MODELING, MONITORING AND SCHEDULING TECHNIQUES FOR NETWORK RECOVERY FROM MASSIVE FAILURES

MODELING, MONITORING AND SCHEDULING TECHNIQUES FOR NETWORK RECOVERY FROM MASSIVE FAILURES

Author:

Zad Tootaghaj, Diman

Graduate Program:

Computer Science and Engineering

Degree:

Doctor of Philosophy

Document Type:

Dissertation

Date of Defense:

May 23, 2018

Committee Members:

• Thomas F Laporta, Dissertation Advisor
• Thomas F Laporta, Committee Chair
• Ting He, Committee Member
• Nilanjan Ray Chaudhuri, Committee Member
• Marek Flaska, Outside Member

Keywords:

• Network Recovery
• Massive Disruption
• Stochastic Optimization
• Uncertainty
• Network Recovery Massive Disruption
• Uncertainty.
• Cascading Failures
• Interdependent Networks
• Power Grid
• Software-Defined Networking

Abstract:

This dissertation explores modeling, monitoring and scheduling techniques for network recovery from massive failures, with a focus on optimization methods under uncertain knowledge of failures. Large-scale failures in communication networks due to natural disasters or malicious attacks can severely affect critical communications and threaten lives of people in the affected area. In 2005, Hurricane Katrina led to outage of over 2.5 million lines in the BellSouth (now AT&T) network. In the absence of a proper communication infrastructure, rescue operation becomes extremely difficult. Progressive and timely network recovery is, therefore, a key to minimizing losses and facilitating rescue missions. Many prior works on failure detection and recovery assume full knowledge of failures and use a deterministic approach for the recovery phase. In real-world scenarios, however, the failure pattern might be unknown or only partially known. Therefore, classic recovery approaches may not work. To this end, I focus on network recovery assuming partial and uncertain knowledge of the failure locations. I first studied large-scale failures in a communication network. In particular, I proposed a new recovery approach under uncertain knowledge of failures. I proposed a progressive multi-stage recovery approach that uses the incomplete knowledge of failure to find a feasible recovery schedule. From the elements of this solution, I selected a node with highest centrality at each iteration step to repair and exploit as a monitor to increase the knowledge of network state, until all critical services are restored. The recovery problem can be addressed by giving different priority to three performance aspects including: 1) Demand loss, 2) computation time and 3) number of repairs (or repair cost). These aspects are in conflict with each other and I studied the trade-off among them. Next, I focused on failure recovery of multiple interconnected networks. In particular, I focused on the interaction between a power grid and a communication network. I modeled the cascading failures in a power gird using a DC power flow model. I tackled the problem of mitigating an ongoing cascade by formulating the minimum cost flow assignment problem as a linear programming optimization. The optimization aimed at finding a minimum cost DC power flow setting that stops the cascading failure, where the total cost is defined as the total weighted amount of unsatisfied load due to the re-distribution of the power in the generators and loads without violating the overload constraint at each line. Then, I focused on network monitoring techniques that can be used for diagnosing the performance of individual links for localizing soft failures (e.g. highly congested links) in a communication network. I studied the optimal selection of the monitoring paths to balance identifiability and probing cost. I considered four closely related optimization problems: (1) Max-IL-Cost that maximizes the number of identifiable links under a probing budget, (2) Max-Rank-Cost that maximizes the rank of selected paths under a probing budget, (3) Min-Cost-IL that minimizes the probing cost while preserving identifiability, and (4) Min-Cost-Rank that minimizes the probing cost while preserving rank. I showed that while (1) and (3) are hard to solve, (2) and (4) possess desirable properties that allow efficient computation, while providing good approximation to (1) and (3). I proposed an optimal greedy-based approach for (4) and proposed a (1-1/e)-approximation algorithm for (2). My experimental analysis revealed that, compared to several greedy approaches that directly solve the identifiability-based optimization (i.e. (1) and (3)), the proposed rank-based optimization (i.e. (2) and (4)) achieved better trade-offs in terms of identifiability and probing cost. Finally, I addressed, a minimum disruptive routing framework in software defined networks. I showed that flow disruption, congestion and violation of policies can occur during updates of flow tables in software defined networks. I aimed to minimize the update disruption and minimize the number of affected flows during the update, while taking into account link capacity constraints and the importance of various flows to upper-layer applications. I formulated the problem as an integer linear programming and showed that it is NP-Hard. I proposed two randomized rounding algorithms with bounded congestion and demand loss to solve this problem. In addition to a small SDN testbed, I performed a large-scale simulation study to evaluate my proposed approaches on real network topologies. Extensive experimental and simulation results show that the two random rounding approaches have a disruption cost close to the optimal while incurring a low congestion factor and a low demand loss.

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• Cool Teen Buddy
  https://sites.google.com/site/coolteenbuddy/
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• Eigenvalues-based Steganalysis
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• FARHATفرحتhttps://sites.google.com/site/farhat83site/
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• Farshid Farhat 's Google-Pageصفحه گوگل فرشيد فرحتhttps://sites.google.com/site/farshidfarhat2/
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• Wireless Mobile Adhoc Network Simulatorشبیه ساز شبکه ادهاکhttps://sites.google.com/site/wmanetsecsim/
• آموزشگاه موسیقی نوای سه گاهآدرس: تهران، نارمک، بالاتر از میدان هفت حوض، ضلع شمال شرقی چهارراه سرسبز، جنب موسسه ملی زبان (دختران)، ساختمان عتیق، پلاک 11. تلفن: 77805234-021https://sites.google.com/site/navayesegah/
• سایت گوگل فرشيد فرحتسایت گوگل فرشيد فرحتhttps://sites.google.com/site/farshid83site/
• شاخه دانشجویي انجمن رمز ایران در دانشگاه صنعتی شریفشاخه دانشجوئی انجمن رمز ایران در دانشگاه صنعتی شریفhttps://sites.google.com/site/sbisc4sut/
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System and methods for UICC-based secure communication

System and methods for UICC-based secure communication US 9461993 B2
System and methods for uicc-based secure communication US 20150222631 A1

ABSTRACT
A system that incorporates the subject disclosure may include, for example, instructions which when executed cause a device processor to perform operations comprising sending a service request to a remote management server; receiving from the management server an authentication management function and an encryption key generator for execution by a secure element and an encryption engine for execution by a secure device processor, sending a request to establish a communication session with a remote device; and communicating with the remote device via a channel established using an application server. The secure element and the secure device processor authenticate each other using a mutual authentication keyset. The secure element, the secure device processor and the device processor each have a security level associated therewith; the security level associated with the secure device processor is intermediate between that of the secure element and that of the device processor. Other embodiments are disclosed.

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System and methods for UICC-based secure communication
SYSTEM AND METHODS FOR UICC-BASED SECURE COMMUNICATION 20150222631

Aesthetic Rating

Aesthetic Rating for Photos

The developed engine gets the image and outputs a score showing the amount of the beauty inside the image! The code is also working on Android/iOS platform. The engine speed is good enough which makes it suitable for a real-time engine. The core computes aesthetic features from the image, and estimates the aesthetic score. Check out > ACQUINE

The contributors: Jia Li, James Wang, Farshid Farhat.

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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.

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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.