The integration of unmanned aerial vehicles (UAVs) and satellite technologies into modern border surveillance offers significant potential, eliminating the deployment limitations of terrestrial networks in remote and dynamic environments. These technologies ensure comprehensive coverage, and provide continuous connectivity in all areas with dynamic deployment. However, the integration presents optimization challenges in terms of resource management, communication, and energy efficiency. In this work, we propose a novel hierarchical optimization framework to address these challenges, modeling a terrestrial-non-terrestrial border surveillance (TNTBS) system in which telecom functions and surveillance services (e.g., synthetic aperture radar) are containerized and dynamically deployed into UAVs and satellites. We deal with battery, backhaul, coverage and surveillance service constraints to optimize the operational cost. To overcome the computational intractability of the large-scale problem of TNTBS, which is modeled as a mixed-integer linear programming (MILP) problem, we introduce a Stackelberg game-based approach that enables scalable and distributed decision making with multiple agents and each agent can leverage a local solver or learning model to optimize its local objective. Our extensive simulations demonstrate that our solution achieves near-optimal performance, while ensuring real-time operation and computational efficiency.

Recently, IoT, SDN and NFV have emerged as significant technological enablers for telemedicine. Because of specific characteristics of telemedicine services, reliability is one of the critical elements to guarantee the quality of services. To maintain high availability of services, existing backup solutions focus on resources constraints where backup instances are placed at the same node (vertical backup) or distributively deployed at different nodes (horizontal backup). While they put more effort to satisfy resource requirements, routing issues are often neglected such as end-to-end latency, multi-path routing, and synchronization in a multi-path scenario. Such aspects are key requirements to deploy high reliability telemedicine services. Therefore, we investigate the dynamic backup mechanism for a telemedicine system. We aim to optimize the reliability of telemedicine service function chains (TSFCs) where a joint horizontal/vertical backup (JHVB) optimization problem is first formulated. Since JHVB is a combinatorial optimization problem, which is NP-Hard, we then solve this problem in both offline and online fashions using Block Successive Upper Bound Minimization (BSUM) and Multi-Armed Bandit (MAB) frameworks. We compare our methods to the benchmarks via intensive simulations based on the Nano Datacenter solution that is used for enabling telemedicine services. The results demonstrates an outstanding performance in terms of failure awareness and service reliability.

Unmanned aerial vehicles (UAVs) are projected to be utilized in a variety of unexpected applications, including agriculture, firefighting, emergency response, intelligent transportation, and so on. Wireless communication is one of the primary facilitators in bringing UAVs into a new phase in such applications. To realize the vision in fifth-generation (5G) networks, we propose a 5G-integration of the flexible multi-UAV system and the Open Radio Access Network (O-RAN) architecture, named U-ORAN. Although different studies have been proposed to optimize the UAV trajectory and resource allocation in the radio access network (RAN), our work is the first study to investigate the benefits of adopting UAVs in the O-RAN architecture. In U-ORAN, we consider a flying base station system and propose a joint optimization problem of multi-UAV trajectory and offloading tasks (UTOT) in which UTOT can optimize the routes from users to the core network as well as resource allocation to process offloading tasks. We decompose UTOT into two sub-problems and provide learning solutions based on the multi-agent reinforcement learning and online learning methodologies, both of which are well supported by the O-RAN architecture. Our intensive numerical simulations show that the proposed approaches outperform in a variety of settings and validation scenarios.

Unmanned aerial vehicles (UAVs) are increasingly deployed as flying base stations to serve various applications, such as smart agriculture, emergency healthcare system, smart transportation, etc, thanks to their advantages of flexible movement, strong wireless communication, and heavy payload capability. To facilitate the deployment of the fifth-generation (5G) networks, the Open Radio Access Network (O-RAN) has presented a distributed architecture for terrestrial and non-terrestrial networks. Unfortunately, O-RAN architecture for wireless sensor networks is still in development. In this paper, we investigate a 5G integration of multi-flying base stations in a wireless sensor network using O-RAN. Specifically, we formulate a joint optimization problem of UAV trajectory and resource allocations to process sensing data, named UTRA. We use decomposition to address it based on two solvable sub-problems and provide learning methodologies solve UTRA based on the multi-agent reinforcement learning and online learning methods, both of which are well supported by the O-RAN architecture. Our extensive numerical simulations show that our proposed approaches are efficient in a variety of settings and validation scenarios.

