@conference {82, title = {Autoscaling for Hadoop Clusters}, year = {2016}, abstract = {

Unforeseen events such as node failures and re- source contention can have a severe impact on the performance of data processing frameworks, such as Hadoop, especially in cloud environments where such incidents are common. SLA compliance in the presence of such events requires the ability to quickly and dynamically resize infrastructure resources. Unfortunately, the distributed and stateful nature of data processing frameworks makes it challenging to accurately scale the system at run-time. In this paper, we present the design and implementation of a model-driven autoscaling solution for Hadoop clusters. We first develop novel gray-box performance models for Hadoop workloads that specifically relate job execution times to resource allocation and workload parameters. We then employ these models to dynamically determine the resources required to successfully complete the Hadoop jobs as per the user-specified SLA under various scenarios including node failures and multi-job executions. Our experimental results on three different Hadoop cloud clusters and across different workloads demonstrate the efficacy of our models and highlight their autoscaling capabilities.

}, author = {Anshul Gandhi and Sidhartha Thota and Parijat Dube and Andrzej Kochut and Li Zhang} } @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} } @article {57, title = {Visual Correlation Analysis of Numerical and Categorical Data on the Correlation Map}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG {\textquoteright}14)}, year = {2014}, month = {12/2014}, abstract = {

Correlation analysis can reveal the complex relationships that often exist among the variables in multivariate data.\  However, as the number of variables grows, it can be difficult to gain a good understanding of the correlation landscape and\  important intricate relationships might be missed. We previously introduced a technique that arranged the variables into a 2D\  layout, encoding their pairwise correlations. We then used this layout as a network for the interactive ordering of axes in parallel\  coordinate displays. Our current work expresses the layout as a correlation map and employs it for visual correlation analysis. In\  contrast to matrix displays where correlations are indicated at intersections of rows and columns, our map conveys correlations\  by spatial proximity which is more direct and more focused on the variables in play. We make the following new contributions,\  some unique to our map: (1) we devise mechanisms that handle both categorical and numerical variables within a unified\  framework, (2) we achieve scalability for large numbers of variables via a multi-scale semantic zooming approach, (3) we\  provide interactive techniques for exploring the impact of value bracketing on correlations, and (4) we visualize data relations\  within the sub-spaces spanned by correlated variables by projecting the data into a corresponding tessellation of the map.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/tvcg14/TVCG_Correlation_MinorRev_Final.pdf}, author = {Zhiyuan Zhang and Kevin T. McDonnell and Erez Zadok and Klaus Mueller} } @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 {60, title = {The Economics of Long-Term Digital Storage}, booktitle = {The Memory of the World in the Digital Age: Digitization and Preservation}, year = {2012}, month = {09/2012}, publisher = {United Nations Educational, Scientific and Cultural Organization (UNESCO)}, organization = {United Nations Educational, Scientific and Cultural Organization (UNESCO)}, address = {Vancouver, Canada}, abstract = {

Paper as the medium for the world{\textquoteright}s memory has one great advantage; it survives benign neglect well. Bits, on the other hand, need continual care, and thus a con- tinual flow of money. A Blue Ribbon panel described economic sustainability as the major issue facing long- term digital preservation. This is despite Kryder{\textquoteright}s Law, the 30-year history of the cost of digital storage media dropping exponentially. If economics are the major con- cern even when Kryder{\textquoteright}s Law holds, what will happen if it slows or stops? We present evidence that it will, and some simulations of the impact on digital preservation costs.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/unesco12/UNESCO2012-storage-econ.pdf}, author = {David S. H. Rosenthal and Daniel C. Rosenthal and Ethan L. Miller and Ian F. Adams and Mark W. Storer 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 {28, title = {Saving Cash by Using Less Cache}, booktitle = {4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud {\textquoteright}12)}, year = {2012}, abstract = {
Everyone loves a large caching tier in their multi-tier cloud-based web service because it both alleviates\ database load and provides lower request latencies. Even\ when load drops severely, administrators are reluctant to\ scale down their caching tier. This paper makes the case\ that (i) scaling down the caching tier is viable with re-\ spect to performance, and (ii) the savings are potentially\ huge; e.g., a 4x drop in load can result in 90\% savings in\ the caching tier size.
}, url = {http://www3.cs.stonybrook.edu/~anshul/hotcloud_2012.pdf}, author = {Timothy Zhu and Anshul Gandhi and Mor Harchol-Balter and Michael Kozuch} } @conference {23, title = {SOFTScale: Stealing Opportunistically For Transient Scaling}, booktitle = {13th International Middleware Conference (Middleware {\textquoteright}12)}, year = {2012}, abstract = {

Dynamic capacity provisioning is a well studied approach to handling gradual changes in data center load. However, abrupt spikes in load are still problematic in that the work in system rises very quickly during the setup time needed to turn on additional capacity. Performance can be severely affected even if it takes only 5 seconds to bring additional capacity online.

