EEC 688/788 Secure and Dependable Computing Lecture 9 Wenbing Zhao Department of Electrical and Computer Engineering Cleveland State University wenbing@ieee.org Maulik no show 10/5/2009

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)

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

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

EEC688/788: Secure & Dependable Computing 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 1/3/2019 EEC688/788: Secure & Dependable Computing

EEC688/788: Secure & Dependable Computing Active Replication Actively Replicated Client Object A Actively Replicated Server Object B Duplicate Invocation Suppressed Duplicate Responses Suppressed RM RM RM RM RM 1/3/2019 EEC688/788: Secure & Dependable Computing

Active Replication with Voting Question: to cope with f number of faults (non-malicious), how many replicas are needed? 1/3/2019 EEC688/788: Secure & Dependable Computing

EEC688/788: Secure & Dependable Computing Passive Replication Passively Replicated Client Object A Passively Replicated Server Object B Primary Replica Primary Replica Response Invocation State Transfer State RM RM RM RM RM Question: can passive replication tolerate non-fail-stop faults? 1/3/2019 EEC688/788: Secure & Dependable Computing

Semi-Active Replication Semi-Actively Replicated Client Object A Semi-Actively Replicated Server Object B Primary Replica Primary Replica Response Invocation Ordering info RM RM RM RM RM 1/3/2019 EEC688/788: Secure & Dependable Computing

EEC688/788: Secure & Dependable Computing 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 1/3/2019 EEC688/788: Secure & Dependable Computing

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 1/3/2019 EEC688/788: Secure & Dependable Computing

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 Not required 1/3/2019 EEC688/788: Secure & Dependable Computing

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

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

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!

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!

Attempting to Fix Write-All-Available Problem caused by accessing the not-fully-recovered replica => how about preventing this?

Attempting to Fix Write-All-Available 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!

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

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 What if RT=1? WT would include all replicas => not fault tolerant!

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

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=vmax+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

Quorum-Based Replication Algorithm: Example

Exercise 1: Quorum-based data replication Consider the following replicas from R1 to R5 with the (Value, Version number) sets respectively as R1 (0,0), R2 (0,0), R3 (0,0), R4 (0,0), R5 (0,0). The following sequence of read/write operations between replicas are performed as follows Read operation on R1,R2,R3 Write operation on R3, R4, R5 with a value 2 Read operation on R4, R3, R2 Write operation on R4, R5, R1 with a value 5 Write operation on R2,R4,R5 with a value 7 Give the final values from R1 to R5 after all the above operations are performed?

Exercise 2: Quorum-based data replication Data replication. Assume we have a distributed system with 6 replicas. If the read quorum has size of 3, what is the minimum size of write quorum? Assume a read quorum consists of 2 replicas. A read operation on data x is mapped to two replicas with one replica has value 2 and version number 1, and the other replica has value 3 and version number 2. Which value will be selected?