We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Part I: AN INTRODUCTION TO INQUIRY. 1. Human Inquiry and Science. 2. Paradigms, Theory, and Research. 3. The Ethics and Politics of Social Research. Part II: THE STRUCTURING OF INQUIRY: QUANTITATIVE AND QUALITATIVE. 4. Research Design. 5. Conceptualization, Operationalization, and Measurement. 6. Indexes, Scales, and Typologies. 7. The Logic of Sampling. Part III: MODES OF OBSERVATION: QUANTITATIVE AND QUALITATIVE. 8. Experiments. 9. Survey Research. 10. Qualitative Field Research. 11. Unobtrusive Research. 12. Evaluation Research. Part IV: ANALYSIS OF DATA:QUANTITATIVE AND QUALITATIVE . 13. Qualitative Data Analysis. 14. Quantitative Data Analysis. 15. Reading and Writing Social Research. Appendix A. Using the Library. Appendix B. Random Numbers. Appendix C. Distribution of Chi Square. Appendix D. Normal Curve Areas. Appendix E. Estimated Sampling Error.

社会研究方法基础 = The Basics of Social Research

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mobility of an IP-based node affects routing paths, and as a result,
can have a significant effect on the protocol operation and state
management. This draft discusses the effects mobility can cause to the
NSIS protocol suite, and how the protocols operate in different
scenarios, with mobility management protocols.

NSIS Protocol Operation in Mobile Environments

The Internet is facing ever-increasing complexity in the construction, configuration and management of heterogeneous networks. New communication paradigms are undermining its original design principles. The mobile Internet demands a level of optimum that is hard to achieve with a strictly-layered protocol stack. Questioning if layering is still an adequate foundation for autonomic protocol stack design, we study the state-of-the-art from both the layered camp and its counterpart. We then outline our vision on protocol stack design for autonomic communication with the POEM model and its internals. A novel cross-layer design approach that combines the advantages of layering and the benefits of holistic and systematic cross-layer optimization is at the core of this work. With inspirations from the natural ecosystem, we are working on the role-based Composable Functional System for self-optimization that features proactive monitoring and control. By doing so step-by-step, we envisage reaching the goal of self-tuning autonomic network with high level of autonomy and efficiency, with minimum human management complexity and user intervention.

Towards self-optimizing protocol stack for autonomic communication : Initial experience

Currently, cloud computing is a primary enabler of new paradigms such as edge and fog computing. One open issue is the pricing of services or resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose OFM, an online learning based marketplace that dynamically determines the price for arbitrary resource types based on supply and demand existing at that period. Unlike state of the art solutions, OFM can handle an arbitrary number of customers and resource types at every instance of time. It further performs integral allocation of resources and thereby avoids the unbounded integrality gap. We evaluate OFM with both real and synthetic datasets to reflect varying buying interests, the number of resources sold and market volatility to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) OFM achieves about 9% of optimal prices and maximizes the Nash social welfare (NSW); (ii) OFM converges faster and works with different data distributions; and (iii) OFM scales for a large number of resources and buyers and computational time is in the order of microseconds, making it applicable for real-time use cases especially in edge markets.

/pdf/ofm-an-online-fisher-market-for-cloud-computing-2zu7pptiqw.pdf

OFM: An Online Fisher Market for Cloud Computing

Nowadays, multiple heterogeneous cellular networks coexist simultaneously, and mobile devices can freely select the appropriate network for data communication. Our measurement studies show that a barrier exists between heterogeneous cellular networks and applications. Triggering handovers between various cellular networks can break the barrier and present the promise of saving energy for cellular data communication. However, most existing network handover triggering methods do not adequately incorporate the characteristics of applications and may cause unnecessary handovers. In this paper, we propose an application-based handover triggering method called AHT. According to the applications that are used by users, AHT triggers handovers between high-performance and energy-efficient cellular networks, thus conserving energy. Based on the user experience (UX) requirements, AHT classifies applications into UX- sensitive and insensitive ones. AHT determines whether and when to switch to the high-performance network (e.g., LTE) in accordance with the predicted UX-sensitive application and estimated usage time, and triggers handovers to the energy- efficient network (e.g., UMTS) through an idle timer. We evaluate the performance of AHT with real application usage traces. Experimental results show that AHT saves up to 60.7% and 32.8% energy compared with the pure LTE network transmission and the screen-based handover triggering scheme Intelli3G, respectively.

AHT: Application-Based Handover Triggering for Saving Energy in Cellular Networks

This paper presents an evaluation of the quality of Service Signalling Layer Protocol (QoS-NSLP) of the NSIS (next steps in signalling) protocol suite. The QoS-NSLP in combination with the NSIS Transport Layer Protocol (NTLP) is proposed by the Internet Engineering Task Force (IETF) as an alternative to the Resource reSerVation Protocol (RSVP). We describe our implementations of the software architecture, both on a network simulator and on a Linux implementation. Both implementations are used in a complimentary manner to illustrate the performance of the QoS-NSLP protocol. The results show the performance of QoS-NSLP with respect to resource consumption, packet processing time, session set up time, refresh interval and protocol overhead. Furthermore, we analyse the protocol performance during route change scenarios.

Xiaoming Fu

Papers

NSIS Protocol Operation in Mobile Environments

Towards self-optimizing protocol stack for autonomic communication : Initial experience

OFM: An Online Fisher Market for Cloud Computing

AHT: Application-Based Handover Triggering for Saving Energy in Cellular Networks

Performance Study of the NSIS QoS-NSLP Protocol