  Cloud computing has become inevitable for every digital service which has
exponentially increased its usage. However, a tremendous surge in cloud
resource demand stave off service availability resulting into outages,
performance degradation, load imbalance, and excessive power-consumption. The
existing approaches mainly attempt to address the problem by using multi-cloud
and running multiple replicas of a virtual machine (VM) which accounts for high
operational-cost. This paper proposes a Fault Tolerant Elastic Resource
Management (FT-ERM) framework that addresses aforementioned problem from a
different perspective by inducing high-availability in servers and VMs.
Specifically, (1) an online failure predictor is developed to anticipate
failure-prone VMs based on predicted resource contention; (2) the operational
status of server is monitored with the help of power analyser, resource
estimator and thermal analyser to identify any failure due to overloading and
overheating of servers proactively; and (3) failure-prone VMs are assigned to
proposed fault-tolerance unit composed of decision matrix and safe box to
trigger VM migration and handle any outage beforehand while maintaining desired
level of availability for cloud users. The proposed framework is evaluated and
compared against state-of-the-arts by executing experiments using two
real-world datasets. FT-ERM improved the availability of the services up to
34.47% and scales down VM-migration and power-consumption up to 88.6% and
62.4%, respectively over without FT-ERM approach.
