Subrota K. Mondal

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.

Computing Defects per Million in Cloud Caused by Virtual Machine Failures with Replication

Virtual machines (VM) are used in cloud computing systems to handle user requests for service. A typical user request goes through several cloud service provider specific processing steps from the instant it is submitted until the service is completed. In the process of providing the service, VM failures cause the users request to be dropped. To mitigate the adverse impact of VM failure, replication mechanisms, either using cold, warm or hot replication, can be used. In this paper, we model the system behavior with a structure-state process to characterize the failure-recovery behavior of a VM in a cloud that uses one of the aforementioned replication schemes. We use a service-oriented dependability metric called Defects Per Million (DPM), defined as the number of user requests dropped out of a million. The structure-state process approach is used to analyze the job completion time distribution and subsequently we compute the DPM by counting the number of requests exceed the specified deadline. The effectiveness of replication schemes are demonstrated through numerical results.

On Boosting Cloud Service Dependability through Optimized Checkpointing

Virtual machines (VMs) are used in cloud computing systems to handle user requests for service. Failure of VMs cause that the users request not being completed. Replication mechanisms can be used to mitigate the impact of VM failures. In this paper, we are primarily interested in characterizing the failure-recovery behavior of a VM in cloud with different replication schemes. We use a service-oriented dependability metric called Defects Per Million (DPM) defined as the number of user requests dropped out of a million, due to VM failures. We present an analytical modeling approach for computing the DPM metric in different replication schemes on the basis of structure-state process and checkpointing method. 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.

Service Reliability Enhancement in Cloud by Checkpointing and Replication

Virtual machines (VMs) are used in cloud computing systems 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 VM failures. In this chapter, we are primarily interested in characterizing the failure–recovery behavior of a VM in the cloud under different replication schemes. We use a service-oriented dependability metric called Defects Per Million (DPM), defined as the number of user requests dropped out of a million due to VM failures. We present an analytical modeling approach for computing the DPM metric in different replication schemes on the basis of the checkpointing method. The effectiveness of replication schemes are demonstrated through experimental results. To verify the validity of the proposed analytical modeling approach, we extend the widely used cloud simulator CloudSim and compare the simulation results with analytical solutions.

Defects per Million (DPM) Evaluation for a Cloud Dealing with VM Failures Using Checkpointing

In cloud computing systems, a user request goes through several cloud service provider specific processing steps from the instant it is submitted until the service is completed. In this paper, we use service-oriented metrics to characterize the dependability of cloud computing systems in order to find the pitfalls and improve the service. We find that it is not possible to fully reflect the impact of a cloud-services dependability behavior through traditional dependability metrics like availability or reliability. We use a user-perceived dependability metric called Defects Per Million (DPM), defined as the number of user requests dropped out of a million. We demonstrate a new formulation for computing DPM metric in cloud computing systems. We incorporate check pointing scheme for job execution in the cloud to mitigate the impact of virtual machine failures, and compute DPM in order to characterize the improvement in the DPM due to the check pointing scheme compared to no-check pointing scheme.

On Dependability, Cost and Security Trade-Off in Cloud Data Centers

The performance, dependability, and security of cloud service systems are vital for the ongoing operation, control, and support. Thus, controlled improvement in service requires a comprehensive analysis and systematic identification of the fundamental underlying constituents of cloud using a rigorous discipline. In this paper, we introduce a framework which helps identifying areas for potential cloud service enhancements. A cloud service cannot be completed if there is a failure in any of its underlying resources. In addition, resources are kept offline for scheduled maintenance. We use redundant resources to mitigate the impact of failures/maintenance for ensuring performance and dependability, which helps enhancing security as well. For example, at least 4 replicas are required to defend the intrusion of a single instance or a single malicious attack/fault as defined by Byzantine Fault Tolerance (BFT). Data centers with high performance, dependability, and security are outsourced to the cloud computing environment with greater flexibility of cost of owing the computing infrastructure. In this paper, we analyze the effectiveness of redundant resource usage in terms of dependability metric and cost of service deployment based on the priority of service requests. The trade-off among dependability, cost, and security under different redundancy schemes are characterized through the comprehensive analytical models.

Defects Per Million (DPM): A user-oriented perspective of telecommunication systems

Defects Per Million (DPM), defined as the number of calls dropped out of a million calls due to failures, is used by the telecommunication systems community as a user-perceived dependability metric. As new standards evolve, with built-in mechanisms to handle and recover from failures, the need for an accurate estimation of metrics like DPM become essential. In this paper, we illustrate the computation of the DPM metric using a system supporting Voice over IP functionality using the Session Initiation Protocol (SIP) as an example to illustrate how the fault-handling mechanisms of the protocol can be accurately reflected in the dependability evaluation. We discuss how can we extend our approach and underlying models to other types of service-oriented environments.