Analyzing consistency properties for fun and profit

Abstract

Motivated by the increasing popularity of eventually consistent keyvalue stores as a commercial service, we address two important problems related to the consistency properties in a history of operations on a read/write register (i.e., the start time, finish time, argument, and response of every operation). First, we consider how to detect a consistency violation as soon as one happens. To this end, we formulate a specification for online verification algorithms, and we present such algorithms for several well-known consistency properties. Second, we consider how to quantify the severity of the violations, if a history is found to contain consistency violations. We investigate two quantities: one is the staleness of the reads, and the other is the commonality of violations. For staleness, we further consider time-based staleness and operation-count-based staleness. We present efficient algorithms that compute these quantities. We believe that addressing these problems helps both key-value store providers and users adopt data consistency as an important aspect of key-value store offerings.

Overview of Auditing Cloud Consistency

“…The violations are measure on two factors: commonality and staleness of the value of read. [9] Finally HAS (Heuristic Auditing Strategy) use to add appropriate reads to find out as many as violations possible.…”

“…Existing solutions can be categorized into trace-based verifications [7], [9] and benchmark-based verifications [15]- [18]. Trace-based verifications focus on three consistency semantics: safety, regularity.…”

“…Ref. [9] offered an online verification algorithm by using the GK algorithm [20], and Used diverse metrics to enumerate the brutality of violations.…”

Maintain the Consistency of Auditing Cloud

“…[1]. from the users' point of view we check the level of consistency provided by cloud service provider .existing solution can be derived into 2 types benchmark-based verifications [5]- [8]and traced base verification [2], [4]. Trace-based verifications contain three consistency semantics:, Lamport who propose these 3 semantic regularity, atomicity and safety…”

“…Ref. [4]he proposed an online verification algorithm by using the GK algorithm [7], and various metrics used to quantify the severity of u n s u c c e s s f u l r e s u l t . The main drawback of the existing trace-based verifications is that a global clock is required among all users.…”