Elastic Resource Research Articles

A machine learning approach to forecast 5G metrics in a commercial and operational 5G platform: 5G and mobility

The demand for more secure, available, reliable, and fast networks emerges in a more interconnected society. In this context, 5G networks aim to transform how we communicate and interact. However, studies using 5G data are sparse since there are only a few number of publicly available 5G datasets (especially about commercial 5G network metrics with real users).In this work, we analyze the data of a commercial 5G deployment with real users, and propose forecasting techniques to help understand the trends and to manage 5G networks. We propose the creation of a metric to measure the traffic load. We forecast the metric using several machine learning models, and we choose LightGBM as the best approach. We observe that this approach obtains results with a good accuracy, and better than other machine learning approaches, but its performance decreases if the patterns contain unexpected events. Taking advantage of the lower accuracy in the performance, this is used to detect changes in the patterns and manage the network in real-time, supporting network resource elasticity by generating alarms and automating the scaling during these unpredictable fluctuations.Moreover, we introduce mobility data and integrate it with the previously traffic load metric, understanding its correlation and the prediction of 5G metrics through the use of the mobility data. We show again that LightGBM is the best model in predicting both types of 5G handovers, intra- and inter-gNB handovers, using the mobility information through Radars in the several roads, and lanes, near the 5G cells.

Validity of IntegratedE-Modules Contextual Teaching and Learning Approaches SubjectMatterElasticity to ImproveCritical Thinking Abilityof Students Senior High School

Students need to develop their critical thinking skills, which include the 4Cs of the 21st century: creativity, collaboration, communication, and critical thinking. These 4Cs are qualities that are essential for success in the modern world. So it's important to acquire knowledge that can sharpen critical thinking abilities. In three high schools in Sungai Punuh City, the pupils' critical thinking abilities were not as good as anticipated. The first research revealed that the lack of connections between the teaching materials and the usage of physics in daily life was the root of the students' poor critical thinking skills. 77% of students said that the professors' teaching resources did not incorporate real-world applications of physics. Textbooks predominate in the instructional resorces utilized, however they do not promote critical thinking or self-learning. Therefore, in order to develop students' critical thinking abilities, autonomous learning tools are required that meet their demands. In order to enhance critical thinking abilities, research was done on the construction of integrated e-modules using the CTL technique on material elasticity. The findings of expert validity tests are especially communicated in this study. Three experts from the Faculty of mathematics and natural sciences UNP Physics Department conducted the validity test, which had an average validity score of 85.7% in the extremely valid category. According to the validity test results, the e-module is combined with the CTL method to resources elasticity to improve critical thinking abilities that are prepared to be evaluated for applicability and efficacy in the actual world.

Efficient resource allocation in cloud computing environments using AI-driven predictive analytics

This paper proposes an innovative AI-driven approach for efficient resource allocation in cloud computing environments using predictive analytics. The study addresses the critical challenge of optimizing resource utilization while maintaining high quality of service in dynamic cloud infrastructures. A hybrid predictive model combining XGBoost and LSTM networks is developed to forecast workload patterns across various time horizons. The model leverages historical data from a large-scale cloud environment, encompassing 1000 servers and over 52 million data points. A dynamic resource scaling algorithm is introduced, which integrates the predictive model outputs with real-time system state information to make proactive allocation decisions. The proposed framework incorporates advanced techniques such as workload consolidation, resource oversubscription, and elastic resource pools to maximize utilization efficiency. Experimental results demonstrate significant improvements in key performance indicators, including increasing resource utilization from 65% to 83%, reducing SLA violation rates from 2.5% to 0.8%, and enhancing energy efficiency, with PUE improving from 1.4 to 1.18. Comparative analysis shows that the proposed model outperforms existing prediction accuracy and resource allocation efficiency methods. The study contributes to the field by presenting a comprehensive, AI-driven solution that addresses the complexities of modern cloud environments and paves the way for more intelligent and autonomous cloud resource management systems.

Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data

<span lang="EN-US">Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.</span>

The Relationship between the Revenue and the Fiscal Capacity of the Spanish Autonomous Regions

This article analyses the degree of inter-territorial redistribution that occurs in Spain through the Autonomous Regions’ financial model and through the flow of current and capital transfers that the regional governments receive outside that model. The results indicate a high degree of redistribution that occurs through the systems of allocation of transfers to the various regional governments. In the case of the common-regime autonomous regions, the elasticity of total resources received by the regional governments with respect to their own fiscal capacity (GDP per capita) is 0.010. This means that the resources available to them are almost unrelated to their own fiscal capacity.

Heterogeneity-Aware Proactive Elastic Resource Allocation for Serverless Applications

Heterogeneity-Aware Proactive Elastic Resource Allocation for Serverless Applications

Resource Management in Aurora Serverless

Amazon Aurora Serverless is an on-demand, autoscaling configuration for Amazon Aurora with full MySQL and PostgreSQL compatibility. It automatically offers capacity scale-up/down (i.e., vertical scaling) based on a customer database application's needs. For customers with time-varying workloads, it offers cost savings compared to provisioned Aurora or other alternatives due to its agile and granular scaling and its usage-based charging model. This paper describes the key ideas underlying Aurora Serverless's resource management. To help meet its goals, Aurora Serverless adapts and fine tunes well-established ideas related to resource over-subscription; reactive control informed by recent measurements; distributed & hierarchical decision-making; and innovations in the DB engine, OS, and hypervisor for efficiency. Perhaps the most challenging goal is to offer a consistent resource elasticity experience while operating hosts at high degrees of utilization. Aurora Serverless implements several novel ideas for striking a balance between these opposing needs. Its technique for mapping workloads to hosts ensures that, in the common case, there is adequate spare capacity within a host to support fast scale-up for a workload. In the rare event this is not so, it live migrates workloads to ensure seamless scale-up. Its load distribution strategy is characterized by "unbalancing" of load across hosts to enable agile live migrations. Finally, it employs a token bucket-based rate regulation mechanism to prevent a growing workload from saturating its host faster than live migration-based remedial actions.

Dynamic variable analysis guided adaptive evolutionary multi-objective scheduling for large-scale workflows in cloud computing

Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm, namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.

OCL-MEC: An online CPU-core prediction based on load balancing framework for offloading resource management in mobile edge computing environment

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0

Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.

Operational experience and R&D results using the Google Cloud for High-Energy Physics in the ATLAS experiment

The ATLAS experiment at CERN relies on a Worldwide Distributed Computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure and conducted an R&D program on Google Cloud Platform. These initiatives leverage key features of commercial cloud providers: lightweight configuration and operation, elasticity and availability of diverse infrastructures. This paper examines the seamless integration of cloud computing services as a conventional Grid site within the ATLAS workflow management and data management systems, while also offering new setups for interactive, parallel analysis. It underscores pivotal results that enhance the on-site computing model and outlines several R&D projects that have benefited from large-scale, elastic resource provisioning models. Furthermore, this study discusses the impact of cloud-enabled R&D projects in three domains: accelerators and AI/ML, ARM CPUs and columnar data analysis techniques.

Exploring Cloud Computing

Cloud computing has reached maturity since Amazon introduced the pioneering cloud services in 2006. Hong Kong, with its vast daily data processing needs across various sectors, is poised to embrace cloud services extensively, despite a gradual uptake initially. Cloud computing now dominates research agendas in computer science due to its profound implications across multiple computing sectors, particularly in managing big data, where it's indispensable. The establishment of a major cloud R&D centre in Hong Kong by Lenovo in January 2015 underscores this trend. Cloud computing, fulfilling the long-standing vision of computing as a utility, possesses the capability to revolutionize a significant portion of the IT industry. It enhances the attractiveness of software as a service and influences the design and procurement of IT hardware. For developers with innovative ideas for new Internet services, the need for substantial capital investment in hardware deployment or ongoing operational expenses is eliminated. Concerns about overprovisioning, which wastes resources, or under provisioning, which can lead to missed opportunities, are alleviated. Additionally, companies with extensive batch-oriented tasks can achieve results at unprecedented speeds, as the scalability of programs allows for efficient utilization of resources without incurring additional costs. This elasticity of resources, where utilizing 1,000 servers for one hour costs the same as using one server for 1,000 hours, represents a significant departure from traditional IT models.

Optimizing IT FinOps and Sustainability through Unsupervised Workload Characterization

The widespread adoption of public and hybrid clouds, along with elastic resources and various automation tools for dynamic deployment, has accelerated the rapid provisioning of compute resources as needed. Despite these advancements, numerous resources persist unnecessarily due to factors such as poor digital hygiene, risk aversion, or the absence of effective tools, resulting in substantial costs and energy consumption. Existing threshold-based techniques prove inadequate in effectively addressing this challenge. To address this issue, we propose an unsupervised machine learning framework to automatically identify resources that can be de-provisioned completely or summoned on a schedule. Application of this approach to enterprise data has yielded promising initial results, facilitating the segregation of productive workloads with recurring demands from non-productive ones.

Joint Request Updating and Elastic Resource Provisioning With QoS Guarantee in Clouds

In a commercial cloud, service providers (e.g., video streaming service provider) rent resources from cloud vendors (e.g., Google Cloud Platform) and provide services to cloud users, making a profit from the price gap. Cloud users acquire services by forwarding their requests to corresponding servers. In practice, as a common scenario, traffic dynamics will cause server overload or load-unbalancing. Existing works mainly deal with the problem by two methods: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">elastic resource provisioning</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">request updating</i> . Elastic resource provisioning is a fast and agile solution but may cost too much since service providers need to buy extra resources from cloud vendors. Though request updating is a free solution, it will cause a significant delay, resulting in a bad users’ QoS. In this paper, we present a new scheme, called real-time request updating with elastic resource provisioning (TRUST), to help service providers pay less cost with users’ QoS guarantee in clouds. In addition, we propose an efficient algorithm for TRUST with a bounded approximation factor based on progressive-rounding. Both small-scale experiment results and large-scale simulation results show the superior performance of our proposed algorithm compared with state-of-the-art benchmarks.

Elasticity unleashed: Fine-grained cloud scaling through distributed three-way decision fusion with multi-head attention

With the proliferation of cloud computing, elastic resource scaling has emerged as a critical challenge. Cloud platforms must efficiently allocate resources to match dynamic workloads. This study addresses the key trade-off between responsiveness and prudence in scaling a novel distributed three-way decision fusion approach. Distributed three-way decisions of immediate, delayed, or no scaling are made at coarse and fine-time granularity levels. Subsequently, a multi-head attention mechanism intelligently fuses these decisions, weighing the relevance of the different granularities to synthesize an integrated scaling strategy. The reactiveness of sudden workload changes are balanced during fusion by considering long-term trends. Comprehensive experiments on real-world data demonstrated that the proposed fusion strategy substantially improves resource efficiency and adherence to service-level agreements compared to existing methods. Multi-head attention imparts autonomy in adapting to diverse operating conditions. The proposed principled methodology and attention-based decision aggregation hold significance for efficient, adaptive, and interpretable cloud scaling mechanisms.

QoS-Driven Slicing Management for Vehicular Communications

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

Fog-computing based mobility and resource management for resilient mobile networks

Mobile networks are facing unprecedented challenges due to the traits of large scale, heterogeneity, and high mobility. Fortunately, the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity, wide-spread geographical distribution, and elastic resource sharing. In this paper, we propose a novel mobile networking framework based on fog computing which outperforms others in resilience. Our scheme is constituted of two parts: the personalized customization mobility management (MM) and the market-driven resource management (RM). The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits; the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices. Synergistically, our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network, which has been practically corroborated by numerical experiments.

An adaptive load balancing strategy for stateful join operator in skewed data stream environments

As one of the most computationally intensive operations in stream processing applications, join operation can cause severe load imbalance problem when dealing with skewed data. Most of the popular solutions focused on monitoring-based dynamic balancing strategies, making it difficult to quickly adapt to the changing frequency of data stream, and sometimes failing the balancing strategies that try to address the skewed load in the cluster. To address these issues, we propose to use the prediction results of a deep reinforcement learning model and adjust the grouping strategy in advance before the frequency change of data stream. It will enable the system to quickly adapt to data stream fluctuation, while managing the resources for effective resource utilization. The following contributions are made in this paper: 1) Explore the main factors that trigger the load skewness problem in distributed stream join systems and carefully model the load balancing problem at the application level. 2) Develop a Gated Recurrent Unit Sequence to Sequence model to predict key frequency distribution of streams, and propose a dynamic grouping algorithm and a feedback-based resource elasticity scaling algorithm to solve the load imbalance problem caused by hot keys in real time. 3) Design and implement an adaptive stream join system Aj-Stream based on the prediction model and the proposed algorithm on Apache Storm. 4) Evaluate the system performance through extensive experiments on a large scale real-world dataset and multiple synthetic datasets. The experimental results demonstrate that the Aj-Stream proposed in this paper exhibits stable throughput and latency performance with both static data streams of varying skewnesses and dynamic data streams. In comparison to existing stream-connected systems, Aj-Stream demonstrated a 22.1% increase in system throughput and a 45.5% decrease in system latency when dealing with frequently fluctuating data streams.

Dynamic Scheduling Stochastic Multiworkflows With Deadline Constraints in Clouds

Nowadays, more and more workflows with different computing requirements are migrated to clouds and executed with cloud resources. In this work, we study the problem of stochastic multi-workflows scheduling in clouds and formalize this problem as an optimization problem that is NP-hard. To solve this problem, an efficient stochastic multi-workflows dynamic scheduling algorithm called SMWDSA is designed to schedule multi-workflows with deadline constraints for optimizing multi-workflows scheduling cost. The proposed SMWDSA consists of three stages including multi-workflows preprocessing, multi-workflow scheduling and scheduling feedback. In SMWDSA, a novel task sub-deadlines assignment stretagy is design to assign the task sub-deadlines to each task of multi-workflows for meeting workflow deadline constraints. Then, we propose a task scheduling method based on the minimal time slot availability to execution task for minimizing workflow scheduling cost while meetingt workflow deadlines. Finally, a scheduling feedback strategy is adopted to update the priorities and sub-deadlines of unscheduled tasks, for further minimizing workflow scheduling cost. We conduct the experiments using both synthetic data and real-world data to evaluate SMWDSA. The results demonstrate the superiority of SMWDSA as compared with the state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Workflow scheduling in clouds is significantly challenging due to not only the large scale of workflows but also the elasticity and heterogeneity of cloud resources. Moreover, minimizing workflow scheduling cost and satisfying workflow deadlines are two critical issues in scheduling with cloud resources, especially the uncertainty of workflow arrive time and task execution time are considered. To meet workflow deadlines, it is an effective strategy to decompose workflow deadline constraints into task sub-deadline constraints. To minimize the workflow scheduling cost, each task in a workflow needs to be assigned to their most suitable VMs for execution. This article presents a novel workflow scheduling algorithm to schedule stochastic multi-workflows in clouds for optimizing multi-workflows scheduling cost and meeting workflows deadlines. This algorithm obtains the task sub-deadline constraints based on the characteristics of workflows for meeting the worklfow deadline constraint. Under the premise of meeting task deadlines, it schedules tasks to a VM with minimum the slot time, for minimizing the cost. Case studies based on well-known real-world workflows data sets suggest that it outperforms traditional ones in terms of success and cost of multi-workflows scheduling. It can thus aid the design and optimization of multi-workflows scheduling in a cloud environment. It can help practitioners better manage the scheduling cost and performance of real-world applications built upon cloud services.

Practice of Alibaba cloud on elastic resource provisioning for large‐scale microservices cluster

SummaryCloud‐native architecture is becoming increasingly crucial for today's cloud computing environments due to the need for speed and flexibility in developing applications. It utilizes microservice technology to break down traditional monolithic applications into light‐weight and self‐contained microservice components. However, as microservices grow in scale and have dynamic inter‐dependencies, they also pose new challenges in resource provisioning that cannot be fully addressed by traditional resource scheduling approaches. The various microservices with different resource demands and latency requirements can create complex calling chains, making it difficult to provide fine‐grained and accurate resource allocation to each component while maintaining the overall quality of service in the chain. Alibaba Cloud has fully embraced cloud‐native and microservice technologies to drive its key business and scenarios, including Double 11 Shopping Festival. In this work, we aim to address the research problem on how to efficiently provision resources for the growing scale of microservice platform and ensure the performance of latency‐critical microservices. To address the problem, we present in‐depth analyses of Alibaba's microservice cluster and propose optimized resource provisioning algorithms to enhance resource utilization while ensuring the latency requirement. First, we analyze the distinct features of microservices in Alibaba's cluster compared to traditional applications. Then we present Alibaba's resource capacity provisioning workflow and framework to address challenges in resource provisioning for large‐scale and latency‐critical microservice clusters. Finally, we propose enhanced resource provisioning algorithms over Alibaba's current practice by making both proactive and reactive scheduling decisions based on different workloads patterns, which can improve resource usage by 10%–15% in Alibaba's clusters, while maintaining the necessary latency for microservices.