On-demand Resource Allocation Research Articles

On-Demand Centralized Resource Allocation for IoT Applications: AI-Enabled Benchmark

The development of emerging information technologies, such as the Internet of Things (IoT), edge computing, and blockchain, has triggered a significant increase in IoT application services and data volume. Ensuring satisfactory service quality for diverse IoT application services based on limited network resources has become an urgent issue. Generalized processor sharing (GPS), functioning as a central resource scheduling mechanism guiding differentiated services, stands as a key technology for implementing on-demand resource allocation. The performance prediction of GPS is a crucial step that aims to capture the actual allocated resources using various queue metrics. Some methods (mainly analytical methods) have attempted to establish upper and lower bounds or approximate solutions. Recently, artificial intelligence (AI) methods, such as deep learning, have been designed to assess performance under self-similar traffic. However, the proposed methods in the literature have been developed for specific traffic scenarios with predefined constraints, thus limiting their real-world applicability. Furthermore, the absence of a benchmark in the literature leads to an unfair performance prediction comparison. To address the drawbacks in the literature, an AI-enabled performance benchmark with comprehensive traffic-oriented experiments showcasing the performance of existing methods is presented. Specifically, three types of methods are employed: traditional approximate analytical methods, traditional machine learning-based methods, and deep learning-based methods. Following that, various traffic flows with different settings are collected, and intricate experimental analyses at both the feature and method levels under different traffic conditions are conducted. Finally, insights from the experimental analysis that may be beneficial for the future performance prediction of GPS are derived.

Secure and Scalable Architectures for Cloud Computing

Cloud computing has emerged as a cornerstone of modern computing infrastructure, offering unparalleled scalability and flexibility for diverse applications. However, the widespread adoption of cloud services has also raised significant concerns regarding security and scalability. As organizations increasingly rely on cloud environments to store and process sensitive data, ensuring robust security measures while maintaining scalability becomes paramount. This paper presents a comprehensive examination of secure and scalable architectures for cloud computing, addressing the challenges and proposing innovative solutions to enhance the resilience and efficiency of cloud infrastructures. The first section of the paper delves into the fundamental principles of cloud computing, outlining its key characteristics and architectural components. By understanding the underlying architecture of cloud systems, stakeholders can better appreciate the intricate interplay between security and scalability. Subsequently, the paper elucidates the multifaceted security threats facing cloud environments, ranging from data breaches and malicious attacks to insider threats and compliance issues. These challenges underscore the critical importance of implementing robust security mechanisms to safeguard sensitive data and infrastructure resources. In response to these challenges, the paper presents a systematic analysis of existing security mechanisms and architectural paradigms designed to fortify cloud environments. This includes encryption techniques, access control mechanisms, authentication protocols, and intrusion detection systems, among others. Furthermore, the paper explores the concept of defense-in-depth strategies, emphasizing the importance of layering security measures to mitigate the impact of potential breaches and vulnerabilities. In tandem with security considerations, the paper examines strategies for achieving scalability in cloud architectures. Scalability is a defining characteristic of cloud computing, enabling on-demand resource allocation and elastic scaling to accommodate fluctuating workloads. However, achieving scalability while ensuring security presents unique challenges, particularly in multi-tenant environments where resources are shared among disparate users. To address these challenges, the paper explores architectural frameworks such as microservices, containerization, and serverless computing, which offer inherent scalability benefits while facilitating robust isolation and security. Moreover, the paper investigates the role of automation and orchestration in optimizing cloud scalability and security. Through the use of DevOps practices and infrastructure- as-code tools, organizations can streamline deployment processes, enforce security policies, and dynamically scale resources in response to evolving demands. Additionally, the paper examines emerging technologies such as edge computing and federated architectures, which extend the scalability and security benefits of cloud computing to edge devices and distributed environments. In conclusion, this paper underscores the intrinsic link between security and scalability in cloud computing architectures. By adopting a holistic approach that integrates robust security measures with scalable design principles, organizations can mitigate risks, enhance operational efficiency, and unlock the full potential of cloud computing. As the landscape of cloud technologies continues to evolve, ongoing research and innovation in secure and scalable architectures will remain essential to address emerging threats and optimize performance in cloud environments.

Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

Internet-of-Things framework for scalable end-of-life condition monitoring in remanufacturing

The worldwide generation of waste electrical and electronic equipment is continuously growing, with electric vehicle batteries reaching their end-of-life having become a key concern for both the environment and human health in recent years. In this context, the proliferation of Internet of Things standards and data ecosystems is advancing the feasibility of data-driven condition monitoring and remanufacturing. This is particularly desirable for the end-of-life recovery of high-value equipment towards sustainable closed-loop production systems. Low-Power Wide-Area Networks, despite being relatively recent, are starting to be conceived as key-enabling technologies built upon the principles of long-range communication and negligible energy consumption. While LoRaWAN is considered the open standard with the highest level of acceptance from both industry and academia, it is its random access protocol (Aloha) that limits its capacity in large-scale deployments to some extent. Although time-slotted scheduling has proved to alleviate certain scalability limitations, the constrained nature of end nodes and their application-oriented requirements significantly increase the complexity of time-slotted network management tasks. To shed light on this matter, a multi-agent network management system for the on-demand allocation of resources in end-of-life monitoring applications for remanufacturing is introduced in this work. It leverages LoRa’s spreading factor orthogonality and network-wide knowledge to increase the number of nodes served in time-slotted monitoring setups. The proposed system is validated and evaluated for end-of-life monitoring where two representative end-node distributions were emulated, with the achieved network capacity improvements ranging from 75.27% to 249.46% with respect to LoRaWAN’s legacy operation. As a result, the suitability of different agent-based strategies has been evaluated and a number of lessons have been drawnaccording to different application and hardware constraints. While the presented findings can be used to further improve the explainability of the proposed models (in line with the concept of eXplainable AI), the overall framework represents a step forward in lightweight end-of-life condition monitoring for remanufacturing.

Dynamic Straggler Mitigation for Large-Scale Spatial Simulations

Spatial simulations have been widely used to study real-world environments, such as transportation systems. Applications like prediction and analysis of transportation require the simulation to handle millions of objects while running faster than real time. Running such large-scale simulation requires high computational power, which can be provided through parallel distributed computing. Implementations of parallel distributed spatial simulations usually follow a bulk synchronous parallel (BSP) model to ensure the correctness of simulation. The processing in BSP is divided into iterations of computation and communication, running on multiple workers, followed by a global barrier synchronisation to ensure that all communications are concluded. Unfortunately, the BSP model is plagued by the straggler problem, where a delay in any worker slows down the entire simulation. Stragglers may occur for many reasons, including imbalanced workload distribution or communication and synchronisation delays. The straggler problem can become more severe with increasing parallelism and continuous change of workload distribution among workers. This article proposes methods to dynamically mitigate stragglers and tackle communication delays. The proposed strategies can rebalance the workload distribution during simulation. These methods employ the spatial properties of the simulated environments to combine a flexible synchronisation model with decentralised dynamic load balancing and on-demand resource allocation. All proposed methods are implemented and evaluated using a microscopic traffic simulator as an example of large-scale spatial simulations. We run traffic simulations for Melbourne, Beijing and New York with different straggler scenarios. Our methods significantly improve simulation performance compared to advanced methods such as global dynamic load balancing.

Multi-Service Network Slicing 5G NR Orchestration via Tailored HARQ Scheme Design and Hierarchical Resource Scheduling

In the radio access network domain, novel applications' requirements and network dynamics claim scalable processing capabilities, decentralized architecture, and on-demand resource allocation. Due to the intrinsic heterogeneous nature of the network resources, a fully context-aware fronthaul management is extremely complex to design. Operators have a high level-of-abstraction of the infrastructure's resources, which limits their decision-making capabilities. In this paper, a novel radio access network orchestrator is proposed, which jointly combines network function placement and resource allocation techniques in a single optimization framework. Given the infrastructure and users' delay constraints, we formulate a constrained linear maximization problem to determine the optimal timing parameters of the downlink hybrid automatic repeat scheme for each user. At inter-slice layer, our proposed resource allocation method is modelled as a Markov decision process, where the resources' sharing policy is tailored according to the real-time slice traffic load. At the intra-slice level, under mild conditions the radio resources are strategically prioritized, following a game theoretic model. Simulation results show that our method performs the hybrid automatic repeat request response 37%-88% compared to the classical LTE schemes. The dynamic allocation method at the inter-slice level improves the resource's utilization up to 50% compared to static network slicing. Moreover, at the intra-slice, the under and over resource provisioning has been decreased by 21.35% and 60.08%, respectively, compared to the classical fair resource allocation schemes.

BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection (BFS) in the proposed work, this further reduces the inappropriate features from the data. The similarities that were hidden can be demoralized by the Support Vector Machine (SVM) classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM. For an unexpected circumstance SVM model can make a resource allocation decision. The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a single-cell multiuser massive Multiple-Input Multiple Output (MIMO) system, with beam allocation problem as an example. The proposed resource allocation based on SVM performs efficiently than the existing conventional methods; this has been proven by analysing its results.

A Hybrid Approach for Task Scheduling in the Cloud Environment

In today’s world, Cloud Computing has been considered as the best concept for the virtualization of various resources. There are various approaches that have been available for improvising the load balancing and also to improvise the job scheduling in the concept of cloud. Cloud Computing has offers the on-demand allocation of resources to users and this feature of cloud makes it a best among various technologies. Cloud computing provides great performance in less maintenance cost. So, Task scheduling has become a very important factor in enhancing the performance of resources dynamically and at low cost, which is the most crucial part of cloud computing. In other words, we can say that allocation of resources and task scheduling are the two major factors that are considered for the better performance and they must be organized precisely. The author has focused on enhancing the task scheduling process by creating a hybrid optimization algorithm and named as Cuckoo Harmony Search Algorithm (CHSA) to remove the task scheduling problem.

Automatic Scaling Mechanism of Intermodal EDI System under Green Cloud Computing

EDI is a hot topic in the research of multimodal transportation informatization, which determines the exchange level of intermodal transportation information. However, its high cost, large system coupling degree and low performance threshold cannot adapt to mass data exchange in high concurrent environment. Therefore, a decentralized, scalable, distributed and efficient data exchange system is formed. It plays a key role in realizing the comprehensive sharing of interdepartmental intermodal information in the cloud environment. In order to solve the problem of mismatching between application load and computing resource capacity and realize on-demand resource allocation and low carbon emission, this paper proposes to build an Extensible EDI system (XEDI) based on MSA and studies the scaling mechanism in container environment. Based on the resource scheduling characteristics of container cloud and considering the distribution and heterogeneity of intermodal cloud computing platform from the perspective of resource allocation, the automatic scaling mechanism of XEDI is established, the scaling model is established, and the automatic scaling algorithm is proposed. For Dominant Resource Fairness for XEDI (XDRF) resource allocation algorithm and Dominant Resource Fairness for XEDI (CXDRF) based on carbon considering energy consumption, the CXDRF algorithm is proved by quantitative experiments to achieve system performance optimization on the basis of ensuring system reliability and effectively reducing energy consumption. XEDI can not only meet the demand of dynamic load and improve service quality, but also reduce resource occupation and save energy by releasing virtual resources when resource utilization rate is low. It has great research significance and practical value for mass data application under low energy consumption conditions.

Flexible Resource Optimization for GEO Multibeam Satellite Communication System

Conventional GEO satellite communication systems rely on a multibeam foot-print with a uniform resource allocation to provide connectivity to users. However, applying uniform resource allocation is inefficient in presence of non-uniform demand distribution. To overcome this limitation, the next generation of broadband GEO satellite systems will enable flexibility in terms of power and bandwidth assignment, enabling on-demand resource allocation. In this paper, we propose a novel satellite resource assignment design whose goal is to satisfy the beam traffic demand by making use of the minimum transmit power and utilized bandwidth. The motivation behind the proposed design is to maximize the satellite spectrum utilization by pushing the spectrum reuse to affordable limits in terms of tolerable interference. The proposed problem formulation results in a non-convex optimization structure, for which we propose an efficient tractable solution. We validate the proposed method with extensive numerical results, which demonstrate the efficiency of the proposed approach with respect to benchmark schemes.

