Elastic Resource Research Articles

Auto-Scaling Provision Basing on Workload Prediction in the Virtualized Data Center

With the development in the Cloud datacenters, the purpose of the efficient resource allocation is to meet the demand of the users instantly with the minimum rent cost. Thus, the elastic resource allocation strategy is usually combined with the prediction technology. This article proposes a novel predict method combination forecast technique, including both exponential smoothing (ES) and auto-regressive and polynomial fitting (PF) model. The aim of combination prediction is to achieve an efficient forecast technique according to the periodic and random feature of the workload and meet the application service level agreement (SLA) with the minimum cost. Moreover, the ES prediction with PSO algorithm gives a fine-grained scaling up and down the resources combining the heuristic algorithm in the future. APWP would solve the periodical or hybrid fluctuation of the workload in the cloud data centers. Finally, experiments improve that the combined prediction model meets the SLA with the better precision accuracy with the minimum renting cost.

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

One of the main concerns of telecommunications operators is related to network coverage. A weak coverage can lead to a performance decrease, not only in the user experience, when using the operators' services, such as multimedia streaming, but also in the overall Quality of Service. This paper presents a novel cloud-based framework of a semi-empirical propagation model that estimates the coverage in a precise way. The novelty of this model is that it is automatically calibrated by using drive test measurements, terrain morphology, buildings in the area, configurations of the network itself and key performance indicators, automatically extracted from the operator's network. Requirements and use cases are presented as motivations for this methodology. The results achieve an accuracy of about 5 dB, allowing operators to obtain accurate neighbour lists, optimise network planning and automate certain actions on the network by enabling the Self-Organising Network concept. The cloud implementation enables a fast and easy integration with other network management and monitoring tools, such as the Metric platform, optimising operators' resource usage recurring to elastic resources on-demand when needed. This implementation was integrated into the Metric platform, which is currently available to be used by several operators.

Security and Privacy Challenges in Connected Vehicular Cloud Computing

Vehicular cloud computing (VCC) is an improvement from conventional cloud computing to new revolutionized computing services including intelligent transportation, autonomous driving and vehicle control, Internet browsing, online documentation, and infotainment. It enables vehicles to autonomously share heterogeneous computational resources while solving unanticipated critical problems dynamically. However, in a world of black hats, technology often has a dark side as well. Therefore, the VCC is limited by considerable security and privacy challenges. The special characteristics of VCC, including the multitenancy nature of clouds, intermittent wireless communications, high mobility of vehicles, and rapid resource elasticity with decentralized operations, have promoted security solutions used in vehicular ad hoc networks and conventional cloud computing to be revised. This paper first presents a state-of-the-art study of VCC that focuses on VCC architecture, its features analysis, and extensive VCC applications. Second, the proposed threats identification taxonomy and an exhaustive survey on security and privacy issues in VCC are presented under a layered approach: physical resource layer, vehicle-to-anything (V2X) network layer, and vehicular cloud layer, as well as at a complete system level. Finally, we highlight and discuss challenges and open research issues that can be considered as future research directions.

Smart Resilient Security Framework and Solutions for Cloud-driven Digital Supply Networks

The cloud-based logistics services allow cost reduction, higher elasticity, flexibility, and maximum utilization of resources for performing high computations and data analytics. Logistics and security are complex issues with the corporate entities when these are to be used for taking critical issues. To understand these, this paper discusses a conceptualized, flexible security framework for cloud-driven digital supply chain in Agricultural. Moreover, a security enhancement layer is included at each layer of the cloud with a feasibility study on protecting user information in the logistics services ambiance. Also, the Data Residency for cloud-based logistics services is elaborated with Data Security analysis. Further, the article discusses the possible solutions to handle the security concerns of the logistic model.

A service‐based framework for building and executing epidemic simulation applications in the cloud

SummaryThe cloud has emerged as an attractive platform for resource‐intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi‐cloud deployment. To address these challenges, this paper proposes a novel service‐based framework called DiFFuSE that enables simulation applications with a bag‐of‐tasks structure to fully exploit cloud platforms. This paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobacterium avium subspecies paratuberculosis, into elastic cloud‐native applications. Experimental results show that the framework enhances application performance and allows exploring different cost‐performance trade‐offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds.

Workload forecasting based elastic resource management in edge cloud

Cloud services are provided at the edge of the network so that data from users can be processed and calculated at the edges. The user's irregular access triggers the fluctuations of the edge cloud workload. Therefore, an elastic resource management method based on workload forecasting in edge clouds is proposed in this paper. When the resource demand is large, more resources are requested from the cloud service provider so that the task can be completed before the deadline. When the resource demand is small, the idle resource is released to meet the cost constraint. The resource demand is judged based on the workload forecasting. In order to improve the accuracy of workload forecasting, a workload forecasting model based on error correction is proposed in this paper. Neither overload nor the light-load status of edge cloud nodes can make full use of the resources. To improve the node processing performance and reduce migration times, a workload migration model for minimizing migration times is proposed in this paper. The experimental results show that the proposed methods can effectively forecast the workload and improve the processing performance of the entire cluster.

ElHealth: Using Internet of Things and data prediction for elastic management of human resources in smart hospitals

Hospitals play an important role towards ensuring proper health treatment to human beings. One of the major challenges faced in this context refers to the increasingly overcrowded patients queues, which contribute to a potential deterioration of patients health conditions. One of the reasons of such an inefficiency is a poor allocation of health professionals. In particular, such allocation process is usually unable to properly adapt to unexpected changes in the patients demand. As a consequence, it is frequently the case where underused rooms have idle professionals whilst overused rooms have less professionals than necessary. Previous works addressed this issue by analyzing the evolution of supply (doctors) and demand (patients) so as to better adjust one to the other, though none of them focused on proposing effective counter-measures to mitigate poor allocations. In this paper, we build upon the concept of smart hospitals and introduce elastic allocation of human resources in healthcare environments (ElHealth), an IoT-focused model able to monitor patients usage of hospital rooms and to adapt the allocation of health professionals to these rooms so as to meet patients needs. ElHealth employs data prediction techniques to anticipate when the demand of a given room will exceeds its capacity, and to propose actions to allocate health professionals accordingly. We also introduce the concept of multi-level predictive elasticity of human resources (which is an extension of the concept of resource elasticity, from cloud computing) to manage the use of human resources at different levels of a healthcare environment. Furthermore, we devise the concept of proactive human resources elastic speedup (which is an extension of the speedup concept, from parallel computing) to properly measure the gain of healthcare time with dynamic parallel use of human resources within hospital environments. ElHealth was thoroughly evaluated based on simulations of a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.71%.

Toward higher-level abstractions based on state machine for cloud resources elasticity

With the dynamic nature of cloud applications and rapid change of their resource requirements, elasticity over cloud resources has to be effectively supported. It represents the ability to dynamically adjust cloud resources that applications use in order to adapt to their varying workloads, while maintaining the desired quality of service. However, implementing elasticity is still challenging task for cloud users as heterogeneous and low-level interfaces are provided to manage cloud resources. To alleviate this, we believe that elasticity features should be provided at resource description level. In this paper, we propose a new Cloud Resource Description Model called cRDM, which is based on State Machine formalism. Using this model, we aim at representing cloud resources while considering their elasticity behavior over the time without referring to any low level interfaces or cloud provider technical constraints. We also propose a software system based on this new specification to support the elasticity-aware orchestration of cloud resources by exploiting the underlying cloud orchestration tools and APIs. We rely on a real use case to demonstrate the applicability of the proposed system and conduct a set of experiments proving the productivity and expressiveness of the cRDM model in comparison to existing solutions. The resulted findings of our evaluation shows the efficiency of our proposal.

Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. A softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this article, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.

When QoE meets learning: A distributed traffic-processing framework for elastic resource provisioning in HetNets

In heterogeneous networks (HetNets), dynamics of user requirements across the temporal domain lead to the order of magnitude traffic to be processed by macro cells. To achieve high quality of experience (QoE) for users and to perform resource allocation for cells intelligently, we first propose a distributed traffic-processing framework (SDVTS) for elastic resource partitioning, to accommodate dynamics from the user-centric and resource-oriented perspectives respectively. Assisted by a software defined infrastructure, SDVTS fulfills the responsibilities of the request-based and push-based services in an interactive loop. Second, we formulate a traffic-processing time model that computes the delay of handling traffic. The non-convex model is decomposed and a dual evolution algorithm is explored to approximate the optimal solution. Furthermore, we introduce a low-complexity reinforcement learning algorithm with the personalized QoE profiling. A distributed algorithm in coalition between user and cell is designed for seamless connection of an advanced reinforcement learning system (ARLS) components and engines embedded in SDVTS. Extensive simulation results with thorough analysis demonstrate that our framework SDVTS dominates in terms of QoE and cell′s system performance when compared with competing approaches.

Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals.

Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

Strategies for daytime slicing in future internet service providers

AbstractInternet became part of the human society, being a crucial tool for people's communication, information exchange, and content consumption. However, the Internet does not provide suitable Quality of Service (QoS) to guarantee the users, where several limitations compromise the users' Quality of Experience. Therefore, Internet Service Providers (ISPs) are including new technologies and management techniques to evolve to Future ISPs (FISPs). One promising technique for FISPs is the slicing of their network resources among clients and delivered services, where a strategy to define the structure of each slice is essential to improve QoS and resource utilization. Additionally, FISPs should consider the elastic resource utilization of the clients within periods of time through the day. Within this context, this article presents several strategies to define slices based on the daytime bandwidth requirements of the clients, where each strategy designed has singular characteristic that enhances aspects of the FISP, such as number of clients, resource saturation, bandwidth availability, adaptation, and others. Numerical results, considering existing network infrastructures, suggest that each strategy improves one or more of the FISP aspects according to its designing.

An elastic controller using Colored Petri Nets in cloud computing environment

Cloud computing is an emerging distributed computing paradigm that has become one of the extremely popular computing paradigms nowadays. One of the reasons for the popularity of cloud computing is due to its elasticity feature. Elasticity is a unique feature that enables the cloud platforms to add and remove resources “on the fly” to handle changes in workload demands. On the other hand, if the elasticity feature is not correctly managed, the cloud platforms may face over-provisioning or under-provisioning problems due to the arrival rate of users to the cloud applications varies over the time. Therefore, it necessitates the resource elasticity management issue as one of the challenging problems to be taken into account in the cloud computing environment. In this paper, we propose an elastic controller based on Colored Petri Nets to manage cloud infrastructures automatically. Finally, we evaluate the efficiency of the proposed elastic controller under three real workloads. The simulation results indicate that the proposed elastic controller reduces the response time by up to 4.8%, and increases the resource utilization and the elasticity by up to 9.3% and 6.7% respectively, compared with other approaches.

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

AbstractIn the present scenario, the state of engineering education worldwide is facing a big challenge of the employability of graduates. Cloud computing technology has changed the educational landscape by allowing educators and administrators to shift to the cloud for making actionable decisions. In this paper, a smart learning framework is proposed where universities/colleges can advocate the management of employability enhancement and start skill‐set enhancement courses through e‐learning. This proposed framework monitors the academic/skill data of students to classify their employability at the early stage of graduation. In addition, an algorithm is proposed for skill‐set assessment to find various clusters of students who lack in required skill‐set. The predicted clusters of students can be offered opportunities to improve their required skill‐set through e‐learning. Further, we used the design science research methodology for conducting online courses. Moreover, the prediction of resource usage of the proposed e‐learning framework is calculated to improve its effectiveness. In our experiment, results show that our proposed hybrid classier achieves 96.45% accuracy of classification. The prediction of resource usage offers better opportunities for adaptive resource elasticity. The results depict that by introducing the proposed framework in engineering education; more effective decisions can be made to improve the employability and overall growth of students.

Pec: Proactive Elastic Collaborative Resource Scheduling in Data Stream Processing

In the Distributed Parallel Stream Processing Systems (DPSPS), elastic resource allocation allows applications to dynamically response to workload fluctuations. However, resource provisioning can be particularly challenging, due to the unpredictability of the workload. In addition, unlike CPU resources, bandwidth resources are often ignored in resource allocation. Moreover, resource allocation and resource placement are considered separately. In this paper, we investigate the proactive elastic resource scheduling problem for computation-intensive and communication-intensive applications, which aims at meeting the latency requirement with the minimal energy cost, and propose a dynamic collaborative strategy from the systemic perspective. Specifically, we first model a collaborative workload prediction pattern to accurately predict the upcoming workload, and construct a latency estimation model to estimate the latency of the application. Then, we design an energy-efficient resource pre-allocation method, in which the CPU frequency adjustment and the stability of resource reconfigurations are both considered. Finally, we present a communication-aware resource placement approach. Simulation results show that, compared with the reactive strategies, our strategy achieves an obviously better latency performance, and effectively avoids unnecessary resource adjustments. Meanwhile, the energy consumption is about saved by 50 percent on average, and the communication cost is maintained at a very low level of 4 percent.

A Predictive and Evolutionary Approach for Cost-Effective and Deadline-Constrained Workflow Scheduling Over Distributed IaaS Clouds

Clouds provide highly elastic resource provisioning styles through which scientific workflows are allowed to acquire desired resources ahead of the execution and build required software environment on virtual machines (VMs). However, various challenges for cloud workflow, especially its optimal scheduling, are yet to be addressed. Traditional approaches mainly consider VMs to be with non-fluctuating, time-invariant, stochastic, or bounded performance. This work describes workflows to be deployed and executed over distributed infrastructure-as-a-service clouds with time-varying performance of VMs and is aimed at reducing the execution cost of workflow while meeting deadline constraints. For this purpose, the authors employ time-series-based prediction approaches to capture dynamic performance fluctuations, feed an evolutionary algorithm with predicted performance information, and generate schedules at real-time. A case study based on multiple randomly-generated workflow templates and third-party commercial clouds shows that their proposed approach outperforms traditional ones.

A development approach for collective opportunistic Edge-of-Things services

Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains. The requirements of such IoT applications, however, pose significant non/functional challenges to engineering efforts, mitigated by emerging computing paradigms. On the infrastructure side, Cloud, Fog, and Edge Computing provide virtualised, on-demand, elastic resource provisioning – at the distant data centres, Network core and Edge – supporting the abstraction and scalability needs of IoT settings while also altogether giving options for QoS-driven trade-offs. However, despite intense research in these fields, there is still a gap of approaches supporting the engineering of dynamic, heterogeneous smart environments, such as those involving “collectives” of devices coordinating in a complex fashion to provide “global” services. In this paper, we integrate the Aggregate Computing and Opportunistic IoT Service models and propose a full-fledged approach for the engineering – from analysis to simulation – of complex “Edge of Things” applications. We compare by simulation two deployment targets for the same collective application: one centralised/Cloud-based, and the other decentralised/Edge-based. We discuss the trade-offs each one introduces, and we draw recommendations on application-driven choices of the appropriate deployment.

ERP: An elastic resource provisioning approach for cloud applications

Elasticity is the key technique to provisioning resources dynamically in order to flexibly meet the users’ demand. Namely, the elasticity is aimed at meeting the demand at any time. However, the aforementioned approaches usually provision virtual machines (VMs) in a coarse-grained manner just by the CPU utilization. Actually, two or more elements are needed for the performance metric, including the CPU and the memory. It is challenging to determine a suitable threshold to efficiently scale the resources up or down. In this paper we present an elastic scaling framework that is implemented by the cloud layer model. First we propose the elastic resource provisioning (ERP) approach on the performance threshold. The proposed threshold is based on the Grey relational analysis (GRA) policy, including the CPU and the memory. Secondly, according to the fixed threshold, we scale up the resources from different granularities, such as in the physical machine level (PM-level) or virtual machine level (VM-level). In contrast, we scale down the resources and shut down the spare machines. Finally, we evaluate the effectiveness of the proposed approach in real workloads. The extensive experiments show that the ERP algorithm performs the elastic strategy efficiently by reducing the overhead and response time.

Harnessing Data Movement in Virtual Clusters for In-Situ Execution

As a result of increasing data volume and velocity, Big Data science at exascale has shifted towards the in-situ paradigm, where large scale simulations run concurrently alongside data analytics. With in-situ, data generated from simulations can be processed while still in memory, thereby avoiding the slow storage bottleneck. However, running simulations and analytics together on shared resources will likely result in substantial contention if left unmanaged, as demonstrated in this work, leading to much reduced efficiency of simulations and analytics. Recently, virtualization technologies such as Linux containers have been widely applied to data centers and physical clusters to provide highly efficient and elastic resource provisioning for consolidated workloads including scientific simulations and data analytics. In this paper, we investigate to facilitate network traffic manipulation and reduce mutual interference on the network for in-situ applications in virtual clusters. In order to dynamically allocate the network bandwidth when it is needed, we adopt SARIMA-based techniques to analyze and predict MPI traffic issued from simulations. Although this can be an effective technique, the naive usage of network virtualization can lead to performance degradation for bursty asynchronous transmissions within an MPI job. We analyze and resolve this performance degradation in virtual clusters.

Designing a hybrid cloud for a supply chain network of Industry 4.0: a theoretical framework

PurposeSupply chain is the area that requires effective and integrated means of communication, shared risk, collaboration and orchestration in order to work successfully and the cloud computing has lot to offer to this domain. Cloud computing has appeared as a modern paradigm in supply chain networks for creating intelligent industries of future. The purpose of this paper is to propose a framework that can transform supply chain stakeholders toward Industry 4.0.Design/methodology/approachCloud computing is attributed with increasing competitiveness by focusing on cost reduction, greater elasticity, flexibility and maximum utilization of resources which results in successfully achieving business goals. This paper proposes a Hybrid Supply Chain Cloud model, which integrates the infrastructure, the resources and the configurations of platforms.FindingsThis research paper is aimed at proposing a hybrid cloud that essentially helps in integrating the supply chain network with the flexibility and efficiency. It is important that a supply chain network adds value to ensure customer satisfaction and this can be best achieved by collaborating it with hybrid cloud.Research limitations/implicationsThis research provides a consistent central management and comprehensive view of all computing resources, which gives organizations the knowledge they need to optimize workload placement.Practical implicationsThe findings derived from this research aim to facilitate policy makers and practitioners to develop effective courses of action in current and future supply chain management. Therefore, upon implementation, this model can provide long-term benefits for the organizations by improving the overall efficiency of its supply chain network.Originality/valueThe proposed hybrid cloud will provide deep level of integration in Industry 4.0 situation and thereby brought up portable comprehensive infrastructure based on resources and required configuration in real-time environment.