Emergent behaviors in the Internet of things: The ultimate ultra-large-scale system

To reach its potential, the Internet of Things (IoT) must break down the silos that limit applications' interoperability and hinder their manageability. Doing so leads to the building of ultra-large-scale systems (ULSS) in several areas, including autonomous vehicles, smart cities, and smart grids. The scope of ULSS is both large and complex. Thus, the authors propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly programming all possible decisions in the vast space of ULSS scenarios, HEB relies on the emergent behaviors induced by local rules at each level of the hierarchy. The authors discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. They also illustrate the HEB concepts in reference to autonomous vehicles. This use case paves the way to the discussion of new lines of research.Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).Peer ReviewedPostprint (author's final draft

A general guide to applying machine learning to computer architecture

The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results. The purpose of this paper is to serve as a foundational base and guide to future computer architecture research seeking to make use of machine learning models for improving system efficiency. We describe a method that highlights when, why, and how to utilize machine learning models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data generation every execution quantum and parameter engineering. This is followed by a survey of a set of popular machine learning models. We discuss their strengths and weaknesses and provide an evaluation of implementations for the purpose of creating a workload performance predictor for different core types in an x86 processor. The predictions can then be exploited by a scheduler for heterogeneous processors to improve the system throughput. The algorithms of focus are stochastic gradient descent based linear regression, decision trees, random forests, artificial neural networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version

Advances in the Hierarchical Emergent Behaviors (HEB) approach to autonomous vehicles

Widespread deployment of autonomous vehicles (AVs) presents formidable challenges in terms on handling scalability and complexity, particularly regarding vehicular reaction in the face of unforeseen corner cases. Hierarchical Emergent Behaviors (HEB) is a scalable architecture based on the concepts of emergent behaviors and hierarchical decomposition. It relies on a few simple but powerful rules to govern local vehicular interactions. Rather than requiring prescriptive programming of every possible scenario, HEB’s approach relies on global behaviors induced by the application of these local, well-understood rules. Our first two papers on HEB focused on a primal set of rules applied at the first hierarchical level. On the path to systematize a solid design methodology, this paper proposes additional rules for the second level, studies through simulations the resultant richer set of emergent behaviors, and discusses the communica-tion mechanisms between the different levels.Peer ReviewedPostprint (author's final draft

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.Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).Peer ReviewedPostprint (author's final draft

Fog function virtualization: A flexible solution for IoT applications

The Internet of Things applications must carefully assess certain crucial factors such as the real-time and largely distributed nature of the “things”. Fog Computing provides an architecture to satisfy those requirements through nodes located from near the “things” till the edge. The problem comes with the integration of the Fog nodes into current infrastructures. This process requires the development of complex software solutions and prevents Fog growth. In this paper we propose three innovations to enhance Fog: (i) a new orchestration policy, (ii) the creation of constellations of nodes, and (iii) Fog Function Virtualization (FFV). All together will complement Fog to reach its true potential as a generic scalable platform, running multiple IoT applications simultaneously. Deploying a new service is reduced to the development of the application code, fact that brings the democratization of the Fog Computing paradigm through ease of deployment and cost reduction.The authors thanks Rodolfo Milito for his insightful comments and revisions. Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. Josue V. Quiroga work was supported by a Doctoral Scholarship provided by the Mexican National Council of Science and Technology (CONACyT). This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).Peer ReviewedPostprint (author's final draft

Scalability of broadcast performance in wireless network-on-chip

Networks-on-Chip (NoCs) are currently the paradigm of choice to interconnect the cores of a chip multiprocessor. However, conventional NoCs may not suffice to fulfill the on-chip communication requirements of processors with hundreds or thousands of cores. The main reason is that the performance of such networks drops as the number of cores grows, especially in the presence of multicast and broadcast traffic. This not only limits the scalability of current multiprocessor architectures, but also sets a performance wall that prevents the development of architectures that generate moderate-to-high levels of multicast. In this paper, a Wireless Network-on-Chip (WNoC) where all cores share a single broadband channel is presented. Such design is conceived to provide low latency and ordered delivery for multicast/broadcast traffic, in an attempt to complement a wireline NoC that will transport the rest of communication flows. To assess the feasibility of this approach, the network performance of WNoC is analyzed as a function of the system size and the channel capacity, and then compared to that of wireline NoCs with embedded multicast support. Based on this evaluation, preliminary results on the potential performance of the proposed hybrid scheme are provided, together with guidelines for the design of MAC protocols for WNoC.Peer ReviewedPostprint (published version

NEMsCAM: A novel CAM cell based on nano-electro-mechanical switch and CMOS for energy efficient TLBs

In this paper we propose a novel Content Addressable Memory (CAM) cell, NEMsCAM, based on both Nano-electro-mechanical (NEM) switches and CMOS technologies. The memory component of the proposed CAM cell is designed with two complementary non-volatile NEM switches and located on top of the CMOS-based comparison component. As a use case for the NEMsCAM cell, we design first-level data and instruction Translation Lookaside Buffers (TLBs) with 16nm CMOS technology at 2GHz. The simulations show that the NEMsCAM TLB reduces the energy consumption per search operation (by 27%), write operation (by 41.9%) and standby mode (by 53.9%), and the area (by 40.5%) compared to a CMOS-only TLB with minimal performance overhead.We thank all anonymous reviewers for their insightful comments. This work is supported in part by the European Union (FEDER funds) under contract TIN2012-34557, and the European Union’s Seventh Framework Programme (FP7/2007-2013) under the ParaDIME project (GA no. 318693)Postprint (author's final draft

An Energy-Efficient Design Paradigm for a Memory Cell Based on Novel Nanoelectromechanical Switches

In this chapter, we explain NEMsCAM cell, a new content-addressable memory (CAM) cell, which is designed based on both CMOS technologies and nanoelectromechanical (NEM) switches. The memory part of NEMsCAM is designed with two complementary nonvolatile NEM switches and located on top of the CMOS-based comparison component. As a use case, we evaluate first-level instruction and data translation lookaside buffers (TLBs) with 16 nm CMOS technology at 2 GHz. The simulation results demonstrate that the NEMsCAM TLB reduces the energy consumption per search operation (by 27%), standby mode (by 53.9%), write operation (by 41.9%), and the area (by 40.5%) compared to a CMOS-only TLB with minimal performance overhead

Evaluating University-Business Collaboration at Science Parks: a Business Perspective

The evaluation of the companies’ performance at University Science Parks (SPs) becomes essential in identifying the needs of the companies and the feasibility of the University-Business Collaboration (ubc). The companies’ real needs are also of interest for universities and SPs, since they face the challenge of designing strategies that best help them to transfer knowledge more effectively. This research article focuses on Key Performance Indicators (kpis) in ubc, needs and business objectives of companies co-located at SPs in Spain and Mexico. This article (i) aims to identify the kpis in ubc used by co-located companies at SPs, and (ii) explore the kpis in ubc and critical success factors of SPs. This article focuses on the perspective of companies, with a secondary focus on the perspectives of SPs and universities. For this study, data was collected through online company surveys in Spain and Mexico.Postprint (published version

Assessing the Performance of Virtualization Technologies for NFV: a Preliminary Benchmarking

The NFV paradigm transforms those applications executed for decades in dedicated appliances, into software images to be consolidated in standard server. Although NFV is implemented through cloud computing technologies (e.g., virtual machines, virtual switches), the network traffic that such components have to handle in NFV is different than the traffic they process when used in a cloud computing scenario. Then, this paper provides a (preliminary) benchmarking of the widespread virtualization technologies when used in NFV, which means when they are exploited to run the so called virtual network functions and to chain them in order to create complex services