Economic analysis on the financial benefits and risks is crucial for deciding whether an IT transformation should take place. While the financial analysis techniques for general investment is well known, and there have been case studies for many IT types of IT transformations, there is no good methodology that an IT professional can follow and readily conduct return on investment or total cost analysis. This paper aims to fill this void and proposes an economic analysis methodology that is applicable to all IT transformations.

The overwhelming amount of various monitoring and log data generated in multitier IT systems makes problem determination one of the most expensive and labor-intensive tasks in IT Services arena. Particularly the initial step of problem classification is complicated by error propagation making secondary problems surfacing on multiple dependent resources. In this paper, we propose to automate the process of problem classification by leveraging machine learning. The main focus is to categorize the problem a user experiences by recognizing the real root cause specificity leveraging available training data such as monitoring and logs across the systems. We transform the structure of the problem into a hierarchy using an existing taxonomy. We then propose an efficient hierarchical incremental learning algorithm which is capable of adjusting its internal local classifier parameters in realtime. Comparing to the traditional batch learning algorithms, this online solution decreases the computational complexity of the training process by learning from new instances on an incremental fashion. Our approach significantly reduces the memory required to store the training instances. We demonstrate the efficiency of our approach by learning hierarchical problem patterns for several issues occurring in distributed web applications. Experimental results show that our approach substantially outperforms previous methods.

Acquiring a private computing cloud is the first step that an enterprise would choose to enable the cloud model and get its considerable benefits while keeping the control within the enterprise. The enterprise level applications that provide the infrastructure enabling cloud computing services are typically built by integrating inter-related complex software components. Critical challenges of these applications are the increasing level of inter-component dependencies and the customized growth, which make recurrent deployment of such applications, as the one required in private clouds, labor intensive and error prone. In this paper we investigate the type of issues faced when deploying a cloud computing management infrastructure and propose a solution to self-assist the deployment. We show how by leveraging virtual image technologies we can detect faulty installations and their signatures early in the deployment process. We also propose a methodology to capture in a shared repository and update these signatures for reuse in subsequent deployments in the form of two level signature patterns. We explore the perspective of our solution and criteria of analysis.

Virtualization has rapidly gained popularity affecting multiple levels of 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. Cloud computing is a paradigm of computing that offers virtualized resources “as a service.” Cloud Managers are responsible for lifecycle management of virtual resources, efficient utilization of physical resources and exposing basic operational APIs to users. Software solutions can then be deployed on these virtual resources. In this paper we propose a Virtual Hypervisor abstraction allowing solution managers to have an improved control over the resource allocation decisions regarding their virtual machines while maintaining cloud manager's role as the ultimate physical resource manager. We also introduce a novel resource allocation algorithm illustrating how the Virtual Hypervisor abstraction can be efficiently realized by the global cloud manager. We also use simulations to illustrate that our algorithm can be used to achieve fairer resource sharing and isolation across different Virtual Hypervisors.

While IT management services represent a mature subject in the IT business arena, the emerging cloud generation of management services require critical enhancements to the current processes and technologies in order to deliver IT management remotely with rapid on-boarding and minimal labor involvement from experts, to be affordable and scale up to the promise of the cloud. Traditional Remote Infrastructure Management (RIM) service providers use their own Network Operations Centers (NOC) to remotely monitor and manage customerspsila IT infrastructure. The primary business value for RIM services is that it helps global enterprises to small and medium businesses (SMB) to outsource the burden of managing their IT infrastructure. Although the IT management service itself delivered this way is more affordable, the RIM customer on-boarding process particularly is not, taking between one to two months of expensive labor. This paper describes what and how IT management processes, technologies and skills can be improved to provide remote customer on-boarding at an appropriate speed for delivery from the cloud. Our contributions consist of major enhancements in a key on-boarding area, namely IT discovery. Experimental results show that our approach aligns the RIM on-boarding methods to the cloud expectations both from a time as well as quality perspective.

Problem determination and resolution PDR is the process of detecting anomalies in a monitored system, locating the problems responsible for the issue, determining the root cause and fixing the cause of the problem. The cost of PDR represents a substantial part of operational costs, and faster, more effective PDR can contribute to a substantial reduction in system administration costs. In this paper, we propose to automate the process of PDR by leveraging machine learning methods. The main focus is to effectively categorize the problem a user experiences by recognizing the problem specificity leveraging all available training data such like the performance data and the logs data. Specifically, we transform the structure of the problem into a hierarchy which can be determined by existing taxonomy in advance. We then propose an efficient hierarchical incremental learning algorithm which is capable of adjusting its internal local classifier parameters in real-time. Comparing to the traditional batch learning algorithms, this online learning framework can significantly decrease the computational complexity of the training process by learning from new instances on an incremental fashion. In the same time this reduces the amount of memory required to store the training instances. We demonstrate the efficiency of our approach by learning hierarchical problem patterns for several issues occurring in distributed web applications. Experimental results show that our approach substantially outperforms previous methods.

Traditional desktop delivery model is based on a large number of distributed PCs executing operating system and desktop applications. Managing traditional desktop environments is incredibly challenging and costly. Tasks like installations, configuration changes, security measures require time-consuming procedures and dedicated deskside support. Also these distributed desktops are typically underutilized, resulting in low ROI for these assets. Further, this distributed computing model for desktops also creates a security concern as sensitive information could be compromised with stolen laptops or PCs. Desktop virtualization, which moves computation to the data center, allows users to access their applications and data using stateless ldquothin-clientldquo devices and therefore alleviates some of the problems of traditional desktop computing. Enterprises can now leverage the flexibility and cost-benefits of running users' desktops on virtual machines hosted at the data center to enhance business agility and reduce business risks, while lowering TCO. Recent research and development of cloud computing paradigm opens new possibilities of mass hosting of desktops and providing them as a service. However, transformation of legacy systems to desktop clouds as well as proper capacity provisioning is a challenging problem. Desktop cloud needs to be appropriately designed and provisioned to offer low response time and good working experience to desktop users while optimizing back-end resource usage and therefore minimizing provider's costs. This paper presents tools and approaches we have developed to facilitate fast and accurate planning for desktop clouds. We present desktop workload profiling and benchmarking tools as well as desktop to cloud transformation process enabling fast and accurate transition of legacy systems to new cloud-based model.

Deploying a modern software service usually involves installing several software components, and configuring these components properly to realize the complex interdependencies between them. This process, which accounts for a significant portion of information technology (IT) cost, is complex and error-prone. In this paper, we propose delta configuration - an approach that reduces the cost of software deployment by eliminating a large number of choices on parameter values that administrators have to make during deployment. In delta configuration, the complex software stack of a distributed service is first installed and tested in a test environment. The resulting software images are then captured and used for deployment in production environments. To deploy a software service, we only need to copy these pre-configured software images into a production environment and modify them to account for the difference between the test environment and a production environment. We have implemented a prototype system that achieves software deployment using delta configuration of the configuration state captured inside virtual machines. We perform a case study to demonstrate that our scheme leads to substantial reduction in complexity for the customer, over the traditional software deployment method.

The need for service differentiation in Internet services has motivated interest in controlling multi-tier web applications. This paper describes a tier-to-tier (T2T) management architecture that supports decentralized actuator management in multi-tier systems, and a testbed implementation of this architecture using commercial software products. Based on testbed experiments and analytic models, we gain insight into the value of coordinated exploitation of actuators on multiple tiers, especially considerations for control efficiency and control granularity. For control efficiency, we show that more effective utilization of tiers can be achieved by using actuators on the bottleneck tier rather than only using actuators on the entry tier. For granularity of control (the ability to achieve a wide range of service level objectives) we show that a fine granularity of control can be achieved through a coordinated, cross-tier exploitation of coarse grained actuators (e.g., multiprogramming level), an approach that can greatly reduce controllerinduced variability.

Analytic applications from various industrial sectors have specific attributes and requirements including relatively long processing time, parallelization, multiple interactive invocations, Web services, and expected quality of service objectives. Current parallel resource management systems for batch-oriented jobs lack the effective support for multiple interactive invocations with consideration in quality of service objectives, while transaction processing systems do not support dynamic creation of parallel application instances. To better serve the analytic applications, a set of additional resource management services, defined as stateful support, introduces the concept of service instance and service instance management. This set of stateful support services can be implemented as extension to existing parallel resource management to serve these analytic applications that rapidly increase in the demand of computing power

In this paper we present a hybrid performance model for modeling differentiated service of multi-tier web applications with per-tier concurrency limits, cross-tier interactions, as well as a work-conserving resource allocation model. The service dependencies between multiple tiers are captured first using a layered queueing model. We then show how to model per-tier concurrency limits and service differentiation between multiple classes while maintaining work conservation at each tier. We use a function approximation approach combined with a coupled processor model. Our model is calibrated from an actual multitier J2EE testbed, and we show the ability of the model to accurately model common performance metrics. Our proposed (layered) model shows 78% improvement in root mean square error over a single-tier machine repair model as well as a tandem queue model. We also demonstrate one application of the model for model-based resource allocation.