This is an old revision of the document!
Intro
Participants: Nha, Dileep, etc
When: Every week, Thu 3pm (or Fri 2pm)
Where: TBA
Topics: Research topics of participants
Presentor's role:
Update agenda table & upload materials in the webpage before meeting
Upload updated material if necessary
Make sure to do TODO list indicated in the meeting note
Participants' role:
Kaist's wiki
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id : cloudlet
pw : guest123
Agenda
5/1/14
Nha on journal paper preparation
[MH] Todo list
Check the movement model of Thosmas Brinkhoff's simulator
Re-think the privacy metric of vehicle-based privacy
Check pedestrian mode of Paramics
5/12/14
[Dileep] Smart Grid Security Paper
Attacks on AMI
False data injection attack
DoS attack
Eavesdropping attack
Privacy threat
Vacancy check leading to intrusive threat
TODO: type of information handled by AMI (stored, communicated)
Pricing Information, Consumption Readings, Person's Living patterns, Health Insurance can determine the usage of medical devices, landlord can tell no. of peoples staying and their time or parties et.
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Impersonation attack (MIM)
Replay attack
TODO: need a detailed discussion of related threats to user & system caused by these attacks
These attacks results in sending incorrect data to control centers.
Incorrect decisions made by control center may lead to:
Inefficient Dispatch / Power Quality Degradation, Unnecessary Load Shedding, False Alarm / Erroneous Fault Detection, System Instability, Financial losses.
And if there are many attacks then it may cause to destroy the appliances.
[Nha] Brinkhoff Generator's limitations
5/30/14
Nha's list
MapReduce: Simplified Data Processing on Large Clusters PDF
Jeffrey Dean and Sanjay Ghemawat
Google Inc
ACM 51 (1) (2008) 107–113.
The authors proposes a simple and powerful interface that enables automatic parallelization and distribution of large-scale computations, combined with an implementation of this interface that achieves high performance on large clusters of commodity PCs.
MapReduce optimization algorithm based on machine learning in heterogeneous cloud environment PDF
LIN Wen-hui, LEI Zhen-ming, LIU Jun,YANG Jie, LIU Fang, HE Gang, WANG Qin
Beijing University of Posts and Telecommunications, China
The Journal of China Universities of Posts and Telecommunications (ELSEVIER), Dec 2013
The authors develop a machine learning module to obtain nodes’ performance values and optimize the reduce task assignment algorithm & the speculative execution mechanism to improve the performance of heterogeneous clusters.
A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues PDF
Yuming Xu, Kenli Li, Jingtong Hu, Keqin Li
College of Information Science and Engineering, Hunan University, China
Information Sciences Volume 270, 20 June 2014, Pages 255–287
In this paper, a task scheduling scheme on heterogeneous computing systems using a multiple priority queues genetic algorithm (MPQGA) is proposed.
Performance Issues of Heterogeneous Hadoop Clusters in Cloud Computing PDF
B.Thirumala Rao, N.V.Sridevi, V.Krishna Reddy, L.S.S.Reddy
Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering,Mylavaram
Global Journal of Computer Science and Technology, May 2011, Volume XI Issue VIII
The overview of Hadoop and some issues that affect the performance of hadoop in heterogeneous clusters in cloud environments. The paper proposed some guidelines on how to overcome these issues to improve the performance of hadoop.
SHadoop: Improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters PDF
Rong Gu, Xiaoliang Yang, Jinshuang Yan, Yuanhao Sun, Bing Wang, Chunfeng Yuan, Yihua Huang
Nanjing University, Intel Asia-Pacific Research and Development Ltd, China
Journal of Parallel and Distributed Computing, Vol 74, Issue 3, March 2014, Pages 2166–2179
The paper analyzed and identified two critical limitations of MapReduce execution mechanism, achieved first optimization by implementing new job setup/cleanup task, and replaced heartbeat with an instant messaging mechanism to speedup task scheduling.