Network slicing is among the most wanted features of the 5G to address the diverse requirements of different classes of services, such as IoT applications. However, bringing the slicing to Wi-Fi networks is very challenging due to the lack of wireless virtualization supports in hardware. This paper proposes a new approach to achieve network slicing in Enterprise WiFi without virtualization techniques. Our approach is based on the dynamic association of user equipment to Wi-Fi access points to meet the requirements of differentiated IoT services. We model this technique as an optimization problem with the objective of maximizing the total throughput of the network while meeting the differentiated IoT service requirements. A heuristic algorithm based on the stable matching mechanism has been proposed to solve the optimization problem in near real-time. Furthermore, to enable a practical implementation, we advocate an online algorithm based on Reinforcement Learning which can mitigate the calculation in each time slot as done by the matching algorithm. Simulation results show that our solutions achieve the total throughput approximately to the optimum one and outperform the traditional RSSI method while guaranteeing the IoT slicing service requirements.

Recently, the exponential growth of the Internet-of-Things (IoT) services with heterogeneous requirements becomes a burden to the traditional cloud/data center platform. Edge computing is an emerging solution to gain business value of IoT services where real-time demands become satisfied by moving computing resources close to data sources. Nevertheless, edge resources are still limited to be able to fulfill all demands at the same time. Among new approaches, resource sharing between edge/cloud service providers has been considered as a promising mechanism to address resource scarcity and pursue cost reduction. In this article, we propose an allocation and sharing model in the edge cloud network where providers team up to efficiently utilize resources, named the share-to-run IoT services (SRIS). In particular, we formulate a resource allocation and sharing optimization model to implement IoT services of multiple edge/cloud providers that can maximize the providers’ utility while satisfying service constraints. We relax SRIS into a tractable form that can be solved efficiently using well-known distributed convex frameworks, such as the dual decomposition and alternating direction method of multipliers. Finally, we evaluate our methods by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes by obtaining a faster convergence, increasing by 6.9% of utilization, and 16% of acceptance rate compared to the nonoptimal approach.

With the capability of decoupling the control plane and the data plane of networks, Software-Defined Network (SDN) enables flexible and efficient implementations in networks. In addition, Network Function Virtualization (NFV) with Virtual Network Function (VNF) service chain capabilities provides high-performance networks with greater scalability, elasticity, and adaptability. Such an elastic deployment of service chains results in different Service Level Agreements (SLA) and resource requirements on the control plane. In this work, we illustrate the impact of service chains on the control plane and formulate the dynamic controller/switch mapping (DCSM) problem in NFV networks in order to reduce the operational cost. We address the combinatorial optimization problem, DCSM, by designing a novel mechanism to relax DCSM into a tractable problem based on the Penalty Successive Upper Bound Minimization (PSUM) method. In doing so, we conduct several simulation scenarios to evaluate the performance. The experimental results show that our proposed algorithms can achieve a near-optimal result and reduce the operational cost up to 31.7% and 28.3% compared to K-Mean and the matching game-based approaches, respectively.

Internet of Things (IoT) services have been implemented for several network applications from smart cities to rural areas. However, there are many barriers to provide an efficient solution for the IoT service deployment underlying innovation SDN/NFV-based technologies. First, though an IoT service can flexibly deploy via virtual network functions (VNFs), a deployment scheme needs to solve the joint routing and resource allocation problem, which becomes more difficult than the traditional centralized cloud/datacenter solution due to distributed resources in the edge-cloud network. In addition, due to uncertain workloads in IoT services, static optimization solutions may not deal with uncompleted knowledge of the entire input, which is often given by assumptions, but unrealistic in current provisioning approaches. Aiming to address these issues, we model an online mechanism for the dynamic IoT service chain deployment to optimize the operational cost in a finite horizon. We propose a JOint Routing and Placement problem for IoT service chain (JORP) that can dynamically scale in/out the number of VNF instances. We then propose a learning method to efficiently solve JORP based on branch-and-bound (BnB). Our proposed learning mechanism can intelligently imitate the branching/pruning actions of BnB, and remove unlikely solutions in the search space based on the deep neural network model to improve the performance. In that respect, we take an intensive simulation that illustrates the promising result of our proposed deep learning method compared to BnB and the greedy baseline in terms of the performance of the algorithm and the operational cost reduction.

The article presents a novel approach to address the interoperability issues in the Internet of Things (IoT) systems. Based on the IoT Reference Architecture, an IoT system architecture integrated with gateways and data converter solutions has been proposed to enhance the information exchange and data sharing across interconnected devices, networks and platforms. In addition, distributed open-source analytic platforms for real-time computational and streaming processing has been introduced to the proposed architecture. A case study of real-time oxygen saturation (SpO2) monitoring and analysis has been implemented to demonstrate the feasibility of the proposed architecture. The experimental results show the efficiency in terms of collecting and analyzing large volumes of data.

The following topics are dealt with: learning (artificial intelligence); optimisation; telecommunication computing; resource allocation; wireless channels; Internet of Things; telecommunication traffic; deep learning (artificial intelligence); mobile computing; cellular radio.

As an emerging solution to latency requirements of Internet of Things (IoT) services, edge computing can bring powerful processing capacity closer to data sources. However, with the limited resources at edge nodes, a major challenge is finding optimal resources in distributed edges to reduce the operational costs of service deployment. Prior works focus mainly on static optimization which may not work efficiently with the time-varying workloads and resource constraints. In this paper, we, therefore, consider a dynamic allocation framework in the edge-cloud network over the long run with uncertainty workloads. In such a system, we introduce a JOint Routing and Placement problem for IoT services, called JORP, that dynamically assigns resources according to workload demand in order to reduce the operational costs in long term. Inspired from the well-known algorithm, branch-and-bound (BnB), for solving the mixed-integer non linear problems (MINLPs) like JORP, we bring the learning concept to address the high complexity of BnB when the search space is huge. Particularly, we design a deep neural network (DNN) and train it under the imitation learning to mimic branching behaviors in BnB for searching the optimal solution. Finally, simulations show our solution outperforms baselines in terms of convergence and operational cost.

Emergence of Internet of Things (IoT) and its affiliated services brought new challenges, such as diversity of service requirements, massive connectivity, security and isolating concerns, and integration issues in networks and business. Network slicing is a powerful tool to address these challenges leveraging technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this article, we propose an architecture to address diverse requirements and resource provisioning challenges of IoT in WiFi networks. The difference between this work and the prior work is on the combination of IoT slicing with WiFi. We utilize the WiFi slicing paradigm to leverage SDN capabilities, such as service classification based on user demands and elastic resource allocation, to map different services into IoT slices. We translate Quality of Service (QoS) requirements into different IoT slices and formulate system requirements as an optimization problem with the objective of maximizing the total throughput of the WiFi system while meeting the IoT slices' constraints. Based on the numerical results, our proposed architecture not only guarantees all slicing service demands but also improves the total throughput of a WiFi network by 8%.

Recently, a rapid growth in the number of vehicles on the road has led to an unexpected surge of parking demand. Consequently, finding a parking space has become increasingly difficult and expensive. One of the viable approaches is to utilize both public and private parking lots (PLs) to effectively share the parking spaces. However, when the parking demands are not balanced among PLs, a local congestion problem occurs where some PLs are overloaded, and others are underutilized. Therefore, in this article, we formulate the parking assignment problem with two objectives: 1) minimizing parking expenses and 2) balancing parking demand among multiple PLs. First, we derive a matching solution for minimizing parking expenses. Then, we extend our study by considering both parking expenses and balancing parking demand, formulating this as a mixed-integer linear programming problem. We solve that problem by using an alternating direction method of multipliers (ADMM)-based algorithm that can enable a distributed implementation. Finally, the simulation results show that the matching game approach outperforms the greedy approach by 8.5% in terms of parking utilization, whereas the ADMM-based algorithm produces performance gains up to 27.5% compared with the centralized matching game approach. Furthermore, the ADMM-based proposed algorithm can obtain a near-optimal solution with a fast convergence that does not exceed eight iterations for the network size with 1000 vehicles. Note to Practitioners-The efficiency of the parking assignment is critical to the parking management systems in order to provide the best parking guides. This article investigates the cost minimization problem for parking assignment while balancing parking demand among multiple parking lots (PLs). Previous parking assignment approaches do not jointly investigate the cost of parking and the cost of PL utilization. Therefore, they can fail to the local congestion problem caused by a large number of vehicles driving toward the same PL. In this article, a new method that considers both of minimizing parking expenses and balancing parking demand is proposed. It is obtained by using the alternating direction method of multipliers (ADMM)-based proposal that distributively solves a constrained optimization problem. Based on the experimental results, the ADMM-based algorithm outperforms the matching-based algorithm and the greedy algorithm in terms of the balancing parking demand and reducing parking expenses. The proposed method can be readily implemented in real-world industrial PLs. In the future work where parking assignments for electric vehicles are needed, our proposed mechanism can then be extended to solve the balanced electricity overload multiple charging stations.

