Early Quality of Service Prediction via Interface-level Metrics, Code-level Metrics, and Antipatterns

https://deepblue.lib.umich.edu/bitstream/2027.42/155332/1/IST___Webservices (12).pd

Design and Implementation of a Distributed Mobility Management Entity (MME) on OpenStack

Network Functions Virtualisation (NFV) involves the implementation of network functions, for example firewalls and routers, as software applications that can run on general-purpose servers. In present-day networks, each network function is typically implemented on dedicated and proprietary hardware. By utilising virtualisation technologies, NFV enables network functions to be deployed on cloud computing infrastructure in data centers. This thesis discusses the application of NFV to the Evolved Packet Core (EPC) in Long Term Evolution (LTE) networks; specifically to the Mobility Management Entity (MME), a control plane entity in the EPC. With the convergence of cloud computing and mobile networks, conventional architectures of network elements need to be re-designed in order to fully harness benefits such as scalability and elasticity. To this end, we design and implement a distributed MME with a three-tier architecture common to web applications. We highlight design considerations for moving MME functionality to the cloud and compare our new distributed design to that of a standalone MME. We deploy and test the distributed MME on two separate OpenStack clouds. Our results indicate that the benefits of scalability and resilience can outweigh the marginal increase in latency for EPC procedures. We find that the latency is dependent on the actual placement of MME components within the data center. Also, we believe that extensions to the OpenStack platform are required before it can meet performance and availability requirements for telecommunication applications

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates

Automatic prediction of computational resource consumption for efficient task migration in cloud

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 백윤흥.In order to accommodate the high demand for performance in smartphones, mobile cloud computing techniques, which aim to enhance a smartphone's performance through utilizing powerful cloud servers, were suggested. Among such techniques, execution offloading, which migrates a thread between a mobile device and a server, is often employed. In such execution offloading techniques, it is typical to dynamically decide what code part is to be offloaded through decision making algorithms. In order to achieve optimal offloading performance, however, the gain and cost of offloading must be predicted accurately for such algorithms. Previous works did not try hard to do this because it is usually expensive to make an accurate prediction. Moreover, existing schemes completely ignore the costs of cloud resources by assuming that idle servers are always available for free of charge. These unrealistic assumptions make each server run only a small load to achieve the guaranteed high offload performance. Therefore, these schemes cannot be applied to real-world commercial clouds which aim to minimize the operation costs by maximizing the server throughput, and then charge users for their resource usage. Thus in this dissertation, I present Mantis, a framework for predicting the Computational Resource Consumption(CRC) of Android applications on given inputs accurately, and efficiently. CRC synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the program's CRC metric yet can be evaluated efficiently from the program's input. I apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Using the techniques, I empirically show they enhance the performance of offloading. Lately, I propose CMcloud, a novel cost-effective mobile-to-cloud offloading platform, which works nicely under the real-world cloud environments. CMcloud minimizes both the server costs and the user service fee by offloading as many mobile applications to a single server as possible, while satisfying the target performance of all applications.Abstract i Chapter 1 Introduction 1 1.1 Mobile Execution Offloading . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Dynamic Code Partitioning . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Cost-effectivity of Mobile Execution Offloading . . . . . . . . . . . . 3 1.4 Dissertation Contributions and Outline . . . . . . . . . . . . . . . . . 4 Chapter 2 Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices 6 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Feature Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 CRC Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.5 Predictor Code Generation . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.2 Slicer Challenges . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5.3 Slicer Design . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6 Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.7.1 Evaluation Environment . . . . . . . . . . . . . . . . . . . . 24 2.7.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . 26 2.8 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Chapter 3 Precise Execution Offloading for Applications with Dynamic Behavior in Mobile Cloud Computing 40 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Background & Motivation . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3 f Mantis : Automatically generation of accurate and efficient performance predictor for mobile execution offloading . . . . . . . . . . . . 48 3.3.1 Performance predictor generation overview . . . . . . . . . . 49 3.3.2 Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3.3 Predictor Generator . . . . . . . . . . . . . . . . . . . . . . 50 3.4 Dynamic code partitioning with predictor generated by f Mantis . . . 52 3.4.1 Architecture for our solver . . . . . . . . . . . . . . . . . . . 52 3.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.2 Evaluation Environment . . . . . . . . . . . . . . . . . . . . 55 3.5.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . 56 3.6 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Chapter 4 CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications 73 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Backgrounds and Limitations . . . . . . . . . . . . . . . . . . . . . . 75 4.2.1 Basic Offload Mechanisms . . . . . . . . . . . . . . . . . . . 76 4.2.2 Limitations of Existing schemes . . . . . . . . . . . . . . . . 77 4.3 CMcloud offloading . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3.1 Design Goals . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3.2 Operation Model . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.3 Architecture Model . . . . . . . . . . . . . . . . . . . . . . . 82 4.4 CMcloud mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.4.1 Reference-model Server Profiling . . . . . . . . . . . . . . . 84 4.4.2 Performance Estimation . . . . . . . . . . . . . . . . . . . . 85 4.4.3 Performance Monitoring . . . . . . . . . . . . . . . . . . . . 92 4.4.4 Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.4.5 Cost-aware Application Scheduling in Cloud . . . . . . . . . 94 4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.5.1 Estimating Target CPI stack . . . . . . . . . . . . . . . . . . 96 4.5.2 Predicting Instruction Count . . . . . . . . . . . . . . . . . . 98 4.5.3 Cost Effectiveness with QoS requirements . . . . . . . . . . . 98 4.5.4 Offloading/migration Overhead . . . . . . . . . . . . . . . . 102 4.6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Chapter 5 Conculsion 105 초록 119 vDocto

Geographically Distributed Database Management at the Cloud's Edge

Request latency resulting from the geographic separation between clients and remote application servers is a challenge for cloud-hosted web and mobile applications. Numerous studies have shown the importance of low latency to the end user experience. Small response time increases on the order of a few hundred milliseconds directly translate to reduced user satisfaction and loss of revenue that persist even after a low latency environment is restored. One way to address this challenge in geo-distributed settings is to push all or part of the application, along with the data it requires, to the edge of the cloud - closer to application clients. This thesis explores the idea of taking advantage of clients' proximity to the edge of the network in order to reduce request latencies. SpearDB is a prototype replicated distributed database system which operates in a star network topology, with a core site and a large number of edge sites that are close to clients. Clients access the nearest edge, which holds replicas of locally relevant portions of the database. SpearDB's edge sites coordinate through the core to provide a global transactional consistency guarantee (parallel snapshot isolation or PSI), while handling as much work locally as possible. SpearDB provides full general purpose transactional semantics with ACID guarantees. Experiments show that SpearDB is effective at reducing workload latencies for applications whose access patterns are geographically localizable. Many applications fit this criteria: bulletin boards (e.g., Craigslist, Kijiji), local commerce or services (e.g., Groupon, Uber), booking and ticketing (e.g., OpenTable, StubHub), location based services (mapping, directions, augmented reality), local news outlets and client-centric services (e-mail, rss feeds, gaming). SpearDB introduces protocols for executing application transactions in a geo-distributed setting under strong consistency guarantees. These protocols automatically hide the complexity as well as much of the latency introduced by geo-distribution from applications. The effectiveness of SpearDB depends on the placement of primary and secondary replicas at core and edge sites. The secondary replica placement problem is shown to be NP-hard. Several algorithms for automatic data partitioning and replication are presented to provide approximate solutions. These algorithms work in a geo-distributed core-edge setting under partial replication. Their goal is to bring data closer to clients in order to lower request latencies. Experimental comparisons of the resulting placements' latency impact show good results. Surprisingly however, the placements produced by the simplest of the proposed algorithms are comparable in quality to those produced by more complex approaches

Semantics-Empowered Communication: A Tutorial-cum-Survey

Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.Comment: Submitted to an IEEE journal. Copyright might be transferred without further notic