Amir Vahid Dastjerdi

Fog Computing: Helping the Internet of Things Realize Its Potential

The Internet of Things (IoT) could enable innovations that enhance the quality of life, but it generates unprecedented amounts of data that are difficult for traditional systems, the cloud, and even edge computing to handle. Fog computing is designed to overcome these limitations.

An Effective Architecture for Automated Appliance Management System Applying Ontology-Based Cloud Discovery

Cloud computing is a computing paradigm which allows access of computing elements and storages on-demand over the Internet. Virtual Appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology to solve the complexities of service deployment on Cloud infrastructure. However, an automated approach to deploy required appliances on the most suitable Cloud infrastructure is neglected by previous works which is the focus of this work. In this paper, we propose an effective architecture using ontology-based discovery to provide QoS aware deployment of appliances on Cloud service providers. In addition, we test our approach on a case study and the result shows the efficiency and effectiveness of the proposed work.

Distributed Intrusion Detection in Clouds Using Mobile Agents

Cloud Computing extends an enterprise ability to meet the computing demands of its everyday operations, while offering flexibility, mobility and scalability. However, the reason that Chief Information Officers (CIOs) and their colleagues hesitate to let their business workloads to move from private Cloud into public Cloud is security. This work tries to offer a line of defense by applying Mobile Agents technology to provide intrusion detection for Cloud applications regardless of their locations. Therefore, CIOs feel safer to use Cloud to extend their on-premise infrastructure by adding capacity on demand.

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

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.

A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service

Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.

Compatibility-Aware Cloud Service Composition under Fuzzy Preferences of Users

When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the user requirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such as discovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the best one among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even new challenges caused by diversity of users with different expertise requiring their applications to be deployed across difference geographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances (software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, we present a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology-based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimize effort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for composition optimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systems that force users to assign exact weights for all preferences.

A dependency-aware ontology-based approach for deploying service level agreement monitoring services in Cloud

Cloud computing offers virtualized computing elements on demand in a pay‐as‐you‐go manner. The major motivations to adopt Cloud services include no upfront investment on infrastructure and transferring responsibility of maintenance, backups, and license management to Cloud Providers. However, one of the key challenges that holds businesses from adopting Cloud computing services is that, by migrating to Cloud, they move some of their information and services out of their direct control. Their main concern is how well the Cloud providers keep their information (security) and deliver their services (performance). To cope with this challenge, several service level agreement management systems have been proposed. However, monitoring service deployment as a major responsibility of those systems have not been deeply investigated yet. Therefore, this paper shows how monitoring services have to be described, deployed (discovered and ranked), and then how they have to be executed to enforce accurate penalties by eliminating service level agreement failure cascading effects on violation detection. Copyright

Fog computing : principles, architectures, and applications

Abstract The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in a centralized cloud does not scale to requirements of such an environment. This is because there are applications such as health monitoring and emergency response that require low latency, so delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides a background and motivations regarding the emergence of Fog computing, and defines its key characteristics. In addition, a reference architecture for Fog computing is presented, and recent related development and applications are discussed.

An Autonomous Reliability-Aware Negotiation Strategy for Cloud Computing Environments

Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.

mCloud: A Context-Aware Offloading Framework for Heterogeneous Mobile Cloud

Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices’ proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.