Virtual Machine Consolidation for Stochastic Load Balancing in Cloud Data Center Management

Cloud computing is able to managing a massive quantity of growing work for the use of enterprise clients in a specified way Virtualization, which makes assumptions the network resources and makes it simple to control, is an important enabling technology for cloud computing. Computing is being used in the proposed work to distribute cloud services tailored to the needs and to promote the smart grid principle. “Skewness” concept was delivered here wherein equal was reducing to combine workloads to enhance the usage of the server. The complexities of on-demand allocation of resources arise from managing customer demands. As a result, the use of vms technologies has proved to be helpful in terms of resource provisioning. The use of virtualized environments is expected to reduce primarily consist connection speed while also executing tasks in accordance with cloud resource availability. This implementation can be use local negotiation based VM consolidation mechanism to predict each job request and reduce overloads to create virtual space at the time of multiple requests. The proposed system implement co-location approach to combine unused small spaces to create new virtual space for improves the performance of server. Also implement self-destruction approach to eliminate the invalid data based on time to live property. The proposed framework is executed in genuine time with effective asset allotment. In this system to begin with broaden a forecast show which will gauge the parcel sizes of decrease commitments at runtime. And it can detect information skewness in real time and allocate extra asses for mordant of large walls that help us complete faster.

A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.

Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers

Cloud computing has been considered a core model of elastic on-demand resource allocation using a pay-as-you go model. One of the big challenges of this environment is to provide high quality service (QoS) through efficient and stringent management of cloud data center resources. With the increasing demand for cloud based services, the traffic volume inside cloud data centers (DC) has been increased exponentially. Accordingly, and to provide high QoS, a proper scheduling mechanism has to be followed by the cloud service provider. Furthermore, accurate scheduling is necessary for advancing the problem of energy consumption and resource utilization. In this paper, we propose an optimal resource allocation and consolidation virtual machine (VM) placement model for multi-tier applications in modern large cloud DCs. The proposed model targets to optimize the DCs’ energy and communication cost that influence the overall cloud performance through Software Defined Networking (SDN) control features. To solve the formulated multi-objective optimization problem, a novel adaptive genetic algorithm is proposed. The experimental results validate the efficacy of the proposed model through extensive simulations using synthetic and real workload traces. These results show that the proposed model jointly optimizes cloud QoS as well as energy consumption.

Sunflower Whale Optimization Algorithm for Resource Allocation Strategy in Cloud Computing Platform

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower Whale Optimization Algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1

Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in video coding and transcoding services. New codecs, such as AV1, are being developed to deal with new demands for high video resolutions with bandwidth constraints and quality requirements. However, these new codecs have high computational requirements and new strategies are needed to accelerate their processing.Cloud computing offers interesting features such as on-demand resource allocation, multitenancy, elasticity, and resiliency among others. Deploying video coding and transcoding services on these infrastructures is suitable because it allows the adaptation of the resources to the workload, offers high availability, and provides ubiquitous access.This work proposes a cloud-based distributed architecture, tuned for video coding, that relies on an elastic pool of workers and media servers to provide fault-tolerance. A distributed application is deployed on the top of the architecture to split the video encoding process of a video in several jobs that can be dynamically assigned among the elastic pool of workers. The proposed solution is analyzed in terms of scalability, resource usage, and job distribution varying the number of workers for three well-known video codecs: H.265, VP9, and AV1. Moreover, the quality of the encoded videos has been evaluated for different bit rates and number of frames per job using full reference metrics like: PSNR, MS-SSIM, and VIF. Results show that our solution achieve similar quality and bitrate compared with full video coding while improving the total encoding time, which can decrease more than 90% depending on the encoder and the number of workers.

A Cloud-Based Distributed Architecture to Accelerate Video Encoders

Nowadays, video coding and transcoding have a great interest and important impact in areas such as high-definition video and entertainment, healthcare and elderly care, high-resolution video surveillance, self-driving cars, or e-learning. This growing demand for high-resolution video boosts the proposal of new codecs and the development of their encoders that require high computational requirements. Therefore, new strategies are needed to accelerate them. Cloud infrastructures offer interesting features for video coding, such as on-demand resource allocation, multitenancy, elasticity, and resiliency. This paper proposes a cloud-based distributed architecture, where the network and the storage layers have been tuned, to accelerate video encoders over an elastic number of worker encoder nodes. Moreover, an application is developed and executed in the proposed architecture to allow the creation of encoding jobs, their dynamic assignment, their execution in the worker encoder nodes, and the reprogramming of the failed ones. To validate the proposed architecture, the parallel execution of existing video encoders, x265 for H.265/HEVC and libvpx-vp9 for VP9, has been evaluated in terms of scalability, workload, and job distribution, varying the number of encoder nodes. The quality of the encoded videos has been analyzed for different bit rates and number of frames per job using the Peak Signal-to-Noise Ratio (PSNR). Results show that our proposal maintains video quality compared with the sequential encoding while improving encoding time, which can decrease near 90%, depending on the codec and the number of encoder nodes.

