Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

The use of Blockchain technology has recently become widespread. It has emerged as an essential tool in various academic and industrial fields, such as healthcare, transportation, finance, cybersecurity, and supply chain management. It is regarded as a decentralized, trustworthy, secure, transparent, and immutable solution that innovates data sharing and management. This survey aims to provide a systematic review of Blockchain application to intelligent transportation systems in general and the Internet of Vehicles (IoV) in particular. The survey is divided into four main parts. First, the Blockchain technology including its opportunities, relative taxonomies, and applications is introduced; basic cryptography is also discussed. Next, the evolution of Blockchain is presented, starting from the primary phase of pre-Bitcoin (fundamentally characterized by classic cryptography systems), followed by the Blockchain 1.0 phase, (characterized by Bitcoin implementation and common consensus protocols), and finally, the Blockchain 2.0 phase (characterized by the implementation of smart contracts, Ethereum, and Hyperledger). We compared and identified the strengths and limitations of each of these implementations. Then, the state of the art of Blockchain-based IoV solutions (BIoV) is explored by referring to a large and trusted source database from the Scopus data bank. For a well-structured and clear discussion, the reviewed literature is classified according to the research direction and implemented IoV layer. Useful tables, statistics, and analysis are also presented. Finally, the open problems and future directions in BIoV research are summarized

Cost-aware virtual machines placement problem under constraints over a distributed cloud infrastructure

Massively Multi-players Online Gaming (MMOG) presents one of the most popular large scale Cloud Gaming services. However, minimizing the overall MMOG operating cost is viewed as a critical task. The main challenge for MMOG providers is to find the best tradeoff between cost and response delay when allocating resources: in fact, allocating powerful resources lead to an excellent operating delay, but it is costly. However, cheapest resources don't guarantee even acceptable latency especially for high peak periods. In this view, the present paper contributes to optimize, under delay constraint, the resources allocation cost of MMOG service over a distributed Cloud infrastructure. We develop a model to capture the intrinsic trade-off between response delays relative to allocated resources and their corespondent costs. Then, we formulate the optimal VMs placement problem, which is NP-hard. Besides, we propose a Cost-aware Virtual Machines placement algorithm that approximates the best trade-off point between the two compromise terms. We evaluate our contribution comparing to a Random, Greedy Allocation Cost (GAC) and Minimum Allocation Cost (MAC) placement algorithms via Matlab tool. Results show elasticity and effectiveness of our contribution in maintaining the balance between cost and delay

Cost, energy, and response delay awareness-solution for cloud resources management: proposition of a predictive dynamic algorithm for VMs allocation over a distributed cloud infrastructure

International audienceVirtual resources allocation and placement problem in a distributed Cloud infrastructure presents a compromising question. The geographic position of data centers; their available free resources; the correspondent delay and energy consumption constraints are factors that involve determining the best allocation and placement decision. Allocation and placement cost will be relatively determined according to that choice. In fact, data centers, installed in cold regions, offer lower costs because they need few cooling maintenances, consequently, energy consumptions are minimized. However, data centers, installed closer to population areas, could impose higher costs because of their limited resources or high need for cooling maintenance, so energy consumptions have to be higher. On the other hand, and within acceptable network conditions, allocating powerful resources placed in closer data centers may guarantee shorter global response delay. This could be helpful to support delay-sensitive applications such as Massively Multi-players Online Gaming (MMOG) and enhance their relative Quality of Experience (QoE). However, it may engender high costs and vice versa. In this view, the present paper highlights the critical relationship between the three basics metrics affecting the QoE of the MMOG service, namely the cost, the energy consumption, and the global response delay. We propose a Predictive Dynamic Virtual Machines (VMs) Allocation and Placement algorithm based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) prediction model that captures the intrinsic trade-off of these metrics and outcomes the best mapping of necessary allocated resources. Our contribution is formulated as a Multiple Multidimensional Knapsack Problem (MMKP). Results show the effectiveness of our contribution in maintaining the balance between low-cost objective, low energy consumption by minimizing the inter-migrations of VMs over data centers, and acceptable delay maintained under a predefined threshold