	t’s	window
Steps	for	PUi
Shared-memory	parallel	stream	join
4	– shared-nothing	vs.	shared-memory	parallelism
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
5	– load	balancing	&	state	transfer	[23,29]
57
If	we	shift	the	processing	of	a	certain	subset	of	
tuples	from	node	A	to	node	B,	how	do	transfer	
its	previous	state?
M
A
M
A
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
State	transfer	challenging	for	stateful	operators
A B
time
58
5	– load	balancing	&	state	transfer
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Window	Recreation	Protocol	[23]
A B
time
A
A
B
Send	to	A
Send	to	B
59
5	– load	balancing	&	state	transfer
+ No	communication	
between	nodes
- Completion	time	
proportional	to	window	
size
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
State	Recreation	Protocol
A B
time
B
B
B
Copy	to	B
60
Tuples	referring
to	caller	A
5	– load	balancing	&	state	transfer
+ Minimizes	completion	
time
- Communication	
between	nodes
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
6	– elasticity	[23,30]
61
How	/	when	to	provision	or	
decommission	new	resources	depending	
on	the	analysis’	costs	fluctuations?
J
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
J J
J
Target	utilization	threshold
Upper	utilization	threshold
Lower	utilization	threshold
0%
100%
CPU	consumption
Memory
Latency
...
………
OP OP OPOP OP
Elasticity	and	load	balancing	actions	on	per-subcluster	basis
62
6	– elasticity	[23]
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
63
6	– elasticity
• How	many	resources	should	be	provisioned	/	
decommissioned?	[23]
• How	to	mix	load	balancing	and	provisioning	/	
decommissioning?	[23]
• How	to	chose	which	resources	to	provision	/	
decommission?	[30]
• How	to	predict	when	more	/	less	resources	will	
be	needed?	[30]
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Load-aware	provisioning
1 2 4 7 11 2617
64
6	– elasticity	[23]
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
7	– fault	tolerance	[related	work	in	18,	31,	32]
65
How	to	replace	a	failed	node	minimizing	
recovery	time	(making	it	transparent	to	
the	end	user)?
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
J
J
J
J
J
J
Active	standby
Primary
Replica
AM
A
66
Cost:
Recovery
Time:
7	– fault	tolerance
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Passive	standby
Primary
Replica
AM
A
Periodic
checkpoints
67
Cost:
Recovery
Time:
7	– fault	tolerance
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Passive	standby
Primary
AM
Periodic
checkpoints
68
Cost:
Recovery
Time:
Disk
7	– fault	tolerance
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Upstream	Backup
AM
Buffer
69
Cost:
Recovery
Time:
7	– fault	tolerance
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Fault	tolerance	and	elasticity	can	also	be	integrated	
together	in	solution	that	externalize	the	management	
of	state	from	the	SPE	[32]
70
7	– fault	tolerance
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
8 – data	sharing	(differential	privacy)	[2,33,34,35]
71
How	to	prevent	privacy	leaks?
Suppose	we	are	interested	in	publishing	vehicles’	
average	speed	over	a	window	of	one	hour...
We	could	aggregate	by	RSU!
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
8 – data	sharing	(differential	privacy)
72
Wait	a	moment!	
what if	a	single	vehicle	is	
connected	to	a	certain	RSU?
Whether	a	certain	mechanism	preserves	or	not	the	privacy	
of	the	underlying	data	depends	on	the	knowledge	of	the	
adversary
Differential	privacy	assumes	the	worst	case	scenario!
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
73
8 – data	sharing	(differential	privacy)
time
[8:00,9:00)
22.3	Km/h 10	Km/h ? 15Km/h
avg	speed:	30	Km/h
without	differential	privacy,	if													knows	the	speed	of												,											and												
and	the	avg	speed,	then	it	can	find	out	the	speed	of	
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
74
8 – data	sharing	(differential	privacy)
time
[8:00,9:00)
22.3	Km/h 10	Km/h ? 15Km/h
avg	speed:	30	Km/h
with	differential	privacy,	if													knows	the	speed	of												,											and												,	it	does	
gain	extra	knowledge	from	the	avg	speed	in	order	to	find	the	speed	of	
+	noise	(calibrated	from	probabilistic	distribution)	so	
that	whether													participates	or	not,	the	results	
will	be	nearly	the	same
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
75
8 – data	sharing	(differential	privacy)
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Agenda
• Introduction:	Fog	architectures
• Data	streaming	basics
• Trade-offs	and	challenges	in	Fog	streaming	analysis
• Conclusions
• Bibliography
76The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Introduction:	Fog	architectures
77
• where	do	we	process	data?
• how	do	we	process	data?
• how	do	we	enforce	security	and	privacy?
...	cannot	really	answer	to	these	questions	
independently	...
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Fog	architectures,	applications	trade-offs	
and	challenges
78
Sensor/Edge	Devices Fog	Devices Cloud	Devices
Cost + ++ +++
Computational	power + ++ +++
Inter-node	heterogeneity +++ ++ +
Intra-node heterogeneity + + +++
Size +++ ++ +
Communication wireless wireless/wired wired
Volume	of	data millions	of	small	streams thousands of	medium	aggregated	streams many	large	streams
Evolution	over	time +++ ++ +
The	data	streaming	paradigm	and	its	use	in	Fog	architectures
Agenda
• Introduction:	Fog	architectures
• Data	streaming	basics
• Trade-offs	and	challenges	in	Fog	streaming	analysis
• Conclusions
• Bibliography
79The	data	streaming	paradigm	and	its	use	in	Fog	architectures
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The	data	streaming	paradigm	and	its	use	in	Fog	architectures 84
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