Cloud service providers started with Infrastructure as a Service (IaaS) offerings and over time expanded into Platform as a Service (PaaS) and Software as a Service (SaaS). Even though each provider has a rich product offering, there are many scenarios where a multi-cloud strategy is desirable: utilizing economic dynamics, preventing data lock-in with one vendor, circumventing geographic restrictions, complying with local regulations, or combining on-premise and public-cloud resources. The challenge from a consumer perspective with multi-cloud deployments is the lack of a common abstraction for the offered products and a standardized way to express all of the application requirements for the resulting deployments. In this paper, we contribute by making yet another case for multi-cloud deployments and by predicting the emergence of a new generation of application-level resource managers which will natively support multi-cloud for enterprise applications. We identify three main components of the feedback loop controlled application-level resource managers: the software life-cycle manager, the data storage and access manager, and the service execution manager.

The performance of streaming media servers has been limited by the dual requirements of high disk throughput (to service more clients simultaneously) and low memory use (to decrease system cost). To achieve high disk throughput, disk drives must be accessed with large IOs to amortize disk access overhead. Large IOs imply an increased requirement of expensive DRAM, and, consequently, greater overall system cost. MEMS-based storage, an emerging storage technology, is predicted to offer a price-performance point between those of DRAM and disk drives. In this study, we propose storage architectures that use the relatively inexpensive MEMS-based storage devices as an intermediate layer (between DRAM and disk drives) for temporarily staging large disk IOs at a significantly lower cost. We present data layout mechanisms and synchronized IO scheduling algorithms for the real-time storage and retrieval of streaming data within such an augmented storage system. Analytical evaluation suggests that MEMS-augmented storage hierarchies can reduce the cost and improve the throughput of streaming servers significantly.

Allowing higher-priority requests to preempt ongoing disk IOs is of particular benefit to delay-sensitive and real-time systems. In this paper, we present semi-preemptible IO, which divides disk IO requests into small temporal units of disk commands to improve the preemptibility of disk access. We first lay out main design strategies to allow preemption of each component of a disk access-seek, rotation, and data transfer, namely, seek-splitting, JIT-seek, and chunking. We then present the preemption mechanisms for single and multidisk systems-JIT-preemption and JIT-migration. The evaluation of our prototype system showed that semi-preemptible IO substantially improved the preemptibility of disk access with little loss in disk throughput and that preemptive disk scheduling could improve the response time for high-priority interactive requests.

It is widely understood that most system downtime is accounted for by programming errors and administration time. However, a growing body of work has indicated an increasing cause of downtime may stem from transient errors in computer system hardware due to external factors, such as cosmic rays. This work indicates that moving to denser semiconductor technologies at lower voltages has the potential to increase these transient errors. In this paper, we investigate the susceptibility of commodity operating systems and applications on commodity PC processors to these soft-errors and we introduce ideas regarding the improved recovery from these transient errors in software. Our results indicate that, for the Linux kernel and a Java virtual machine running sample workloads, many errors are not activated, mostly due to overwriting. In addition, given current and upcoming microprocessor support, our results indicate that those errors activated, which would normally lead to system reboot, need not be fatal to the system if software knowledge is used for simple software recovery. Together, they indicate the benefits of simple memory soft error recovery handling in commodity processors and software.

The performance of streaming media servers has been limited due to the dual requirements of high throughput and low memory use. Although disk throughput has been enjoying a 40% annual increase, slower improvements in disk access times necessitate the use of large DRAM buffers to improve the overall streaming throughput. MEMS-based storage is an exciting new technology that promises to bridge the widening performance gap between DRAM and disk-drives in the memory hierarchy. We explore the impact of integrating these devices into the memory hierarchy on the class of streaming media applications. We evaluate the use of MEMS-based storage for buffering and caching streaming data. We also show how a bank of k MEMS devices can be managed in either configuration and that they can provide a k-fold improvement in both throughput and access latency. An extensive analytical study shows that using MEMS storage can reduce the buffering cost and improve the throughput of streaming servers significantly.

The use of interactive media has already gained considerable popularity. Interactivity gives viewers VCR controls like slow-motion, pause, fast-forward, and instant replay. However, traditional server-based or client-based approaches for supporting interactivity either consume too much network bandwidth or require large client buffering; and hence they are economically unattractive. We propose the architecture and design of an interactive media proxy (IMP) server that transforms noninteractive broadcast or multicast streams into interactive ones for servicing a large number of end users. For IMP to work cost-effectively, it must carefully manage its storage devices, which are needed for caching voluminous media data. In this regard, we propose a fine-grained device management strategy consisting of three complementary components: disk profiler, data placement, and IO scheduler. Through quantitative analysis and experiments, we show that these fine-grained strategies considerably improve device throughput under various workload scenarios.

This paper presents the architecture and implementation of XTREAM, a high-performance streaming multimedia system. XTREAM is supported by its three core components: IO scheduler, request handler, and admission controller. Via extensive experiments, we show that, thanks to these core components, XTREAM can achieve a low response time as well as high throughput and high-quality service to simultaneous clients.

Supporting preemptible disk access is essential for interactive multimedia applications that require short response time. In this study, we propose Virtual IO, an abstraction for disk IO, that transforms a non-preemptible IO request into a preemptible one. In order to achieve its objective efficiently, Virtual IO uses disk profiling to obtain accurate and detailed knowledge about the disk. Upon implementation of Virtual IO, we show that not only does Virtual IO enable highly preemptible disk access, but it does so with little or no loss in disk throughput.