Virtual Machine Replication on Achieving Energy-Efficiency in a Cloud

The rapid growth in cloud service demand has led to the establishment of large-scale virtualized data centers in which virtual machines (VMs) are used to handle user requests for service. A user’s request cannot be completed if the VM fails. Replication mechanisms can be used to mitigate the impact of failures. Further, data centers consume a large amount of energy resulting in high operating costs and contributing to significant greenhouse gas (GHG) emissions. In this paper, we focus on Infrastructure as a Service (IaaS) cloud where user job requests are processed by VMs and analyze the effectiveness of VM replications in terms of job completion time performance as well as energy consumption. Three different schemes: cold, warm, and hot replications are considered. The trade-offs between job completion time and energy consumption in different replication schemes are characterized through comprehensive analytical models which capture VM state transitions and associated power consumption patterns. The effectiveness of replication schemes are demonstrated through experimental results. To verify the validity of the proposed analytical models, we extend the widely used cloud simulator CloudSim and compare the simulation results with analytical solutions

Defects per Million Computation in Service-Oriented Environments

Traditional system-oriented dependability metrics like reliability and availability do not fully reflect the impact of system failure-repair behavior in service-oriented environments. The telecommunication systems community prefers to use Defects Per Million (DPM), defined as the number of calls dropped out of a million calls due to failures, as a user-perceived dependability metric. In this paper, we provide new formulation for the computation of the DPM metric for a system supporting Voice over IP functionality using the Session Initiation Protocol (SIP). We evaluate different replication schemes that can be used at the SIP application server. They include the effects of software failure, failure detection, recovery mechanisms, and imperfect coverage for recovery mechanisms. We derive closed-form expressions for the DPM taking into account the transient behavior of recovery after a failure. Our approach and underlying models can be readily extended to other types of service-oriented environments