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Direct air capture (DAC) systems that capture carbon dioxide (CO2) directly from the atmosphere are garnering considerable attention for their potential role as negative emission technologies in achieving net-zero CO2 emission goals. Common DAC technologies are based either on liquid–solvent (L-DAC) or solid–sorbent (S-DAC) to capture CO2. A comprehensive multi-factor comparative economic analysis of the deployment of L-DAC and S-DAC facilities across various United States (U.S.) cities is presented in this paper. The analysis considers the influence of various factors on the favorability of DAC deployment, including local climatic conditions such as temperature, humidity, and CO2 concentrations; the availability of energy sources to power the DAC system; and costs for the transport and storage of the captured CO2 along with the consideration of the regional market and policy drivers. The deployment analysis in over 70 continental U.S. cities shows that L-DAC and S-DAC complement each other spatially, as their performance and operational costs vary in different climates. L-DAC is more suited to the hot, humid Southeast, while S-DAC is preferrable in the colder, drier Rocky Mountain region. Strategic deployment based on regional conditions and economics is essential for promoting the commercial adoptability of DAC, which is a critical technology to meet the CO2 reduction targets. [ABSTRACT FROM AUTHOR]

Natural-gas-fueled solid oxide fuel cell (SOFC) systems have the potential for high-efficiency conversion of carbon to power due to the underlying electrochemical conversion process while readily facilitating carbon capture through the separation of the fuel and oxidant sources. Compressed air energy storage (CAES) technology can potentially store significant quantities of energy for later use with a high round-trip efficiency and lower cost when compared with state-of-the-art battery technology. The base load generation capability of SOFC can be coupled with CAES technology to provide a potentially flexible, low-carbon solution to meet the fluctuating electricity demands imposed by the increasing share of intermittent variable renewable energy (VRE) production. SOFC and CAES can be hybridized through thermal integration to maximize power output during periods of high electrical demand and then store power when either demand is low or renewable generation reduces power prices. The techno-economics of a low-carbon hybrid SOFC and CAES system was developed and investigated in the present study. The proposed hybrid system was found to be cost-competitive with other power-generating base-load facilities when power availability was considered. The hybrid system shows increased resilience to changes in a high VRE grid market scenario. [ABSTRACT FROM AUTHOR]

Cloud-based Natural Language Understanding (NLU) services are becoming more popular with the development of artificial intelligence. More applications are integrated with cloud-based NLU services to enhance the way people communicate with machines. However, with NLU services provided by different companies powered by unrevealed AI technology, how to choose the best one is a problem for developers. Existing tools that can provide guidance to developers and make recommendations based on their needs are severely limited. This article comprehensively evaluates multiple state-of-the-art NLU services, and the results indicate that there is no absolute winner for different usage requirements. Motivated by this observation, we provide several insights and propose NLUBroker, a Quality of Experience-driven (QoE-driven) broker system, to select the proper service according to the environment. NLUBroker senses the client and service status and leverages a solution to the multi-armed bandit problem to conduct online learning, aiming to achieve maximum expected QoE. The performance of NLUBroker is evaluated in both simulation and real-world environments, and the evaluation results demonstrate that NLUBroker is an efficient solution for selecting NLU services. It is adaptive to changes in the environment, outperforms three baseline methods we evaluated and improves overall QoE up to 1.5× for the evaluated state-of-the-art NLU services. [ABSTRACT FROM AUTHOR]

In recent years, edge computing has emerged as a popular mechanism to deliver dynamic Web content to clients. However, many existing edge cache networks have not been able to harness the full potential of edge computing technology. In this paper, we argue and experimentally demonstrate that cooperation among the individual edge caches coupled with scalable serverdriven document consistency mechanisms can significantly enhance the capabilities and performance of edge cache networks in delivering fresh dynamic content. However, designing large-scale cooperative edge cache networks presents many research challenges. Toward addressing these challenges, this paper presents cooperative edge cache grid (cooperative EC grid, for short)--a large-scale cooperative edge cache network for efficiently delivering highly dynamic Web content with varying server update frequencies. The design of the cooperative EC grid focuses on the scalability and reliability of dynamic content delivery in addition to cache hit rates, and it incorporates several novel features. We introduce the concept of cache clouds as a generic framework of cooperation in large-scale edge cache networks. The architectural design of the cache clouds includes dynamic hashing-based document lookup and update protocols, which dynamically balance lookup and update loads among the caches in the cloud. We also present cooperative techniques for making the document lookup and update protocols resilient to the failures of individual caches. This paper reports a series of simulation-based experiments which show that the overheads of cooperation in the cooperative EC grid are very low, and our architecture and techniques enhance the performance of the cooperative edge networks. Index Terms--Dynamic content caching, edge computing, cooperative caching, cache clouds.