In this paper, we propose SOFTScale, an approach to handling load spikes in multi-tier data centers without having to over-provision resources. SOFTScale works by opportunistically stealing resources from other tiers to alleviate the bottleneck tier, even when the tiers are carefully provisioned at capacity. SOFTScale is especially useful during the transient overload periods when additional capacity is being brought online.

Via implementation on a 28-server multi-tier testbed, we investigate a range of possible load spikes, including an artificial doubling or tripling of load, as well as large spikes in real traces. We find that SOFTScale can meet our stringent response time Service Level Agreement goal of 500ms without using any additional resources even under some extreme load spikes that would normally cause the system (without SOFTScale) to exhibit response times as high as 96 seconds.

}, keywords = {Data centers, load spikes, power management, resource provisioning}, url = {http://www3.cs.stonybrook.edu/~anshul/middleware12.pdf}, author = {Anshul Gandhi and Timothy Zhu and Mor Harchol-Balter and Michael Kozuch} } @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 {64, title = {Evaluating Performance and Energy in File System Server Workloads}, booktitle = {USENIX Conference on File and Storage Technologies (FAST {\textquoteright}10)}, year = {2011}, month = {07/2011}, address = {Orlando, FL}, abstract = {

Recently, power has emerged as a critical factor in designing components of storage systems, especially for power-hungry data centers. While there is some research into power-aware storage stack components, there are no systematic studies evaluating each component{\textquoteright}s impact separately. This paper evaluates the file system{\textquoteright}s impact on energy consumption and performance. We studied several popular Linux file systems, with various mount and format options, using the FileBench workload generator to emulate four server workloads: Web, database, mail, and file server. In case of a server node consisting of a single disk, CPU power generally exceeds diskpower consumption. However, file system design, implementation, and available features have a significant effect on CPU/disk utilization, and hence on performance and power. We discovered that default file system options are often suboptimal, and even poor. We show that a careful matching of expected workloads to file system types and options can improve power-performance efficiency by a factor ranging from 1.05 to 9.4 times.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/fsgreen-bench/fs-green-comparison.pdf}, author = {Priya Sehgal and Vasily Tarasov 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} } @article {58, title = {Optimizing Energy and Performance for Server-Class File System Workloads}, journal = {ACM Transactions on Storage (TOS {\textquoteright}10)}, volume = {6}, year = {2010}, month = {09/2010}, abstract = {

Recently, power has emerged as a critical factor in designing components of storage systems, especially for power-hungry data centers. While there is some research into power-aware storage stack components, there are no systematic studies evaluating each component{\textquoteright}s impact separately. Various factors like workloads, hardware configurations, and software configurations impact the performance and energy efficiency of the system. This article evaluates the file system{\textquoteright}s impact on energy consumption and performance. We studied several popular Linux file systems, with various mount and format options, using the FileBench workload generator to emulate four server workloads: Web, database, mail, and fileserver, on two different hardware configurations. The file system design, implementation, and available features have a significant effect on CPU/disk utilization, and hence on performance and power. We discovered that default file system options are often suboptimal, and even poor. In this article we show that a careful matching of expected workloads and hardware configuration to a single software configuration{\textemdash}the file system{\textemdash}can improve power-performance efficiency by a factor ranging from 1.05 to 9.4 times.

}, url = {http://dl.acm.org/citation.cfm?id=1837918}, author = {Priya Sehgal and Vasily Tarasov and Erez Zadok} } @conference {65, title = {Energy and Performance Evaluation of Lossless File Data Compression on Server Systems}, booktitle = {The Israeli Experimental Systems Conference (SYSTOR {\textquoteright}09)}, year = {2009}, month = {05/2009}, publisher = {ACM}, organization = {ACM}, address = {Haifa, Israel}, abstract = {

Data compression has been claimed to be an attractive solution to save energy consumption in high-end servers and data centers. However, there has not been a study to explore this. In this paper, we present a comprehensive evaluation of energy consumption for various file compression techniques implemented in software. We apply various compression tools available on Linux to a variety of data files, and we try them on server class and workstation class systems. We compare their energy and performance results against raw reads and writes. Our results reveal that software based data compression cannot be considered as a universal solution to reduce energy consumption. Various factors like the type of the data file, the compression tool being used, the read-to-write ratio of the workload, and the hardware configuration of the system impact the efficacy of this technique. In some cases, however, we found compression to save substantial energy and improve performance.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/greencomp/green-compress.pdf}, author = {Rachita Kothiyal and Vasily Tarasov and Priya Sehgal and Erez Zadok} }