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Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

Cloud-based analytics allow for inexpensive processing of large amount of data. However, processing protected health information (PHI) in cloud is a challenging task due to strict regulations (e.g., HIPAA) requiring features (e.g., dataisolation) which most cloud-based platforms do not currentlysupport in their offerings. This makes it difficult to leveragemany technologies well suited to the cloud (e.g., Apache Spark)to process PHI. To address this issue, we have developedthe Watson Health Cloud (WHC), a cloud-based platformfor the storage and analysis of large amount of PHI. TheWHC enables all the features necessary to store and processPHI, with little customization needed by the end-user. Thispaper describes the lessons learned from developing a cloudplatform for PHI. Specifically, we discuss the architecture andimplementation challenges we faced throughout development. We hope the insights gained from our experiences help otherswhen designing frameworks and applications which processPHI.

IT Autopilot is a flexible architecture to support the delivery of information technology (IT) systems management services. Complex services that involve several tools require integration between the tools and automated processes that can invoke multiple tools. Designed primarily for the small and mid-sized enterprise, the architecture of IT Autopilot allows it to be deployed as a set of local and remote services delivered by the enterprise or by service providers as a flexible and extensible service offering. The IT Autopilot integrated IT service management platform is able to combine different tools and services to create specific, customized IT service solutions. Using the analogy of an autopilot on an airplane, the pilot first performs a set of manual operations to get the airplane off the ground and flying. Next, the autopilot is engaged to carry on normal flight operations. In our vision, there is an initial manual configuration step before IT Autopilot is enabled to take over and maintain the customer's normal IT operational state. In this paper we explain our vision and describe the prototype system we have implemented.

Recently enterprises have been able to leverage two revolutionary new tools for gaining a competitive advantage for their business -- cloud computing and analytic applications. Cloud computing unburdens them from running and maintaining their compute resources, whereas analytic applications comb through their big data to provide new insights for a competitive advantage in the market. Analytic applications are carefully tailored to their target problems. While there is a lot of work published on both the mechanics of cloud computing as well as analytic methods for distilling insights from a variety of data, there is little work available about the cloud influence on the analytics platforms which aim at lowering the barrier for the creation, deployment, scaling and maintenance of next generation analytic workloads. This paper discusses the challenges we are facing today in order to provide an analytics platform to reduce cost and increase performance of analytics applications in the cloud computing environment.

We design a framework for solution validation and couple it with the ability for VM and elasticity management. We implement our design in the form of a "dashboard which captures all elements of a comprehensive cloud solution management framework. We illustrate the applicability of the dashboard with a cloud solution we have developed.

With the proliferation of cloud based services - IaaS, PaaS, and SaaS - enterprises are increasingly consuming all type of IT services delivered by cloud providers. These cloud services are increasingly being used to create integrated solutions where some of the component services are delivered from on-premise private cloud environments and the remaining are provided by off-premise providers. While this best-of-breed approach results in flexible and agile business solutions, it also raises significant problems related to metering, billing, charge back and accounting. In this paper, after reviewing common hybrid cloud integration patterns, we discuss the importance of usage metering and the associated challenges in hybrid cloud environments. We then present a novel solution for metering of services delivered from multiple cloud providers. In this approach, service metering is keyed off of the life-cycle events generated by the service management controls across the hybrid cloud. By identifying life-cycle events associated with services consumed, service usage is tracked from all sources and then filtered and aggregated in a centralized manner. The aggregated information can then be used in an on-line manner for policy-based service delivery, charge-back, billing and reporting, and auditing. Based on this approach we have developed an end-to-end service usage metering and billing system for hybrid cloud environments. Some of the key underlying technologies have been incorporated in IBM SmartCloud Orchestrator - an IBM offering for managing workload patterns in multi cloud environments.

In this paper, we describe architecture and design of an innovative integrated monitoring solution for monitoring workloads in a hybrid cloud. Using off-the-shelf commercially available monitoring products and services, the solution described here allows for monitoring of a workload deployed across public and private clouds separated by enterprise firewall. The monitored data from endpoints distributed across the hybrid cloud is aggregated in a centralized monitoring system for analysis and for performing higher level management functions. Our solution is automated and can be integrated with other IT service management functions. The solution can leverage any endpoint monitoring functions and services a cloud service provider may offer. The monitoring framework described in this paper forms the basis for the hybrid cloud monitoring capability in IBM's hybrid cloud offering,IBM Service Management Extensions for Hybrid Cloud.

Desktop virtualization is a new computing approach to application delivery and management. It leverages OS virtualization and remoting protocols to provide users with remote access to virtual machines running in a centralized data center. It promises significant benefits in terms of improved data security, reduced management complexity, and more efficient and flexible resource usage. However, it brings a lot of management challenges typical for centralized systems, with performance and quality of service management being one of the most important. This paper proposes a management algorithm suitable for efficient resource allocation in virtualized desktop environments and takes application performance QoS features into consideration. It proposes a novel, non-intrusive method for application and remoting protocol agnostic desktop responsiveness monitoring. Moreover, it is based on studies of desktop workload usage which enabled us to discover and leverage workload patterns that can lead to increased efficiency both in terms of desktop responsiveness and resource usage. We have prototyped the system and discuss several case studies validating the approach and illustrating the most important features of the solution.

The thin-client computing model has been recently regaining popularity in a new form known as the virtual desktop. That is where the desktop is hosted on a virtualized platform. Even though the interest in this computing paradigm is broad there are relatively few tools and methods for benchmarking virtual client infrastructures. We believe that developing such tools and approaches is crucial for the future success of virtual client deployments and also for objective evaluation of existing and new algorithms, communication protocols, and technologies. We present DeskBench, a virtual desktop benchmarking tool, that allows for fast and easy creation of benchmarks by simple recording of the user's activity. It also allows for replaying the recorded actions in a synchronized manner at maximum possible speeds without compromising the correctness of the replay. The proposed approach relies only on the basic primitives of mouse and keyboard events as well as screen region updates which are common in window manager systems. We have implemented a prototype of the system and also conducted a series of experiments measuring responsiveness of virtual machine based desktops under various load conditions and network latencies. The experiments illustrate the flexibility and accuracy of the proposed method and also give some interesting insights into the scalability of virtual machine based desktops.

Virtualization rapidly gains popularity affecting multiple levels of the computing stack. Since it decouples resources from their users it provides greater flexibility in terms of resource allocation but also brings new challenges for optimal design, provisioning, and runtime management of systems. This paper presents an analytical model of virtual machine migration that provides an estimate of the expected improvement in response time due to a migration decision. The model explicitly accounts for predictability of resource demands and characteristics of the virtualization infrastructure, such as migration time and overhead. It brings new insights into the decision strategies that should be used to improve the overall responsiveness of the system when dynamically managing virtual machines. The model is validated using simulations driven by data center resource utilization traces.

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.

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.

This paper explores merging SAN performance management tools into the host system thus dissolving the traditional boundary between hosts and storage. The host service time distribution is exploited to locate caches in the SAN, detect and isolate contention in the communication channels and backend disk drives. We define essential metrics and measure I/O workloads over extended periods to define baseline behavior. We have implemented architecture to acquire configuration and performance data from agents on the host and SAN elements. In addition to performance management this data is the basis of management functions such as capacity planning, and sizing host and SAN resources required for application rollout and expansion. The static dependency map (SAN topology or 'wiring' diagram), provided by current SAN management tools, is extended into the host server. We then attempt to develop a corresponding dynamic dependency view showing how performance metrics at the application and host level, such as I/O queuing levels and service times, relate to response times in the SAN. The dynamic view is also an important complement to the static dependency graph because the actual traffic flow may be difficult to deduce from the topology.