Workload placement on servers has been traditionally driven by mainly performance objectives. In this work, we investigate the design, implementation, and evaluation of a power-aware application placement controller in the context of an environment with heterogeneous virtualized server clusters. The placement component of the application management middleware takes into account the power and migration costs in addition to the performance benefit while placing the application containers on the physical servers. The contribution of this work is two-fold: first, we present multiple ways to capture the cost-aware application placement problem that may be applied to various settings. For each formulation, we provide details on the kind of information required to solve the problems, the model assumptions, and the practicality of the assumptions on real servers. In the second part of our study, we present the pMapper architecture and placement algorithms to solve one practical formulation of the problem: minimizing power subject to a fixed performance requirement. We present comprehensive theoretical and experimental evidence to establish the efficacy of pMapper.

/pdf/pmapper-power-and-migration-cost-aware-application-placement-2o53nt8erw.pdf

pMapper: power and migration cost aware application placement in virtualized systems

Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness&#x201D; to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.

Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.

Power and performance management of virtualized computing environments via lookahead control

Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.

/pdf/key-challenges-for-delivering-clinical-impact-with-4ha0epal3a.pdf

Key challenges for delivering clinical impact with artificial intelligence.

https://repository.tudelft.nl/islandora/object/uuid%3A7bdae86a-0209-4e53-83d0-c531dd3465ec/datastream/OBJ/download

Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

A dynamic server migration and consolidation algorithm is introduced. The algorithm is shown to provide substantial improvement over static server consolidation in reducing the amount of required capacity and the rate of service level agreement violations. Benefits accrue for workloads that are variable and can be forecast over intervals shorter than the time scale of demand variability. The management algorithm reduces the amount of physical capacity required to support a specified rate of SLA violations for a given workload by as much as 50% as compared to static consolidation approach. Another result is that the rate of SLA violations at fixed capacity may be reduced by up to 20%. The results are based on hundreds of production workload traces across a variety of operating systems, applications, and industries.

http://ants.iis.sinica.edu.tw/3BkMJ9lTeWXTSrrvNoKNFDxRm3zFwRR/91/Dynamic%20Placement%20of%20Virtual%20Machines.pdf

Dynamic Placement of Virtual Machines for Managing SLA Violations

As businesses have grown, so has the need to deploy I/T applications rapidly to support the expanding business processes. Often, this growth was achieved in an unplanned way: each time a new application was needed a new server along with the application software was deployed and new storage elements were purchased. In many cases this has led to what is often referred to as "server sprawl", resulting in low server utilization and high system management costs. An architectural approach that is becoming increasingly popular to address this problem is known as server virtualization. In this paper we introduce the concept of server consolidation using virtualization and point out associated issues that arise in the area of application performance. We show how some of these problems can be solved by monitoring key performance metrics and using the data to trigger migration of Virtual Machines within physical servers. The algorithms we present attempt to minimize the cost of migration and maintain acceptable application performance levels.

Application Performance Management in Virtualized Server Environments

A computer implemented method, a computer program product, and data processing system clone virtual machines in a virtual desktop environment. A request to clone a new virtual machine is intercepted, wherein the request was initially sent to a virtual management server. Responsive to intercepting the request, the new virtual machine is cloned from a snapshot of a master virtual machine. A delta file is created for the new virtual machine, wherein the delta file includes a base disk reference and a copied delta file. The new virtual machine is configured to read a base disk image from a snapshot of the master virtual machine. The new virtual machine is configured to write data to the copied delta file in the delta file.

Provisioning Storage-Optimized Virtual Machines Within a Virtual Desktop Environment

Historical data is measured for a computer server system. Future demand for service in the computer server system is forecast based on the historical data, and the mapping of virtual machines to physical machines is updated based on the forecast of the future demand. Measurement, forecasting, and placement modules can be employed.

Dynamic placement of virtual machines for managing violations of service level agreements (SLAs)

Integrating data processing resources across a hybrid cloud includes providing a plug-in software component configured to be installed in a software framework. When the plug-in software component is activated, it may generate an interface for a consumer of data processing resources. Using this interface, the consumer of data processing resources may request the use of data processing resources provided as a service on the hybrid cloud. In response, the plug-in software component may acquire the requested data processing resources via interfaces provided by the various data processing resources. A number of different data processing resources may have different interface requirements. However, the first interface generated by the plug-in software component may provide a single vendor neutral interface for accessing data processing resources on the hybrid cloud by the consumer of data processing resources.

Kirk A. Beaty

Papers

Dynamic Placement of Virtual Machines for Managing SLA Violations

Application Performance Management in Virtualized Server Environments

Provisioning Storage-Optimized Virtual Machines Within a Virtual Desktop Environment

Dynamic placement of virtual machines for managing violations of service level agreements (SLAs)

Hybrid cloud integrator plug-in components