Deadline-aware multi-objective IoT services placement optimization in fog environment using parallel FFD-genetic algorithm

Nowadays, Internet of things has become as an inevitable aspect of humans’ IT-based life. A huge number of geo-distributed IoT enabled devices such as smart phones, smart cameras, health care systems, vehicles, etc. are connected to the Internet and manage users’ applications. The IoT applications are generally time sensitive, so that giving them up to Cloud and receiving the response may violate their required deadline, due to distance between user device and centralized Cloud data center and consequently increasing network latency. Fog environment, as an intermediate layer between Cloud and IoT devices, brings a smaller scales of Cloud capabilities closer to user location. Processing real time applications in Fog layer helps more deadlines to be met. Although Fog computing enhances quality of service parameters, limited resources and power of Fog nodes is a challenge in processing applications. Furthermore, the network latency is still an issue for communications between applications’ services and between user device and Fog node, which seriously threatens deadline condition. Regarding to mentioned points, this paper proposes a 3-partite deadline-aware applications’ services placement optimization model in Fog environment which optimizes total power consumption, total resources wastage, and total network latency, simultaneously. The proposed model prioritizes applications in 3 levels based on their associated deadline, and then the model is solved using a parallel model of first fit decreasing and genetic algorithm combination. Simulations results indicates the superiority of proposed approach against counterpart algorithms in terms of reducing power consumption, resource wastage, network latency, and service rejection rate.     

MIFaaS: A Mobile-IoT-Federation-as-a-Service Model for dynamic cooperation of IoT Cloud Providers

In the Internet of Things (IoT) arena, a constant evolution is observed towards the deployment of integrated environments, wherein heterogeneous devices pool their capacities to match wide-ranging user requirements. Solutions for efficient and synergistic cooperation among objects are, therefore, required. This paper suggests a novel paradigm to support dynamic cooperation among private/public local clouds of IoT devices. Differently from device-oriented approaches typical of Mobile Cloud Computing, the proposed paradigm envisages an IoT Cloud Provider (ICP)-oriented cooperation, which allows all devices belonging to the same private/public owner to participate in the federation process. Expected result from dynamic federations among ICPs is a remarkable increase in the amount of service requests being satisfied. Different from the Fog Computing vision, the network edge provides only management support and supervision to the proposed Mobile-IoT-Federation-as-a-Service (MIFaaS), thus reducing the deployment cost of peripheral micro data centers. The paper proposes a coalition formation game to account for the interest of rational cooperative ICPs in their own payoff. A proof-of-concept performance evaluation confirms that obtained coalition structures not only guarantee the satisfaction of the players’ requirements according to their utility function, but also these introduce significant benefits for the cooperating ICPs in terms of number of tasks being successfully assigned.     

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Mobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.     

A systematic approach toward security in Fog computing: Assets,vulnerabilities, possible countermeasures

Fog computing is an emerging paradigm in the Internet of Things (IoT) space, consisting of a middle computation layer, sitting between IoT devices and Cloud servers. Fog computing provides additional computing, storage, and networking resources in close proximity to where data is being generated and/or consumed. As the Fog layer has direct access to data streams generated by IoT devices and responses/commands sent from the Cloud, it is in a critical position in terms of security of the entire IoT system. Currently, there is no specific tool or methodology for analysing the security of Fog computing systems in a comprehensive way. Generic security evaluation procedures applicable to most information technology products are time consuming, costly, and badly suited to the Fog context. In this article, we introduce a methodology for evaluating the security of Fog computing systems in a systematic way. We also apply our methodology to a generic Fog computing system, showcasing how it can be purposefully used by security analysts and system designers.     

CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog

Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies.     

面向延迟敏感型物联网应用的计算迁移策略

针对云计算网络延迟较长、能耗过高和边缘服务器计算资源有限的问题,提出了一种提高延迟敏感型物联网（IoT）应用服务质量（QoS）的边缘-云合作的漂移加惩罚计算迁移策略（DPCO）。首先,建立物联网-边缘-云系统模型,对业务模式、计算任务所经历的传输延迟和计算延迟、系统产生的计算能耗和传输能耗等进行数学建模;然后,以系统能耗和任务平均延迟为优化目标,以边缘服务器的队列稳定性为限制条件构建边缘-云合作的计算迁移优化模型;接着,以优化目标为惩罚函数,基于李雅普诺夫稳定性理论推导出计算迁移优化模型的漂移加惩罚函数特性。最后,基于推导结果提出了DPCO计算迁移算法,通过每时隙选择使当前漂移加惩罚函数最小化的计算迁移策略来降低长期的单位时间能耗和缩短系统平均延迟。与轻流雾处理（LFP）、基准边缘计算（EC）、基准云计算（CC）策略相比,DPCO的系统能耗最低,约是CC策略的2/3;任务平均延迟也最小,可减少为CC的1/5。实验结果表明,DPCO能够有效降低边缘-云计算系统的能量消耗,减少计算任务的端到端延迟,满足延迟敏感型IoT应用的QoS要求。     

Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.     

Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN

World Wide Web - The Fog Computing was proposed to extend the computing task to the network edge in lots of Internet of Things (IoT) scenario, such as Internet of Vehicle (IoV). However, the...     

An energy efficient FPGA partial reconfiguration based micro-architectural technique for IoT applications

Low power consumption and high computational performance are two important processor design goals for IoT applications. Achieving both design goals in one processor architecture is challenging due to their conflicting requirements. This paper introduces a reconfigurable micro-architectural level technique that allows a Reduced Instruction Set Computing (RISC) processor to support IoT applications with different performance and energy trade-off requirements. The processor can be reconfigured into either multi-cycle execution mode (low computational speed with low dynamic power consumption) or pipeline execution mode (high computational speed at the expense of high dynamic power), based on dynamic workload characteristics in IoT applications. Switching between modes is accomplished by exploiting the partial reconfiguration (PR) feature offered by the recent advancements in modern FPGAs. A RISC processor was designed based on the proposed micro-architectural level technique and implemented on FPGA as IoT sensor node. Experimental results demonstrate that the proposed technique with reconfigurable micro-architecture is able to significantly reduce the dynamic energy consumption, compared to conventional multi-cycle and pipeline only micro-architectures, while allowing better performance-energy trade-off in IoT applications.     

云边环境下基于博弈论的两阶段任务迁移策略

移动边缘计算（MEC）为计算密集型应用和资源受限的移动设备之间的冲突提供了有效解决办法,但大多关于MEC迁移的研究仅考虑移动设备与MEC服务器之间的资源分配,忽略了云计算中心的巨大计算资源。为了充分利用云和MEC资源,提出一种云边协作的任务迁移策略。首先,将云边服务器的任务迁移问题转化为博弈问题;然后,证明该博弈中纳什均衡（NE）的存在以及唯一性,并获得博弈问题的解决方案;最后,提出了一种基于博弈论的两阶段任务迁移算法来求解任务迁移问题,并通过性能指标对该算法的性能进行了评估。仿真结果表明,采用所提算法所产生的总开销分别比本地执行、云中心服务器执行和MEC服务器执行的总开销降低了72.8%、47.9%和2.65%,数值结果证实了所提策略可以实现更高的能源效率和更低的任务迁移开销,并且随着移动设备数量的增加可以很好地扩展规模。     

云环境下绿色任务调度策略

针对云计算数据中心的能耗问题,提出了绿色云计算体系理论,设计了绿色云系统架构;基于该架构,将能量作为一种系统资源进行分配,提出了三种绿色任务调度算法分别是STF-OS、LTF-OS和RT-OS算法;对三种绿色任务调度算法可行性做了相关的理论分析,三种算法可以有效地减少能源消耗;通过扩展云计算仿真平台CloudSim实现了模拟实验,结果表明STF-OS算法降低数据中心能耗的能力最优。     

Dynamic application placement in the Mobile Cloud Network

To meet the challenges of consistent performance, low communication latency, and a high degree of user mobility, cloud and Telecom infrastructure vendors and operators foresee a Mobile Cloud Network that incorporates public cloud infrastructures with cloud augmented Telecom nodes in forthcoming mobile access networks. A Mobile Cloud Network is composed of distributed cost- and capacity-heterogeneous resources that host applications that in turn are subject to a spatially and quantitatively rapidly changing demand. Such an infrastructure requires a holistic management approach that ensures that the resident applications’ performance requirements are met while sustainably supported by the underlying infrastructure. The contribution of this paper is three-fold. Firstly, this paper contributes with a model that captures the cost- and capacity-heterogeneity of a Mobile Cloud Network infrastructure. The model bridges the Mobile Edge Computing and Distributed Cloud paradigms by modelling multiple tiers of resources across the network and serves not just mobile devices but any client beyond and within the network. A set of resource management challenges is presented based on this model. Secondly, an algorithm that holistically and optimally solves these challenges is proposed. The algorithm is formulated as an application placement method that incorporates aspects of network link capacity, desired user latency and user mobility, as well as data centre resource utilisation and server provisioning costs. Thirdly, to address scalability, a tractable locally optimal algorithm is presented. The evaluation demonstrates that the placement algorithm significantly improves latency, resource utilisation skewness while minimising the operational cost of the system. Additionally, the proposed model and evaluation method demonstrate the viability of dynamic resource management of the Mobile Cloud Network and the need for accommodating rapidly mobile demand in a holistic manner.     

Real-time edge framework (RTEF): task scheduling and realisation

With the big success of the Cloud Computing, or the Cloud, new research areas appeared. Edge Computing (EC) is one of the recent paradigms that is expected to overcome the Quality of Service (QoS) and latency issues caused by the best-effort behaviour of the Cloud. EC aims to bring the computation power close to the end devices as much as possible and reduce the dependency to the Cloud. Bringing computing power close to the source also enables real-time applications. In this paper, we propose a novel software reference architecture for Edge Servers, which is operating system (OS) and hardware-agnostic. Edge Servers can collaborate and execute (near) real-time tasks on time, either by downscaling or scheduling them according to their deadlines or offloading them to other Edge Servers in the network. Decision making for offloading, resource planning, and task scheduling are challenging problems in decentralized systems. The paper explains how resource planning and task scheduling can be overcome with software approach. Finally, the article realises the architecture as a framework, called Real-Time Edge Framework (RTEF) and validates its correctness with a use case.