This paper investigates an efficient placement and chaining of Virtual Network Functions (VNFs) to provide cloud based IoT services with minimal resource usage cost. We take into account bandwidth capacity and link delay of network connection between clouds where VNFs are allocated and underlying IoT networks where sensors and IoT gateways are deployed. Regarding the constantly changing network dynamics, input traffic of service components is considered at the lower granularity level of messages based on the communication between each VNF and corresponding sensors via IoT gateways. From the algorithm perspective, the specific topology of multiple edge clouds is leveraged to improve the solution. In this paper, we present an NFV-based high-level architecture for a system that enables the deployment of IoT services across multiple edges and clouds. We formulate the VNF placement problem using a non-convex Integer Programming model. Taking into account different IoT topologies, we devise two algorithms for small- and large-scale networks to find the near optimal solution: i) a customized Markov approximation with two techniques, i.e., multi-start and batching, and a node ranking-based heuristic. Simulation and experimental results show that the proposed solution improves the cost up to 21% compared to state-of-the-art schemes.

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges in terms of adaptation to diverse network appliances and reduction of the capital and operational expenses of the service providers. In particular, to deploy service chains, providers must consider different objectives, such as minimizing the network latency or the operational cost, which are coupled objectives that have traditionally been addressed separately. In this paper, the problem of virtual network function (vNF) placement for service chains is studied for the purpose of energy and traffic-aware cost minimization. This problem is formulated as an optimization problem named the joint operational and network traffic cost (OPNET) problem. First, a sampling-based Markov approximation (MA) approach is proposed to solve the combinatorial NP-hard problem, OPNET. Even though the MA approach can yield a near-optimal solution, it requires a long convergence time that can hinder its practical deployment. To overcome this issue, a novel approach that combines the MA with matching theory, named as SAMA, is proposed to find an efficient solution for the original problem OPNET. Simulation results show that the proposed framework can reduce the total incurred cost by up to 19 percent compared to the existing non-coordinated approach.

Software in IoT devices needs to be improved regularly to adapt security issues and new user requirements. In advanced IoT networks, devices employ the component-based software architecture in which components can be updated at run-time, such devices can download software components from neighbors, enabling fast distribution of updates in the entire network. One of the most energy consuming operations in the update process is flash re-writing in which the order of re-writing components into the flash memory is decisive for energy consumption. In this paper, we propose a mechanism that schedules updates in an entire IoT network to minimize the energy consumption, while satisfying the deadline constraint for updating all the devices. We mathematically formulate the problem of energy efficient update scheduling as an optimization problem with a novel energy model of the update process, then propose an algorithm to approximate the optimal schedule for updating all devices in the network. We examine the proposed algorithm in three different network instances including a tree, a partial mesh and a full mesh topology. Simulation results illustrate that our algorithm can obtain a near optimum which is, in the best case, only 3.2% different from the minimum.

The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, namely controller-switch assignment and Virtual Network Function (VNF) placement. Unlike prior works, our purpose is to jointly solve these two problems, accounting for the complex and counter-intuitive manner they are related to each other. We present a service chain aware framework (SACO) that enables the controller-switch association in a multi-controller network regarding the relationship of switches via their connected VNFs that implement service components of the chain. We also propose a model and formulate the joint optimization problem of dynamic controller-switch mapping and VNF allocation. We apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problem and employ the Markov approximation method to find a near-optimal solution. Simulation results show that our service chain aware approach improves the system cost up to 10 ~ 43% compared to the state-of-the-art solutions.

Due to security issues and incremental user requirements, software in IoT devices needs to be changed frequently. Recently, advanced IoT devices employ the component-based software architecture in which components can be updated at run-time. In such IoT networks, devices can download updated components from neighbor nodes, enabling quick deployment of updates in the entire network. A key operation which consumes a significant amount of energy in the update process is flash re-writing, in which the order of re-writing components into the memory is decisive for energy consumption. In this paper, we propose a mechanism that schedules updates on all devices in an IoT network to minimize the energy consumption, taking into account the deadline constraint for updating the entire network. We introduce a novel energy model of the update process, then propose an algorithm to approximate the optimal schedule for updating all devices in the network. Simulation results show that our algorithm can obtain a near optimal which is, on average, 7.1% different from the global minimum.

