Contention-Aware Game-theoretic Model for Heterogeneous Resource Assignment

CAGE: A Contention-Aware Game-theoretic Model for Heterogeneous Resource Assignment

Traditional resource management systems rely on a centralized approach to manage users running on each resource. The centralized resource management system is not scalable for large-scale servers as the number of users running on shared resources is increasing dramatically and the centralized manager may not have enough information about applications' need. In this paper we propose a distributed game-theoretic resource management approach using market auction mechanism to find optimal strategy in a resource competition game. The applications learn through repeated interactions to choose their action on choosing the shared resources. Specifically, we look into two case studies of cache competition game and main processor and co-processor congestion game. We enforce costs for each resource and derive bidding strategy. Accurate evaluation of the proposed approach show that our distributed allocation is scalable and outperforms the static and traditional approaches.

Draft > CAGE

Big Data, Conference Paper, Research Auction Theory, Biding Strategy, Computer Architecture, Contention Aware, Diman Tootaghaj, Distributed Resource Management, Game Theory, Resource Allocation, Resource Assignment, Resource Sharing on October 5, 2017.

Modeling and Optimization of MapReduce

ABSTRACT

MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of mappers increases, the map phase can take much longer than expected. This paper analytically shows that stochastic behavior of mapper nodes has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of mappers without accurate scheduling can degrade the overall performance. We analytically capture the effects of stragglers (delayed mappers) on the performance. Based on an observed delayed exponential distribution (DED) of the response time of mappers, we then model the map phase by means of hardware, system, and application parameters. Mean sojourn time (MST), the time needed to sync the completed map tasks at one reducer, is mathematically formulated. Following that, we optimize MST by finding the task inter-arrival time to each mapper node. The optimal mapping problem leads to an equilibrium property investigated for different types of inter-arrival and service time distributions in a heterogeneous datacenter (i.e., a datacenter with different types of nodes). Our experimental results show the performance and important parameters of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.

[Tech Report] [Master Thesis] [IEEE Trans]

Last version > MapReduce_Performance_Optimization

This entry was posted in Conference Paper, Journal Paper, Research, Technical Report, Thesis Diman Tootaghaj, Fork-Join Queue, Mapper, MapReduce, Mean Sojourn Time, Modeling, MST, Reducer, Stochastic Optimization, Straggler, Straggling Mapper

PIAKAP

Authentication and Key Agreement Protocol in 4G

Private Identification, Authentication and Key Agreement Protocol with Security Mode Setup, Farshid Farhat, Somayeh Salimi, Ahmad Salahi.

Abstract
Identification, authentication and key agreement protocol of UMTS networks with security mode setup has some weaknesses in the case of mutual freshness of key agreement, DoS-attack resistance, and efficient bandwidth consumption. In this article we consider UMTS AKA and some other proposed schemes. Then we explain the known weaknesses of the previous frameworks suggested for the UMTS AKA protocol. After that we propose a new protocol called private identification, authentication, and key agreement protocol (PIAKAP), for UMTS mobile network. Our suggested protocol combines identification and AKA stages of UMTS AKA protocol while eliminates disadvantages of related works and brings some new features to improve the UMTS AKA mechanism. These features consist of reducing the interactive rounds of the UMTS AKA with security mode setup and user privacy establishment.

ePrint, Research, Security and tagged Ahmad Salahi, Authentication, Identification, Key Agreement, Mobile Network, PIAKAP, Privacy, Security Protocol, Somayeh Salimi, UMTS on March 25, 2011.

Bid Data Analytics

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Professional Photography using Deep Learning

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspective-ness, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.

Art, Computer Vision, Conference Paper, Deep Learning, Image Processing, Machine Learning, Pattern Recognition, Photography, Research, Thesis ACM Multimedia, ACMM, Aesthetics, AI, Art Theory, Artificial Intelligence, CBIR, Computer Vision, Content-based Image Retrieval, Deep Learning, Deep-Learned Model Transfer, Human Pose, Human Pose Estimation, Image Aesthetics, Image Processing, Image Retrieval, Intelligent Portrait Composition Assistance, IPCA, Machine Learning, Object Detection, Pattern Recognition, Penn State Deep Learning, Photography, Photography Idea, Portrait, Portrait Composition, Portrait Dataset, Portrait Photography, Pose Estimation, Pose Recommendation, Scene Parsing, Step-by-Step-Guide on July 2, 2017. 

A Public Key Encryption Algorithm for Network Security

Enhanced Public Key Encryption Algorithm for Security of Network

Abstract -- Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet,

security became a major concern and the history of security allows a better understanding of the emergence of security technology. The internet

structure itself allowed for many security threats to occur. When the architecture of the internet is modified it can reduce the possible attacks that can be

sent across the network. Knowing the attack methods, allows for the appropriate security to emerge. By means of firewalls and encryption mechanisms

many businesses secure themselves from the internet. The businesses create an "intranet" to remain connected to the internet but secured from

possible threats. Data integrity is quite a issue in security and to maintain that integrity we tends to improve as to provides the better encryption

processes for security. In our proposed work we will make encryption harder with enhanced public key encryption protocol for security and will discuss

the applications for proposed work. We will enhance the hardness in security by improving the Diffie-Hellman encryption algorithm by making changes or

adding some more security codes in current algorithm.

REFERENCES

[1] Farhat, Farshid, Somayeh Salimi, and Ahmad Salahi. "Private

Identification, Authentication and Key Agreement Protocol with

Security Mode Setup." IACR Cryptology ePrint Archive 2011.

[2] Emmanuel Bresson, Olivier Chevassut, David

Pointcheva, Jean-Jacques Quisquater, "Authenticated

Group Diffie-Hellman Key Exchange", Computer and

Communication Security- proc of ACM CSS'01,

Philadelphia, Pennsylvania, USA, Pages 255-264, ACM Press,

November 5-8, 2001.

[3] Mario Cagaljm, Srdjan Capkun and Jean-Pierre

Hubaux," Key agreement in peer-to-peer wireless

networks", Ecole Polytechnique F´ed´erale de Lausanne

(EPFL), CH-1015 Lausanne.

[4] Michel Abdalla, Mihir Bellare, Phillip Rogaway,"

DHIES: An encryption scheme based on the Diffie-Hellman

Problem", September 18, 2001.

[5] Jean-Fran¸cois Raymond, Anton Stiglic," Security Issues

in the Diffie-Hellman Key Agreement Protocol".

[6] Whitfield Diffie and Martin E. Hellman," New Directions

in Cryptography", invited paper.

[7] F. Lynn Mcnulty," Encryption's importance to

economic and infrastructure security" in 2002.

[8] Tony Chung and Utz Roedig," Poster Abstract: DHBKEY -A Diffie-Hellman Key Distribution Protocol for

Wireless Sensor Networks", Infolab21, Lancaster University,

UK.

[9] A. Chandrasekar, V.R. Rajasekar, V. Vasudevan,"

Improved Authentication and Key Agreement Protocol

Using Elliptic Curve Cryptography" in 2006.

[10] SANS Institute Info Sec Reading Room," A Review of

the Diffie-Hellman Algorithm and its use in Secure

Internet Protocols".

*11+ Paul C. Kocher, "Timing Attacks on Implementations of

Diffie-Hellman, RSA, DSS, and Other Systems", Cryptography

Research, Inc. 607 Market Street, 5th Floor, San Francisco, CA

94105, USA.

[12] Brita Vesterås," Analysis of Key Agreement Protocols",

Mtech Thesis, Department of Computer Science and Media

Technology, Gjovik University College, 2006

[13] (2006) The YouTube website [online]. Available:

youtube.com/watch?v=40i9ujVJ040

[14] (2008) The YouTube website [online]. Available:

youtube.com/watch?v=3QnD2c4Xovk.

[15] (2011) The Wikipedia website [online]. Available:

wikipedia.org/wiki/Key-agreement_protocol.

[16] (2009) The Wikipedia website [online]. Available:

wikipedia.org/wiki/Diffie%E2%80%93Hellman_key_e

xchange.

Mirzakhani, Maryam

Maryam Mirzakhani was first women to win Fields Medal in math also professor at Stanford university.

RIP #MaryamMirzakhani

in Farshid Farhat 's Twitter

Deep Learning at Pennsylvania State University

Integrating Deep-learned Models and Photography Idea Retrieval

Intelligent Portrait Composition Assistance (IPCA) – Integrating Deep-learned Models and Photography Idea Retrieval, Farshid Farhat, Mohammad Kamani, Sahil Mishra, James Wang, ACM Multimedia 2017, Mountain View, CA, USA.

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspectiveness, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.

Full Text @ intelligent-portrait-composition-assistance-deep-learning-image-retrieval

Art, Computer Vision, Conference Paper, Deep Learning, Image Processing, Machine Learning, Pattern Recognition, Photography, Research, Thesis and tagged ACM Multimedia, ACMM, Aesthetics, AI, Art Theory, Artificial Intelligence, CBIR, Computer Vision, Content-based Image Retrieval, DEEP LEARNING, Deep-Learned Model Transfer, Human Pose, Human Pose Estimation, Image Aesthetics, Image Processing, Image Retrieval, Intelligent Portrait Composition Assistance, IPCA, Machine Learning, Object Detection, Pattern Recognition, Penn State Deep Learning, Photography, Photography Idea, Portrait, Portrait Composition, Portrait Dataset, Portrait Photography, Pose Estimation, Pose Recommendation, Scene Parsing