An IoT-oriented data placement method with privacy preservation in cloud environment

© 2018 Elsevier Ltd IoT (Internet of Things) devices generate huge amount of data which require rich resources for data storage and processing. Cloud computing is one of the most popular paradigms to accommodate such IoT data. However, the privacy conflicts combined in the IoT data makes the data placement problem more complicated, and the resource manager needs to take into account the resource efficiency, the power consumption of cloud data centers, and the data access time for the IoT applications while allocating the resources for the IoT data. In view of this challenge, an IoT-oriented Data Placement method with privacy preservation, named IDP, is designed in this paper. Technically, the resource utilization, energy consumption and data access time in the cloud data center with the fat-tree topology are analyzed first. Then a corresponding data placement method, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is designed to achieve high resource usage, energy saving and efficient data access, and meanwhile realize privacy preservation of the IoT data. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method

Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios