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

The General Internet Signaling Transport (GIST) protocol currently
uses TCP or TLS over TCP for connection mode operation. This document
describes the usage of GIST over the Stream Control Transmission
Protocol (SCTP). The use of SCTP can take the advantage of features
provided by SCTP, namely streaming-based transport, support of
multiple streams to avoid head of line blocking, and the support of
multi-homing to provide network level fault tolerance. Additionally,
the support for some extensions of SCTP is also discussed, namely its
Partial Reliability Extension and the usage of TLS over SCTP.

General Internet Signaling Transport (GIST) over SCTP

Network-layer mobility protocols have been developed to keep continuous connectivity for mobile hosts while transparent to the higher layers. However, Due to its distinct characteristics of different from traditional TCP/IP environment, mobility poses substantial impacts on TCP performance in mobile environments. This paper proposes a new cross-layer approach, by introducing a mobility detection element in the network layer which interacts with the transport layer to optimize TCP operations. As changes are only made to the endpoints, this approach preserves the end-to-end semantics of TCP. Different from most exiting works, which utilize either transport or network layer alone without much cross-layer cooperation, our approach allows the use of mobility information in TCP. We analytically compare this approach against existing approaches and show that our approach outperforms prior approaches in terms of effective data resumption time. Through performance simulations, our approach demonstrates that it can effectively improve TCP performance in Mobile IPv6-based mobile environments.

/pdf/a-cross-layer-approach-for-improving-tcp-performance-in-11dawby25z.pdf

A Cross-Layer Approach for Improving TCP Performance in Mobile Environments

In current cloud computing systems, when leveraging virtualization technology, the customer's requested data computing or storing service is accommodated by a set of communicated virtual machines (VM) in a scalable and elastic manner. These VMs are placed in one or more server nodes according to the node capacities or failure probabilities. The VM placement availability refers to the probability that at least one set of all customer's requested VMs operates during the requested lifetime. In this paper, we first study the problem of placing at most H groups of k requested VMs on a minimum number of nodes, such that the VM placement availability is no less than $\delta$, and that the specified communication delay and connection availability for each VM pair under the same placement group are not violated. We consider this problem with and without Shared-Risk Node Group (SRNG) failures, and prove this problem is NP-hard in both cases. We subsequently propose an exact Integer Nonlinear Program (INLP) and an efficient heuristic to solve this problem. We conduct simulations to compare the proposed algorithms with two existing heuristics in terms of performance. Finally, we study the related reliable routing problem of establishing a connection over at most w link-disjoint paths from a source to a destination, such that the connection availability requirement is satisfied and each path delay is no more than a given value. We devise an exact algorithm and two heuristics to solve this NP-hard problem, and evaluate them via simulations.

Reliable Virtual Machine Placement and Routing in Clouds

Multicasting can provide an efficient way of delivering data from a sender to a group of receivers. It has received much attention over the past decade because of an increasing demand for group communication applications such as multimedia streaming. However, native IP multicast has not become widespread largely due to its technical and operational issues. To overcome these obstacles of deployment, various application layer and overlay multicast approaches have been proposed. Compared with IP multicast, they provide a new way of handling multicast without upgrading the infrastructure in a large scale. Nevertheless, they introduce a number of challenges and are still plagued with concerns on scalability, heterogeneity and dynamic performance. In this paper we propose a new protocol framework for addressing these issues, so-called the Dynamic Mesh-based Overlay Multicast Protocol or DMMP, which intends to provide an efficient and resilient multicast support by dynamically managing an overlay core comprised of end hosts. Moreover, DMMP can be used for media streaming which is contracted by a limited resource in stream supplying entities and requires good scalability and reliability. Initial analysis shows that DMMP has the potential to efficiently deliver multicast services for large groups. KeywordsOverlay, multicast, end host, application layer multicast, media streaming

Xiaoming Fu

Papers

General Internet Signaling Transport (GIST) over SCTP

A Cross-Layer Approach for Improving TCP Performance in Mobile Environments

Reliable Virtual Machine Placement and Routing in Clouds

DMMP: A New Dynamic Mesh-based Overlay Multicast Protocol Framework

19th IEEE International Conference on Network Protocols (ICNP), 2011