Elastic Resource Provisioning Research Articles

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.

Elastic Provisioning of Network and Computing Resources at the Edge for IoT Services

The fast growth of Internet-connected embedded devices demands new system capabilities at the network edge, such as provisioning local data services on both limited network and computational resources. The current contribution addresses the previous problem by enhancing the usage of scarce edge resources. It designs, deploys, and tests a new solution that incorporates the positive functional advantages offered by software-defined networking (SDN), network function virtualization (NFV), and fog computing (FC). Our proposal autonomously activates or deactivates embedded virtualized resources, in response to clients' requests for edge services. Complementing existing literature, the obtained results from extensive tests on our programmable proposal show the superior performance of the proposed elastic edge resource provisioning algorithm, which also assumes an SDN controller with proactive OpenFlow behavior. According to our results, the maximum flow rate for the proactive controller is 15% higher; the maximum delay is 83% smaller; and the loss is 20% smaller compared to when the non-proactive controller is in operation. This improvement in flow quality is complemented by a reduction in control channel workload. The controller also records the time duration of each edge service session, which can enable the accounting of used resources per session.

Modeling and optimizing micro-service based cloud elastic management system

Cloud elasticity is a comprehensive solution that takes into account all running applications and resources, including the cloud management system. Cloud resources and applications are constantly changing in terms of capacity and behavior, implying a dynamic change in the architecture and characteristics of the cloud management system. Following the micro-service architecture pattern, the new trend in application modeling is to decompose it into components and make them standalone cooperated modules. By customizing the application operation modules to match new tasks, this design provides the application with rapid adaptation agility to changes in requirements. This paper proposes a full-stack micro-service-based elastic cloud management system that elastically scales and manages cloud resources. The proposed model focuses on the elastic scaling performance of micro-service management modules by analyzing cloud management in three areas: interactions, end-to-end delay, and communication. In addition, optimizing the decoupling and orchestration scheduling of micro-service cloud management system components to achieve elastic management scaling. To the best of our knowledge, no work has investigated cloud management as a workload required for elastic resource provisioning. In comparison to a monolithic architecture, the proposed micro-service-based model achieves 45% to 65% enhancement in elastic scaling performance.

Elastic Resource Provisioning Using Data Clustering in Cloud Service Platform

Currently, cloud computing has received great attention in commerce and scientific research due to its flexibility and strong data processing capability. However, in view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this article, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of proposed method consists of three core components: tasks clustering, the amount of tasks prediction in cluster, dynamic resource provisioning and scheduling. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster to meet their performance requirements. We implement the experiments in Google cloud traces dataset and the results show that our method achieves 92.3, 91.2 percent, and 3679.2 kW<inline-formula><tex-math notation="LaTeX">$ \cdot$</tex-math><alternatives><mml:math><mml:mo>&#x00B7;</mml:mo></mml:math><inline-graphic xlink:href="zhu-ieq1-3002755.gif"/></alternatives></inline-formula>h respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

Improving the energy-efficiency of virtual machines by I/O compensation

With the development and application of cloud computing, virtualization technology has been widely applied in modern data centers to provide performance isolation and elastic resource provision. Although the performance overheads of resource virtualization have been extensively studied, the energy-efficiency loss caused in virtualization layer are seldom mentioned in previous studies. In this paper, we firstly analyze the VM scheduling model and I/O virtualization model from the perspective of energy-efficiency optimization; then, we propose a power-fairness credit scheduling model, which introduces a novel I/O compensation mechanism for achieving quick I/O response and better energy-efficiency at the same time. To investigate the performance and effectiveness of the proposed scheduling model, extensive experiments are conducted by using variety of benchmarks as workloads. The experimental results show that the proposed power-fairness credit scheduling model can significantly reduce the energy-efficiency loss caused by scheduling delay and I/O virtualization. In addition, it also exhibits better robustness and adaptiveness when a virtualized system is facing I/O-intensive workloads or mixed workloads.

Expelliarmus: Semantic-centric virtual machine image management in IaaS Clouds

Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads. In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it models VMIs as semantic graphs to facilitate their similarity computation. Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, it assembles VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed. Experimental results show that our semantic-centric approach is able to optimize the repository size by 2.3−22 times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.