The last few years have witnessed a significant increase in the deployment of heterogeneous Internet of Things (IoT) networks. IoT devices send data with different requirements such as tolerated delay and data rates. Emerging multi-interface IoT devices bring the flexibility of connecting to multiple heterogeneous access networks, which thus improves the network capacity. However, each network interface has its own constraints in terms of network coverage, capacity, packet loss rates, etc. An efficient utilization of the available multiple interfaces in IoT gateways would improve the network performance. Therefore, it is crucial to design a flow assignment mechanism to select the appropriate interface that best satisfies the flow's requirements and maximizes the amount of data accepted by an IoT gateway. In this work, we model and formulate the optimized flow assignment problem (OFAP) in a multi-interface IoT gateway. Then, we develop two heuristic algorithms to find a feasible solution for OFAP. The first algorithm is based on the greedy approach and the second uses dynamic programming to assign flows to interfaces. We provide simulation results that show the effectiveness of our algorithms.

This paper investigates an efficient placing mechanism for placing Virtual Network Function (VNF) on the cloud networks to enable the construction of Internet of Things (IoT) service chains with minimal resource usage cost. In particular, we propose a model taking IoT network infrastructure into account. Such the network composed of numerous sensors with constrained resource, dynamic connectivity toward multi-homing IoT gateways makes the problem of optimally placing VNFs with expected Quality of Service (QoS) more challenging and has not been considered in prior works. From the model, we formulate a non-convex Integer Programming (IP) placement problem and devise a batching Markov approximation placement (BMAP) algorithm to find the optimal solution. Simulation results show that the proposed approach improves the cost compared to those that do not consider the IoT network.

Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN deployments, such as i) where to place SDN controllers in a given network, ii) how to assign connections from controllers to switches in terms of satisfying multiple objectives (e.g., resource utilization, failure, quality of services, etc.). In this work, we focus on the efficient assignment between SDN controllers and switches to guarantee a low operational cost, quality of services, and fault tolerance, in which the complexity of virtual links in network services, an omitted factor in current works, is considered and addressed. We formulate an optimization problem for dynamic controller/switch mapping (DCSM) in the network virtualization. We then proposed approximation algorithms in terms of relaxing the binary variables to solve the NP-hard problem, DCSM, in both centralized and distributed mechanisms. We also create various simulation schemes to evaluate our methods where they outperform state-of-the-art methods.

Installation of backup power supply plays a vital role in maintaining communication services which can save billions of dollars as well as human lives during natural disasters. Due to the higher capital and operational expense compared to public power, pooling and sharing the backup power supplies can be an economical solution since the backup power capacity can be sized based on the aggregate demand of co-located operators. However, how to pool and share the backup power at multi-operator cellular sites in a fair manner should be considered due to the limited capacity and high user demands. In this paper, we adopt the Nash Bargaining Solution (NBS) of a bargaining problem which can guarantee the fairness of backup power sharing and design a decentralized algorithm approach with limited information exchange among the operators. Our simulation demonstrates that the sharing the backup power reduces the average delay and requires less BS power consumption than the non-sharing approach, especially for high traffic load scenarios. In addition, we also extend the formulation with respect to admission control for very high traffic demand cases.

Phishing detection is recognized as a criminal issue of Internet security. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks. However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the network and embedded robust machine learning techniques for phishing detection. In this paper, we use uniform resource locator features and Web traffic features to detect phishing websites based on a designed neuro-fuzzy framework (dubbed Fi-NFN). Based on the new approach, fog computing as encouraged by Cisco, we design an anti-phishing model to transparently monitor and protect fog users from phishing attacks. The experiment results of our proposed approach, based on a large-scale dataset collected from real phishing cases, have shown that our system can effectively prevent phishing attacks and improve the security of the network.

Keeping wireless base stations operating continually and providing uninterrupted communications services can save billions of dollars as well as human lives during natural disasters and/or electricity outages. Toward this end, wireless operators need to install backup power supplies whose capacity is sufficient to support their peak power demand, thus incurring a significant capital expense. Hence, pooling together backup power supplies and sharing it among co-located wireless operators can effectively reduce the capital expense, as the backup power capacity can be sized based on the aggregate demand of co-located operators instead of individual demand. Turning this vision into reality, however, faces a new challenge: how to fairly share the backup power supply? In this paper, we propose fair sharing of backup power supply by multiple wireless operators based on the Nash bargaining solution (NBS). In addition, we integrate our analysis with multiple time slots for emergency cases in which the study the backup energy sharing based on model predictive control and NBS subject to an energy capacity constraint regarding future service availability. Our simulations demonstrate that sharing backup power/energy improves the communications service quality with lower cost and consumes less base station power than the non-sharing approach.

