Obtaining response time moments in processor sharing (PS) queues is difficult due to serving of multiple jobs. Egalitarian PS (EPS) queues are limited to one class of arriving jobs. Discriminatory PS (DPS) assigns weights to different job classes and offers more diverse modeling capabilities than EPS. It is known that response time is the representative metric for delay as specified in service level agreements (SLAs), which consider higher moments important. Hence, we build an automated numerical algorithm for calculating higher moments of response time in M/M/1-DPS queues for multiple job classes and test two different case studies.
The purpose of this paper is to articulate the need for an open, extensible robot platform to support swarm robotic research using vision and to propose one such platform. The platform proposed here is intended for research which trades smaller population size with more sophisticated individual robot capabilities. The validation of proposed swarm robotic algorithms using real-world hardware is essential, but is fraught with difficulty due to the expense and complexity of developing and maintaining multiple operational units. A number of open hardware platforms have been proposed, although most prioritize small size and low cost over advanced capabilities such as vision. We are interested in a number of different research directions which utilize vision as a core capability and find the existing open hardware platforms to be insufficient (and existing commercial platforms too expensive). In this paper we describe a set of desirable characteristics for an open, extensible visually-guided robot platform. We then present our solution, the BuPiGo (pronounced buppy-go), describing the hardware itself and a model developed for simulation purposes. We also present some initial results on using the BuPiGo for visual homing an individual navigation task that we hope to exploit for swarm tasks in the future.
Feature-oriented programming (FOP) and aspect-oriented programming (AOP) focus are to modularize additional classes behavior and crosscutting concerns, respectively, for soft- ware evolution. So, these software development approaches represent advanced paradigms for modular software product lines development. Thereby, a FOP and AOP symbiosis would permit reaching pros and cons of both approaches. FOP allows developers to modularly refine classes collaboration for Software Product Lines (SPLs), an adequate approach to represent heterogeneous crosscutting concerns. Similarly, traditional AOP structurally modularizes in a re- fined manner homogeneous crosscutting concerns. Since traditional AOP presents implicit dependencies and strong coupling between classes and aspects, these AOP issues are solved by the Join Point Interface JPI approach. This article presents JPI Feature Modules for FOP + JPI SPL components modularization, i.e., classes, aspects, and join point interfaces along with their evolution, for a SPL transparent implementation in a FOP + JPI context. In addition, this article shows JPI Feature Modules of a case study to highlight mutual benefits of FOP and JPI approaches for a modular SPL software conception.
Many modern programming languages rely on memory management environments that are responsible for allocation and deallocation of objects. Garbage collection phases are used in order to detect inaccessible objects on the heap so they can be deallocated. The performance of garbage collection techniques depends heavily on the environment, implementation specific parameters and the benchmark used. The contribution of this publication is an extendable memory management simulator, which aims to assist developers in memory management evaluation and research. The simulator is capable of reading operations from a trace file extracted from a virtual machine and simulating the memory management needed by the simulated mutator. The framework aims to provide an isolated experimentation and comparison platform in the field of automatic memory management. New algorithms can be added to the framework in order to compare them to established algorithms.
Model checking of Markov chains using logics like CSL or asCSL proves whether a logical formula holds for a state of the Markov chain. It has been developed in the last decade to a widely used approach to express performance and dependability quantities for models from a wide range of application areas. In this paper, model checking is extended to prove formulas for distributions rather than single states. This is a very natural way to express certain performance or dependability measures that depend on the state of the system rather than on a specific state in the state space of the Markov chain. It is shown that the mentioned logics can be easily extended from states to distributions and model checking algorithms can also be easily adopted. Furthermore, new equivalences will be introduced that are weaker than bisimulation but still characterize the extended logics.
The Internet increasingly focuses on content, as exemplified by the now popular Information Centric Networking paradigm. This means, in particular, that estimating content popularities becomes essential to manage and distribute content pieces efficiently. In this paper, we show how to properly estimate content popularities from a traffic trace. Specifically, we consider the problem of the popularity inference in order to tune content-level performance models, e.g. caching models. In this context, special care must be brought on the fact that an observer measures only the flow of requests, which differs from the model parameters, though both quantities are related by the model assumptions. Current studies, however, ignore this difference and use the observed data as model parameters. In this paper, we highlight the inverse problem that consists in determining parameters so that the flow of requests is properly predicted by the model. We then show how such an inverse problem can be solved using Maximum Likelihood Estimation. Based on two large traces from the Orange network and two synthetic datasets, we eventually quantify the importance of this inversion step for the performance evaluation accuracy.
We make use of a Physarum machine that is a biological computing device implemented in the plasmodium of Physarum polycephalum and/or Badhamia utricularis which are one-cell organisms able to build complex networks for solving different computational tasks. The plasmodium behavior can model an ancient Chinese game called Go. In the paper, we describe implementation of the Go game on the Physarum machines. A special version of the game is presented, where payoffs are assessed by means of the measure defined on the basis of rough set theory. Theoretical foundations given in the paper are supplemented with description of a specialized software tool developed, among others, for simulation of the described game.
To sell a good before a deadline, a monopolist chooses between posting a price and running a costly reserve-price auction each period. Buyers with independent private values arrive over time. For a wide range of auction costs, the profit-maximizing mechanism sequence is to post prices first and then to run auctions. The optimality of the prices-then-auctions mechanism sequence provides a new justification for the hybrid sales mechanism of allowing the "Buy It Now" option before a standard auction on eBay.
The development of various sensor and sensor network made it possible to collect environmental date from specific area, however, there is a lack of practical application to share useful information and knowledge with using the sensor data, Thus this study is to establish data domain ontology and to predict the information on the growth environment of crop based on this already built domain ontology. The inference model suggested in this paper is collected from weather center.
Network traffic analysis is a process to infer patterns in communication. Reliance on computer network and increasing connectivity of these networks makes it a challenging task for the network managers to understand the nature of the traffic that is carried in their network. However, it is an important data analysis task, given the amount of network traffic generated. Summarization is a key data mining concept, which is considered as a solution for creating concise yet accurate summary of network traffic. In this paper, we propose a new definition of summary for network traffic which outperforms the existing state-of-the-art summarization techniques. Our approach is based on clustering algorithm which reduces the information loss incurred by the existing techniques. By analysing the traffic summarization results using most up to date evaluation metrics, we demonstrate that our approach achieves better summaries than others on benchmark KDD cup 1999 dataset and also on real life network traffic including simulated attacks.
Towards the realization of smart building applications, buildings are increasingly instrumented with diverse sensors and actuators. These sensors generate large volumes of data which can be analyzed for optimizing building operations. Many building energy management tasks such as energy forecasting, disaggregation, among others require complex analytics leveraging collected sensor data. While several standalone and cloud-based systems for archiving, sharing and visualizing sensor data have emerged, their support for analyzing sensor data streams is primitive and limited to rule-based actions based on thresholds and simple aggregation functions. We develop OpenBAN, an open source sensor data analytics middleware for buildings, to make analytics an integral component of modern smart building applications. OpenBAN provides a framework of extensible sensor data processing elements for identifying various building context, which different applications can leverage. We validate the capabilities of OpenBAN by developing three representative real-world applications which are deployed in our test-bed buildings: (i) household energy disaggregation, (ii) detection of sprinkler usage from water meter data, and (iii) electricity demand forecasting. We also provide a preliminary system performance of OpenBAN when deployed in the cloud and locally.
Human sensory systems allow individuals to see, hear, touch, and interact with the surrounding physical environment. Understanding human perception and its limit enables us to better exploit the psychophysics of human perceptual systems to design more efficient, adaptive algorithms and develop perceptually-inspired computational models., In this talk, I will survey some of recent efforts on perceptually-inspired computing with applications to crowd simulation and multimodal interaction. In particular, I will present data-driven personality modeling based on the results of user studies, example-guided physics-based sound synthesis using auditory perception, as well as perceptually-inspired simplification for multimodal interaction. These perceptually guided principles can be used to accelerating multi-modal interaction and visual computing, thereby creating more natural human-computer interaction and providing more immersive experiences. I will also present their use in interactive applications for entertainment, such as video games, computer animation, and shared social experience. I will conclude by discussing possible future research directions.
MyHealthAvatar is a project designed to collect lifestyle and health data to promote citizen's wellbeing. As a lifetime companion of citizens the amount of data to be collected is large. It is almost impossible for citizens, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. Visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3D avatar, dashboard, diary, timeline, clock view and map. These components can be used cooperatively to achieve flexible visual analysis of spatial temporal lifestyle and health data.
In this paper, an open and generic storage simulator is proposed. It simulates with accuracy multi-tiered storage systems based on heterogeneous devices including HDDs, SSDs and the connecting buses. The target simulated sys- tem is constructed from the hardware configuration input, then sent to the simulator modules along with the trace file and the appropriate simulator functions are selected and executed. Each module of the simulator is executed by a thread, and communicates with the others via ZeroMQ, a message transmission API using sockets for the information transfer. The result is an accurate behavior of the simulated system submitted to a specific workload and represented by performance and reliability metrics. No restriction is put on the input hardware configuration which can handle differ- ent levels of details and makes this simulator generic. The diversity of the supported devices, regardless to their na- ture: disks, buses, ..etc and organisation: JBOD, RAID, ..etc makes the simulator open to many technologies. The modularity of its design and the independence of its exe- cution functions, makes it open to handle any additional mapping, access, maintenance or reconstruction strategies. The conducted tests using OLTP and scientific workloads show accurate results, obtained in a competitive runtime.
Apache Pig system generates MapReduce jobs by compiling program scripts written in Pig Latin to process large data sets in parallel on distributed computing nodes. There are inefficient features in Pig due to the limitation of the MapReduce, e.g., the MapReduce is used only for batch processing. As various smart devices are extensively utilized recently, streams of data are generated explosively and the need to process streams of data in real-time is required. In this paper, we propose a data flow language processing system, called LAMA-CEP, by generating DAG-based stream processing services to process unbounded streams of data in real-time continuously. We present a stream processing language, called Pig Latin Stream extended from Pig Latin. Programs written in Pig Latin Stream are translated into distributed stream processing jobs and then the jobs are executed on a highly scalable distributed stream processing system to process large streams of data in real-time.
Using different modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. In this paper, we propose a generic approach to modeling the performance of data center networks. The approach offers multiple performance models but requires to use only a single modeling language. We propose a two-step modeling methodology, in which a high-level descriptive model of the network is built in the first step, and in the second step model-to-model transformations are used to automatically transform the descriptive model to different network simulation models. We automatically generate three performance models defined at different levels of abstraction to analyze network throughput. By offering multiple simulation models in parallel, we provide flexibility in trading-off between the modeling accuracy and the simulation overhead. We analyze the simulation models by comparing the prediction accuracy with respect to the simulation duration. We observe, that in the investigated scenarios the solution duration of coarser simulation models is up to 300 times shorter, whereas the average prediction accuracy decreases only by 4 percent.
Online route planning services compute routes from any given location to a desired destination address. Unlike offline implementations, they do so in a traffic-aware fashion by taking into consideration up-to-date map data and real-time traffic information. In return, users have to provide precise location information about a route’s endpoints to a not necessarily trusted service provider. As suchlike leakage of personal information threatens a user’s privacy and anonymity, this paper presents PrOSPR, a comprehensive approach for using current online route planning services in a privacy-preserving way, and introduces the concept of k-immune route requests to avert inference attacks based on restricted space information. Using a map-based approach for creating cloaked regions for the start and destination addresses, our solution queries the online service for routes between subsets of points from these regions. This, however, might result in the returned path deviating from the optimal route. By means of empirical evaluation on a real road network, we demonstrate the feasibility of our approach regarding quality of service and communication overhead.
Nowadays, HTTP adaptive streaming (HAS) has become a cost-effective solution in delivering the video content. Different from the one-client HAS, the HAS over multi-client faces many challenges. Due to the lack of the knowledge of the networks, one-client HAS will leads to the low efficiency in utilizing the limited bandwidth over the multi-client circumstance. In this paper, a playout buffer aware adaptation scheme over multi-client (PBMC) LTE networks is proposed to improve the performance of HAS. Firstly, a QoE metric is given that considers both the static quality and the quality smoothness. Secondly, to maximize the QoE, an integer programming problem is formulated. By considering the playout buffer level, the resource requirement, which can guarantee the playout continuity, is formulated as a constraint of the problem. Then, the feasibility of the problem is analyzed. If the problem is infeasible, a method is given to exclude the users as less as possible to obtain the feasibility. When the problem is feasible, a graph model can be constructed based on the resource requirement and the playout buffer level. With the model, the solution can be converted to finding a feasible path with the highest QoE in the graph, which can be solved by employing the dynamic programming. Finally, the simulation results can verify the performance of PBMC in both improving the static quality and guaranteeing the playout continuity.
The Internet remains an unfinished work. There are several approaches to enhancing it that have been experimentally validated within federated testbed environments. To best gain scientific knowledge from these studies, reproducibility and automation are needed in all areas of the experiment life cycle. Within the GENI and FIRE context, several architectures and protocols have been developed for this purpose. However, a major open research issue remains, namely the description and discovery of the heterogeneous resources involved. To remedy this, we propose a semantic information model that can be used to allow declarative interoperability, build dependency graphs, validate requests, infer knowledge and conduct complex queries. The requirements for such an information model have been extracted from current international Future Internet research projects and the practicality of the model is being evaluated through initial implementations. The main outcome of this work is the definition of the Open-Multinet Upper Ontology and related sub-ontologies, which can be used to describe and manage federated infrastructures and their resources.
Analysis of contemporary Big Data collections require an effective and efficient content-based access to data which is usually unstructured. This first implies a necessity to uncover descriptive knowledge of complex and heterogeneous objects to make them findable. Second, multimodal search structures are needed to efficiently execute complex similarity queries possibly in outsourced environments while preserving privacy. Four specific research objectives to tackle the challenges are outlined and discussed. It is believed that a relevant solution of these problems is necessary for a scalable similarity search operating on Big Data.
