Rodolfo A. Milito

Fog computing and its role in the internet of things

Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).

Fog Computing: A Platform for Internet of Things and Analytics

Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disruptions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.

Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing

This paper examines some of the most promising and challenging scenarios in IoT, and shows why current compute and storage models confined to data centers will not be able to meet the requirements of many of the applications foreseen for those scenarios. Our analysis is particularly centered on three interrelated requirements: 1) mobility; 2) reliable control and actuation; and 3) scalability, especially, in IoT scenarios that span large geographical areas and require real-time decisions based on data analytics. Based on our analysis, we expose the reasons why Fog Computing is the natural platform for IoT, and discuss the unavoidable interplay of the Fog and the Cloud in the coming years. In the process, we review some of the technologies that will require considerable advances in order to support the applications that the IoT market will demand.

A Massively Multithreaded Packet Processor

Publisher Summary This chapter presents Porthos, a new packet processor designed for stateful applications. The design of Porthos is derived from fundamental observations about stateful networking applications: a significant number of off-chip accesses with little locality need to be supported for each packet. To efficiently implement the requisite large number of parallel packets, Porthos implements a massively multithreaded processor with a strong bias toward efficiency over single-threaded performance. Besides providing a massive multithreading packet processor, another key innovation in Porthos is the implementation of a loosely coupled processor/memory architecture. This allows the performance to be optimized by software and provides topological flexibility and a uniform software model. Furthermore, the use of flow gating allows efficient memory synchronization with minimal software porting issues.

Radio Resource Allocation for a High Capacity Vehicular Access Network

Vehicular networks will enable a high number of services in intelligent transportation systems, and are also expected to improve the availability of wireless services to vehicles. In this paper we address the problem of establishing a vehicular network with high capacity using 802.11 and/or DSRC, and in particular we focus on frequency allocation and power control. We advocate a regular frequency reuse pattern that guarantees SINR requirements of the air interface. Resulting co-channel interference is then reduced using an adaptive power control algorithm. Simulation results for a highway scenario reveal that high capacities are possible with moderate infrastructure access point density.

An Abstraction Methodology for the Evaluation of Multi-core Multi-threaded Architectures

As the evolution of multi-core multi-threaded processors continues, the complexity demanded to perform an extensive trade-off analysis, increases proportionally. Cycle-accurate or trace-driven simulators are too slow to execute the large amount of experiments required to obtain indicative results. To achieve a thorough analysis of the system, software benchmarks or traces are required. In many cases when an analysis is needed most, during the earlier stages of the processor design, benchmarks or traces are not available. Analytical models overcome these limitations but do not provide the fine grain details needed for a deep analysis of these architectures. In this work we present a new methodology to abstract processor architectures, at a level between cycle-accurate and analytical simulators. To apply our methodology we use queueing modeling techniques. Thus, we introduce Q-MAS, a queueing based tool targeting a real chip (the Ultra SPARC T2 processor) and aimed at facilitating the quanti?cation of trade-offs during the design phase of multi-core multi-threaded processor architectures. The results demonstrate that Q-MAS, the tool that we developed, provides accurate results very close to the actual hardware, with a minimal cost of running what-if scenarios.

Tackling IoT Ultra Large Scale Systems: Fog Computing in Support of Hierarchical Emergent Behaviors

The Internet of Things (IoT) marks a phase transition in the evolution of the Internet, distinguished by a massive connectivity and the interaction with the physical world. The organic evolution of IoT requires the consideration of three dimensions: scale, organization, and context. These dimensions are particularly relevant in Ultra Large Scale Systems (ULSS), of which autonomous vehicles is a prime example. Fog Computing is well positioned to support contextual awareness and communication, critical for ULSS. The design and orchestration of ULSS require fresh approaches, new organizing principles. A recent paper proposed Hierarchical Emergent Behaviors (HEB), an architecture that builds on established concepts of emergent behaviors and hierarchical decomposition and organization. HEB’s local rules induce emergent behaviors, i.e., useful behaviors not explicitly programmed. In this chapter we take a first step to validate HEB concepts through the study of two basic self-driven car “primitives”: exiting a platoon formation, and maneuvering in anticipation of obstacles beyond the range of on-board sensors. Fog nodes provide the critical contextual information required.