Cascading Cache Layer in Content Management System

Caching involves the temporal storing of data in a separate folder. Cascading is the arrangement of something in sequence from top to bottom. Cascading cache layer in content management system places data in layers and sequence in order of importance. The cached data are also removed based on their order of importance. Caching is majorly about input and output of content and data, this brings the need for cascading management system to make accessing data easier than usual. This work takes a look into caching and how it works. It considers various levels of caching in the content management systems. It tries to explain what cascading is in a content management system as well as its importance. This work explains how cascading cache in layers would make it faster and more efficient to access data

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area

엣지 클라우드 환경을 위한 연산 오프로딩 시스템

학위논문(박사)--서울대학교 대학원 :공과대학 전기·정보공학부,2020. 2. 문수묵.The purpose of my dissertation is to build lightweight edge computing systems which provide seamless offloading services even when users move across multiple edge servers. I focused on two specific application domains: 1) web applications and 2) DNN applications. I propose an edge computing system which offload computations from web-supported devices to edge servers. The proposed system exploits the portability of web apps, i.e., distributed as source code and runnable without installation, when migrating the execution state of web apps. This significantly reduces the complexity of state migration, allowing a web app to migrate within a few seconds. Also, the proposed system supports offloading of webassembly, a standard low-level instruction format for web apps, having achieved up to 8.4x speedup compared to offloading of pure JavaScript codes. I also propose incremental offloading of neural network (IONN), which simultaneously offloads DNN execution while deploying a DNN model, thus reducing the overhead of DNN model deployment. Also, I extended IONN to support large-scale edge server environments by proactively migrating DNN layers to edge servers where mobile users are predicted to visit. Simulation with open-source mobility dataset showed that the proposed system could significantly reduce the overhead of deploying a DNN model.본 논문의 목적은 사용자가 이동하는 동안에도 원활한 연산 오프로딩 서비스를 제공하는 경량 엣지 컴퓨팅 시스템을 구축하는 것입니다. 웹 어플리케이션과 인공신경망 (DNN: Deep Neural Network) 이라는 두 가지 어플리케이션 도메인에서 연구를 진행했습니다. 첫째, 웹 지원 장치에서 엣지 서버로 연산을 오프로드하는 엣지 컴퓨팅 시스템을 제안합니다. 제안된 시스템은 웹 앱의 실행 상태를 마이그레이션 할 때 웹 앱의 높은 이식성(소스 코드로 배포되고 설치하지 않고 실행할 수 있음)을 활용합니다. 이를 통해 상태 마이그레이션의 복잡성이 크게 줄여서 웹 앱이 몇 초 내에 마이그레이션 될 수 있습니다. 또한, 제안된 시스템은 웹 어플리케이션을 위한 표준 저수준 인스트럭션인 웹 어셈블리 오프로드를 지원하여 순수한 JavaScript 코드 오프로드와 비교하여 최대 8.4 배의 속도 향상을 달성했습니다. 둘째, DNN 어플리케이션을 엣지 서버에 배포할 때, DNN 모델을 전송하는 동안 DNN 연산을 오프로드 하여 빠르게 성능향상을 달성할 수 있는 점진적 오프로드 방식을 제안합니다. 또한, 모바일 사용자가 방문 할 것으로 예상되는 엣지 서버로 DNN 레이어를 사전에 마이그레이션하여 콜드 스타트 성능을 향상시키는 방식을 제안 합니다. 오픈 소스 모빌리티 데이터셋을 이용한 시뮬레이션에서, DNN 모델을 배포하면서 발생하는 성능 저하를 제안 하는 방식이 크게 줄일 수 있음을 확인하였습니다.Chapter 1. Introduction 1 1.1 Offloading Web App Computations to Edge Servers 1 1.2 Offloading DNN Computations to Edge Servers 3 Chapter 2. Seamless Offloading of Web App Computations 7 2.1 Motivation: Computation-Intensive Web Apps 7 2.2 Mobile Web Worker System 10 2.2.1 Review of HTML5 Web Worker 10 2.2.2 Mobile Web Worker System 11 2.3 Migrating Web Worker 14 2.3.1 Runtime State of Web Worker 15 2.3.2 Snapshot of Mobile Web Worker 16 2.3.3 End-to-End Migration Process 21 2.4 Evaluation 22 2.4.1 Experimental Environment 22 2.4.2 Migration Performance 24 2.4.3 Application Execution Performance 27 Chapter 3. IONN: Incremental Offloading of Neural Network Computations 30 3.1 Motivation: Overhead of Deploying DNN Model 30 3.2 Background 32 3.2.1 Deep Neural Network 33 3.2.2 Offloading of DNN Computations 33 3.3 IONN For DNN Edge Computing 35 3.4 DNN Partitioning 37 3.4.1 Neural Network (NN) Execution Graph 38 3.4.2 Partitioning Algorithm 40 3.4.3 Handling DNNs with Multiple Paths. 43 3.5 Evaluation 45 3.5.1 Experimental Environment 45 3.5.2 DNN Query Performance 46 3.5.3 Accuracy of Prediction Functions 48 3.5.4 Energy Consumption. 49 Chapter 4. PerDNN: Offloading DNN Computations to Pervasive Edge Servers 51 4.1 Motivation: Cold Start Issue 51 4.2 Proposed Offloading System: PerDNN 52 4.2.1 Edge Server Environment 53 4.2.2 Overall Architecture 54 4.2.3 GPU-aware DNN Partitioning 56 4.2.4 Mobility Prediction 59 4.3 Evaluation 63 4.3.1 Performance Gain of Single Client 64 4.3.2 Large-Scale Simulation 65 Chapter 5. RelatedWorks 73 Chapter 6. Conclusion. 78 Chapter 5. RelatedWorks 73 Chapter 6. Conclusion 78 Bibliography 80Docto

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure