@article{Lam2006441,
title = "Adaptive schemes for location update generation in execution location-dependent continuous queries ",
journal = "Journal of Systems and Software ",
volume = "79",
number = "4",
pages = "441 - 453",
year = "2006",
note = "",
issn = "0164-1212",
doi = "http://dx.doi.org/10.1016/j.jss.2005.07.015",
url = "//www.sciencedirect.com/science/article/pii/S0164121205001299",
author = "Kam-Yiu Lam and Özgür Ulusoy",
keywords = "Location-dependent continuous queries",
keywords = "Location update",
keywords = "Moving object database",
keywords = "Location management ",
abstract = "An important feature that is expected to be owned by today’s mobile computing systems is the ability of processing location-dependent continuous queries on moving objects. The result of a location-dependent query depends on the current location of the mobile client which has generated the query as well as the locations of the moving objects on which the query has been issued. When a location-dependent query is specified to be continuous, the result of the query can continuously change. In order to provide accurate and timely query results to a client, the location of the client as well as the locations of moving objects in the system has to be closely monitored. Most of the location generation methods proposed in the literature aim to optimize utilization of the limited wireless bandwidth. The issues of correctness and timeliness of query results reported to clients have been largely ignored. In this paper, we propose an adaptive monitoring method (AMM) and a deadline-driven method (DDM) for managing the locations of moving objects. The aim of our methods is to generate location updates with the consideration of maintaining the correctness of query evaluation results without increasing location update workload. Extensive simulation experiments have been conducted to investigate the performance of the proposed methods as compared to a well-known location update generation method, the plain dead-reckoning (pdr). "
}
@article{Lee20091984,
title = "Selectivity-sensitive shared evaluation of multiple continuous \{XPath\} queries over \{XML\} streams ",
journal = "Information Sciences ",
volume = "179",
number = "12",
pages = "1984 - 2001",
year = "2009",
note = "Special Section: Web Search ",
issn = "0020-0255",
doi = "http://dx.doi.org/10.1016/j.ins.2009.01.022",
url = "//www.sciencedirect.com/science/article/pii/S0020025509000309",
author = "Hyun-Ho Lee and Won-Suk Lee",
keywords = "\{XML\} stream",
keywords = "Multiple continuous \{XPath\} queries",
keywords = "XP-table",
keywords = "Early-query-termination strategy",
keywords = "Stream relation",
keywords = "Selectivity",
keywords = "Adaptive optimization ",
abstract = "One of the primary issues confronting \{XML\} message brokers is the difficulty associated with processing a large set of continuous \{XPath\} queries over incoming \{XML\} streams. This paper proposes a novel system designed to present an effective solution to this problem. The proposed system transforms multiple \{XPath\} queries before their run-time into a new data structure, called an XP-table, by sharing their common constraints. An XP-table is matched with a stream relation (SR) transformed from a target \{XML\} stream by a \{SAX\} parser. This arrangement is intended to minimize the run-time workload of continuous query processing. In addition, an early-query-termination strategy is proposed as an improved alternative to the basic approach. It optimizes query processing by arranging the evaluation sequence of the member-lists (m-lists) of an XP-table adaptively and offers increased efficiency, especially in cases of low selectivity. System performance is estimated and verified through a variety of experiments, including comparisons with previous approaches such as \{YFilter\} and LazyDFA. The proposed system is practically linear-scalable and stable for evaluating a set of \{XPath\} queries in a continuous and timely fashion. "
}
@article{Yuan20131573,
title = "Adaptive resource management for \{P2P\} live streaming systems ",
journal = "Future Generation Computer Systems ",
volume = "29",
number = "6",
pages = "1573 - 1582",
year = "2013",
note = "Including Special sections: High Performance Computing in the Cloud & Resource Discovery Mechanisms for \{P2P\} Systems ",
issn = "0167-739X",
doi = "http://dx.doi.org/10.1016/j.future.2012.09.002",
url = "//www.sciencedirect.com/science/article/pii/S0167739X12001756",
author = "Xiaoqun Yuan and Geyong Min and Yi Ding and Qiong Liu and Jinhong Liu and Hao Yin and Qing Fang",
keywords = "\{P2P\} live streaming",
keywords = "Multiple channel",
keywords = "Channel streaming quality",
keywords = "Resource allocation",
keywords = "Scalability model ",
abstract = "Peer-to-Peer (P2P) has become a popular live streaming delivery technology owing to its scalability and low cost. \{P2P\} streaming systems often employ multi-channels to deliver streaming to users simultaneously, which leads to a great challenge of allocating server resources among these channels appropriately. Most existing \{P2P\} systems resort to over-allocating server resources to different channels, which results in low-efficiency and high-cost. To allocate server resources to different channels efficiently, we propose a dynamic resource allocation algorithm based on a streaming quality model for \{P2P\} live streaming systems. This algorithm can improve the channel streaming quality for multi-channel \{P2P\} live streaming system and also guarantees the streaming quality of the channels under extreme Internet conditions. In an experiment, the proposed algorithm is validated by the trace data. "
}
@article{Birke20122237,
title = "A delay-based aggregate rate control for \{P2P\} streaming systems ",
journal = "Computer Communications ",
volume = "35",
number = "18",
pages = "2237 - 2244",
year = "2012",
note = "",
issn = "0140-3664",
doi = "http://dx.doi.org/10.1016/j.comcom.2012.07.005",
url = "//www.sciencedirect.com/science/article/pii/S0140366412002332",
author = "Robert Birke and Csaba Kiraly and Emilio Leonardi and Marco Mellia and Michela Meo and Stefano Traverso",
keywords = "Peer-to-Peer",
keywords = "Video streaming",
keywords = "Measurement",
keywords = "Flow control ",
abstract = "In this paper we consider mesh based \{P2P\} streaming systems focusing on the problem of regulating peer transmission rate to match the system demand while not overloading each peer upload link capacity. We propose Hose Rate Control (HRC), a novel scheme to control the speed at which peers offer chunks to other peers, ultimately controlling peer uplink capacity utilization. This is of critical importance for heterogeneous scenarios like the one faced in the Internet, where peer upload capacity is unknown and varies widely. \{HRC\} nicely adapts to the actual peer available upload bandwidth and system demand, so that Quality of Experience is greatly enhanced. To support our claims we present both simulations and actual experiments involving more than 1000 peers to assess performance in real scenarios. Results show that \{HRC\} consistently outperforms the Quality of Experience achieved by non-adaptive schemes. "
}
@article{Pozueco2013775,
title = "Adaptable system based on Scalable Video Coding for high-quality video service ",
journal = "Computers & Electrical Engineering ",
volume = "39",
number = "3",
pages = "775 - 789",
year = "2013",
note = "Special issue on Image and Video ProcessingSpecial issue on Recent Trends in Communications and Signal Processing ",
issn = "0045-7906",
doi = "http://dx.doi.org/10.1016/j.compeleceng.2013.01.015",
url = "//www.sciencedirect.com/science/article/pii/S0045790613000219",
author = "Laura Pozueco and Xabiel García Pañeda and Roberto García and David Melendi and Sergio Cabrero",
abstract = "Content adaptation to a heterogeneous environment like the Internet is a key process for improving the perceived quality of the user. This paper presents an adaptive streaming system using Scalable Video Coding (SVC) technology. Using feedback information from clients about the transmission status, the server is able to select the most suitable combination of \{SVC\} layers for the available bandwidth. The estimation of the available bandwidth is carried out with non-intrusive methods, based on classic metrics such as packet loss, jitter and novel metrics like the linearity of reception times of \{RTP\} packets. The system is implemented in real equipment and the results show the correct operation and the accuracy of the system when adapting to different variations of the available bandwidth. We also study the scalability of the system when several clients access the service simultaneously, demonstrating that our system is as scalable as a non-adaptive system with SVC. "
}
@article{Works2015127,
title = "Practical Identification of Dynamic Precedence Criteria to Produce Critical Results from Big Data Streams ",
journal = "Big Data Research ",
volume = "2",
number = "4",
pages = "127 - 144",
year = "2015",
note = "",
issn = "2214-5796",
doi = "http://dx.doi.org/10.1016/j.bdr.2015.09.001",
url = "//www.sciencedirect.com/science/article/pii/S2214579615000490",
author = "Karen Works and Elke A. Rundensteiner",
keywords = "Big data streams",
keywords = "Critical result production",
keywords = "Rapid online adaption ",
abstract = "Abstract During periods of high volume, big data stream applications may not have enough resources to process all incoming tuples. To maximize the production of the most critical results under such resource shortages, a recent solution, \{PR\} (short for Preferential Result), utilizes both static criteria (defined at compile-time) and dynamic criteria (identified online at run-time) to prioritize the processing of tuples throughout the query pipeline. Unfortunately, locating the optimal criteria placement (i.e., where in the query pipeline to evaluate each prioritization criteria) is extremely compute-intensive and runs in exponential time. This makes \{PR\} impractical for complex big data stream systems. Our proposed criteria selection and placement approach, PR-Prune (short for Preferential Result-Pruning), is practical. PR-Prune prunes ineffective dynamic criteria and combines multiple criteria along the same pipeline. To achieve this, PR-Prune seeks to expand the duration in the query pipeline that tuples identified as critical are pulled forward. Our experiments use a real data stream from the S&P 500 stocks, synthetic data streams, and a diverse set of queries. The results substantiate that PR-Prune increases the production of the most critical results compared to the state-of-the-art approaches. In addition, PR-Prune significantly lowers the optimization search time compared to PR. "
}
@article{Hu201414,
title = "Exploring the optimal substream scheduling and distribution mechanism for data-driven \{P2P\} media streaming ",
journal = "Computer Communications ",
volume = "44",
number = "",
pages = "14 - 25",
year = "2014",
note = "",
issn = "0140-3664",
doi = "http://dx.doi.org/10.1016/j.comcom.2014.02.018",
url = "//www.sciencedirect.com/science/article/pii/S0140366414000711",
author = "Chao Hu and Ming Chen and Changyou Xing and Guomin Zhang",
keywords = "\{P2P\} live streaming",
keywords = "Substream",
keywords = "Scheduling and distribution",
keywords = "Data-driven overlay ",
