Distributed Cloud Infrastructure Research Articles

A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming demand surges and resource outages. Numerous comprehensive methods still overlook certain aspects, such as the inclusion of multiple objectives, addressing local minimum problems, and optimizing resource utilization to its fullest potential. Our research focus is on exploring cloud-deployed three-tier applications to address these challenges. This paper presents a new approach to load balancing, introducing a novel weight assignment algorithm. The algorithm effectively distributes tasks among virtual machines using three carefully optimized algorithms. To begin with, we convert the user tasks into the directed acyclic graph (DAG) structured workflow, organizing them according to their execution sequence. The iterative critical path search algorithm, implemented with a layer-based priority assigning technique, addresses the critical path challenge. This process involves finding a critical path, dividing tasks into distinct layers, classifying them based on their computational complexity and priorities, and distinguishing between critical and non-critical tasks. Following the classification process, the tasks are organized and prioritized using an improved Moth Flame Optimization approach based on multiple criteria, which consider criticality, priority, and makespan. This approach significantly reduces scheduling latency and delays. Finally, task allocation is proposed using the high convergence rock hyrax optimization algorithm with a divide-and-conquer strategy, enabling effective exploration and exploitation of overcrowded resources for improved load balancing. The CloudSim simulation tool is used to carry out the simulation, where it assesses various performance aspects such as latency, bandwidth, completion time, throughput, energy usage, instances of not meeting QoS violations, and overhead.

Evaluating the Deployment of a Disaggregated Open RAN Controller On a Distributed Cloud Infrastructure

Evaluating the Deployment of a Disaggregated Open RAN Controller On a Distributed Cloud Infrastructure

IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

Edge Workloads Monitoring and Failover: a StarlingX-Based Testbed Implementation and Measurement Study

With the ever-growing amount of time-critical, compute-intensive, and private IoT applications, the need for High Availability (HA) Edge Clouds becomes indispensable. Realizing HA Edge Clouds is inherently challenging due to the geographically-dispersed hierarchy of the Distributed Cloud Infrastructure (DCI). For example, frequent isolation between the central Cloud and Edge Clouds due to networking instability necessitates some autonomous operations at the Edge Clouds. Furthermore, because Edge Clouds have fewer resources than central Clouds, configuring the Edge functions (i.e., control, compute, and storage) in HA clusters will undoubtedly reduce downtime. However, it will limit the Edge scalability. To that end, StarlingX is developing an HA-protected and scalable DCI virtualization platform based on the open-source ecosystem, focusing on low-touch management of Edge Clouds. StarlingX provides a fault management service that realizes DCI-wide alarming and logging capabilities, allowing for rapid response to virtualized infrastructure events. Recently, the IETF Network Working Group proposed that monitoring both the DCI and the Edge workloads (software containers) is critical for an Edge Computing Platform to maintain HA IoT application deployment. Indeed, the possibility of the infrastructure remaining stable and healthy while the workloads suffer a fatal failure simultaneously necessitates failover functionality that monitors both the infrastructure and the Edge workloads. In this paper, we first propose a dynamic failover functionality that centrally monitors Edge workloads to recover from deployment or Edge node failures, motivated by the IETF direction. Second, we experimentally optimize the failover functionality for monitoring a microservice-architected IoT application deployed on a StarlingX-based DCI testbed to collect temperature sensor readings from Raspberry Pis. Regardless of how quickly the Edge workload health checks are collected, the recorded failover measurements reveal that the recovery time will not drop below a predetermined level controlled by Edge resources and network speed. Furthermore, reducing the statistics collection timeout reduces the recovery time of an Edge node failure. When the timeout value is less than the minimum achievable recovery time, false-positive failures (FPFs) can occur. Third, to supplement the StarlingX fault management service, we provide a modular implementation of the proposed failover functionality. Finally, we present the first-ever introduction of the StarlingX platform’s software stack to promote its use in academic research.

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

Virtual 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.

A Novel Local and Global Cooperative Approach for Distributed Mobile Cloud Computing

In the cloud service paradigm, service providers aim to cost-efficiently serve Internet of Things (IoT) devices’ applications that often request computation-intensive services. In this study, we focus on the cloud resource sharing problem in the distributed mobile cloud computing (DMCC) platform. By considering different IoT applications and distributed cloud infrastructure, we design a novel DMCC resource sharing scheme for data offloading services. Based on the cooperative game theory, five different value solutions are adopted, and they are implemented for each case cooperative process to provide the fair-efficient solution. According to the service characteristics, our proposed scheme explores the mutual benefits of local and global MCC providers’ interactions, and effectively shares the distributed cloud resources. To dynamically adapt the current DMCC system conditions, our approach can provide an appropriate guidance to improve the efficiency of cloud resource usage while enhancing the service quality. The main novelty of our proposed scheme is the ability to leverage a reciprocal consensus between different control viewpoints. Therefore, we can ensure a relevant tradeoff between efficiency and fairness among multiple service providers.

Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks

Recently, with the advent of the Internet of Things (IoT), Smart Cities have emerged as a potential business opportunity for most cloud service providers. However, centralized cloud architectures cannot sustain the requirements imposed by many IoT services. High mobility coverage and low latency constraints are among the strictest requirements, making centralized solutions impractical. In response, theoretical foundations of Fog Computing have been introduced to set up a distributed cloud infrastructure by placing computational resources close to end-users. However, the acceptance of its foundational concepts is still in its early stages. A key challenge still to answer is Service Function Chaining (SFC) in Fog Computing, in which services are connected in a specific order forming a service chain to fully leverage on network softwarization. Also, Low Power Wide Area Networks (LPWANs) have been getting significant attention. Opposed to traditional wireless technologies, LPWANs are focused on low bandwidth communications over long ranges. Despite their tremendous potential, many challenges still arise concerning the deployment and management of these technologies, making their wide adoption difficult for most service providers. In this article, a Mixed Integer Linear Programming (MILP) formulation for the IoT service allocation problem is proposed, which takes SFC concepts, different LPWAN technologies and multiple optimization objectives into account. To the best of our knowledge, our work goes beyond the current state-of-the-art by providing a complete end-to-end (E2E) resource provisioning in Fog-cloud environments while considering cloud and wireless network requirements. Evaluations have been performed to evaluate in detail the proposed MILP formulation for Smart City use cases. Results show clear trade-offs between the different provisioning strategies. Our work can serve as a benchmark for resource provisioning research in Fog-cloud environments since the model approach is generic and can be applied to a wide range of IoT use cases.

The DODAS Experience on the EGI Federated Cloud

The EGI Cloud Compute service offers a multi-cloud IaaS federation that brings together research clouds as a scalable computing platform for research accessible with OpenID Connect Federated Identity. The federation is not limited to single sign-on, it also introduces features to facilitate the portability of applications across providers: i) a common VM image catalogue VM image replication to ensure these images will be available at providers whenever needed; ii) a GraphQL information discovery API to understand the capacities and capabilities available at each provider; and iii) integration with orchestration tools (such as Infrastructure Manager) to abstract the federation and facilitate using heterogeneous providers. EGI also monitors the correct function of every provider and collects usage information across all the infrastructure. DODAS (Dynamic On Demand Analysis Service) is an open-source Platform-as-a-Service tool, which allows to deploy software applications over heterogeneous and hybrid clouds. DODAS is one of the so-called Thematic Services of the EOSC-hub project and it instantiates on-demand container-based clusters offering a high level of abstraction to users, allowing to exploit distributed cloud infrastructures with a very limited knowledge of the underlying technologies.This work presents a comprehensive overview of DODAS integration with EGI Cloud Federation, reporting the experience of the integration with CMS Experiment submission infrastructure system.

Future edge clouds

Widespread deployment of centralized clouds has changed the way internet services are developed, deployed and operated. Centralized clouds have substantially extended the market opportunities for online services, enabled new entities to create and operate internet-scale services, and changed the way traditional companies run their operations. However, there are types of services that are unsuitable for today's centralized clouds such as highly interactive virtual and augmented reality (VR/AR) applications, high-resolution gaming, virtualized RAN, mass IoT data processing and industrial robot control. They can be broadly categorized as either latency-sensitive network functions, latency-sensitive applications, and/or high-bandwidth services. What these basic functions have in common is the need for a more distributed cloud infrastructure—an infrastructure we call edge clouds. In this paper, we examine the evolution of clouds, and edge clouds especially, and look at the developing market for edge clouds and what developments are required in networking, hardware and software to support them.

Cloud infrastructure of INP’S Astana branch - PE “NULITS” and its integration with distributed JINR cloud infrastructure

The article describes the Institute of Nuclear Physics’ (INP) Astana branch - private establishments “Nazarbayev University Library and IT services” (PE NULITS) cloud and its integration with the distributed cloud infrastructure consisting of the Laboratory of Information Technologies of the Joint Institute for Nuclear Research (JINR) cloud as well as clouds of some JINR Member State organizations. It explains a motivation of that work, an approach it is based on, working plan of the integration.

Topology-Aware Resource-Efficient Placement for High Availability Clusters Over Geo-Distributed Cloud Infrastructure

A management and orchestration framework (MANO) in network function virtualization (NFV) enables the agile deployment and operation of virtual network functions over a geographically distributed cloud infrastructure. This facilitates the deployment of redundancy models (i.e., high availability clusters) over different cloud centers, to guarantee the high availability of network services. In particular, in the telecommunications field, availability and resiliency are always required at a high level. Existing placement algorithms only consider one type of redundancy model at a given time. However, in reality, different redundancy configurations can be utilized to ensure the availability of virtual functions. In this article, we present an optimization model and topology-aware resource-efficient placement algorithm (TARE), which can be employed to optimally deploy high availability clusters with different redundancy configurations over geo-distributed cloud infrastructures. This model takes into account the different requirements of various high availability clusters in terms of bandwidth and computing resource demands. By simulation, the TARE has better performance than other baseline solutions in terms of the bandwidth usage, while maintaining an acceptable level of availability.

A distributed load balancing and admission control algorithm based on Fuzzy type-2 and Game theory for large-scale SaaS cloud architectures

With the advent of cloud computing and virtualization technologies, business models are created to serve users with applications, platforms or processing resources. One of these models is providing software as a service (SaaS) in cloud environments. Due to its user-friendly nature, the demand for SaaS is increasing exponentially in recent years, so geographically distributed cloud infrastructures are required for hosting such services. On the other hand, with the world wide spread of applications, users need to access them all over the world. Common centralized architecture of resources is not responsive and clusters of large-scale distributed servers are required to host applications in different geographical locations. In this paper, a novel two-layer (regional–local) distributed resource management algorithm is proposed which can handle management tasks such as requests admission control and load balancing in large scales. The proposed algorithm is scalable, in which the control tasks are divided among application and proxy servers. Therefore, each component performs the task of managing resources independently. To control the admission of requests, a Self-Adaptive Fuzzy type-2 Controller containing two fuzzy controllers is introduced. Also, for distributing requests and balancing load in the proxy servers, a Game theory based algorithm proposed which converged to Nash equilibrium. The proposed algorithm is implemented and tested using four physical machines. The algorithm is evaluated using two standard dynamic workloads. By deploying this algorithm, the average percentage of rejected requests and LBM value are improved compared to rival algorithms about 15% and 4.5% respectively.

Integrated cloud infrastructure of the LIT JINR, PE “NULITS” and INP's Astana branch

The article describes the distributed cloud infrastructure deployed on the basis of the resources of the Laboratory of Information Technologies of the Joint Institute for Nuclear Research (LIT JINR) and some JINR Member State organizations. It explains a motivation of that work, an approach it is based on, lists of its participants among which there are private entity “Nazarbayev University Library and IT services” (PE “NULITS”) Autonomous Education Organization “Nazarbayev University” (AO NU) and The Institute of Nuclear Physics’ (INP's) Astana branch.

A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT

The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.

Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers

In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing solutions that are not suitable for a geographically distributed cloud infrastructure. In this paper, exact as well as heuristic solutions based on Integer Linear programming (ILP) formulations are proposed. Our work focuses also on the problem of scheduling the VM migration by finding the best migration sequence of intercommunicating VMs that minimizes the resulting traffic on the backbone network. The proposed algorithms execute within a reasonable time frame to readjust VM placement scheme according to the perceived demand. Our aim is to use VM migration as a tool for dynamically adjusting the VM placement scheme while minimizing the network traffic generated by VM communication and migration. Finally, we demonstrate the effectiveness of our proposed algorithms by performing extensive experiments and simulation.

Implementation and Provisioning of Federated Networks in Hybrid Clouds

Federated cloud networking is needed to allow the seamless and efficient interconnection of resources distributed among different clouds. This work introduces a new cloud network federation framework for the automatic provision of Layer 2 (L2) and Layer 3 (L3) virtual networks to interconnect geographically distributed cloud infrastructures in a hybrid cloud scenario. This framework provides a simple, secure and elastic solution to create and manage federated networks across heterogeneous cloud providers, with abstraction of the underlying technologies. The paper also analyzes the main technologies and topologies that can used the framework to construct L2 and L3 overlay networks within hybrid clouds. In order to demonstrate the viability of the proposed solution and compare the different topologies, the article shows a proof-of-concept of a real federated network deployment in a hybrid cloud, which spans a local private cloud, managed with OpenNebula, and two public clouds, two different regions of Amazon EC2. Results show that L2 and L3 overlay connectivity can be achieved with a minimal bandwidth overhead, lower than 10%.

