09-10-2014, 09:59 AM
Consistency as a Service:
Auditing Cloud Consistency
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. INTRODUCTION
CLOUD computing has become commercially popular,
as it promises to guarantee scalability, elasticity, and
high availability at a low cost [1], [2]. Guided by the trend
of the everything-as-a-service (XaaS) model, data storages,
virtualized infrastructure, virtualized platforms, as well as
software and applications are being provided and consumed as
services in the cloud. Cloud storage services can be regarded
as a typical service in cloud computing, which involves the
delivery of data storage as a service, including database-like
services and network attached storage, often billed on a utility
computing basis, e.g., per gigabyte per month. Examples
include Amazon SimpleDB1, Microsoft Azure storage2, and
so on. By using the cloud storage services, the customers
can access data stored in a cloud anytime and anywher
PRELIMINARIES
In this section, we first illustrate the consistency as a service
(CaaS) model. Then, we describe the structure of the user
operation table (UOT), with which each user records his
operations. Finally, we provide an overview of the two-level
auditing structure and related definitions
DISCUSSION
In this section, we will discuss some additional issues
about CaaS in terms of the election of an auditor and other
consistency models.
A. Election of An Auditor
In section III, an auditor is simply elected from the auditor
cloud in turn, where each user becomes the auditor with the
same probability. However, different users have different levels
of ability in terms of available bandwidth, CPU, and Memory
of clients. The users with a higher ability should have a higher
probability of being selected as auditor. In this section, we
provide a more comprehensive solution to elect an auditor
as follows: We construct an ID ring for a group of users,
where each node is associated with a node ID, and each user
is denoted by a set of nodes in the ring. Suppose the number
of nodes in the ring is n. To elect an auditor, we can randomly
generate a number r, and let the user who is denoted by the
node with an ID of (r mod n) in the ring to be the auditor.
Note that the selection of each user does not have to be
uniform. The number of nodes associated with a user can be
determined by his abilities, e.g., the capability of his client, his
trusted rank, and so on. In this way, the probability of a user
with a higher ability of being chosen as the auditor becomes
higher. For example, given 3 users and 6 nodes, user Alice
is denoted by 3 nodes, user Bob is denoted by 2 nodes, and
user Clark is denoted by 1 node. Therefore, the probability of
Alice being the auditor is 50%, for Bob it is 33%, and for
Clark it is 17%.
X. CONCLUSION
In this paper, we presented a consistency as a service
(CaaS) model and a two-level auditing structure to help users
verify whether the cloud service provider (CSP) is providing
the promised consistency, and to quantify the severity of
the violations, if any. With the CaaS model, the users can
assess the quality of cloud services and choose a right CSP
among various candidates, e.g, the least expensive one that
still provides adequate consistency for the users’ applications.
For our future work, we will conduct a thorough theoretical
study of consistency models in cloud computing.