abstract = "Abstract Most \{P2P\} live streaming systems divide video stream into fine-grained blocks, and paralleled distribute these blocks in overlay network to utilize the bandwidth and storage resources of end users, which improves the scalability. Although transmitting video stream with these blocks greatly guarantees the system robustness, it also brings long distribution delay and high protocol overhead issues. Therefore, aggregating these blocks to substreams and delivering them in data-driven mode can promote the distribution efficiency under the network environment with peer churn. In this paper, we study the substream scheduling and distribution mechanism in data-driven overlay network, and formulate it as a minimax problem. Subsequently, we propose a global centralized approach to solve this problem, and derive the optimal substream scheduling and distribution scheme. According to the design idea of global centralized solution, we present a distributed substream scheduling and distribution mechanism, which is adaptive to dynamic \{P2P\} network. Finally, we compare the performance of global centralized solution and distributed substream scheduling and distribution mechanism with block-based live streaming. The experiment results show substream-based design achieves better efficiency in video stream dissemination, and distributed substream scheduling and distribution mechanism can preserve high performance when peer churn appears. "
}
@article{Li2010537,
title = "On the source switching problem of Peer-to-Peer streaming ",
journal = "Journal of Parallel and Distributed Computing ",
volume = "70",
number = "5",
pages = "537 - 546",
year = "2010",
note = "",
issn = "0743-7315",
doi = "http://dx.doi.org/10.1016/j.jpdc.2010.01.005",
url = "//www.sciencedirect.com/science/article/pii/S0743731510000122",
author = "Zhenhua Li and Jiannong Cao and Guihai Chen and Yan Liu",
keywords = "Peer-to-Peer",
keywords = "Multimedia streaming",
keywords = "Source switching ",
abstract = "Peer-to-Peer(P2P) streaming has been proved a popular and efficient paradigm of Internet media streaming. In some applications, such as an Internet video distance education system, there are multiple media sources which work alternately. A fundamental problem in designing such kind of \{P2P\} streaming system is how to achieve fast source switching so that the startup delay of the new source can be minimized. In this paper, we propose an efficient solution to this problem. We model the source switch process, formulate it into an optimization problem and derive its theoretical optimal solution. Then we propose a practical greedy algorithm, named fast source switch algorithm, which approximates the optimal solution by properly interleaving the data delivery of different media sources. The algorithm can adapt to the dynamics and heterogeneity of real Internet environments. We have carried out extensive simulations on various real-trace \{P2P\} overlay topologies to demonstrate the effectiveness of our model and algorithm. The simulation results show that our proposed algorithm outperforms the normal source switch algorithm by reducing the source switch time by 20%–30% without bringing extra communication overhead. The reduction in source switching time is more obvious as the network scale increases. "
}
@article{Li2009901,
title = "Content and overlay-aware scheduling for peer-to-peer streaming in fluctuating networks ",
journal = "Journal of Network and Computer Applications ",
volume = "32",
number = "4",
pages = "901 - 912",
year = "2009",
note = "",
issn = "1084-8045",
doi = "http://dx.doi.org/10.1016/j.jnca.2009.01.001",
url = "//www.sciencedirect.com/science/article/pii/S1084804509000022",
author = "Jiaming Li and Chai Kiat Yeo",
keywords = "Overlay",
keywords = "Content-aware",
keywords = "Transmission scheduling",
keywords = "Peer-to-peer networking",
keywords = "Video streaming ",
abstract = "Due to the scalability of peer-to-peer structure, it is widely applied in multimedia streaming applications to provide services for large number of clients concurrently. A lot of research work has been done on improving the data distribution efficiency by adapting the overlay structure to the dynamically changing networks. Instead of changing overlay structure, we propose a data packet scheduling algorithm to improve transmission efficiency of the streaming system. Our scheduling algorithm distributedly prioritizes the data packets and sends the most important data to the whole system first. It avoids wasting network resources while frequently switching peer connections. The algorithm serves to provide an optimal ordering solution to minimize overall latency based on overlay structure information. With the help of content-aware weighting scheme, the scheduling algorithm also improves the streaming quality at the peers. Our algorithm shows good performance even under a dynamic and challenging network environment with negligible algorithm overhead. "
}
@article{Evensen2012312,
title = "Using bandwidth aggregation to improve the performance of quality-adaptive streaming ",
journal = "Signal Processing: Image Communication ",
volume = "27",
number = "4",
pages = "312 - 328",
year = "2012",
note = "Modern Media Transport – Dynamic Adaptive Streaming over \{HTTP\} (DASH) ",
issn = "0923-5965",
doi = "http://dx.doi.org/10.1016/j.image.2011.10.007",
url = "//www.sciencedirect.com/science/article/pii/S0923596511001317",
author = "Kristian Evensen and Dominik Kaspar and Carsten Griwodz and Pål Halvorsen and Audun F. Hansen and Paal Engelstad",
keywords = "Multihoming",
keywords = "Bandwidth aggregation",
keywords = "Video streaming",
keywords = "HTTP",
keywords = "Quality adaptive streaming ",
abstract = "Devices capable of connecting to multiple, overlapping networks simultaneously is becoming increasingly common. For example, most laptops are equipped with LAN- and WLAN-interface, and smart phones can typically connect to both \{WLANs\} and 3G mobile networks. At the same time, streaming high-quality video is becoming increasingly popular. However, due to bandwidth limitations or the unreliable and unpredictable nature of some types of networks, streaming video can be subject to frequent periods of rebuffering and characterized by a low picture quality. In this paper, we present a multilink extension to the data retrieval part of the \{DAVVI\} adaptive, segmented video streaming system. \{DAVVI\} implements the same core functionality as the \{MPEG\} \{DASH\} standard. It uses \{HTTP\} to retrieve data, segments video, provides clients with a description of the content, and allows clients to switch quality during playback. Any DAVVI-data retrieval extensions can also be implemented in a DASH-solution. The multilink-enabled \{DAVVI\} client divides video segments into smaller subsegments, which are requested over multiple interfaces simultaneously. The size of each subsegment is dynamic and calculated on the fly, based on the throughput of the different links. This is an improvement over our earlier subsegment approach, which divided segments into fixed size subsegments. The quality of the video is adapted based on the measured, aggregated throughput. Both the static and the dynamic subsegment approaches were evaluated with on-demand streaming and quasi-live streaming. The new subsegment approach reduces the number of playback interruptions and improves video quality significantly for all cases where the earlier approach struggled. Otherwise, they show similar performance. "
}
@article{Moon2016664,
title = "Adaptive interface selection over cloud-based split-layer video streaming via multi-wireless networks ",
journal = "Future Generation Computer Systems ",
volume = "56",
number = "",
pages = "664 - 674",
year = "2016",
note = "",
issn = "0167-739X",
doi = "http://dx.doi.org/10.1016/j.future.2015.09.022",
url = "//www.sciencedirect.com/science/article/pii/S0167739X15003027",
author = "Seonghoon Moon and Juwan Yoo and Songkuk Kim",
keywords = "Video streaming",
keywords = "Scalable video coding",
keywords = "Adaptive interface selection",
keywords = "Layer-splitting",
keywords = "Multiple wireless interfaces ",
abstract = "Abstract As mobile devices such as tablet \{PCs\} and smartphones proliferate, the online video consumption over a wireless network has been accelerated. From this phenomenon, there are several challenges to provide the video streaming service more efficiently and stably in the heterogeneous mobile environment. In order to guarantee the QoS of real-time \{HD\} video services, the steady and reliable wireless mesh is necessary. Furthermore, the video service providers have to maintain the QoS by provisioning streaming servers to respond the clients’ request of different video resolution. In this paper, we propose a reliable cloud-based video delivery scheme with the split-layer \{SVC\} encoding and real-time adaptive multi-interface selection over \{LTE\} and WiFi links. A split-layer video streaming can effectively scale to manage the required channels on each layer of various client connections. Moreover, split-layer \{SVC\} model brings streaming service providers a remarkable opportunity to stream video over multiple interfaces (e.g. WiFi, LTE, etc.) with a separate controlling based on their network status. Through the adaptive interface selection, the proposed system aims to ensure the maximizing video quality which the bandwidth of LTE/WiFi accommodates. In addition, the system offers cost-effective streaming to mobile clients by saving the \{LTE\} data consumption. In our system, an adaptive interface selection is developed with two different algorithms, such as \{INSTANT\} and \{EWMA\} methods. We implemented a prototype of mobile client based on iOS particularly by using iPhone5S. Moreover, we also employ the split-layer \{SVC\} encodes in streaming server-side as the add-on module to \{SVC\} reference encoding tool in a virtualized environment of \{KVM\} hypervisor. We evaluated the proposed system in an emulated and a real-world heterogeneous wireless network environments. The results show that the proposed system not only achieves to guarantee the highest quality of video frames via WiFi and \{LTE\} simultaneous connection, but also efficiently saves \{LTE\} bandwidth consumption for cost-effectiveness to client-side. Our proposed method provides the highest video quality without deadline misses, while it consumes 50.6% \{LTE\} bandwidth of ‘LTE-only’ method and 72.8% of the conventional (non-split) \{SVC\} streaming over a real-world mobile environment. "
}
@article{DeHon2006334,
title = "Stream computations organized for reconfigurable execution ",
journal = "Microprocessors and Microsystems ",
volume = "30",
number = "6",
pages = "334 - 354",
year = "2006",
note = "Special Issue on FPGA’s ",
issn = "0141-9331",
doi = "http://dx.doi.org/10.1016/j.micpro.2006.02.009",
url = "//www.sciencedirect.com/science/article/pii/S0141933106000287",
author = "André DeHon and Yury Markovsky and Eylon Caspi and Michael Chu and Randy Huang and Stylianos Perissakis and Laura Pozzi and Joseph Yeh and John Wawrzynek",
keywords = "FPGA",
keywords = "Reconfigurable",
keywords = "Scalability",
keywords = "Design reuse",
keywords = "Streaming",
keywords = "System architecture",
keywords = "Design patterns",
keywords = "Pipe-and-filter",
keywords = "Productivity ",
