6306 Advanced Operating Systems
Instructor : Dr. Mohan Kumar
Room : 315 NH
[email protected]
Class : TTh 7- 8:20PM
Office Hours : TTh1-3 PM
GTA : Byung Sung
[email protected]
Kumar
CSE@UTA
1
Distributed Systems
R Chow, Distributed Operating Systems
 Provide a conceptually simple model

Efficient, flexible, consistent and robust
 Goals

Efficiency
 Communication delays??
 Scheduling, load balancing
 Flexibility
 Users decide how, where, and when to use the system
 Environment for building additional tools and services
 System’s view - scalability, portability, and interoperability

Consistency


Lack of global information, potential replication and partitioning
of data, component failures and interaction among modules
Robustness
Kumar
CSE@UTA
2
Transparency
 Logical view of a Physical System
 Reduce the effect and awareness of the physical system
 System-dependent information
 Tradeoff between simplicity and effectiveness
 Access transparency –local and remote objects
 Location transparency –objects refs by logical names
 Migration transparency –location independence
 Concurrency transparency
 Replication transparency
 Parallelism, Failure, Performance, Size (modularity
and scalability), Revision (software)
Kumar
CSE@UTA
3
Transparency
System Goals
Efficiency
Transparencies
Flexibility
Access, Location,
Migration, size, revision
Consistency
Access
Replication
Performance
Robustness
Failure
Replication
Size
Revision
Kumar
Concurrency
Parallelism
Performance
CSE@UTA
4
Transparency
Major Issues
Transparencies
Communication
Synchronization
Distributed algorithms
Process scheduling
Deadlock handling
Load balancing
Resource scheduling
File sharing
Concurrency control
Failure handling
Configuration
Redundancy
Interaction and control
transparency
Kumar
Performance transparency
Resource transparency
Failure transparency
CSE@UTA
5
Services
 Primitive
 Message passing primitives – send and receive
 Synchronization
 Process management

Process creation, deletion, tracking, resource allocation
 System servers
 Name server/directory server


Network server



Users,processes, machines,files,ports
Path selection and routing
Broadcast/multicast server
Clock Synchronization
Kumar
CSE@UTA
6
Architecture Models
 Workstation-server model
 Processor-pool model
 Communication network protocols
Kumar
CSE@UTA
7
Distributed Coordination
 Barrier Synchronization
 Condition coordination
 Mutual Exclusion
Interprocess communication
Distributed Resources
Distributed Shared memory
Kumar
CSE@UTA
8
Two Processor Model
Indurkhya and H Stone, IEEE Software, 1997
Consider execution of application program that contains M
tasks on a Two processor (or PE) system
We make the following assumptions to simplify analysis
1. Each task executes in R units of time
all tasks are of equal complexity
2. Each task communicates with every other task with an
overhead cost of C units of time when the communicating
tasks are on different PEs and with zero cost when the
communicating tasks are co-resident
3. Non-overlapped computation/communication
Kumar
CSE@UTA
9
Two PE model
(Contd.)
(k)
(M-k)
Kumar
CSE@UTA
10
Two PE model (Contd.)
* Assign all tasks to one PE or
* Partition tasks among the two PEs
Total execution time (TET) is the sum of the total computation
time and the communication overheads
Let us assign (M-k) tasks to one PE and k tasks to the other
Computation time = R Maximum (M-k,k)
Communication time = C (M-k)*k
Total Execution time = R * (Max (M-k,k) + C *(M-k)*k
Linear
Quadratic
What is the maximum TET as a function of k?
PA252/502 Week 8
Kumar
CSE@UTA
11
Two PE model (Contd.)
R/C = 10
100
R
80
40
comput
.
comm.
20
TET
60
0
0
10
20
25
30
40
50
k
PA252/502 Week 8
Kumar
CSE@UTA
12
Two PE model (Contd.)
R/C = 40
60
50
30
comput
.
comm.
20
TET
40
10
0
comput.
comm.
TET
0
10
20
25
30
40
50
50
0
50
40
10
50
30
15
45
25
15.63
40.63
30
15
45
40
10
50
50
0
50
Minimum execution time when R/C = M/2
Kumar
CSE@UTA
13
Two PE model (Contd.)
R/C = 25
60
50
comput
.
comm.
40
30
TET
20
10
0
comput.
comm.
TET
0
10
20
25
30
40
50
50
0
50
40
16
56
30
24
54
25
25
50
30
24
54
40
16
56
50
0
50
Kumar
CSE@UTA
14
Two PE model (Contd.)
Total time on one processor = R* M units … (1)
R/C = M/2 is the critical point for 2 processor system
if R/C  M/2 assign all tasks to one Processor
if R/C > M/2 divide tasks equally among all processors
Divide equally among the two processors
Then TET = R(M/2) + C(M-M/2)*M/2 … (2)
To get break even point equate (1) and (2)
R(M/2) + C(M-M/2)*M/2 = R*M,
Simplifying, we get R/C = M/2
Kumar
CSE@UTA
15
Two PE model (Contd.)
If R/C high then C is low, communication
overheads are low,
we are justified in parallelising
If R/C is low then C is high, communication
overheads are high
better to execute on one processor
Speedup = Serial time/Parallel time
> 1 if and only if R/C >M/2
Check with the times
Kumar
CSE@UTA
16
N PE model
All processor share
a common communication
channel
Assign ki tasks to Pei
k1 - PE1
k2 - PE2
…
kN - PEN
k1+ k2+ . . . + kN= M
Computation time = R Max (ki)
Communication time = C/2[k1*(M-k1)+k2*(M-k2)+ . . . +kN*(M-kN)
Kumar
CSE@UTA
17
N PE model
Communication time = C/2[k1*(M-k1)+k2*(M-k2)+ . . . +kN*(M-kN)
= C/2  ki(M-ki)
= C/2[ (M ki) -  (ki2)]
= C/2 [M2 - ki2]
3
3
Kumar
CSE@UTA
18
N PE model
Total Execution time = R* Max(ki) + C/2 [M2 - ki2]
assuming all tasks to be of equal complexity,
we assign M/N tasks per PE
Total execution time = R(M/N)+CM2/2(1-1/N) … (3)
ki2 = N(M/N)2 = M2/N; because, ki = M/N
Equate equation 3 with RM to get the break even point
R(M/N)+CM2/2(1-1/N) = RM
RM(1-1/N) = CM2/2(1-1/N)
R/C = M/2
if R/C  M/2 assign all tasks to one Processor
if R/C > M/2 divide tasks equally among N PEs
Kumar
CSE@UTA
19
N PEs with overlapped communication
The PEs can compute and communicate at the same time
TET = Max {computation time, communication time}
= Max {R* Max(ki), C/2 [M2 - ki2}
assuming all tasks to be of equal complexity,
= Max {R(M/N), (CM2/2)*(1-1/N)}
optimum performance when
computation time = communication overhead
R(M/N) = (CM2/2) * (1-1/N), ignoring 1/N if N is large,
R(M/N) = CM2/2
R/C = MN/2; optimum number of PEs, N = 2R/MC
Kumar
CSE@UTA
20
N PEs with multiple communication links
N Communication channels in the network
Total Execution time = R* Max(ki) + [C/(2N)]* [M2 - ki2]
assuming all tasks to be of equal complexity,
Total execution time = R(M/N)+CM2/2N(1-1/N)
M C
1
RM  R 
(N  )
N 2N
N
1
C
1
RM ( N  ) 
(N  )
N
2N
N
R M

