An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks

Network Functions Virtualization (NFV) in Software Defined Networks (SDN) emerged as a new technology for creating virtual instances for smooth execution of multiple applications. Their amalgamation provides flexible and programmable platforms to utilize the network resources for providing Quality of Service (QoS) to various applications. In SDN-enabled NFV setups, the underlying network services can be viewed as a series of virtual network functions (VNFs) and their optimal deployment on physical/virtual nodes is considered a challenging task to perform. However, SDNs have evolved from single-domain to multi-domain setups in the recent era. Thus, the complexity of the underlying VNF deployment problem in multi-domain setups has increased manifold. Moreover, the energy utilization aspect is relatively unexplored with respect to an optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the VNF deployment problem in multi-domain SDN setup has been addressed with a primary emphasis on reducing the overall energy consumption for deploying the maximum number of VNFs with guaranteed QoS. The problem in hand is initially formulated as a "Multi-objective Optimization Problem" based on Integer Linear Programming (ILP) to obtain an optimal solution. However, the formulated ILP becomes complex to solve with an increasing number of decision variables and constraints with an increase in the size of the network. Thus, we leverage the benefits of the popular evolutionary optimization algorithms to solve the problem under consideration. In order to deduce the most appropriate evolutionary optimization algorithm to solve the considered problem, it is subjected to different variants of evolutionary algorithms on the widely used MOEA framework (an open source java framework based on multi-objective evolutionary algorithms).Comment: Accepted for publication in IEEE INFOCOM 2019 Workshop on Intelligent Cloud Computing and Networking (ICCN 2019

Polling-Based Downlink Communication Protocol for LoRaWAN using Traffic Indication

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 김종권.LPWAN (Low Power Wide Area Network) technologies such as LoRa and SigFox are emerging as a technology of choice for the Internet of Things (IoT) applications where tens of thousands of untethered devices are deployed over a wide area. In such operating environments, energy conservation is one of the most crucial concerns and network protocols adopt various power saving schemes to lengthen device lifetimes. For example, to avoid idle listening, LoRaWAN restricts downlink communications. However, the confined design philosophy impedes the deployment of IoT applications that require asynchronous downlink communications. In this thesis, we design and implement an energy efficient downlink communication mechanism, named TRILO, for LoRaWAN. We aim to make TRILO be energy efficient while obeying an unavoidable trade-off that balances between latency and energy consumption. TRILO adopts a beacon mechanism that periodically alerts end-devices which have pending downlink frames. We implement the proposed protocol on top of commercially available LoRaWAN components and confirm that the protocol operates properly in real-world experiments. Experimental results show that TRILO successfully transmits downlink frames without losses while uplink traffic suffers from a slight increase in latency because uplink transmissions should halt during beacons and downlink transmissions. Computer simulation results also show that the proposed scheme is more energy efficient than the legacy LoRaWAN downlink protocol.전력 공급이 제한적인 수 만개의 디바이스들을 이용하여 넓은 지역을 바탕으로 운영되는 사물인터넷 시스템을 구축하는 데에 있어서 LoRa, SigFox와 같은 저전력 광역 네트워크 기술(LPWA)이 주목받고 있다. 이러한 시스템 환경에서 에너지 절약은 중요한 관심사 중 하나이며 네트워크 프로토콜들은 다양한 절전 방식을 채택하여 디바이스의 수명을 보장하려 하고 있다. 예를 들어, 불필요한 대기 청취로 인한 에너지 손실을 방지하기 위해서 LoRaWAN은 다운링크 통신을 제한하고 있는데, 이러한 설계 철학은 비동기적인 다운링크 통신을 필요로 하는 IoT 애플리케이션의 요구 사항을 충족시키지 못하는 문제점을 가지고 있다. 따라서 본 논문에서는 LoRaWAN에서 다운링크를 효과적으로 컨트롤할 수 있도록 TRILO라는 에너지 효율적인 다운링크 통신 메커니즘을 설계하고 구현하였다. TRILO는 다운링크 프레임이 팬딩되어 있는 엔드 디바이스들의 리스트 정보를 주기적으로 네트워크에 알리는 비콘 메커니즘을 채택하였고, 서버와 디바이스들이 각각 정해진 순서에 따라 다운링크 전송 및 수신을 스케줄링하도록 하였다. 설계한 프로토콜이 제대로 동작하는지 검증하기 위해서 기존 LoRaWAN의 구성 요소 위에 제안된 프로토콜을 구현한 후 실제 테스트 베드를 구축하여서 실험을 진행하였다. 실험 결과에 따르면 TRILO는 기존 프로토콜의 업링크 통신 성능을 저해하지 않으면서도 추가적인 다운링크 프레임을 손실 없이 성공적으로 전송 및 수신하였고, 컴퓨터 시뮬레이션 결과 또한 제안된 기법이 기존의 LoRaWAN 다운링크 프로토콜보다 더 에너지 효율적으로 동작하는 것을 보여주었다.ABSTRACT ........................................................................................................... ⅰ CONTENTS ........................................................................................................... ⅲ LIST OF FIGURES ............................................................................................ ⅳ LIST OF TABLES .............................................................................................. ⅵ CHAPTER Ⅰ: Introduction ................................................................................ 1 CHAPTER Ⅱ: Related Work ............................................................................. 8 CHAPTER Ⅲ: A Primer on LoRa and LoRaWAN .................................. 11 CHAPTER Ⅳ: Downlink Communications Scheme .................................. 17 4.1 Comparison of Two Polling Schemes ..................................... 19 4.2 Proposed Downlink Communications Scheme ....................... 26 CHAPTER Ⅴ: Implementation ........................................................................ 28 CHAPTER Ⅵ: Evaluation ................................................................................. 31 6.1 Experimental Results .................................................................... 32 6.2 Simulation Results ......................................................................... 37 CHAPTER Ⅶ: Discussion ................................................................................. 42 CHAPTER Ⅷ: Conclusion ................................................................................. 45 BIBLIOGRAPHY ................................................................................................... 47 초록 ........................................................................................................................... 51Maste

