This work proposes a dithered-digital-mixing background timing-skew calibration method for time-interleaved (TI) analog-to-digital converters (ADCs). Unlike prior digital-mixing methods that limit the input bandwidth or lead the calibration process to a limit cycle, the proposed method enhances the input bandwidth to the Nyquist frequency and guarantees the convergence. This is achieved by a pseudo-random binary sequence generator that produces a dither signal. Practical considerations including thermal noise and the step size of variable delay lines are discussed. Behavioral simulation results demonstrate the effectiveness of the proposed method with an improvement of signal-to-noise-and-distortion ratio from 26.3dB to 52.6dB for a 5GS/s 9b 16-channel TI ADC.

High-speed time-interleaved analog-to-digital converters (TI-ADCs) are sensitive to timing skew mismatch. Autocorrelation-based background timing skew calibration techniques require small hardware overhead as they rely on the TI-ADC input signal for calibration. However, such techniques suffer from a very long convergence time. This paper proposes a new correlation-based technique that boosts convergence speed by orders of magnitude compared to existing autocorrelation-based techniques. The technique uses a digital window detector and calculates the signal correlation funnction around zero-crossings only. Practical design considerations including thermal noise, clock jitter, quantization and offset mismatch are discussed. Behavioral simulation results for two TI-ADCs with different speeds, resolutions and interleaving factors are presented.

This paper presents a second-order VCO-based $\Delta\Sigma$ ADC with a fully digital feedback adder, which is highly digital, area efficient and low power. Both the first and second loop integrator are implemented by VCOs and are free of OTA. A novel digital adder is proposed to realize the secondary feedback, significantly reducing the power and area of the second stage. The proposed ADC is designed in a 28nm CMOS technology under 0.9V supply, consuming only 1.14mW. The simulated SNDR and SFDR are 72.5dB and 84.1dB respectively over a 5MHz signal bandwidth.

With the on-demand service provision ability and elastic nature, cloud computing provides a suitable development and running environment for internetware, to support its cooperation and evolvement. Among the three service models of cloud computing, Platform-as-a-Service (PaaS) provides internetware application developers with sufficient and convenient computing resources to develop and run their applications. As with other cloud computing models, monitoring is the basis of on-demand service provision and elastic scalability in PaaS. Apart from this, monitoring is also a required service that should be provided to PaaS consumers. However, due to the diversity and heterogeneity of the objects to be monitored in PaaS, monitoring is not easy to implement. Additionally, it is difficult to perform manual or automatic administration on PaaS based on raw monitoring data in the form of metric data set. In this paper, we propose a model-driven monitoring approach for PaaS. By transforming low-level monitoring data to high-level monitoring model based on meta-model definitions and transforming rules, this approach not only simplifies the implementation of PaaS monitoring system, but also presents a high-level abstraction of the whole PaaS, which reduces the difficulty of PaaS management. We built a graphic modeling tool to assist the definition of the meta-model and transforming rules, and applied this approach in the implementation of a PaaS monitoring system in practice.

Monitoring plays a significant role in improving the quality of service in cloud computing. It helps clouds to scale resource utilization adaptively, to identify defects in services for service developers, and to discover usage patterns of numerous end users. However, due to the heterogeneity of components in clouds and the complexity arising from the wealth of runtime information, monitoring in clouds faces many new challenges. In this paper, we propose a runtime model for cloud monitoring (RMCM), which denotes an intuitive representation of a running cloud by focusing on common monitoring concerns. Raw monitoring data gathered by multiple monitoring techniques are organized by RMCM to present a more intuitive profile of a running cloud. We applied RMCM in the implementation of a flexible monitoring framework, which can achieve a balance between runtime overhead and monitoring capability via adaptive management of monitoring facilities. Our experience of utilizing the monitoring framework on a real cloud demonstrates the feasibility and effectiveness of our approach.

Application Programming Interface (API) constraints on objects are rules that API client code must follow in order to get expected results from these objects. Runtime verification, an important approach for detecting API constraint violations, usually suffers from high runtime overhead. This paper focuses on temporal API constraints on multiple interacting objects. Violation detection of such constraints is more challenging than violation detection of single object constraints, and may induce higher runtime overhead. To reduce the runtime overhead, without compromising the effectiveness of verification, we propose a Lazy Verification Approach (LAVA), which enables verification lazily. Verification probes in LAVA are loaded automatically during the program execution as late as possible. And only probes on objects that have been bound by a binding point (a special method invocation that binds involved objects together) are enabled. Based on these optimization strategies, we implemented an efficient and flexible runtime verification framework. We show the effectiveness of our approach by applying it to verify five constraints in the DaCapo [1] benchmark. The empirical results show that our approach can reduce the number of method invocation events sent by probes, which is the main cause of runtime overhead, by 74% to 100% on average, and bring about an optimization ratio of 44.1% to 89.9% on runtime overhead.

Web service technology aims to enable the interoperation of heterogeneous systems and the reuse of distributed functions in an unprecedented scale and has achieved significant success. There are still, however, challenges to realize its full potential. One of these challenges is to ensure the behavior of Web services consistent with their requirements. Monitoring events that are relevant to Web service requirements is, thus, an important technique. This paper introduces an online monitoring approach for Web service requirements. It includes a pattern-based specification of service constraints that correspond to service requirements, and a monitoring model that covers five kinds of system events relevant to client request, service response, application, resource, and management, and a monitoring framework in which different probes and agents collect events and data that are sensitive to requirements. The framework analyzes the collected information against the prespecified constraints, so as to evaluate the behavior and use of Web services. The prototype implementation and experiments with a case study shows that our approach is effective and flexible, and the monitoring cost is affordable.