C 2N
Kumar
CSE@UTA
21
N PEs with multiple communication
links
if R/C  M/2N assign all tasks to one Processor
if R/C > M/2N divide tasks equally among N PEs
Speedup = TET on ONE PE / (TET on N PEs)
RN
=
RM
C

N
2
R M ( N  1)
RM CM
1


(1  )
C
2N
N
2N
N
If R/C >> M/2 then speedup increases with N
if R/C << M/2 then speedup is given by,
2NR/MC
Kumar
CSE@UTA
22
Comments on Performance
Multiple number of PEs produce overhead costs
- scheduling
- contention for shared resources
- message passing
- synchronisation
- lack of load balancing
Overhead costs increase with the number of PEs
R/C is a measure of the amount of program execution per
unit of overhead
If R/C is large, Communication overheads are low and the
problem execution is efficient
If R/C is small, Communication overheads are high …..
Kumar
CSE@UTA
23
Active Middleware Services in a Decision Support
System for Managing Highly Available
Distributed Resources
S A Fakhouri, W F Jerome, V K Naik, A Raina and P Varma
 The cluster based system is called Mounties
 Manages resources and applications using rule based
constraints
 Mounties has four service components

Resource proxy objects


Manipulates cluster configuration
Event notification mechanism

Monitors and controls interdependent and distributed resources

Rule Evaluation and Decision processing mechanism
 Global optimization service

Provides decision making capabilities
Kumar
CSE@UTA
24
What’s in the paper?
 Architecture and design of service components
 Interaction between the components and the
decision making component
 General programming paradigm
Kumar
CSE@UTA
25
Cluster?
Nodes,disks,
adapters,databases etc.
Heterogeneous
Components of a
Cluster
Transparent
Modular
Scalable
Achieved by providing coordination and
mapping of physical distributed resources
onto a set of virtual resources and their
services – very complex?
Kumar
CSE@UTA
26
Authors’ approach
 Clusters and resources – 2 dimensions
 Semistatic nature of resource



Type and quality of supporting services needed to enable
its services
Formalized as simple rules
Dynamic state of the services provided by the
cluster – captured by events
 Coordination and mapping – centralized and
rule-based
 Events and rules are combined only when
necessary
Kumar
CSE@UTA
27
Resources
 Attributes
 Unique Name
 Type – functionality
 Capacity – number of dependent sources it
can serve
 Priority (L1 .. 10H)
 State
ONLINE
 OFFLINE
 FAILED

Kumar
CSE@UTA
28
Mounties Approach
 Constraint-based methodology
 cluster configuration, startup and recovery
 Constraints are used build relationships among supporting
and dependent resources/services
 Nominal State – ONLINE, OFFLINE
 Inter-resource relationships

DependsOn
 CollacatedWith
 Anti-CollocatedWith
 Equivalency

Set of resources with similar functionality
– Choose one of these
Kumar
CSE@UTA
29
Mounties..
 Distinct
 Decision making, Resource allocation
 Monitor, control, manipulate
 Example from the paper
 E1: DA0,DA1; E2: NA0,NA1, NA2
Database 1
DO E1, E2
CW E1,E2
Database 1
Database 2
E1: DA0
E1: DA1
E2 : NA0
E2 : NA1
Node 0
Node 1
Kumar
CSE@UTA
30
Mounties..
Webserver
E2: NA2
D : E2,E3
E3: DB1
CW : E2
Node 2
Kumar
CSE@UTA
31
Events and Clusters
 Resource related
 Response to a command
 Enduser interactions/directives
 Alerts/alarms
Resource Specific Constraints
capacity, dependency, location,
Kumar
CSE@UTA
32
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CSE@UTA
33
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CSE@UTA
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