Control Orchestration Protocol: Unified Transport API for Distributed Cloud and Network Orchestration

In the context of the fifth generation of mobile technology (5G), multiple technologies will converge into a unified end-to-end system. For this purpose, software defined networking (SDN) is proposed, as the control paradigm will integrate all network segments and heterogeneous optical and wireless network technologies together with massive storage and computing infrastructures. The control orchestration protocol is presented as a unified transport application programming interface solution for joint cloud/network orchestration, allowing interworking of heterogeneous control planes to provide provisioning and recovery of quality of service (QoS)-aware end-to-end services. End-to-end QoS is guaranteed by provisioning and restoration schemes, which are proposed for optical circuit/packet switching restoration by means of signal monitoring and adaptive modulation and adaptive route control, respectively. The proposed solution is experimentally demonstrated in an international multi-partner test bed, which consists of a multi-domain transport network comprising optical circuit switching and optical packet switching domains controlled by SDN/OpenFlow and Generalized Multiprotocol Label Switching (GMPLS) control planes and a distributed cloud infrastructure. The results show the dynamic provisioning of IT and network resources and recovery capabilities of the architecture.

Need for a Transport API in 5G for Global Orchestration of Cloud and Networks Through a Virtualized Infrastructure Manager and Planner [Invited]

The new 5G paradigm seeks for a scalable architecture that is able to efficiently manage the increasing volume of traffic generated by smart devices to be processed in a distributed cloud infrastructure. To this end, coordinated management of the network and the cloud resources forming an end-to-end system is of great importance. Software defined networking and network function virtualization architectures are the key enablers for integrating network and cloud resources, enabling cross optimization on both sides. This optimization requires efficient resource allocation algorithms, which take into account computing and network resources. In this paper, we propose an end-to-end orchestration architecture for distributed cloud and network resources aligned with the European Telecommunications Standards Institute management and orchestration architecture. The proposed architecture includes the virtual infrastructure manager and planner (VIMaP) component to enable dynamic resource allocation for interconnected virtual instances in distributed cloud locations. A heuristic algorithm for dynamic virtual machine graphs resource allocation is included to validate the VIMaP architecture and exploit its functionalities. Moreover, the control orchestration protocol is included between the architecture components to offer end-to-end transport services. Finally, the proposed architecture is experimentally validated, and the heuristic algorithm performance is evaluated.

The CTTC 5G End-to-End Experimental Platform : Integrating Heterogeneous Wireless/Optical Networks, Distributed Cloud, and IoT Devices

The Internet of Things (IoT) will facilitate a wide variety of applications in different domains, such as smart cities, smart grids, industrial automation (Industry 4.0), smart driving, assistance of the elderly, and home automation. Billions of heterogeneous smart devices with different application requirements will be connected to the networks and will generate huge aggregated volumes of data that will be processed in distributed cloud infrastructures. On the other hand, there is also a general trend to deploy functions as software (SW) instances in cloud infrastructures [e.g., network function virtualization (NFV) or mobile edge computing (MEC)]. Thus, the next generation of mobile networks, the fifth-generation (5G), will need not only to develop new radio interfaces or waveforms to cope with the expected traffic growth but also to integrate heterogeneous networks from end to end (E2E) with distributed cloud resources to deliver E2E IoT and mobile services. This article presents the E2E 5G platform that is being developed by the Centre Tecnol\`ogic de Telecomunicacions de Catalunya (CTTC), the first known platform capable of reproducing such an ambitious scenario.

Enhancing Telco Service Quality with Big Data Enabled Churn Analysis: Infrastructure, Model, and Deployment

The penetration of mobile phones is nearly saturated in both developing and developed regions. In such a circumstance, how to prevent subscriber churn has become an important issue for today’s telecom operators, as the cost to acquire a new subscriber is much higher than that to retain an existing subscriber. In this paper, we propose to leverage the power of big data to mitigate the problem of subscriber churn and enhance the service quality of telecom operators. As the information hub, telecom operators have accumulated a huge volume of valuable data on subscriber behaviors, service usage, and network operations. To enable efficient big data processing, we first build a dedicated distributed cloud infrastructure that integrates both online and offline processing capabilities. Second, we develop a complete churn analysis model based on deep data mining techniques, and utilize inter-subscriber influence to improve prediction accuracy. Finally, we use real datasets obtained from a large telecom operator in China to verify the accuracy of our churn analysis models. The dataset contains the information of over 3.5 million subscribers, which generate over 600 million call detail records (CDRs) per month. The empirical results demonstrate that our proposed method can achieve around 90% accuracy for T + 1 testing periods and identify subscribers with high negative influence successfully.