Centralized Cloud Data Center Research Articles

Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.

A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing

Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.

Fog Computing in Next Generation Networks: A Review

Cloud, Edge, and Fog computing has recently attracted significant attention in both industry and academia. However, finding their definition in computing paradigms and the correlation between them is difficult. In order to support modern computing systems, the cloud, edge devices, and fog computing offer high-quality services, lower latency, multi-tenancy, mobility support, and many other features. Fog/edge computing is an emerging computing paradigm that uses decentralized resources at the edge of a network to process data closer to user devices, like smartphones and tablets, as an alternative to using remote and centralized cloud data center resources. Fog networking or fogging is one of the best used models recently. By addressing this issue, this work serves as a valuable resource for those who will come after. Initially, we present an overview modern computing models and associated areas of interest research. After that, we discuss each paradigm. After that, we go into great detail about fog computing, highlighting its exceptional function as the link between edge, cloud, and IoT computing. Finally, we briefly outline open research questions and future directions in Edge, Fog, Cloud, and IoT computing.

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.

Optimized Processing Placement Over a Vehicular Cloud

Modern vehicles equipped with on-board units (OBU) are playing an essential role in the emerging smart city revolution. The vehicular processing resources, however, are not used to their full potential. The concept of vehicular clouds is proposed to exploit the underutilized vehicular resources to supplement cloud computing services to relieve the burden on centralized cloud data centers and improve quality of service. In this paper we introduce a vehicular cloud architecture supported by fixed edge computing nodes and a central cloud data center. A mixed integer linear programming (MILP) model is developed to optimize the allocation of the processing demands in the distributed architecture while minimizing the overall power consumption. The results show power savings as high as 84% compared to processing in the conventional cloud.Variations in the test cases to include processing demand and traffic demand splitting showed power saving of 71% and 16% respectively, even for large demand volumes. A heuristic algorithm with performance approaching that of the MILP model is developed to validate the MILP model and allocate processing demands in real time.

A Novel Centralized Cloud-Based Mobile Data Rollover Management

Mobile service providers (SPs) offer various data plans to satisfy the needs of their customers and to maximize their own profits. One of these plans is unlimited Internet access. However, in practice there is a concern for customers who do not want to pay premium prices for unlimited plans, as exceeding the maximum data allowance in a limited plan may result in a high penalty. But when users travel abroad, roaming services based on current technology have limited coverage and high prices, which do not meet customer expectations. A new paradigm to overcome these problems is a virtual SIM card (V-SIM), which can be established based on 5G network architecture. This new approach uses cloud technology to enable customers to have the data they require wherever they want. In this work, we propose a cloud-based service for users to provide data all over the world by designing the novel architecture of V-SIM technology based on 5G communication systems. More precisely, a new scheme, called a centralized cloud data center, or `data-pool' for short, is designed to present data rent and release facilities for mobile users any time and everywhere. The benefits of the proposed scheme for both SPs and their customers are modeled and analyzed based on real-world experiments. Finally, by considering the user's behavior, the performance of the proposed approach is compared to the conventional method in terms of the monthly data usage, monthly data rent/offload status, and the payment with and without considering the proposed method.

Fog Computing in Healthcare: A Review

The Internet of things (IoT) connects multiple devices worldwide. It is a growing field in the healthcare system such as health monitoring and tracking, fitness program, and remote medical assistance. With the advent of IoT based technologies in healthcare, it can alleviate the pressure on healthcare systems and can reduce the healthcare cost, and increase the computing and processing speed. Cloud computing was introduced to manage larger and complex healthcare data in the IoT environment. Cloud computing uses centralized cloud data centers. The central server manages the data for all the IoT devices. The integration of IoT with the cloud has some major issues such as latency, bandwidth overuse, real-time response delays, protection, and privacy. So the concept of edge computing and fog computing came into existence to overcome these issues. This paper review the IoT-Fog-based system model architectures, similar paradigm, issues, and difficulties in the area of cloud computing and finally, the performance of some of these proposed systems is assessed using the iFogSim simulator.

A Case for Elevating the Edge to be a Peer of the Cloud

Over the last 20 years, mobile computing has evolved to encompass a wide array of increasingly data-rich applications. Many of these applications were enabled by the Cloud computing revolution, which commoditized server hardware to support vast numbers of mobile users from a few large, centralized data centers. Today, mobile's next stage of evolution is spurred by interest in emerging technologies such as Augmented and Virtual Reality (AR/VR), the Internet of Things (IoT), and Autonomous Vehicles. New applications relying on these technologies often require very low latency response times, increased bandwidth consumption, and the need to preserve privacy. Meeting all of these requirements from the Cloud alone is challenging for several reasons. First, the amount of data generated by devices can quickly saturate the bandwidth of backhaul links to the Cloud. Second, achieving low-latency responses for making decisions on sensed data becomes increasingly difficult the further mobile devices are from centralized Cloud data centers. And finally, regulatory or privacy restrictions on the data generated by devices may require that such data be kept locally. For these reasons, enabling next-generation technologies requires us to reconsider the current trend of serving applications from the Cloud alone.

High-performance flow classification using hybrid clusters in software defined mobile edge computing

Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage-intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI.In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of 29.19 MPPS and a speedup 58 times as great as the serial mode, the second scenario is 12 times faster due to using the shared memory. The best performance, however, belongs to the hybrid cluster mode. The hybrid cluster achieves a throughput of 30.59 which is 1.4 MPPS more than the GPU cluster.

An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment

The development of Internet of Things leads to an increase in edge devices, and the traditional cloud is unable to meet the demands of the low latency of numerous devices in edge area. On the hand, the media delivery requires high-quality solution to meet ever-increasing user demands. The edge cloud paradigm is put forward to address the issues, which facilitates edge devices to acquire resources dynamically and rapidly from nearby places. However, in order to complete as many tasks as possible in a limited time to meet the needs of users, and to complete the consistency maintenance in as short a time as possible, a two-level scheduling optimization scheme in an edge cloud environment is proposed. The first-level scheduling is by using our proposed artificial fish swarm-based job scheduling method, most jobs will be scheduled to edge data centers. If the edge data center does not have enough resource to complete, the job will be scheduled to centralized cloud data center. Subsequently, the job is divided into same-sized tasks. Then, the second-level scheduling, considering balance load of nodes, the edge cloud task scheduling is proposed to decrease completion time, while the centralized cloud task scheduling is presented to reduce total cost. The experimental results show that our proposed scheme performs better in terms of minimizing latency and completion time, and cutting down total cost.

IntelliTC : intelligent inter‐DC traffic controller for the Internet of everything service based on fog computing

As the data size on smart things continue to grow, it becomes inefficient to transfer the massive raw data to centralized cloud datacenters for the analysis of raw data and the deployment of innovative and novel services into various areas such as smart cities, etc. As the concept of Fog Computing, the structure of distributed intelligent infrastructure can address those problems by providing elastic resources and services to end users with smart devices at the edge of network. Hence, through the functions of analytics and flexible control based on the Software-Defined Network (SDN), it is necessary to provide the SDN-driven management intelligence, which can maximize network utilization subject to constraints on service priority and resources fairness by differentiating service flows according to the quality requirements. This paper proposes the SDN controller for intelligent inter-datacenter (inter-DC) cloud networking, which enables optimal traffic loads distribution across the distributed datacenters with the global inter-DC view of link states and flow statistics by applying the network-aware flow scheduling on a policy-driven inter-DC traffic control layer. The proposed intelligent inter-DC controller performs a global resources scheduling mechanism that uses different weights according to the priority of service class in allocating bandwidths based on multi-layers networking interconnecting the distributed datacenters.

Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization

Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.

Resource Management in Fog/Edge Computing

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

Resource identification in fog-to-cloud systems: toward an identity management strategy

Fog-to-Cloud (F2C) is a novel paradigm aiming at extending the cloud computing capabilities to the edge of the network through the hierarchical and coordinated management of both, centralized cloud datacenters and distributed fog resources. It will allow all kinds of devices that are capable to connect to the F2C network to share its idle resources and access both, service provider and third parties’ resources to expand its own capabilities. However, despite the numerous advantages offered by the F2C model, such as the possibility of offloading delay-sensitive tasks to a nearby device and using the cloud infrastructure in the execution of resource-intensive tasks, the list of open challenges that needs to be addressed to have a deployable F2C system is pretty long. In this paper we focus on the resource identification challenge, proposing an identity management system (IDMS) solution that starts assigning identifiers (IDs) to the devices in the F2C network in a decentralized fashion using hashes and afterwards, manages the usage of those IDs applying a fragmentation technique. The obtained results during the validation phase show that our proposal not only meets the desired IDMS characteristics, but also that the fragmentation strategy is aligned with the constrained nature of the devices in the lowest tier of the network hierarchy.

Edge Computing and Networking: A Survey on Infrastructures and Applications

As a concept to enhance and extend cloud-computing capabilities, edge computing aims to provide Internet-based services in the close proximity to users by placing IT infrastructures at the network edge in forms of tiny datacenters. Taking advantage of the close distance to end user and access networks, edge datacenters can provide low-latency and context-aware services and further improve users' quality of experience. As the network edge is a geographically spread concept, the edge datacenters are usually highly distributed so that they can provide nearby storage and processing capabilities to most of the end users. Furthermore, edge datacenters also co-work with centralized cloud datacenters for service orchestration. Such decentralization and collaboration are expected to introduce significant transformations to both infrastructures and applications. To provide an overview of how edge can be integrated with cloud-computing and how edge computing can benefit applications, this paper studies the infrastructure and application issues of edge computing and networking in several sub-aspects, including related concepts, infrastructures, resource management and virtualization, performance, and applications.