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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 cloud is a new delivery model in which end users connect to virtual desktops running in remote data centers. This paradigm offers multiple benefits both in terms of manageability as well as efficiency improvements. However, realizing this potential requires better understanding of desktop workload and its implications for desktop consolidation. We analyze CPU and memory usage on a sample of 35 desktops using a fine-grained 10 second averaging interval. Results provide insights into achievable efficiency improvements from desktop consolidation as well as detailed autocorrelation and variability behavior as a function of number of aggregated desktops. We also propose an interactivity classification method leading to functional form suitable for estimating residual durations of interactivity states. This finding can be leveraged in on-line proactive management algorithms for desktop cloud optimization.

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.

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 “thin-client“ 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.

This paper describes APL2/X, an interface between X-Windows and APL2, designed and built at the IBM Cambridge Scientific Center. It currently runs under VM/CMS and AIX. The APL2/X VM version of the interface uses the APL2 associated processor 11 to communicate with X. The APL2/X AIX version uses a new auxiliary processor, AP144, to achieve the same functionality.APL2/X enables all of the X-Windows Xlib calls and data-structures for use from the APL2 environment. In so doing, it enables APL2 to make use of a true windowing environment. Several APL2 sample programs using the interface have been coded to illustrate and validate the interface.The basic X-Windows Xlib is actually a large application written in C. APL2/X has therefore been built upon, as well as heavily influenced, a general APL2-to-C interface that runs on various platforms: APL2 under VM/CMS and MVS/TSO, APL2 for the RISC System/6000™ under AIX, and APL2/PC under PC/DOS.