The IT industry is experiencing a disruptive trend for which the entire data center infrastructure is becoming software defined and programmable. IT resources are provisioned and optimized continuously according to a declarative and expressive specification of the workload requirements. The software defined environments facilitate agile IT deployment and responsive data center configurations that enable rapid creation and optimization of value-added services for clients. However, this fundamental shift introduces new challenges to existing data center management solutions. In this paper, we focus on the storage aspect of the IT infrastructure and investigate its unique challenges as well as opportunities in the emerging software defined environments. Current state-of-the-art software defined storage (SDS) solutions are discussed, followed by our novel framework to advance the existing SDS solutions. In addition, we study the interactions among SDS, software defined compute (SDC), and software defined networking (SDN) to demonstrate the necessity of a holistic orchestration and to show that joint optimization can significantly improve the effectiveness and efficiency of the overall software defined environments.

Abstract-We propose a new MapReduce cloud service model, Cura, for data analytics in the cloud. We argue that performing MapReduce analytics in existing cloud service models -either using a generic compute cloud or a dedicated MapReduce cloud -is inadequate and inefficient for production workloads. Existing services require users to select a number of complex cluster and job parameters while simultaneously forcing the cloud provider to use those potentially sub-optimal configurations resulting in poor resource utilization and higher cost. In contrast Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs so as to obtain a global resource optimization from the provider perspective. Secondly, to better serve modern MapReduce workloads which constitute a large proportion of interactive real-time jobs, Cura uses a unique instant VM allocation technique that reduces response times by up to 65%. Thirdly, our system introduces deadline-awareness which, by delaying execution of certain jobs, allows the cloud provider to optimize its global resource allocation and reduce costs further. Cura also benefits from a number of additional performance enhancements including cost-aware resource provisioning, VMaware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that along with response time improvements, our techniques lead to more than 80% reduction in the compute infrastructure cost of the cloud data center.

Today's application environments combine Cloud and onpremise infrastructure, as well as platforms and services from different providers to enable quick development and delivery of solutions to their intended users. The ability to use Cloud platforms to stand up applications in a short time frame, the wide availability of Web services, and the application of a continuous deployment model has led to very dynamic application environments. In those application environments, managing quality of service has become more important. The more external service vendors are involved the less control an application owner has and must rely on Service Level Agreements (SLAs). However, SLA management is becoming more difficult. Services from different vendors expose different instrumentation. In addition, the increasing dynamism of application environments entails that the speed of SLA monitoring set up must match the speed of changes to the application environment.This paper proposes the rSLA service and language that is both flexible enough to instrument virtually any environment and agile enough to scale and update SLA management as needed. Using rSLA the time of setting up SLA compliance monitoring of application environments involving infrastructure, platform, and application services can be significantly reduced.

With billions at stake in new product development, acquisitions and alliances, IBM's Strategic IP insight Platform (SIIP) delivers transformative results, helping clients gain strategic insights. Applied to the pharmaceutical industry, SIIP accelerates the discovery of information to more quickly and accurately answer questions such as: which chemical compounds are good for which targets? What's the likelihood this compound will succeed? What diseases could be treated with this target? What are the candidate drugs that can be repurposed for a given disease? Applied to drug discovery in life sciences, the SIIP platform leverages and integrates a wide range of public and private content, rich set of deep analytics and a massive-scale architecture to improve patient outcomes. SIIP was born prior to the proliferation of the many big data tools available today. We describe what tools and architecture decisions have been helpful in this first phase of solution development, and what tools and architectures we are relying on as we raise our own standard for performance and service delivery.

Storage services are an essential part of an organization's IT infrastructure services and contribute a significant part of total IT costs. For this reason, various service management techniques are applied to optimize a service's storage resource usage while still addressing requirements related to performance, high availability, or disaster recovery. While storage virtualization has been the basis for many storage service management optimizations, the relatively stable environments of enterprise IT enabled all management activity to proceed in the context of change processes on specialized storage controllers. Completely virtualized environments require frequent topological changes but also enable optimized resource usage across shared resource pools. This enables lower resource and service management costs if the right storage service management architecture is deployed. This chapter focuses on cloud service management from a storage perspective, providing a set of proven methods and services to optimize storage resource usage and the management architecture that enables them.