1. EEC 688/788 Secure and Dependable Computing Lecture 9 Wenbing Zhao Department of Electrical and Computer Engineering Cleveland State University wenbing@ieee.org

2. Data and Service Replication Replication resorts to the use of space redundancy to achieve high availability  Instead of running a single copy of the service, multiple copies are used  Usually deployed across a group of physical nodes for fault isolation Data and service replication  Usually use different approaches  Transactional data replication  Optimistic replication (omitted)  Balance consistency and performance: CAP theorem (omitted)

3. Data and Service Replication Service replication: State machine replication  Each replica is modeled as a state machine: state, interface, deterministic state change via interface  Replica consistency issue: coordination needed Total order of requests to the server replicas Sequential execution of requests Data replication:  Direct access on data  Operation on data: read or write  Context: transaction processing => concurrent access to replicated data essential

4. Service Replication State is encapsulated Clients interact with exported interfaces (APIs) Replication algorithm used to coordinate replicas (for consistency) Fault tolerance middleware

5. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Replication Styles Active replication  Every input (request) is executed by every replica  Every replica generates the outputs (replies)  Voting is needed to cope with non-fail-stop faults Passive replication  One of the replicas is designated as the primary replica  Only the primary replica executes requests  The state of the primary replica is transferred to the backups periodically or after every request processing Semi-active replication  One of the replicas is designated as the leader (or primary)  The leader determines the order of execution  Every input is executed by every replica per the leader’s instruction

6. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Duplicate Invocation Suppressed Duplicate Responses Suppressed Active Replication Actively Replicated Client Object A Actively Replicated Server Object B RM

7. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Active Replication with Voting Question: to cope with f number of faults (non-malicious), how many replicas are needed?

8. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao State Transfer State Response Invocation Passive Replication Passively Replicated Client Object A Passively Replicated Server Object B Primary Replica Primary Replica RM Question: can passive replication tolerate non-fail-stop faults?

9. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Ordering info Response Invocation Semi-Active Replication Semi-Actively Replicated Client Object A Semi-Actively Replicated Server Object B Primary Replica Primary Replica RM

10. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Ensuring Strong Replica Consistency Implementation of Service Replication: Ensuring Strong Replica Consistency For active replication,  use a group communication system or a consensus algorithm that guarantees total ordering of all messages (plus deterministic processing in each replica) Passive replication with systematic checkpointing Semi-active replication Use two-phase commit

11. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Total Ordering of Messages What is total ordering of messages?  All replicas receive the same set of messages in the same order  Atomic multicast – If a message is delivered to one replica, it is also delivered to all non-faulty replicas With replication, we need to ensure total ordering of messages sent by a group of replicas to another group of replicas  FIFO ordering between one sender and a group is not sufficient m1 m2 m1 m2 m1

12. 2/18/2016 EEC688/788: Secure & Dependable Computing Wenbing Zhao Potential Sources of Non-determinisms Multithreading  The order of accesses of shared data by different threads might not be the same at different replicas System calls/library calls  A call at one replica might succeed while the same call might fail at another replica. E.g., memory allocation, file access Host/process specific information  Host name, process id, etc.  Local clocks - gettimeofday() Interrupts  Delivered and handled asynchronously – big problem

13. Data Replication Transactional data replication  Read/write ops on a set of data items within the scope of a transaction  At the transaction level, executions appear to be sequential (One-copy serializable)  Actual ops on each data item often concurrent Optimistic data replication  Eventual consistency: eventually, all updates will be propagated to all data items

14. Transactional Data Replication One-copy serializable  A transactional data replication algorithm should ensure that the replicated data appear to the clients as a single copy  The interleaving of the execution of the transactions be equivalent to a sequential execution of those transactions on a single copy of the data. Make read ops cheaper than updates: read ops are more prevalent It is challenging to design sound replication algorithms

15. Wrong Data Replication Algorithms Write-all  A read op on a data item x can be mapped to any replica of x  Write on x must be applied to all replicas of x Problem: what if a replica becomes faulty?  Blocking! Any single replica fault => bring down the entire system!

16. Wrong Data Replication Algorithms Write-all-available  A read op on a data item x can be mapped to any replica of x  Write on x is applied to available replicas of x Problem: cannot ensure one-copy serializable execution!

17. Attempting to Fix Write-All-Available Problem caused by accessing the not-fully-recovered replica => how about preventing this? Still won’t work  Ti does not precedes Tj because Tj reads y before Ti writes to y  Tj does not precedes Ti because Ti reads x before Tj writes to x  Ti: R(x), W(y)  Tj: R(y), W(x)  Hence, Ti and Tj are not serializable!

19. Insight to the Problem The problem is caused by the fact that conflicting operations are performed at difference replicas We must prevent this from happening A solution: use quorum-based consensus What is a quorum?  Given a system with n processes, a quorum is formed by a subset of the processes in the system  Any two quorums must intersect in at least one process  Read quorum: a quorum formed for read ops  Write quorum: a quorum formed for write ops

20. A Quorum-Based Replication Algorithm Basic idea:  Write ops apply to a write quorum  Read ops apply to a read quorum  Fault tolerance: given total number replicas N and write quorum size W (>= read quorum size R), can tolerate up to N-W failures Quorum rule  Each replica assigned a positive weight, e.g., 1  A read quorum has a min total weight RT  A write quorum has a min total weight WT  RT+WT > total weight && 2WT > total weight

21. A Quorum-Based Replication Algorithm Since update is applied to a quorum of replicas, we need to track which replica has the latest value => use version numbers  Version number is incremented after each update Read rule  A read on data x is mapped to a read quorum replicas of x  Each replica returns both the value of x and its version number  The client select the value that has the highest version number

22. A Quorum-Based Replication Algorithm Write rule  A write op on data x is mapped to a write quorum replicas of x  First, retrieve version numbers from the replicas, set v=v max +1 for this write op  Write to the replicas (in the write quorum) with new value and version # v. A replica overwrites both the value and version number v

23. Quorum-Based Replication Algorithm: Example