Two new fast heuristics for mapping parallel applications on cloud computing PDF
I. De Falco, U. Scafuri, E. Tarantino
Institute of High Performance Computing and Networking, National Research Council of Italy
Future Generation Computer Systems Volume 37, July 2014, Pages 1–13
In this paper two new heuristics, named Min–min-C and Max–min-C, are proposed able to provide near-optimal solutions to the mapping of parallel applications, modeled as Task Interaction Graphs, on computational clouds.
Time-optimized contextual information forwarding in mobile sensor networks PDF
Christos Anagnostopoulos
School of Computing Science, University of Glasgowm, UK
Journal of Parallel and Distributed Computing Volume 74, Issue 5, May 2014, Pages 2317–2332
The paper studies on the scheduling of Context Forwarding (CF) in Mobile Sensor Networks (MSNs), formulates the CF scheduling problem as an optimal stopping time problem, proposes an optimal CF policy over MSN, and proposes policy exhibits efficient CF in MSN.
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Daniel John Messier, Joseph James Salvo, John William Carbone, Charles Burton Theurer, Li Zhang
General Electric Company
Patent US8321870 B2, 2012
A system for processing a computational task is presented.
Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system PDF
Andrew J. Page, Thomas M. Keane, Thomas J. Naughton
Wellcome Trust Sanger Institute in Cambridge
Journal of Parallel and Distributed Computing Volume 70, Issue 7, July 2010, Pages 758–766
They present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system.
An effective and robust two-phase resource allocation scheme for interdependent tasks in mobile ad hoc computational Grids PDF
Sayed Chhattan Shah, Qurat-Ul-Ain Nizamani, Sajjad Hussain Chauhdary, Myong-Soon Park
Electronics and Telecommunications Research Institute in Daejeon, South Korea
Journal of Parallel and Distributed Computing Volume 72, Issue 12, December 2012, Pages 1664–1679
They propose a two-phase resource allocation scheme to reduce communication cost between dependent tasks
An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems PDF
Qinma Kang, Hong He, Jun Wei
Shandong University, China
Journal of Parallel and Distributed Computing Volume 73, Issue 8, August 2013, Pages 1106–1115
This paper investigates the problem of allocating parallel application tasks to processors in heterogeneous distributed computing systems with the goal of maximizing the system reliability.
Ganga: A tool for computational-task management and easy access to Grid resources PDF
J.T. Mościcki, F. Brochu, J. Ebke,…
CERN, Geneva, Switzerland; University of Cambridge, United Kingdom
Computer Physics Communications Volume 180, Issue 11, November 2009, Pages 2303–2316
Present the computational task-management tool Ganga, which allows for the specification, submission, bookkeeping and post-processing of computational tasks on a wide set of distributed resources.
Dryad: distributed data-parallel programs from sequential building blocks PDF
Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, Dennis Fetterly
Microsoft Research, Silicon Valley
ACM SIGOPS Operating Systems Review - EuroSys'07 Conference Proceedings Volume 41 Issue 3, June 2007 Pages 59-72
Dryad is a popular programming model for implementing parallel and distributed programs that can scale up capability of processing from a very small cluster to a large cluster.
Optimal task partition and distribution in grid service system with common cause failures PDF
Yuan-Shun Dai, Gregory Levitin, Xiaolong Wang
Department of Computer and Information Science, Purdue University, USA
Future Generation Computer Systems Volume 23, Issue 2, February 2007, Pages 209–218
This paper solved an important optimization problem for grid service. The problem was to maximize the expected profit by partitioning the service task into subtasks and by distributing them among the available resources. A genetic algorithm was presented to solve this type of optimization problem.
Dynamic tuning of the workload partition factor and the resource utilization in data-intensive applications PDF
Claudia Rosas, Anna Sikora, Josep Jorba, Andreu Moreno, Antonio Espinosa, Eduardo César
Universitat Autònoma de Barcelona, Spain
Future Generation Computer Systems Volume 37, July 2014, Pages 162–177
This work proposes a methodology to dynamically improve the performance of data-intensive applications based on: (i) adapting the size and the number of data partitions to reduce the overall execution time; and (ii) adapting the number of processing nodes to achieve an efficient execution.
An energy-efficient process clustering assignment algorithm for distributed system PDF
Anan Niyom, Peraphon Sophatsathit , Chidchanok Lursinsap
Chulalongkorn University, Thailand
Simulation Modelling Practice and Theory Volume 40, January 2014, Pages 95–111
A new set of task scheduling and assignment algorithms focusing on minimizing energy consumption, named Energy-Efficient Process Clustering Assignment algorithm or EPC algorithm, was proposed.
Parallel partitioning for distributed systems using sequential assignment PDF
Simon Spacey, Wayne Luk, Daniel Kuhn, Paul H.J. Kelly
Department of Computing, Imperial College London, UK
Journal of Parallel and Distributed Computing Volume 73, Issue 2, February 2013, Pages 207–219
This paper introduces a method to combine the advantages of both task parallelism and fine-grained co-design specialisation to achieve faster execution times than either method alone on distributed heterogeneous architectures.
Two-phase grouping-based resource management for big data
processing in mobile cloud computing PDF
JiSu Park, Hyongsoon Kim, Young-Sik Jeong and Eunyoung Lee
Korea University, Dongduk Womens University, Korea
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2013
This paper proposes a grouping technique based on the utilization and movement rates to cope the movement problem and the utilization problem of mobile devices in cloud computing.
Programming support and scheduling for communicating parallel tasks PDF
Jörg Dümmler, Thomas Rauber, Gudula Rünger
Chemnitz University of Technology, Germany
Journal of Parallel and Distributed Computing Volume 73, Issue 2, February 2013, Pages 220–234
This paper describes the parallel programming model of communicating M-tasks (CM-tasks), discusses the scheduling of CM-task programs and presents an appropriate algorithm.
Distributed and Cloud Computing: From Parallel Processing to the Internet of things PDF
Kai Hwang, Jack Dongarra, Geoffrey C. Fox
Univ. of Southern California (USC), University College Dublin
Elsivier, Published: October 2011, Paperback, 672 Pages
The book provides comprehensive coverage of distributed and cloud computing, including:
Unordered List ItemFacilitating management, debugging, migration, and disaster recovery through virtualization
Clustered systems for research or ecommerce applications
Designing systems as web services
Social networking systems using peer-to-peer computing
Principles of cloud computing using examples from open-source and commercial applications
An Energy-efficient Task Scheduler for Multi-core Platforms with per-core DVFS Based on Task Characteristics PDF
Ching-Chi Lin, You-Cheng Syu, Chao-Jui Chang, Jan-Jan Wu, Pangfeng Liu, Po-Wen Cheng and Wei-Te Hsu
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Cost Minimization for Scheduling Parallel, Single-threaded, Heterogeneous, Speed-scalable Processors PDF
Rashid Khogali & Olivia Das
Department of Electrical & Computer Engineering Ryerson University, Toronto, Canada
Dileep's list
ACE: Exploiting Correlation for Energy-Efficient and
Continuous Context Sensing
Suman Nath
Microsoft Research
MobiSys’12, June 25–29, 2012, Low Wood Bay, Lake District, UK
ACE (Acquisitional Context Engine), a middleware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user’s current context to applications running on it. In addition, it dynamically learns relationships among various context attributes (e.g., whenever the user is Driving, he is not AtHome). ACE exploits these automatically learned relationships for two powerful optimizations.
CITA: Code In The Air Simplifying Sensing and Coordination
Tasks on Smartphones
Lenin Ravindranath, Arvind Thiagarajan, Hari Balakrishnan, and Samuel Madden
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
HotMobile’12 February 28–29, 2012, San Diego, CA, USA
This paper introduced the CITA architecture for simplifying the tasking applications for users and developers. Developers, who can create tasks by writing only server side code, even for tasks that involve multiple end users and their devices, a variety of sensors, and the server. In our current implementation, these tasks are written in JavaScript. End users, who are able to specify their own tasks by “mixing and matching” available activities and tasks via a web UI (or a smartphone UI)
Snooze: Energy Management in 802.11n WLANs
Ki-Young Jang, Shuai Hao, Anmol Sheth, Ramesh Govindan
University of Southern California, Technicolor Research
ACM CoNEXT’11, December 6-9, 2011
The design and implementation of Snooze, an energy management technique for 802.11n which uses two novel and inter-dependent mechanisms: client micro-sleeps and antenna configuration management. In Snooze, the AP monitors traffic on the WLAN and directs client sleep times and durations as well as antenna configurations, without significantly affecting throughput or delay. Snooze achieves 30~85% energy-savings over CAM across workloads ranging from VoIP and video streaming to file downloads and chats.
COCA: Computation Offload to Clouds using AOP
Hsing-Yu Chen, Yue-Hsun Lin and Chen-Mou Cheng
National Taiwan University and Carnegie Mellon University
2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
COCA is a programming framework that allows smartphones application developers to offload part of the computation to servers in the cloud easily. COCA works at the source level. By harnessing the power of AOP, COCA inserts appropriate offloading code into the source code of the target application based on the result of static and dynamic profiling.
Collaborative Sensing over Smart SensorsPDF Just 2 pages? It is Workshop paper and contains 4 pages?
Vassileios Tsetsos, Nikolaos Silvestros, and Stathes Hadjiefthymiades
Pervasive Computing Research Group, Dept of Informatics and Telecommunications, University of Athens Panepistimiopolis, Ilissia,Greece.
2nd Student Workshop on Wireless Sensor Netwotks, Athens 2009
IPAC (Integrated Platform for Autonomic Computing) adopts a novel and pragmatic approach to context-aware computing.
Enhanced Collaborative Sensing Scheme
for User Activity RecognitionPDF Just Poster! Find full paper It is just Demo poster paper of two pages. No Full paper available.
Yuki Nishida, Yoshihiro Kawahara and Tohru Asami
The University of Tokyo
SenSys’11, November 1–4, 2011, Seattle, WA, USA.
Share raw sensor data among users, yield new information
add information of noisy environment.
CoSense – A Collaborative Sensing Platform for Mobile DevicesPDF Fine, but just poster. Check full paper It need payment
Samuli Hemminki,Kai Zhao , Aaron Yi Ding, Martti Rannanjärv
Department of Computer Science, University of Helsinki, Finland
SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Opportunistically distributes sensing tasks between familiar devices in close proximity.
Energy-efficient collaborative context sensing on mobile phonesPDF This is proposal, not paper! But check this paper OptiMuS
Sensing as a Service: A Cloud Computing System
for Mobile Phone SensingPDF
Xiang Sheng, Xuejie Xiao, Jian Tang and Guoliang Xue
Syracuse University and Arizona State University
SENSORS'2012: IEEE Sensors Conference; October 29-31, 2012, Taipei
Identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions.
Crowdsourcing to Smartphones: Incentive Mechanism Design for Mobile Phone SensingPDF
Dejun Yang, Guoliang Xue, Xi Fang
Arizona State University
Mobicom '12 Proceedings of the 18th annual international conference on Mobile computing and networking
Designed incentive mechanisms for mobile phone sensing.
Mobile Sensing for Social CollaborationsPDF
Chi Harold Liu, Pan Hui
IBM Research - China and Deutsche Telekom Laboratories
ACM CSCW Workshop of Mobile Collaboration in the Developing World, Hangzhou China, March 2011
The main objective of this paper is to receive early feedback from researchers and practitioners in the areas of mobile computing and social collaboration, and to brainstorm and synergy new collaboration opportunities.
Energy-Efficient Sensor Node Control Based on Sensed Data and Energy Monitoring PDF
Ho-Guen Song, Dae-Cheol Jeon, Hee-Dong Park
Korea Nazarene University, Cheonan
Springer-Verlag Berlin Heidelberg 2011
Proposes an energy-efficient sensor node control mechanism to prolong sensor networks’ lifespan by minimizing and equalizing energy consumption of sensor nodes.
Energy-efficient Tasking in Participatory Sensing SystemsPDF
Kevin Wiesner, Sebastian Feld
Institute for Computer Science Ludwig-Maximilians-Universität München
Location-based applications and services (LBAS) 2013
Proposed to energy-efficient task distribution and monitoring concept for participatory sensing system and evaluate it by Means of simulation.
Wireless Sensor Network:
A Promising Approach for Distributed Sensing Tasks PDF
Prof. Madhav Bokare, Mrs. Anagha Ralegaonkar
SSBES`s Institute Of Technology and Management ,Nanded, India
Journal of Engineering Technology and Management Science Vol. I No.1 December-January 2012
Most sensor networks actively monitor their surroundings, and it is often easy to deduce information other than the data monitored.
Sensing task assignment via sensor selection for maximum target coverage in WSNsPDF
Marjan Naderan,Mehdi Dehghan , Hossein Pedram
Amirkabir University of Technology,Tehran,Iran
Assigning the sensing task to cover maximum number of targets while minimizing the energy consumption of the sensing operation.
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDENPDF Good reference for platform design
Prem Prakash Jayaraman, Charith Perera, Dimitrios Georgakopoulos and Arkady Zaslavsky
CSIRO Computational Informatics Canberra, Australia
9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing October 20–23, 2013 Austin, Texas, United States.
To develop a platform that is autonomous, scalable, interoperable and supports efficient sensor data collection, processing, storage and sharing.
Opportunistic Collaboration in Participatory Sensing EnvironmentsPDF Interesting paper
Niwat Thepvilojanapong, Shinichi Konomi , Yoshito Tobe, Yoshikatsu Ohta
Tokyo Denki University
MobiArch’10, September 24, 2010, Chicago, Illinois, USA.
To achieve energy efficiency and reduce data redundancy, we propose Aquiba protocol that exploits opportunistic collaboration of pedestrians.
Dynamic Mobile Cloud Computing: Ad Hoc and
Opportunistic Job Sharing pdf
Niroshinie Fernando, Seng W. Loke and Wenny Rahayu
La Trobe University, Australia
2011 Fourth IEEE International Conference on Utility and Cloud Computing, 281-286
Explore the feasibility of a mobile cloud computing framework to use local resources to solve these problems. The framework aims to determine a priori the usefulness of sharing workload at runtime. The results of experiments conducted in Bluetooth transmission and an initial prototype are also presented.
To offload or not to offload: an efficient code
partition algorithm for mobile cloud computing pdf
Yuan Zhang, Hao Liu, Lei Jiao, Xiaoming Fu
University of Göttingen, Germany, Tsinghua University, China
2012 IEEE International Conference on Cloud Networking, 80-86
Proposed an efficient code partition algorithm for mobile code offloading. Our algorithm is based on the observation that when a method is offloaded, the subsequent invocations will be offloaded with a high chance. Unlike the current approach which makes an individual decision for each component, our algorithm finds the offloading and integrating points on a sequence of calls by depth-first search and a linear time searching scheme.
6/9/14
MapReduce by Nha
Computation with parallelism and distribution and faults
Input –> Map –> Interm. Output –> Reduce –> Final Output
Input: (K, V)
sample: counting word accurances
K: key: doc name, line no
V: value: contents of line
TODO by Nha: figure out how each reducer selectively process words that belong to specific range that it is responsible for.
Answer
Parallelism
mappers can run simultaneously
reducers can run simultaneously
but reducer can run after all mappers are done
Fault tolerance
WSN by Dileep
6/19/14
Nha
Egalitarian distribution: reduce sampling rates of each device to get collective sampling rate of R_i
Selective distribution: choose k nodes out of p_i+1 so that collective sampling rate of them meets R_i, unchosen nodes don't sample/report.
Centralized:
Distributed:
Peformance metric
Energy saving percentage
TODO for Nha
Dileep
7/1/14
HydroCast: Underwater Pressure Routing by Uichin Lee
Opti
Mobility Model
Next meeting plan on 7/8
7/21/14
Cuckoo 1
Java
Use Activity/Service framework of Android
QR-code/Resource Description file-based master-slave binding
Use ibis frame work for RPC
Question
Cuckoo 2
7/24/14
8/14/14
8/20/14
Code in the Air
8/27/14
Acquisitional Context Engine
9/19/14
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