abstract = "Reconfigurable systems can offer the high spatial parallelism and fine-grained, bit-level resource control traditionally associated with hardware implementations, along with the flexibility and adaptability characteristic of software. While reconfigurable systems create new opportunities for engineering and delivering high-performance programmable systems, the traditional approaches to programming and managing computations used for hardware systems (e.g., Verilog, VHDL) and software systems (e.g., C, Fortran, Java) are inappropriate and inadequate for exploiting reconfigurable platforms. To address this need, we develop a stream-oriented compute model, system architecture, and execution patterns which can capture and exploit the parallelism of spatial computations while simultaneously abstracting software applications from hardware details (e.g., timing, device capacity, and microarchitectural implementation details) and consequently allowing applications to scale to exploit newer, larger, and faster hardware platforms. Further, we describe hardware and software techniques that make this late-bound platform mapping viable and efficient. "
}
@article{Liu20061889,
title = "Adaptive segment-based patching scheme for video streaming delivery system ",
journal = "Computer Communications ",
volume = "29",
number = "11",
pages = "1889 - 1895",
year = "2006",
note = "",
issn = "0140-3664",
doi = "http://dx.doi.org/10.1016/j.comcom.2005.10.036",
url = "//www.sciencedirect.com/science/article/pii/S0140366405004111",
author = "Yunqiang Liu and Songyu Yu and Jun Zhou",
keywords = "Video streaming",
keywords = "Request rate",
keywords = "Channel transition",
keywords = "Network bandwidth ",
abstract = "In on-demand video streaming system, periodic broadcast technique scheme has been shown to be very effective for serving a popular video in reducing the demand on server bandwidth. On the contrary, reactive server transmission approach is more suitable for the video that is not popular enough. However, the level of demand on a video may change by time. In this paper, we propose a segment-based patching scheme which allocates adaptively transmission resources according to the varying client request rate. Our technique smoothly adjusts itself to cope with the changing workloads. The scheme tries to dynamically search the optimal number of channels assigned to the video by the newly updated request rate so as to minimize the bandwidth requirement. We also show how to seamlessly perform the transition of changing the number of channels with the guarantee that the clients viewing this video will not experience any disruption. Simulation results indicate that the scheme adapts very well to the changing client request rate and improves the system performance significantly in terms of the total server bandwidth requirement. "
}
@article{Atzori20121049,
title = "Streaming video over wireless channels: Exploiting reduced-reference quality estimation at the user-side ",
journal = "Signal Processing: Image Communication ",
volume = "27",
number = "10",
pages = "1049 - 1065",
year = "2012",
note = "",
issn = "0923-5965",
doi = "http://dx.doi.org/10.1016/j.image.2012.09.005",
url = "//www.sciencedirect.com/science/article/pii/S0923596512001798",
author = "Luigi Atzori and Alessandro Floris and Giaime Ginesu and Daniele Giusto",
keywords = "Video streaming",
keywords = "Reduced-reference quality estimation",
keywords = "Wireless channels",
keywords = "Adaptive rate control ",
abstract = "We propose a source rate control scheme for streaming video sequences over wireless channels by resorting on a reduced-reference (RR) quality estimation approach. It works as follows: the server extracts important features of the original video, which are coded and sent through the channel along with the video sequence and then exploited at the decoder to compute the actual quality; the observed quality is analyzed to obtain information on the impact of the source rate at the given system configuration; at the receiver, decisions are taken on the optimal source rate to be applied next at the encoder to maximize the quality as perceived at the user-side. The rate is adjusted on a per-window basis to compensate low-throughput periods with high-throughput periods so as to avoid abrupt video quality changes, which can be caused by sudden variations in the channel throughput. The use of the \{RR\} quality estimation represents the main novelty of the proposed work. This has the advantage of allowing the rate control to optimize the user-perceived video quality after all the streaming system impairments have affected the signal, including actual channel errors, playback buffer starvation occurrences and error concealment. This approach is new in this context, since in the past proposals video models are used to predict the relationships of the quality with the coding rate, channel errors and starvation occurrences. Numerical simulations show how the proposed approach is able to achieve results similar to those obtained with model-based approaches, but with the significant benefit of not requiring any knowledge on the signal and channel characteristics. "
}
@article{DeMatteis2016,
title = "Proactive elasticity and energy awareness in data stream processing ",
journal = "Journal of Systems and Software ",
volume = "",
number = "",
pages = " - ",
year = "2016",
note = "",
issn = "0164-1212",
doi = "http://dx.doi.org/10.1016/j.jss.2016.08.037",
url = "//www.sciencedirect.com/science/article/pii/S0164121216301467",
author = "Tiziano De Matteis and Gabriele Mencagli",
keywords = "Data stream processing",
keywords = "Elasticity",
keywords = "Model predictive control",
keywords = "Frequency scaling ",
abstract = "Abstract Data stream processing applications have a long running nature (24 hr/7 d) with workload conditions that may exhibit wide variations at run-time. Elasticity is the term coined to describe the capability of applications to change dynamically their resource usage in response to workload fluctuations. This paper focuses on strategies for elastic data stream processing targeting multicore systems. The key idea is to exploit Model Predictive Control, a control-theoretic method that takes into account the system behavior over a future time horizon in order to decide the best reconfiguration to execute. We design a set of energy-aware proactive strategies, optimized for throughput and latency QoS requirements, which regulate the number of used cores and the \{CPU\} frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support offered by modern multicore CPUs. We evaluate our strategies in a high-frequency trading application fed by synthetic and real-world workload traces. We introduce specific properties to effectively compare different elastic approaches, and the results show that our strategies are able to achieve the best outcome. "
}
@article{Chakravarthy20152648,
title = "Adapting Stream Processing Framework for Video Analysis ",
journal = "Procedia Computer Science ",
volume = "51",
number = "",
pages = "2648 - 2657",
year = "2015",
note = "International Conference On Computational Science, \{ICCS\} 2015Computational Science at the Gates of Nature ",
issn = "1877-0509",
doi = "http://dx.doi.org/10.1016/j.procs.2015.05.372",
url = "//www.sciencedirect.com/science/article/pii/S1877050915011801",
author = "S. Chakravarthy and A. Aved and S. Shirvani and M. Annappa and E. Blasch",
keywords = "Stream processing",
keywords = "Image pre-processing",
keywords = "Video stream processing ",
abstract = "Abstract Stream processing (SP) became relevant mainly due to inexpensive and hence ubiquitous deployment of sensors in many domains (e.g., environmental monitoring, battle field monitoring). Other continuous data generators (surveillance, traffic data) have also prompted processing and analysis of these streams for applications such as traffic congestion/accidents and personalized marketing. Image processing has been researched for several decades. Recently there is emphasis on video stream analysis for situation monitoring due to the ubiquitous deployment of video cameras and unmanned aerial vehicles for security and other applications. This paper elaborates on the research and development issues that need to be addressed for extending the traditional stream processing framework for video analysis, especially for situation awareness. This entails extensions to: data model, operators and language for expressing complex situations, QoS (Quality of service) specifications and algorithms needed for their satisfaction. Specifically, this paper demonstrates inadequacy of current data representation (e.g., relation and arrable) and querying capabilities to infer long-term research and development issues. "
}
@article{Hidalgo2016,
title = "Self-adaptive processing graph with operator fission for elastic stream processing ",
journal = "Journal of Systems and Software ",
volume = "",
number = "",
pages = " - ",
year = "2016",
note = "",
issn = "0164-1212",
doi = "http://dx.doi.org/10.1016/j.jss.2016.06.010",
url = "//www.sciencedirect.com/science/article/pii/S0164121216300796",
author = "Nicolas Hidalgo and Daniel Wladdimiro and Erika Rosas",
keywords = "Stream processing",
keywords = "Self-adaptable graph",
keywords = "Elastic processing",
keywords = "Scalable processing",
keywords = "S4 ",
abstract = "Abstract Nowadays, information generated by the Internet interactions is growing exponentially, creating massive and continuous flows of events from the most diverse sources. These interactions contain valuable information for domains such as government, commerce, and banks, among others. Extracting information in near real-time from such data requires powerful processing tools to cope with the high-velocity and the high-volume stream of events. Specially designed distributed processing engines build a graph-based topology of a static number of processing operators creating bottlenecks and load balance problems when processing dynamic flows of events. In this work we propose a self-adaptive processing graph that provides elasticity and scalability by automatically increasing or decreasing the number of processing operators to improve performance and resource utilization of the system. Our solution uses a model that monitors, analyzes and changes the graph topology with a control algorithm that is both reactive and proactive to the flow of events. We have evaluated our solution with three stream processing applications and results show that our model can adapt the graph topology when receiving events at high rate with sudden peaks, producing very low costs of memory and \{CPU\} usage. "
}
@article{Smit20132103,
title = "Distributed, application-level monitoring for heterogeneous clouds using stream processing ",
journal = "Future Generation Computer Systems ",
volume = "29",
number = "8",
pages = "2103 - 2114",
year = "2013",
note = "Including Special sections: Advanced Cloud Monitoring Systems & The fourth \{IEEE\} International Conference on e-Science 2011 — e-Science Applications and Tools & Cluster, Grid, and Cloud Computing ",
issn = "0167-739X",
doi = "http://dx.doi.org/10.1016/j.future.2013.01.009",
url = "//www.sciencedirect.com/science/article/pii/S0167739X1300023X",
author = "Michael Smit and Bradley Simmons and Marin Litoiu",
keywords = "Cloud computing",
keywords = "Monitoring",
keywords = "Utility computing",
keywords = "Distributed",
keywords = "Monitoring-as-a-Service ",