Constructing Web pages from fragments has been shown to provide significant benefits for both content generation and caching. In order for a Web site to use fragment-based content generation, however, good methods are needed for fragmenting the Web pages. Manual fragmentation of Web pages is expensive, error prone, and unscalable. This paper proposes a novel scheme to automatically detect and flag fragments that are cost-effective cache units in Web sites serving dynamic content. Our approach analyzes Web pages with respect to their information sharing behavior, personalization characteristics, and change patterns. We identify fragments which are shared among multiple documents or have different lifetime or personalization characteristics. Our approach has three unique features. First, we propose a framework for fragment detection, which includes a hierarchical and fragment-aware model for dynamic Web pages and a compact and effective data structure for fragment detection. Second, we present an efficient algorithm to detect maximal fragments that are shared among multiple documents. Third, we develop a practical algorithm that effectively detects fragments based on their lifetime and personalization characteristics. This paper shows the results when the algorithms are applied to real Web sites. We evaluate the proposed scheme through a series of experiments, showing the benefits and costs of the algorithms. We also study the impact of using the fragments detected by our system on key parameters such as disk space utilization, network bandwidth consumption, and load on the origin servers. Index Terms--Dynamic content caching, fragment-based caching, fragment detection.

The emerging edge services architecture promises to improve the availability and performance of Web services by replicating servers at geographically distributed sites. A key challenge in such systems is data replication and consistency, so that edge server code can manipulate shared data without suffering the availability and performance penalties that would be incurred by accessing a traditional centralized database. This article explores using a distributed object architecture to build an edge service data replication system for an e-commerce application, the TPC-W benchmark, which simulates an online bookstore. We take advantage of application-specific semantics to design distributed objects that each manages a specific subset of shared information using simple and effective consistency models. Our experimental results show that by slightly relaxing consistency within individual distributed objects, our application realizes both high availability and excellent performance. For example, in one experiment, we find that our object-based edge server system provides five times better response time over a traditional centralized cluster architecture and a factor of nine improvement over an edge service system that distributes code but retains a centralized database. Index Terms--Availability, data replication, distributed objects, edge services, performance, Wide Area Networks (WAN).

Location-based services (LBSs) provide enhanced functionality and convenience of ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the users’ privacy. The leakage of private location data in the LBS context has drawn significant attention from academics and industry due to its importance, leading to numerous research efforts aiming to confront the related challenges. However, to the best of our knowledge, none of relevant studies have performed a qualitative and quantitative comparison and analysis of the complex topic of designing countermeasures and discussed the viability of their use with different kinds of services and the potential elements that could be deployed to meet new challenges. Accordingly, the purpose of this survey is to examine the privacy-preserving techniques in LBSs. We categorize and provide an inside-out review of the existing techniques. Performing a retrospective analysis of several typical studies in each category, we summarize their basic principles and recent advances. Additionally, we highlight the use of privacy-preserving techniques in LBSs for enabling new research opportunities. Providing an up-to-date and comprehensive overview of existing studies, this survey may further stimulate new research efforts into this promising field. [ABSTRACT FROM AUTHOR]

Data stores and cloud services are typically accessed using a client-server paradigm wherein the client runs as part of an application process which is trying to access the data store or cloud service. This paper presents the design and implementation of enhanced clients for improving both the functionality and performance of applications accessing data stores or cloud services. Our enhanced clients can improve performance via multiple types of caches, encrypt data for providing confidentiality before sending information to a server, and compress data for reducing the size of data transfers. Our clients can perform data analysis to allow applications to more effectively use cloud services. They also provide both synchronous and asynchronous interfaces. An asynchronous interface allows an application program to access a data store or cloud service and continue execution before receiving a response which can significantly improve performance. We present a Universal Data Store Manager (UDSM) which allows an application to access multiple different data stores and provides a common interface to each data store. The UDSM also can monitor the performance of different data stores. A workload generator allows users to easily determine and compare the performance of different data stores. We also present NLU-SA, an application for performing natural language understanding and sentiment analysis on text documents. NLU-SA is implemented on top of our enhanced clients and integrates text analysis with Web searching. We present results from NLU-SA on sentiment on the Web towards major companies and countries. We also present a performance analysis of our enhanced clients. [ABSTRACT FROM AUTHOR]

Cloud computing has made the resources needed to execute large-scale in-memory distributed computations widely available. Specialized programming models, e.g., MapReduce, have emerged to offer transparent fault tolerance and fault recovery for specific computational patterns, but they sacrifice generality. In contrast, the Resilient X10 programming language adds failure containment and failure awareness to a general purpose, distributed programming language. A Resilient X10 application spans over a number of places. Its formal semantics precisely specify how it continues executing after a place failure. Thanks to failure awareness, the X10 programmer can in principle build redundancy into an application to recover from failures. In practice, however, correctness is elusive, as redundancy and recovery are often complex programming tasks. This article further develops Resilient X10 to shift the focus fromfailure awareness to failure recovery, from both a theoretical and a practical standpoint. We rigorously define the distinction between recoverable and catastrophic failures. We revisit the happens-before invariance principle and its implementation. We shift most of the burden of redundancy and recovery from the programmer to the runtime system and standard library. We make it easy to protect critical data from failure using resilient stores and harness elasticity--dynamic place creation--to persist not just the data but also its spatial distribution. We demonstrate the flexibility and practical usefulness of Resilient X10 by building several representative high-performance in-memory parallel application kernels and frameworks. These codes are 10× to 25× larger than previous Resilient X10 benchmarks. For each application kernel, the average runtime overhead of resiliency is less than 7%. By comparing application kernels written in the Resilient X10 and Spark programming models, we demonstrate that Resilient X10's more general programming model can enable significantly better application performance for resilient in-memory distributed computations. [ABSTRACT FROM AUTHOR]

Ballooning is a popular solution for dynamic memory balancing. However, existing solutions may perform poorly in the presence of heavy guest swapping. Furthermore, when the host has sufficient free memory, guest virtual machines (VMs) under memory pressure is not be able to use it in a timely fashion. Even after the guest VM has been recharged with sufficient memory via ballooning, the applications running on the VM are unable to utilize the free memory in guest VM to quickly recover from the severe performance degradation. To address these problems, we present MemFlex , a shared memory swapper for improving guest swapping performance in virtualized environment with three novel features: (1) MemFlex effectively utilizes host idle memory by redirecting the VM swapping traffic to the host-guest shared memory area. (2) MemFlex provides a hybrid memory swapping model, which treats a fast but small shared memory swap partition as the primary swap area whenever it is possible, and smoothly transits to the conventional disk-based VM swapping on demand. (3) Upon ballooned with sufficient VM memory, MemFlex provides a fast swap-in optimization, which enables the VM to proactively swap in the pages from the shared memory using an efficient batch implementation. Instead of relying on costly page faults, this optimization offers just-in-time performance recovery by enabling the memory intensive applications to quickly regain their runtime momentum. Performance evaluation results are presented to demonstrate the effectiveness of MemFlex when compared with existing swapping approaches. [ABSTRACT FROM PUBLISHER]

Checkpointing and rollback techniques enhance reliability and availability of virtual machines and their hosted IT services. This paper proposes VM-μCheckpoint, a light-weight pure-software mechanism for high-frequency checkpointing and rapid recovery for VMs. Compared with existing techniques of VM checkpointing, VM-μCheckpoint tries to minimize checkpoint overhead and speed up recovery by means of copy-on-write, dirty-page prediction and in-place recovery, as well as saving incremental checkpoints in volatile memory. Moreover, VM-μCheckpoint deals with the issue that latency in error detection potentially results in corrupted checkpoints, particularly when checkpointing frequency is high. We also constructed Markov models to study the availability improvements provided by VM-μCheckpoint (from 99 to 99.98 percent on reasonably reliable hypervisors). We designed and implemented VM-μCheckpoint in the Xen VMM. The evaluation results demonstrate that VM-μCheckpoint incurs an average of 6.3 percent overhead (in terms of program execution time) for 50 ms checkpoint intervals when executing the SPEC CINT 2006 benchmark. Error injection experiments demonstrate that VM-μCheckpoint, combined with error detection techniques in RMK, provides high coverage of recovery. [ABSTRACT FROM AUTHOR]