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 user's 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.

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

Excerpt] At the core of performance management and human resources more generally is the idea that people make the difference in organizations. Research has shown time and time again that effective deployment of human capital is a key differentiator in business. People, and more specifically their distinct knowledge and skills, function as the most reliable source of competitive advantage for companies in today’s economy. Realizing the need for continual development of the workforce, American companies spent an estimated total of $164.2 billion, or $1,196 per employee, on workforce training and development in 2012. Effective performance management has the ability to significantly enhance such development efforts; employees could increase their productivity by as much as 26% under effective performance management systems. As companies strive to do more with less, properly designed and executed performance management functions as an essential mechanism for increasing employee productivity and continually improving business results.

From Measurement to Ownership: The Evolution and Organizational Implications of Modern Performance Management

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-service's 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.

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

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.

Service Reliability Enhancement in Cloud by Checkpointing and Replication

Challenges in the implementation of the performance management system in the ministry of social development in Lesotho

BENEFITS Performance management has a number of benefits: Promotes better systems, leading to better outcomes  Results in improved performance: In a survey of state health agencies, 76 percent reported that their performance management efforts resulted in improved performance  Provides better results for the public Provides data to illustrate the importance of public health  Shifts the conversation from wasteful government spending to the value created for citizens  Provides a better understanding of public health’s importance for employees, decision makers, and the public Demonstrates greater accountability for funding Provides meaningful data and benchmarks  Establishes common standards/indicators and data, to continually measure and improve value Emphasizes quality, not just quantity Increases productive teamwork Improves problem-solving skills

Phillip C. Howard

Papers

Introduction to Performance Management.

Planning Capacity to Meet User Service Requirements.