16-07-2012, 01:20 PM
Adaptive Fault Tolerance in Real Time Cloud Computing
Adaptive Fault Tolerance in Real Time Cloud Computing.pptx (Size: 305.05 KB / Downloads: 87)
introduction
Cloud support for real time system is really important because, today we found a lot of real time systems around us.
Use of cloud infrastructure for real time applications increases the chances of errors.
There is an increased need to tolerate the fault.
Here is the model for the fault tolerance of real time applications running at cloud infrastructure.
Literature survey
Present System
X. Kong et. al. [1, 5] presented a model for virtual infrastructure performance and fault tolerance. But it is not well suited for the fault tolerance of real time cloud applications.
For the non-cloud applications, a baseline model for distributed RTS is, distributed recovery block [11] proposed by K. H. Kim which is very basic in nature.
Another model is “time stamped fault tolerance of distributed RTS” [9], which is proposed by S.Malik and M. J. Rehman.
All of these models were defined for the real time systems based on standard computing architecture. No one has introduced the fault tolerance on the basis of reliability of node and reliability assessment of node.
Fault Tolerance Mechanism
Reliability Assessment Algorithm:
Begin
Initially reliability:=1, n :=1
Input from configuration RF, maxReliability,minReliability
Input nodestatus
if nodeStatus =Pass then
reliability := reliability + (reliability * RF)
if n > 1 then
n := n-1;
else if nodeStatus = Fail then
reliability := reliability – (reliability * RF * n)
n := n+1;
if reliability >= maxReliability then
reliability := maxReliability
if reliability < minReliability then
nodeStatus :=dead
call_proc: remove_this_node
call_proc: add_new_node
End
Contnd…..
Decision Mechanism Algorithm
Begin
Initially reliability:=1, n :=1
Input from RA nodeReliability, numCandNodes
Input from configuration SRL
bestReliability := find_reliability of node with highest
reliability
if bestReliability >= SRL
status := success
else
perform_backward_recovery
call_proc: remove_node_minReliability
call_proc: add_new_nod
End
Here we can see that convergence towards decrement in reliability is much higher.
Advantages:
It has a dynamic behaviour of reliability configuration.
The scheme is highly fault tolerant.
The reason behind adaptive reliability is that the scheme can take advantage of dynamic scalability of cloud infrastructure.
This system takes the full advantage of using diverse software.
Probability of failure is very less in our devised scheme.
The system assures the reliability by providing the backward recovery.
Conclusion
The proposed scheme is a good option to be used as a fault tolerance mechanism for real time computing on cloud infrastructure.
The reason behind adaptive reliability is that the scheme can take advantage of dynamic scalability of cloud infrastructure.
Probability of failure is very less in our devised scheme.
It does not suffer from domino effect as check pointing is made in the end when all the nodes have produced the result.