next up previous
Next: Related Work Up: Lazy Consistency Using Previous: Lazy Consistency Using


This paper describes a new algorithm that insures transactions running at clients in a client/server system always view a consistent state as they run. The algorithm is useful in conjunction with optimistic concurrency control mechanisms (e.g., [1]), and also for ensuring that read-only transactions can commit without interfering with read/write transactions in a multi-version system (e.g., [30][7][2]).

The algorithm is intended for use in a distributed environment in which servers provide reliable persistent storage for online information (e.g., a database or objects). Applications run at client machines that are distinct from the servers. Clients maintain caches that store copies of persistent objects; clients run application transactions locally on cached copies of persistent objects and send modifications to the servers when transactions commit. The scheme is designed to work in a very large system, e.g., tens of thousands of servers and hundreds of thousands of clients.

The new algorithm propagates information about consistency using multipart timestamps or _multistamps_. Today the utility of multistamps is limited because their size is proportional to system size and therefore they don't scale to very large systems. We show how to reduce the size of multistamps. Unlike other multistamp (or vector clock) schemes, e.g., [15][8][25][17][4], our scheme is based on time rather than on logical clocks: each entry in a multistamp contains a timestamp representing the clock time at some server in the system. Using time rather than logical clocks allows us to keep multistamps small by removing old information. As a result, multistamps need little storage at nodes and little space in messages. Furthermore, because we prune based on time, the discarded information is likely to already be known to interested parties; therefore discarding it has little impact on system performance. We assume that clocks are loosely synchronized; such an assumption is realistic in today's environment [23]. The correctness of the scheme is not affected if information of interest is pruned too early or clocks get out of synch (although performance may be).

This paper describes the new scheme in conjunction with an optimistic concurrency control algorithm, AOCC, which was developed for use in a distributed client/server environment. AOCC has been shown to outperform other concurrency control mechanisms for all common workloads and realistic system assumptions [13][1]. In particular, AOCC outperforms the best locking approach, adaptive callback locking [6]. One reason for AOCC's good performance is that it allows transactions to use cached information without communication with servers; locking mechanisms require such communication at least when objects are modified. However, the reduced communication comes at a cost: unlike with locking, with AOCC transactions can view an inconsistent state while they run. Such transactions cannot harm the persistent state since they will abort, but it is nevertheless desirable to provide transactions with a consistent view, which allows application programmers to depend on invariants. This means code need not check whether invariants hold (as it would need to do if it could not depend on invariants) and it can display consistent information to users.

The paper describes how to augment AOCC to provide running transactions with consistent views. The consistency provided by our scheme is weaker than serializability; we discuss this point further in Section 3. We call it ``lazy'' consistency because it is what we can provide with very little work. We believe lazy consistency is appropriate for our system since we commit in the standard way (by communicating with the server); a transaction may still abort even though it viewed a consistent state.

The consistency mechanism uses multistamps to warn clients of potential violations of consistency. Multistamps are sent to clients on messages that are already flowing in the system. We guarantee they arrive at clients before a transaction might observe an inconsistency. Clients are also lazy; they act on the multistamp information only if it might affect the current transaction. Being lazy buys time so that the needed consistency information is highly likely to be present by the time it is needed.

The paper presents results of simulation experiments to evaluate the cost of the lazy scheme. Our results show that this cost is very small. The cost is manifested by ``fetch stalls'' in running transactions; these are events where a fetch done by a client because of a cache miss causes it to communicate with another server before continuing to run the transaction, where the communication would not have been required in the basic AOCC scheme. Our results show that when contention is low, fewer than one in a thousand fetches lead to stalls; even in stressful workloads, less than one in one hundred fetches lead to stalls. Therefore we believe the cost of the scheme is negligible. The studies also evaluate the effectiveness of pruning multistamps and show that small multistamps are as effective as larger ones in preventing stalls.

Thus the paper makes three contributions:

  1. It presents a new efficient scheme that provides consistency for running transactions.

  2. It presents a new way of implementing multistamps that allows them to be pruned safely and effectively. This keeps multistamps small so that they can be used even in very large systems where otherwise they would not be practical.

  3. It presents the results of simulation studies that show the scheme provides consistency with almost no impact on system performance.

The remainder of the paper is organized as follows. Section 2 describes related work. Section 3 defines lazy consistency and discusses how it relates to serializability and causality. Section 4 describes our system and how AOCC works; Section 5 describes our implementation of lazy consistency; and Section 6 presents our performance results. We conclude with a summary of our results.

next up previous
Next: Related Work Up: Lazy Consistency Using Previous: Lazy Consistency Using

Atul Adya
Wed Jun 25 15:09:14 EDT 1997