abstract = "As utility computing is widely deployed, organizations and researchers are turning to the next generation of cloud systems: federating public clouds, integrating private and public clouds, and merging resources at all levels (IaaS, PaaS, SaaS). Adaptive systems can help address the challenge of managing this heterogeneous collection of resources. While services and libraries exist for basic management tasks that enable implementing decisions made by the manager, monitoring is an open challenge. We define a set of requirements for aggregating monitoring data from a heterogeneous collections of resources, sufficient to support adaptive systems. We present and implement an architecture using stream processing to provide near-realtime, cross-boundary, distributed, scalable, fault-tolerant monitoring. A case study illustrates the value of collecting and aggregating metrics from disparate sources. A set of experiments shows the feasibility of our prototype with regard to latency, overhead, and cost effectiveness. "
}
@article{Dahal20156853,
title = "Event stream processing for improved situational awareness in the smart grid ",
journal = "Expert Systems with Applications ",
volume = "42",
number = "20",
pages = "6853 - 6863",
year = "2015",
note = "",
issn = "0957-4174",
doi = "http://dx.doi.org/10.1016/j.eswa.2015.05.003",
url = "//www.sciencedirect.com/science/article/pii/S095741741500322X",
author = "N. Dahal and O. Abuomar and R. King and V. Madani",
keywords = "Data mining",
keywords = "Situational awareness",
keywords = "Stream processing",
keywords = "Synchrophasor",
keywords = "Wide area monitoring ",
abstract = "Abstract Deployment of Phasor Measurement Units (PMU) in the United States transmission grid has brought a new data stream to be processed and an opportunity to improve situational awareness on the grid. This new data stream offers opportunity for a faster detection and response algorithm to minimize wide spread outages. High rate of data collection of \{PMU\} systems has also brought a challenge on how to extract information from fast moving \{PMU\} data stream in real time to improve situational awareness inside a control room. Despite the fact that mathematical and probabilistic methods are the most accurate methods of stability analysis, online decision making algorithms cannot afford the latency brought by those methods. Traditional batch processing Artificial Intelligence (AI) techniques have been extensively studied as potential replacements for these approaches, however conventional \{AI\} techniques do not deal with continuous streams of fast moving phasor data. This paper presented a novel application of the stream mining algorithms for synchrophasor data to meet quick decision making requirement of future situational awareness applications in power systems. To prove that the proposed methods are efficient and capable of handling huge amounts of data with reasonable accuracy and within limited resources of memory and computational power, four different experiments with different conditions (changing/unchanging the load conditions of Real Power and Reactive Power, fixing the size of memory, and comparing the performance of non-adaptive Hoeffding tree with traditional decision tree algorithms) were conducted. The algorithms discussed in this paper support decisions inside the control rooms helping stakeholders make informed decisions to improve reliability of the future smart grid. "
}
@article{Tudoran2016274,
title = "JetStream: Enabling high throughput live event streaming on multi-site clouds ",
journal = "Future Generation Computer Systems ",
volume = "54",
number = "",
pages = "274 - 291",
year = "2016",
note = "",
issn = "0167-739X",
doi = "http://dx.doi.org/10.1016/j.future.2015.01.016",
url = "//www.sciencedirect.com/science/article/pii/S0167739X15000333",
author = "Radu Tudoran and Alexandru Costan and Olivier Nano and Ivo Santos and Hakan Soncu and Gabriel Antoniu",
keywords = "Cloud computing",
keywords = "Big Data",
keywords = "Multi-site",
keywords = "Stream processing ",
abstract = "Abstract Scientific and commercial applications operate nowadays on tens of cloud datacenters around the globe, following similar patterns: they aggregate monitoring or sensor data, assess the QoS or run global data mining queries based on inter-site event stream processing. Enabling fast data transfers across geographically distributed sites allows such applications to manage the continuous streams of events in real time and quickly react to changes. However, traditional event processing engines often consider data resources as second-class citizens and support access to data only as a side-effect of computation (i.e. they are not concerned by the transfer of events from their source to the processing site). This is an efficient approach as long as the processing is executed in a single cluster where nodes are interconnected by low latency networks. In a distributed environment, consisting of multiple datacenters, with orders of magnitude differences in capabilities and connected by a WAN, this will undoubtedly lead to significant latency and performance variations. This is namely the challenge we address in this paper, by proposing JetStream, a high performance batch-based streaming middleware for efficient transfers of events between cloud datacenters. JetStream is able to self-adapt to the streaming conditions by modeling and monitoring a set of context parameters. It further aggregates the available bandwidth by enabling multi-route streaming across cloud sites, while at the same time optimizing resource utilization and increasing cost efficiency. The prototype was validated on tens of nodes from \{US\} and Europe datacenters of the Windows Azure cloud with synthetic benchmarks and a real-life application monitoring the \{ALICE\} experiment at CERN. The results show a 3× increase of the transfer rate using the adaptive multi-route streaming, compared to state of the art solutions. "
}
@article{Kranjc2015187,
title = "Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform ",
journal = "Information Processing & Management ",
volume = "51",
number = "2",
pages = "187 - 203",
year = "2015",
note = "",
issn = "0306-4573",
doi = "http://dx.doi.org/10.1016/j.ipm.2014.04.001",
url = "//www.sciencedirect.com/science/article/pii/S0306457314000296",
author = "Janez Kranjc and Jasmina Smailović and Vid Podpečan and Miha Grčar and Martin Žnidaršič and Nada Lavrač",
keywords = "Active learning",
keywords = "Stream mining",
keywords = "Sentiment analysis",
keywords = "Stream-based active learning",
keywords = "Workflows",
keywords = "Data mining platform ",
abstract = "Abstract Sentiment analysis from data streams is aimed at detecting authors’ attitude, emotions and opinions from texts in real-time. To reduce the labeling effort needed in the data collection phase, active learning is often applied in streaming scenarios, where a learning algorithm is allowed to select new examples to be manually labeled in order to improve the learner’s performance. Even though there are many on-line platforms which perform sentiment analysis, there is no publicly available interactive on-line platform for dynamic adaptive sentiment analysis, which would be able to handle changes in data streams and adapt its behavior over time. This paper describes ClowdFlows, a cloud-based scientific workflow platform, and its extensions enabling the analysis of data streams and active learning. Moreover, by utilizing the data and workflow sharing in ClowdFlows, the labeling of examples can be distributed through crowdsourcing. The advanced features of ClowdFlows are demonstrated on a sentiment analysis use case, using active learning with a linear Support Vector Machine for learning sentiment classification models to be applied to microblogging data streams. "
}
@article{Wu2009454,
title = "Data-driven memory management for stream join ",
journal = "Information Systems ",
volume = "34",
number = "4–5",
pages = "454 - 467",
year = "2009",
note = "Data Warehousing and On-Line Analytical Processing ",
issn = "0306-4379",
doi = "http://dx.doi.org/10.1016/j.is.2009.02.001",
url = "//www.sciencedirect.com/science/article/pii/S0306437909000064",
author = "Ji Wu and Kian-Lee Tan and Yongluan Zhou",
keywords = "Data stream",
keywords = "Stream join",
keywords = "Data-driven memory management ",
abstract = "Memory management is a critical issue in stream processing involving stateful operators such as join. Traditionally, the memory requirement for a stream join is query-driven: a query has to explicitly define a window for each (potentially unbounded) input. The window essentially bounds the size of the buffer allocated for that stream. However, output produced this way may not be desirable (if the window size is not part of the intended query semantic) due to the volatile input characteristics. We discover that when streams are ordered or partially ordered, it is possible to use a data-driven memory management scheme to improve the performance. In this work, we present a novel data-driven memory management scheme, called Window-Oblivious Join (WO-Join), which adaptively adjusts the state buffer size according to the input characteristics. Our performance study shows that, compared to traditional Window-Join (W-Join), WO-Join is more robust with respect to the dynamic input and therefore produces higher quality results with lower memory costs. "
}
@article{Hershberger2008191,
title = "Adaptive sampling for geometric problems over data streams ",
journal = "Computational Geometry ",
volume = "39",
number = "3",
pages = "191 - 208",
year = "2008",
note = "",
issn = "0925-7721",
doi = "http://dx.doi.org/10.1016/j.comgeo.2006.10.004",
url = "//www.sciencedirect.com/science/article/pii/S0925772106001076",
author = "John Hershberger and Subhash Suri",
keywords = "Stream processing",
keywords = "Single-pass algorithms",
keywords = "Convex hulls",
keywords = "Geometric approximations ",
abstract = "Geometric coordinates are an integral part of many data streams. Examples include sensor locations in environmental monitoring, vehicle locations in traffic monitoring or battlefield simulations, scientific measurements of earth or atmospheric phenomena, etc. This paper focuses on the problem of summarizing such geometric data streams using limited storage so that many natural geometric queries can be answered faithfully. Some examples of such queries are: report the smallest convex region in which a chemical leak has been sensed, or track the diameter of the dataset, or track the extent of the dataset in any given direction. One can also pose queries over multiple streams: for instance, track the minimum distance between the convex hulls of two data streams, report when datasets A and B are no longer linearly separable, or report when points of data stream A become completely surrounded by points of data stream B, etc. These queries are easily extended to more than two streams. In this paper, we propose an adaptive sampling scheme that gives provably optimal error bounds for extremal problems of this nature. All our results follow from a single technique for computing the approximate convex hull of a point stream in a single pass. Our main result is this: given a stream of two-dimensional points and an integer r, we can maintain an adaptive sample of at most 2 r + 1 points such that the distance between the true convex hull and the convex hull of the sample points is O ( D / r 2 ) , where D is the diameter of the sample set. The amortized time for processing each point in the stream is O ( log r ) . Using the sample convex hull, all the queries mentioned above can be answered approximately in either O ( log r ) or O ( r ) time. "
}