@conference {59, title = {On the Importance of Evaluating Storage Systems{\textquoteright} $Costs}, booktitle = {The 6th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage {\textquoteright}14)}, year = {2014}, month = {06/2014}, address = {Philadelphia, PA}, abstract = {

Modern storage systems are becoming more complex, combining different storage technologies with different behaviors. \ Performance alone is not enough\  to charac- terize storage systems: energy efficiency, durability, and more are becoming equally\  important. We posit that one must evaluate storage systems from a monetary cost per- spective as\  well as performance. \ We believe that cost should consider the workloads used over the storage sys-\  tems{\textquoteright} expected lifetime. \ We designed and developed a versatile hybrid storage system under Linux\  that com- bines HDD and SSD. The SSD can be used as cache or as primary storage for hot data. Our\  system includes tun- able parameters to enable trading off performance, en- ergy use, and\  durability. We built a cost model and eval- uated our system under a variety of workloads and pa-\  rameters, to illustrate the importance of cost evaluations of storage systems.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/hotstorage14-cost/cost.pdf}, author = {Zhichao Li and Amanpreet Mukker and Erez Zadok} } @conference {33, title = {Jigsaw: Indoor Floor Plan Reconstruction via Mobile Crowdsensing}, booktitle = {20th Annual International Conference on Mobile Computing and Networking (MobiCom {\textquoteright}14)}, year = {2014}, month = {09/2014}, address = {Hawaii, USA}, abstract = {

The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and timeconsuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1 \~{} 2m and 5 \~{} 9{\textopenbullet}, while the hallway connectivity is 100\% correct.

}, keywords = {indoor floor plan reconstruction; mobile crowdsensing}, url = {http://www.ece.sunysb.edu/~fanye/papers/jigsaw-mobicom14.pdf}, author = {Ruipeng Gao and Mingmin Zhao and Tao Ye and Fan Ye and Yizhou Wang and Kaigui Bian and Tao Wang and Xiaoming Li} } @conference {34, title = {NETWRAP: An NDN Based Real-Time Wireless Recharging Framework for Wireless Sensor}, booktitle = { IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS {\textquoteright}14)}, year = {2014}, abstract = {

Using vehicles equipped with wireless energy transmission technology to recharge sensor nodes over the air is a game-changer for traditional wireless sensor networks. The recharging policy regarding when to recharge which sensor nodes critically impacts the network performance. So far only a few works have studied such recharging policy for the case of using a single vehicle. In this paper, we propose NETWRAP, an NDN based Real Time Wireless Recharging Protocol for dynamic wireless recharging in sensor networks. The real-time recharging framework supports single or multiple mobile vehicles. Employing multiple mobile vehicles provides more scalability and robustness. To efficiently deliver sensor energy status information to vehicles in real-time, we leverage concepts and mechanisms from NDN (Named Data Networking) and design energy monitoring and reporting protocols. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. Then we study how to minimize the total traveling cost of vehicles while guaranteeing all the sensor nodes can be recharged before their batteries deplete. We formulate the recharge optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions with low overhead, we present an algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also ensures the perpetual operation of networks. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed design. The results also validate the correctness of the theoretical analysis and show significant improvements that cut the number of nonfunctional nodes by half compared to the static scheme while maintaining the network overhead at the same level.

}, keywords = {mobile energy replenishing, mobile vehicles, named data networking, perpetual operation, recharge coordination, wireless recharging, Wireless sensor networks}, url = {http://www.ece.sunysb.edu/~fanye/papers/tmc14-netwrap.pdf}, author = {Cong Wang and Ji Li and Fan Ye and Yuanyuan Yang} } @conference {35, title = {Recharging Schedules for Wireless Sensor Networks with Vehicle Movement Costs and Capacity Constraints}, booktitle = {IEEE International Conference on Sensing, Communication, and Networking (SECON {\textquoteright}14)}, year = {2014}, abstract = {

Several recent works have studied the schedule for mobile vehicles to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked the important factors of the vehicles{\textquoteright} moving energy consumption and limited recharging capacity. These oversights may lead to problematic schedules or even stranded vehicles. In this paper, we study the recharging schedule that maximizes the recharging profit - the amount of replenished energy less the cost of vehicle movements - under these important constraints. We first derive the minimum number of vehicles needed for energy neutral condition and discover a set of desired network properties. Then we formulate the recharge schedule optimization into a Profitable Traveling Salesmen Problem with capacity and battery deadline constraints, which we prove to be NP-hard. We propose two algorithms to solve the problem. The first one is a greedy algorithm that maximizes the recharge profit at each step; the second one first adaptively partitions the network based on recharge requests, then forms Capacitated Minimum Spanning Tree in each partition followed by route improvements. Finally, we evaluate and compare the performance of proposed algorithms and validate the correctness of theoretical results through extensive simulations. Given a sufficient number of vehicles, the adaptive algorithm can keep the number of nonfunctional nodes at zero. Compared to the greedy algorithm, it reduces the percentage of transient energy depletion by 30-50\% with 10-20\% energy saving on vehicles.

}, keywords = {adaptive network partitioning, data collection, perpetual operations, vehicle scheduling, Wireless rechargeable sensor networks}, url = {http://www.ece.sunysb.edu/~fanye/papers/secon14.pdf}, author = {Cong Wang and Ji Li and Fan Ye and Yuanyuan Yang} } @article {69, title = {Cpuidle from user space}, year = {2013}, month = {12/2013}, institution = {Stony Brook University}, type = {Technical Report}, address = {Stony Brook, NY}, abstract = {

In this paper we present a user space cpuidle governor. In addition to providing a user space interface to pick idle states for individual cores in a multicore system, the governor also ensures that each core stays in the specified idle state forever. In other words, the cores do not wake up from a specified idle state unless specified by the user. This gives a user complete control over a core{\textquoteright}s idle states without worrying about any kind of wake-ups. A user space governor can be very useful in scenarios where every workload is run with a customized cpuidle power saving algorithm. Coding different algorithms and dynamically switching between them is fairly simpler in user space when compared to kernel space. From our evaluation results, we have concluded that this technique does not hurt power consumption savings or performance benefits in any way.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/cpuidle/cpuidle-from-userspace.pdf}, author = {Madhu Palmur and Zhichao Li and Erez Zadok} } @conference {61, title = {Power Consumption in Enterprise-Scale Backup Storage Systems}, booktitle = {USENIX Conference on File and Storage Technologies (FAST {\textquoteright}12)}, year = {2012}, month = {02/2012}, publisher = {USENIX Association}, organization = {USENIX Association}, address = {San Jose, CA}, abstract = {

Power consumption has become an important factor in modern storage system design. Power efficiency is particularly beneficial in disk-based backup systems that store mostly cold data, have significant idle periods, and must compete with the operational costs of tape-based backup. There are no prior published studies on power consumption in these systems, leaving researchers and practitioners to rely on existing assumptions. In this paper we present the first analysis of power consumption in real-world, enterprise, disk-based backup storage systems. We uncovered several important observations, including some that challenge conventional wisdom. We discuss their impact on future power-efficient designs.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/fast-emc-energy/power_management.pdf}, author = {Z. Li and K. M. Greenan and A. W. Leung and E. Zadok} } @conference {63, title = {On the Energy Consumption and Performance of Systems Software}, booktitle = {4th Israeli Experimental Systems Conference (ACM SYSTOR {\textquoteright}11)}, year = {2011}, month = {06/2011}, address = {Haifa, Israel}, abstract = {

Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system{\textquoteright}s energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/erss11model/erss11model.pdf}, author = {Zhichao L and Radu Grosu and Priya Sehgal and Scott A. Smolka and Scott D. Stoller and Erez Zadok} } @conference {62, title = {Model Discovery for Energy-Aware Computing Systems: An Experimental Evaluation}, booktitle = {1st Workshop on Energy Consumption and Reliability of Storage Systems (ERSS {\textquoteright}11)}, year = {2011}, month = {07/2011}, address = {Orlando, FL}, abstract = {
We present a model-discovery methodology for energy-aware computing systems that achieves high prediction accuracy. Model discovery, or system identification, is a critical first step in designing advanced controllers that can dynamically manage the energy-performance trade-off in an optimal manner. Our methodology favors Multiple-Inputs-Multiple-Outputs (MIMO) models over a collection of Single-Input-Single-Output (SISO) models, when the inputs and outputs of the system are coupled in a nontrivial way. In such cases, MIMO is generally more accurate than SISO over a wide range of inputs in predicting system behavior. Our experimental evaluation, carried out on a representative server workload, validates our approach. We obtained an average prediction accuracy of 77\% and 76\% for MIMO power and performance, respectively. We also show that MIMO models are consistently more accurate than SISO ones.
}, url = {http://www.fsl.cs.stonybrook.edu/docs/erss11model/erss11model.pdf}, author = {Zhichao Li and Radu Grosu and Koundinya Muppalla and Scott A. Smolka and Scott D. Stoller and Erez Zadok} }