Recommender agents are widely used in online markets, social networks and search engines. The recent online news recommendation systems such as Google News and Yahoo! News produce real-time decisions for ranking and displaying highlighted stories from massive news and users access per day. The more relevant highlighted items are suggested to users, the more interesting and better feedback from users achieve. Therefore, the distributed online learning can be a promising approach that provides learning ability for recommender agents based on side information under dynamic environment in large scale scenarios. In this work, we propose a distributed algorithm that is integrated online K-Means user contexts clustering with online learning mechanisms for selecting a highlighted news. Our proposed algorithm for online clustering with lower bound confident clustering approximates closer to offline K-Means clusters than greedy clustering and gives better performance in learning process. The algorithm provides a scalability, cheap storage and computation cost approach for large scale news recommendation systems.

Even though demand response of datacenters recently has received increasing attention due to huge demands and flexible power control knobs, most of current studies focus on the owner-operated datacenters, leaving behind another critical segment of datacenter business: multi-tenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides other facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has its unique feature that challenges any attempts to design its demand response program: uncoordinated power management among tenants. To tackle this challenge, we consider incentive mechanisms that can coordinate tenants' power consumption for emergency demand response, where a fixed energy reduction target must be fulfilled. For two types of price-taking and price-anticipating tenants, we propose two incentive schemes with distributed algorithms that can achieve the same optimal social cost. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes.

In this paper, the problem of resource allocation in cloud datacenters, that own highly complex and heterogeneous tasks and servers, is considered. To address this problem, a novel framework, dubbed joint operation cost and network traffic cost (JOT) framework, is proposed. This framework combines notions from Gibbs sampling and matching theory to find an efficient solution addressing the NP-hard problem JOT. The proposed model is shown to be capable of controlling the active server set, in a coordinated manner while allocating VMs in order to reduce both operation cost and network traffic cost of the cloud datacenter. We also conduct a case-study to validate our proposed algorithm and the results show that JOT can reduce the total incurred cost by up to 19% compared to the existing non-coordinated approach.

In the area of cloud infrastructure environment, the management tool to monitor and control the cloud resources is the important factor that can drive the cost benefit of the cloud vendors. But most these tools are bundled within the high cost commercial platforms and are optimized to run on desktop computers. With the vision that Mobile Cloud Computing will be the future technology paradigm that dominates the IT industry, we want to create a cloud management tool that is open source, fast, lightweight and mobile friendly. We take the initial steps by implementing our framework using several popular technologies such as RESTful, Java Message Service, JSON, and we call it “High performance and Lightweight Mobile Cloud Infrastructure Monitor and Benchmark Service” or HiLiCloud. The initial testings show competitive evaluation results.

We take the first step to study the price competition in a heterogeneous market cloud computing formed by public provider and cloud broker, all of which are also known as cloud service providers. We formulate a price competition between cloud broker and public provider as a two-stage non-cooperative game. In stage one, where cloud service providers set their service prices to maximize their revenue, we use the Nash equilibrium concept to study the equilibria for the price setting game. Cloud users can select the services (from the cloud broker or public provider) that provide them the best payoff in terms of performance (i.e., delay) and price. To that end, cloud users can adapt their service selection behavior by observing the variations in price and quality of service offered by the different cloud service providers. For the service selection game of cloud users in stage two, we use the evolutionary game model to study the evolution and the dynamic behavior of cloud users. Furthermore, the Wardrop equilibrium and replicator dynamics is applied to determine the equilibrium and its convergence properties of the service selection game. Numerical results illustrate that our game model captures the main factors behind the heterogeneous market cloud pricing and service selection, thus represents a promising framework for the design and understanding of the heterogeneous market cloud computing.

Vehicular Ad hoc NETworks (VANETs) should provide the reliable safety message broadcasts and the efficient non-safety message transmissions to vehicles. The IEEE 1609.4 MAC, which supports multi-channel operations in VANETs, is not reliable enough for the safety message broadcast and not efficient in the Service CHannel (SCH) resources utilization. In this paper, we propose a MAC protocol which supports a Reliable Multi-hop Safety message Broadcast (RMSB-MAC) in VANETs. Each Multi-hop Forwarder (MF) collects the safety messages from the neighbor vehicle nodes, and then the MF uses its reserved time slot to broadcast them to all vehicle nodes in its transmission range as well as to forward them to the next MF. Moreover, by allowing vehicle nodes to exchange non-safety messages during the Control CHannel Interval (CCHI), the RMSB-MAC utilizes the SCH resources more efficiently.