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During the past few years, enterprises have been increasingly aggressive in moving mission-critical and performance-sensitive applications to the cloud, while at the same time many new mobile, social, and analytics applications are directly developed and operated on cloud computing platforms. These two movements are encouraging the shift of the value proposition of cloud computing from cost reduction to simultaneous agility and optimization. These requirements (agility and optimization) are driving the recent disruptive trend of software defined computing, for which the entire computing infrastructureVcompute, storage and networkVis becoming software defined and dynamically programmable. The key elements within software defined environments include capability-based resource abstraction, goal-based and policy-based workload definition, and outcome-based continuous mapping of the workload to the available resources. Furthermore, software defined environments provide the tooling and capabilities to compose workloads from existing components that are then continuously and autonomously mapped onto the underlying programmable infrastructure. These elements enable software defined environments to achieve agility, efficiency, and continuous outcome-optimized provisioning and management, plus continuous assurance for resiliency and security. This paper provides an overview and introduction to the key elements and challenges of software defined environments. focuses on identifying better outcomes within a cloud environment with respect to the value of the service, the cost of the required resources, and the risk of failure in an unpredictable environment and under constant usage changes. This requires a high degree of automation and programmability of the infrastructure itself. Hence, this shift led to the recent disruptive trend of software defined computing for which the entire system infrastructureVcompute, storage and networkVis becoming software defined and dynamically programmable. Software defined computing receives considerable focus across academia and enterprises [1-7]. Software defined computing originated from the compute environment in which the computing resources are virtualized and managed as virtual machines [8-11]. This enabled mobility and higher resource utilization as several virtual machines are collocated on the same server,

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Technological advances over the last several years have resulted in digital image and video libraries that, today, comprise tens of terabytes of on-line data. Due to the continued proliferation of image data, these libraries will grow signi cantly larger over the next few years. Consequently, the need for databases that can e ectively support storage, search, retrieval and transmission of this kind of nontraditional data will grow signi cantly. This paper describes a project currently in progress under joint sponsorship by NASA, GSFC, that explores some of these challenges. In this paper, we describe the architecture and implementation of a framework to perform content-based search of an image database, where content is specied by the user at one or more of the three abstraction levels: pixel, feature and semantic. This framework incorporates a progressive methodology that allows a computationally efcient implementation of image processing algorithms, yielding the e cient extraction and manipulation of user-speci ed featured and content during the query execution.

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