A lightweight blockchain based framework for underwater ioT

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Streamed Data Analysis Using Adaptable Bloom Filter

With the coming up of plethora of web applications and technologies like sensors, IoT, cloud computing, etc., the data generation resources have increased exponentially. Stream processing requires real time analytics of data in motion and that too in a single pass. This paper proposes a framework for hourly analysis of streamed data using Bloom filter, a probabilistic data structure where hashing is done by using a combination of double hashing and partition hashing; leading to less inter-hash function collision and decreased computational overhead. When size of incoming data is not known, use of Static Bloom filter leads to high collision rate if data flow is too much, and wastage of storage space if data is less. In such cases it is difficult to determine the optimal Bloom filter parameters (m, k) in advance, thus a target threshold for false positives (f_p) cannot be guaranteed. To accommodate the growing data size, one of the major requirements in Bloom filter is that filter size m should grow dynamically. For predicting the array size of Bloom filter Kalman filter has been used. It has been experimentally proved that proposed Adaptable Bloom Filter (ATBF) efficiently performs peak hour analysis, server utilization and reduces the time and space required for querying dynamic datasets

A novel approach for energy- and memory-efficient data loss prevention to support Internet of Things networks

Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios

Building interactive distributed processing applications at a global scale

Along with the continuous engagement with technology, many latency-sensitive interactive applications have emerged, e.g., global content sharing in social networks, adaptive lights/temperatures in smart buildings, and online multi-user games. These applications typically process a massive amount of data at a global scale. In this cases, distributing storage and processing is key to handling the large scale. Distribution necessitates handling two main aspects: a) the placement of data/processing and b) the data motion across the distributed locations. However, handling the distribution while meeting latency guarantees at large scale comes with many challenges around hiding heterogeneity and diversity of devices and workload, handling dynamism in the environment, providing continuous availability despite failures, and supporting persistent large state. In this thesis, we show how latency-driven designs for placement and data-motion can be used to build production infrastructures for interactive applications at a global scale, while also being able to address myriad challenges on heterogeneity, dynamism, state, and availability. We demonstrate a latency-driven approach is general and applicable at all layers of the stack: from storage, to processing, down to networking. We designed and built four distinct systems across the spectrum. We have developed Ambry (collaboration with LinkedIn), a geo-distributed storage system for interactive data sharing across the globe. Ambry is LinkedIn's mainstream production system for all its media content running across 4 datacenters and over 500 million users. Ambry minimizes user perceived latency via smart data placement and propagation. Second, we have built two processing systems, a traditional model, Samza, and the avant-garde model, Steel. Samza (collaboration with LinkedIn) is a production stream processing framework used at 15 companies (including LinkedIn, Uber, Netflix, and TripAdvisor), powering >200 pipelines at LinkedIn alone. Samza minimizes the impact of data motion on the end-to-end latency, thus, enabling large persistent state (100s of TB) along with processing. Steel (collaboration with Microsoft) extends processing to the emerging edge. Integrated with Azure, Steel dynamically optimizes placement and data-motion across the entire edge-cloud environment. Finally, we have designed FreeFlow, a high performance networking mechanisms for containers. Using the container placement, FreeFlow opportunistically bypasses networking layers, minimizing data motion and reducing latency (up to 3 orders of magnitude)

Secure and Space Efficient Accounts Storage System Using Three-Dimensional Bloom Filter

This paper investigates the application of a three dimensional Bloom Filter (3DBF) to accomplish a secure and efficient accounts storage system by exploiting hashes of usernames and their corresponding passwords. We conducted numerical experiments and mathematical analysis to study the efficiency level of several 3DBF schemes. Our experimental results and analysis show that the level of occupancy for 3DBF is positively correlated to the value of its false positive rate, viz., if the occupancy level increases then so does the value of the false positive rate. Moreover, we also derive a formula for determining the minimum number of bits for storing some data in a 3DBF scheme given the value of its acceptable false positive rate and its occupancy level. We infer that the product of the dimensional parameter of a 3DBF scheme is inversely proportional to the false positive rate and occupancy level used in the scheme

Secure monitoring system for industrial internet of things using searchable encryption, access control and machine learning

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries. In addition, the experiments demonstrated the scalability and efficiency of ELSA with an increasing number of keywords and complexity. Based on the experimental results and feedback received from the publication and presentation of this work, we published our third technical paper. In this paper, we improved ELSA by minimising the lookup table size and summarising the data records by integrating machine-learning (ML) methods suitable for execution at the edge. This integration removes records of unnecessary data by evaluating added value to further processing. This process results in the minimisation of the lookup table size, the cloud storage, and the network traffic, taking full advantage of the edge architecture benefits. We demonstrated the mini-ELSA expanded method on two well-known IIoT datasets. Our results reveal a reduction of storage requirements by > 21% while improving execution time by > 1.39× and search time by > 50% and maintaining an optimal balance between prediction accuracy and space reduction. In addition, we present the computational complexity analysis that reinforces these experimental results