Techno-Economic Impact of Filterless Data Plane and Agile Control Plane in the 5G Optical Metro

Optical metro networks evolution driven by 5G requirements face enormous challenges. Network functions virtualized in the data centers spread to the metro nodes, IP, and optical technologies must cooperate to meet the metro traffic aggregation role. Multiple technological options exist, and carriers confront the need to economically assess them, benchmarked in realistic deployments. This paper gives relevant insights to this aim. We first construct a set of metro network benchmarks. A strategic and distinctive effort is made to incorporate metro WDM topologies, traffic profiles and daily variation patterns, fault-tolerance requisites, and network operational choices, that faithfully reflect the expected 5G metro progression for a national carrier. Then, we use these networks to assess two technological choices. On one hand, the cost-effectiveness limits in terms of CAPEX reductions and energy efficiency brought from the possibility of having an agile control plane in the metro, capable of on-demand instantiation of IT and network resources. On the other hand, we investigate the benefits of replacing ROADMs by more cost-effective filterless technologies, but just limiting this replacement to degree-1 and degree-2 optical nodes, that are prevalent (e.g. >50%) in regional metro topologies. A novel capacity planning algorithm has been developed for IT, IP and optical resources allocation and dimensioning, providing fault-tolerant designs for the realistic scenarios defined. Simulation results have been obtained using the Net2Plan NIW (NFV over IP over WDM) open-source framework. Developed algorithms and part of the testing scenarios are available for inspection in public repositories of the EU METRO-HAUL project, the umbrella for our work. Our results show CAPEX benefits in the order of 10% and energy savings in the order of 20–30% stemming from the on-demand resource allocation in the metro. In addition, degree 1 and degree 2 optical nodes have shown to be a sweet spot for applying filterless switching, with mitigated impact of the associated spectrum waste.

Toward Latency-Optimal Placement and Autoscaling of Monitoring Functions in MEC

Multi-Access Edge Computing (MEC) promises to provide sufficient computing capacity close to users and realize smart management at the edge of mobile network. To achieve the aforementioned objectives, it is indispensable to implement real-time monitoring of the whole MEC network. However, the geo-distributed deployment of MEC infrastructure dramatically increases the communication latency of gathering the state information (e.g. current status and resource utilization) from servers at the edge. This paper addresses latency-optimal placement and autoscaling of monitoring functions in MEC. First, we formally formulate latency-optimal placement of monitoring functions as an integer linear programming problem and proposed a genetic algorithm-based meta-heuristic to obtain the optimal solution with fast convergence. Moreover, to serve the time-varying demand on resource capacity from diversified mobile services, an online VNF scaling scheme is designed for realizing on-demand resource allocation. The effectiveness of our heuristic algorithm is verified through both numerical simulation and experiments in real cloud environment. Experimental results demonstrate performance superiority of the proposed approach over the state-of-art researches, in terms of algorithm CPU time, total network latency and long-term scaling cost.

Potential applications of 5G communication technologies in collaborative intelligent manufacturing

Nowadays most of the communication technologies used in the manufacturing industry are still wired, including various fieldbuses and dedicated industrial Ethernet technologies, though wireless communication technologies including WiFi and ZigBee are recently being adopted. This study is to investigate the integration of 5G wireless communication technologies with collaborative intelligent manufacturing (CIM) processes and systems. 5G technologies and typical scenarios including enhanced mobile broadband, massive machine type communications, and ultra-reliable low latency communication are introduced. Various possible applications or business slices of 5G in CIM are analysed, including human–machine interfaces and production IT, process automation, factory automation, logistics and warehousing, monitoring, and maintenance. These applications are analysed by the functions as modules with the extended 5C architecture. A modular collaborative production approach is suggested to combine modules to achieve on-demand resource allocation and complete flexible tasks in CIM systems. Related research challenges and opportunities are also discussed.

On-demand virtual research environments using microservices.

The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.