6 research outputs found
Tenant Level Checkpointing of Meta-data for Multi-tenancy SaaS
Traditional checkpointing techniques are facing a grave challenge when applied to multi-tenancy software-as-a-service (SaaS) systems due to the huge scale of the system state and the diversity of users' requirements on the quality of services. This paper proposes the notion of tenant level checkpointing and an algorithm that exploits Big Data techniques to checkpoint tenant's meta-data, which are widely used in configuring SaaS for tenant-specific features. The paper presents a prototype implementation of the proposed technique using NoSQL database Couchbase and reports the experiments that compare it with traditional implementation of checkpointing using file systems. Experiments show that the Big Data approach has a significantly lower latency in comparison with the traditional approach
Fail Over Strategy for Fault Tolerance in Cloud Computing Environment
YesCloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected
system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly
possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the
application execution and hardware performance, various fault tolerant techniques exist for building self-autonomous
cloud systems. In comparison to current approaches, this paper proposes a more robust and reliable architecture using
optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using
pass rates and virtualised mechanisms, the proposed Smart Failover Strategy (SFS) scheme uses components such as
Cloud fault manager, Cloud controller, Cloud load balancer and a selection mechanism, providing fault tolerance via
redundancy, optimized selection and checkpointing. In our approach, the Cloud fault manager repairs faults generated
before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme
is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future
request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and
backward recovery using diverse software tools. Compared to existing approaches, preliminary experiment of the SFS
algorithm indicate an increase in pass rates and a consequent decrease in failure rates, showing an overall good
performance in task allocations. We present these results using experimental validation tools with comparison to other
techniques, laying a foundation for a fully fault tolerant IaaS Cloud environment
Heterogeneous Strong Computation Migration
The continuous increase in performance requirements, for both scientific
computation and industry, motivates the need of a powerful computing
infrastructure. The Grid appeared as a solution for inexpensive execution of
heavy applications in a parallel and distributed manner. It allows combining
resources independently of their physical location and architecture to form a
global resource pool available to all grid users. However, grid environments
are highly unstable and unpredictable. Adaptability is a crucial issue in this
context, in order to guarantee an appropriate quality of service to users.
Migration is a technique frequently used for achieving adaptation. The
objective of this report is to survey the problem of strong migration in
heterogeneous environments like the grids', the related implementation issues
and the current solutions.Comment: This is the pre-peer reviewed version of the following article:
Milan\'es, A., Rodriguez, N. and Schulze, B. (2008), State of the art in
heterogeneous strong migration of computations. Concurrency and Computation:
Practice and Experience, 20: 1485-1508, which has been published in final
form at http://onlinelibrary.wiley.com/doi/10.1002/cpe.1287/abstrac