SIGMETRICS '12:ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Jun 11, 2012-Jun 15, 2012 London, United Kingdom. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

Efficiency is a crucial concern across computing systems, from the edge to the cloud. Paradoxically, even as the latencies of bottleneck components such as storage and networks have dropped by up to four orders of magnitude, software path lengths have progressively increased due to overhead from the very frameworks that have revolutionized the pace of information technology. Such overhead can be severe enough to overshadow the benefits from switching to new technologies like persistent memory and low latency interconnects. Resource Proportional Software Design for Emerging Systems introduces resource proportional design (RPD) as a principled approach to software component and system development that counters the overhead of deeply layered code without removing flexibility or ease of development. RPD makes resource consumption proportional to situational utility by adapting to diverse emerging needs and technology systems evolution. Highlights: Analysis of run-time bloat in deep software stacks, an under-explored source of power-performance wastage in IT systems Qualitative and quantitative treatment of key dimensions of resource proportionality Code features: Unify and broaden supported but optional features without losing efficiency Technology and systems evolution: Design software to adapt with changing trade-offs as technology evolves Data processing: Design systems to predict which subsets of data processed by an (analytics or ML) application are likely to be useful System wide trade-offs: Address interacting local and global considerations throughout software stacks and hardware including cross-layer co-design involving code, data and systems dimensions, and non-functional requirements such as security and fault tolerance Written from a systems perspective to explore RPD principles, best practices, models and tools in the context of emerging technologies and applications This book is primarily geared towards practitioners with some advanced topics for researchers. The principles shared in the book are expected to be useful for programmers, engineers and researchers interested in ensuring software and systems are optimized for existing and next generation technologies. The authors are from both industry (Bhattacharya and Voigt) and academic (Gopinath) backgrounds.

With the number of machine-to-machine (M2M)–enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology. Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology. Details a practical scheme for the forward error correction code design Investigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communications Identifies algorithms that will ensure functionality, performance, reliability, and security of M2M systems Illustrates the relationship between M2M systems and the smart power grid Presents techniques to ensure integration with and adaptation of existing communication systems to carry M2M traffic Providing authoritative insights into the technologies that enable M2M communications, the book discusses the challenges posed by the use of M2M communications in the smart grid from the aspect of security and proposes an efficient intrusion detection system to deal with a number of possible attacks. After reading this book, you will develop the understanding required to solve problems related to the design, deployment, and operation of M2M communications networks and systems.

This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.

This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture. It also comprises current research trends in emerging areas, such as data security, data protection management, and network resource management in data centers. Specific attention is devoted to industry needs associated with the challenges faced by data centers, such as various power, cooling, floor space, and associated environmental health and safety issues, while still working to support growth without disrupting quality of service. The contributions cut across various IT data technology domains as a single source to discuss the interdependencies that need to be supported to enable a virtualized, next-generation, energy efficient, economical, and environmentally friendly data center. This book appeals to a broad spectrum of readers, including server, storage, networking, database, and applications analysts, administrators, and architects. It is intended for those seeking to gain a stronger grasp on data center networks: the fundamental protocol used by the applications and the network, the typical network technologies, and their design aspects. The Handbook of Data Centers is a leading reference on design and implementation for planning, implementing, and operating data center networks.

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book