Summary
Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

All one needs to know about fog computing and related edge computing paradigms: A complete survey

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.

Fog Computing: A Taxonomy, Survey and Future Directions

Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters

Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

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A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( $\text{CO}_2$ ) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, $\text{CO}_2$ emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the $100$ highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with $50$ percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by $50.09$ percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.

Assessment of the Suitability of Fog Computing in the Context of Internet of Things

This paper focuses on the theoretical modeling of sensor cloud, which is one of the first attempts in this direction. We endeavor to theoretically characterize virtualization, which is a fundamental mechanism for operations within the sensor-cloud architecture. Existing related research works on sensor cloud have primarily focused on the ideology and the challenges that wireless sensor network (WSN)-based applications typically encounter. However, none of the works has addressed theoretical characterization and analysis, which can be used for building models for solving different problems to be encountered in using sensor cloud. We present a mathematical formulation of sensor cloud, which is very important for studying the behavior of WSN-based applications in the sensor-cloud platform. We also suggested a paradigm shift of technology from traditional WSNs to sensor-cloud architecture. A detailed analysis is made based on the performance metrics, i.e., energy consumption, fault tolerance, and lifetime of a sensor node. A thorough evaluation of the cost effectiveness of sensor cloud is also done by examining the cash inflow and outflow characteristics from the perspective of every actor of the sensor cloud. Analytical results show that the sensor-cloud architecture outperforms a traditional WSN, by increasing the sensor lifetime by 3.25% and decreasing the energy consumption by 36.68%. We also observe that the technology shift to sensor cloud reduces the expenditure of an end user by 14.72%, on average.

On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network

Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization

This paper proposes a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) [1] within the sensor-cloud infrastructure. Existing cloud pricing models are limited in terms of the homogeneity in service-types, and hence, are not compliant for the heterogeneous service oriented architecture of Se-aaS. We propose a new pricing model comprising of two components, applicable for Se-aaS architecture: pricing attributed to Hardware ( pH ) and pricing attributed to Infrastructure ( pI ). pH addresses the problem of pricing the physical sensor nodes subject to variable demand and utility of the end-users. It maximizes the profit incurred by every sensor owner, while keeping in mind the end-users’ utility. pI mainly focuses on the pricing incurred due to the virtualization of resources. It takes into account the cost for the usage of the infrastructural resources, inclusive of the cost for maintaining virtualization within sensor-cloud. pI maximizes the profit of the sensor-cloud service provider (SCSP) by considering the user satisfaction. Simulation results depict improved performance of pH in comparison to the traditional hardware pricing algorithms, viz. PPM and Sprite, in terms of the residual energy, proximity to the base station (BS), received signal strength (RSS), overhead, and cumulative energy consumption. The results also show the tendency of the sensor-owners to converge to the end-user utility, but not exceed it. We also analyze the performance of pI. The results show the optimality in the profit incurred by SCSP and the user satisfaction.

Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure

We introduce Rosetta, a probabilistic range filter designed specifically for LSM-tree based key-value stores. The core intuition is that we can sacrifice filter probe time because it is not visible in end-to-end key-value store performance, which in turn allows us to significantly reduce the filter false positive rate for every level of the tree. Rosetta indexes all binary prefixes of a key using a hierarchically arranged set of Bloom filters. It then converts each range query into multiple probes, one for each non-overlapping binary prefix. Rosetta has the ability to track workload patterns and adopt a beneficial tuning for each individual LSM-tree run by adjusting the number of Bloom filters it uses and how memory is spread among them to optimize the FPR/CPU cost balance. We show how to integrate Rosetta in a full system, RocksDB, and we demonstrate that it brings as much as a 40x improvement compared to default RocksDB and 2-5x improvement compared to state-of-the-art range filters in a variety of workloads and across different levels of the memory hierarchy (memory, SSD, hard disk). We also show that, unlike state-of-the-art filters, Rosetta brings a net benefit in RocksDB's overall performance, i.e., it improves range queries without losing any performance for point queries.

Subarna Chatterjee

Papers

Assessment of the Suitability of Fog Computing in the Context of Internet of Things

On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network

Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization

Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure

Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores