This book constitutes the refereed proceedings of the 8th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2013, held in Zhangjiajie, China, in August 2013. The 25 revised full papers presented together with 18 invited papers were carefully reviewed and selected from 80 submissions. The papers cover the following topics: effective and efficient state-of-the-art algorithm design and analysis, reliable and secure system development and implementations, experimental study and testbed validation, and new application exploration in wireless networks.

This book presents the state-of-the-art work in terms of searchable storage in cloud computing. It introduces and presents new schemes for exploring and exploiting the searchable storage via cost-efficient semantic hashing computation. Specifically, the contents in this book include basic hashing structures (Bloom filters, locality sensitive hashing, cuckoo hashing), semantic storage systems, and searchable namespace, which support multiple applications, such as cloud backups, exact and approximate queries and image analytics. Readers would be interested in the searchable techniques due to the ease of use and simplicity. More importantly, all these mentioned structures and techniques have been really implemented to support real-world applications, some of which offer open-source codes for public use. Readers will obtain solid backgrounds, new insights and implementation experiences with basic knowledge in data structure and computer systems.

Real-time embedded systems are widely deployed in mission-critical applications, such as avionics mission computing, highway traffic control, remote patient monitoring, wireless communications, navigation, etc. These applications always require their real-time and embedded components to work in open and unpredictable environments, where workload is volatile and unknown. In order to guarantee the temporal correctness and avoid severe underutilization or overload, it is of vital significance to measure, control, and optimize the processor utilization adaptively. This monograph examines utilization control and optimization in real-time embedded systems. In many practical real-time embedded applications, it is desired to keep the processors' utilizations at the schedulable upper bounds. In this way, the systems deliver their best Quality of Service (QoS), and, at the same time, all real-time tasks remain schedulable. In order to achieve this goal, the authors present several effective solutions that adaptively adjust task rates and/or processor frequencies to enforce the desired utilization. Feedback control and optimization techniques have been leveraged to ensure that a system is neither overloaded nor underutilized.

Datacenter Power Management in Smart Grids overviews recent work on managing and minimizing the cost of data centers in the context of smart grids. It starts by reviewing the operation of smart grids and analyzing how power is consumed in datacenters. Then, it presents various cost minimization approaches using techniques from the fields of optimization, algorithmics, and feedback control. In particular, it focuses on approaches that utilize time-of-use pricing and demand response features to cut the datacenter electricity cost. In a cloud computing environment, companies or individuals offload their computing to the cloud, which is supported by the computing infrastructure called datacenters. The operation of these datacenters consumes large amounts of electricity, bringing high costs and negatively impacting the environment. In the mean time, a new kind of electrical grid, the smart grid, is emerging. Smart grids enable two-way communications between the power generators and the power consumers. Smart grid technology brings many salient features to help deliver power efficiently and reliably. While a lot of research has been conducted on both datacenters and smart grids, Datacenter Power Management in Smart Grids takes the novel approach of considering both together and focuses on cost-aware datacenter power management in the presence of smart grids. This work reviews recent developments in this area and explains how a smart grid operates, where power goes in datacenters, and, most importantly, how to reduce the power cost and/or negative environmental impact when operating datacenters.

Performance management and fault tolerance are two important issues faced by computing systems research. In this dissertation, we exploit the use of feedback control for performance management and fault tolerance. Specifically, we propose Queueing Model Based Feedback Control scheme to achieve performance regulation. It integrates the descriptive'' power of queueing theory and the prescriptive'' power of feedback control to control computing system's performance. In the second part of this dissertation, we further exploit the use of feedback control to achieve fault tolerance for real-time embedded control systems. We propose ORTEGA (On-demand Real-TimE GuArd), a new fault tolerance architecture which utilizes feedback control based software execution. It can be deployed in a wide range of real-time embedded applications to provide fault tolerance. We implemented ORTEGA in an inverted pendulum testbed to demonstrate its efficacy and efficiency. Based on the ORTEGA design, we discuss the fault tolerance and scheduling co-design problem and its solutions.

Fluid-particle and granular flows exhibit rather complex behavior, for example, the occurrence of bubbles and clusters in gas-particle flows, and clogging and size segregation in granular flows. This work is to advance our understanding of granular and gas particle flows using computational simulations. In gas-particle fluidized beds, confined between parallel solid walls, non-uniform solids distribution is observed and the flow profiles are strongly related to the physical and operating parameters, such as particle inelasticity, gravity, bed width and mean solids fraction. A stability analysis has been carried out to investigate the instabilities in gas-particle flows and the generation of cluster, bubbles or streamers in gas-fluidized beds. For boundary driven and body-force-driven granular flows and gas-particle fluidized beds with polydisperse particle mixtures, the particle species segregation is enhanced with a decrease in the system elasticity, an increase in the average solids fraction or an increase in the size ratio, due to the competition of diffusion forces. The distribution of granular energy and its effect on the segregation is also considered in this work.