     

Journey from cloud of things to fog of things: Survey,new trends,and research directions

With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency-sensitive and real-time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client-server model to edge computing along with their objectives and limitations. A state-of-the-art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping-stone in the field of emerging computing paradigms.     

Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications

With the incoming 5G access networks, it is forecasted that Fog computing (FC) and Internet of Things (IoT) will converge onto the Fog-of-IoT paradigm. Since the FC paradigm spreads, by design, networking and computing resources over the wireless access network, it would enable the support of computing-intensive and delay-sensitive streaming applications under the energy-limited wireless IoT realm. Motivated by this consideration, the goal of this paper is threefold. First, it provides a motivating study the main “killer” application areas envisioned for the considered Fog-of-IoT paradigm. Second, it presents the design of a CoNtainer-based virtualized networked computing architecture. The proposed architecture operates at the Middleware layer and exploits the native capability of the Container Engines, so as to allow the dynamic real-time scaling of the available computing-plus-networking virtualized resources. Third, the paper presents a low-complexity penalty-aware bin packing-type heuristic for the dynamic management of the resulting virtualized computing-plus-networking resources. The proposed heuristic pursues the joint minimization of the networking-plus-computing energy by adaptively scaling up/down the processing speeds of the virtual processors and transport throughputs of the instantiated TCP/IP virtual connections, while guaranteeing hard (i.e., deterministic) upper bounds on the per-task computing-plus-networking delays. Finally, the actual energy performance-versus-implementation complexity trade-off of the proposed resource manager is numerically tested under both wireless static and mobile Fog-of-IoT scenarios and comparisons against the corresponding performances of some state-of-the-art benchmark resource managers and device-to-device edge computing platforms are also carried out.     

Prediction-based proactive load balancing approach through VM migration

The ever-growing intricacy and dynamicity of Cloud Computing Systems has created a need for Proactive Load Balancing which is an effective approach to improve the scalability of today’s Cloud services. In order to manage the load proactively on the Cloud system during application execution, load should be predicted through machine learning approaches and handled through VM migration approaches. Thus, this paper formulates an effort to focus on the research problem of designing a prediction-based approach for facilitating proactive load balancing through the prediction of multiple resource utilization parameters in Cloud. The involvement of this paper is twofold. Firstly, various machine learning approaches have been tested and compared for predicting host overutilization as well as underutilization. Secondly, the load prediction model having maximum accuracy from the tested models has been utilized for implementing the proactive VM migration using multiple resource utilization parameters. Further, the proposed technique has been validated through performance evaluation parameters using CloudSim and Weka toolkits. The simulation results clearly demonstrate that the proposed approach is effective for handling VM migration, reducing SLA Violations, VM migrations, execution mean and standard deviation time.     

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.     

ARMCO: Advanced topics in resource management for ubiquitous cloud computing: An adaptive approach

Cloud Computing can be seen as one of the latest major evolution in computing offering unlimited possibility to use ICT in various domains: business, smart cities, medicine, environmental computing, mobile systems, design and implementation of cyber-infrastructures. The recent expansion of Cloud Systems has led to adapting resource management solutions for large number of wide distributed and heterogeneous datacenters. The adaptive methods used in this context are oriented on: self-stabilizing, self-organizing and autonomic systems; dynamic, adaptive and machine learning based distributed algorithms; fault tolerance, reliability, availability of distributed systems. The pay-per-use economic model of Cloud Computing comes with a new challenge: maximizing the profit for service providers, minimizing the total cost for customers and being friendly with the environment.This special issue presents advances in virtual machine assignment and placement, multi-objective and multi-constraints job scheduling, resource management in federated Clouds and in heterogeneous environments, dynamic topology for data distribution, workflow performance improvement, energy efficiency techniques and assurance of Service Level Agreements.     

Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application. When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained. In addition, every workload or application required for the framework is characteristic and these essentials change over time. But, the existing method was failed to ensure the high Quality of Service (QoS). In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced. A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources (i.e., CPU, RAM, and network bandwidth utilization) based on the usage history in each cloud server. Then, Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration. Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics. The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine (VM) Migrations as compared to the state-of-the-art methods.     

MidHDC: Advanced topics on middleware services for heterogeneous distributed computing. Part 2

Currently distributes systems support different computing paradigms like Cluster Computing, Grid Computing, Peer-to-Peer Computing, and Cloud Computing all involving elements of heterogeneity. These computing distributed systems are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use.The second part of this special issue presents advances in allocation algorithms, service selection, VM consolidation and mobility policies, scheduling multiple virtual environments and scientific workflows, optimization in scheduling process, energy-aware scheduling models, failure Recovery in shared Big Data processing systems, distributed transaction processing middleware, data storage, trust evaluation, information diffusion, mobile systems, integration of robots in Cloud systems.