Elastic Resource Provisioning for Increased Energy Efficiency and Resource Utilization in Cloud-RANs

Distributed Radio Access Networks (D-RANs), characterized by static deployment of Base Stations (BSs), are facing critical difficulties in handling the temporal and geographical fluctuations of capacity demands. Recently, Cloud RAN (C-RAN) has been introduced as a centralized cloud computing based paradigm for wireless cellular networks in which the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this article, a novel framework is proposed for C-RAN to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. The RRHs and their corresponding VBSs are divided into clusters within which the active RRH density, transmission power, and size of Virtual Machines (VMs) are adjusted dynamically. To characterize the computational requirements of a VBS, a programmable C-RAN testbed is implemented using the OpenAirInterface (OAI) software platform and USRP boards. Testbed experiment results are presented to describe the consumption and utilization of computing resources in the cloud; simulation results are also presented to illustrate the performance gains of this elastic solution against the current static deployment.

Improving the efficiency of HPC data movement on container-based virtual cluster

Today, lightweight virtualization technologies have been widely deployed on data centers and HPC clusters to provide highly efficient and elastic resource provisioning. Virtualization has also been extended to the I/O stack in operating system. For example, virtual switch has become the primary provider of I/O services for data movement among various light-weight virtual machines, such as Docker and Kubernetes. However, I/O stack virtualization introduces performance degradation and scalability bottleneck to the data movements of HPC computing framework, such as MPI based collective data movements and bursty asynchronous data movements. In order to study the bottleneck, we quantify and analyze the performance degradation involving with HPC data movements on virtual clusters. Then, we design a set of two-stage methods to proactively adapt the virtual network and data movement procedures. This can enhance the performance of HPC collective data movements by up to 3$$\times $$. Meanwhile, a cross-layer middleware is designed to improve the performance and scalability of bursty asynchronous data movements. Our evaluation shows that it can improve the performance of real scientific application by 34.6%.

Reliability-Aware Service Function Chaining With Function Decomposition and Multipath Routing

Network Function Virtualization (NFV) converts network functions executed by costly middleboxes into instances of Virtual Network Functions (VNFs) hosted by industry-standard Physical Machines (PMs). This has proven to be quite an efficient approach when it comes to enabling automated network operations and the elastic provisioning of resources to support heterogeneous services. Today’s revolutionary services impose a remarkably elevated reliability together with ultra-low latency requirements. Therefore, in addition to having highly reliable VNFs, these VNFs have to be optimally placed in such a way to rapidly route traffic among them with the least utilization of bandwidth. Hence, the proper selection of PMs to meet the above-mentioned reliability and delay requirements becomes a remarkably challenging problem. None of the existing publications addressing such a problem concurrently adopts VNF decomposition to enhance the flexibility of the VNFs’ placement and a hybrid routing scheme to achieve an optimal trade-off between the above-mentioned objectives. In this paper, a VNF-decomposition-based backup strategy is proposed together with a delay-aware hybrid multipath routing scheme for enhancing the reliability of NFV-enabled network services while jointly reducing delays these services experience. The problem is formulated as a Mixed Integer Linear Program (MILP) whose resolution yields an optimal VNF placement and traffic routing policy. Next, the delay-aware hybrid shortest path-based heuristic algorithm is proposed to work around the MILP’s complexity. Thorough numerical analysis and simulations are conducted to validate the proposed algorithm and evaluate its performance. Results show that the proposed algorithm outperforms its existing counterparts by 7.53% in terms of computing resource consumption.

ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing

The recent increase in the number of Internet of Things (IoT) devices has led to the generation of a large amount of data. These data are generally processed by cloud servers because of their high scalability and ability to provide resources on demand. However, processing large amounts of data in the cloud is an impractical solution for the strict requirements of IoT services, such as low latency and high bandwidth. Fog computing, which brings computational resources closer to the IoT devices, has emerged as a suitable solution to mitigate these problems. Resource provisioning and application orchestration are two of the key challenges when running IoT applications in a Fog computing environment. In this article, we present ElasticFog, which runs on top of the Kubernetes platform and enables real-time elastic resource provisioning for containerized applications in Fog computing. Specifically, ElasticFog collects network traffic information in real time and allocates computational resources proportionally to the distribution of network traffic. The experimental results prove that ElasticFog achieves a significant improvement in terms of throughput and network latency compared with the default mechanism in Kubernetes.

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.

SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

An adaptive load management service in federated cloud platforms

Recently, cloud platforms have become a promising distributed paradigm for providing flexible computing capability to various high-end applications. Unfortunately, traditionally load management service can not meet the requirements of cloud platforms due to its elastic resource provision characteristic. In this paper, we present a novel adaptive load management service which applies the fuzzy inference model to implement the load-dispatching rules. In this way, we can quantitatively describe the highly nonlinear workload patterns through a small number of simple rules and then make robust load-dispatching decisions. Extensive experiments are conducted in a campus-oriented federated cloud platform to investigate the effectiveness and performance of the proposed load management service, and the results indicate that it can precisely capture the runtime characteristics of workloads and significantly improve the performance of load management service in large-scale cloud environments.

An adaptive load management service in federated cloud platforms

Recently, cloud platforms have become a promising distributed paradigm for providing flexible computing capability to various high-end applications. Unfortunately, traditionally load management service can not meet the requirements of cloud platforms due to its elastic resource provision characteristic. In this paper, we present a novel adaptive load management service which applies the fuzzy inference model to implement the load-dispatching rules. In this way, we can quantitatively describe the highly nonlinear workload patterns through a small number of simple rules and then make robust load-dispatching decisions. Extensive experiments are conducted in a campus-oriented federated cloud platform to investigate the effectiveness and performance of the proposed load management service, and the results indicate that it can precisely capture the runtime characteristics of workloads and significantly improve the performance of load management service in large-scale cloud environments.

Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment

The most challenging problem for a cloud service provider is maintaining the quality of service parameters like reliability, elasticity, keeping the deadline and minimizing the makespan time as also the task rejection ratio. Therefore, the cloud service provider needs a dynamic task scheduling algorithm that reduces the makespan time while increasing the utilization ratio of cloud resources and meeting the user defined QoS parameters. In this paper, we have developed a dynamic scheduling algorithm that balances the workload among all the virtual machines with elastic resource provisioning and deprovisioning based on the last optimal k-interval. Further, the algorithm has been tested on variable number of tasks (10 to 30) to achieve better scalability. The computational results (Figs. 5–10) show that the developed algorithm decreases the makespan time and increases the task to meet the deadline ratio compared with the min-min, the first come-first-serve and the shortest-job-first algorithms in all conditions.

ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) approach in cloud computing

Affordability of appropriate computing resources for satisfying prerequisites of Service Level Agreement (SLA) of clients and optimal utilization of cloud service providers are limited in the present scenario of cloud computing. To overcome these limitations, researchers have exploited various scheduling algorithms to process the deadline based autonomous jobs. The scheduling algorithms however do not support multiprocessor demand and adaptive resource provisioning. This inference triggers to propose a new approach called ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) in cloud computing to handle independent jobs that processes the jobs in a multilevel manner. The SHARP approach embeds multiple criteria decision analysis to preprocess the jobs, multiple attribute job scheduling to prioritize the jobs and adaptive resource provisioning to provide resources dynamically. These contributions alleviate SLA violations in terms of deadline, upgrade client satisfaction and enhance resource utilization. The empirical studies verify the proposed approach in a cloud environment and show the necessity of the proposed approach to support elastic resource provisioning and meet SLA requirements.

Elastic Resource Provisioning for Cloud Based on Docker

Elastic resource provisioning is one of the key techniques for cloud computing, as it can make resources to be organized and provided on demand, resolve the contradiction between resource utilization and user satisfaction, and ensure higher service level agreement. Considering the deficiency of the present cloud resource managements, we propose an innovative elastic resource provisioning approach with a loosely coupled and scalable architecture for cloud based on docker. The elastic resource provisioning method combines both dynamic virtual clustering techniques and economic benefit analysis theories. The results show that our approach can meet the resource supply requirements of elastic cloud with excellent scalability, great agility, strong robustness and high resource utilization.

Elastic Resource Provisioning for Cloud Workflow Applications

Many workflow applications are moved to clouds for elastic capacities. Elastic resource provisioning is one of the most important problems. Realistic factors are involved, including an interval-based charging model, data transfer time, VM loading time, software setup time, resource utilization, and the workflow deadline. A multirule-based heuristic is proposed for the problem under study which contains two components: a deadline division and task scheduling. Taking into account the gaps between tasks, the impact of different critical paths and the precedence constraints, the workflow deadline is properly divided into task deadlines based on the solution of a relaxed problem. The relaxed problem is modeled by integer programming and solved by CPLEX. All tasks are sorted in terms of the developed depth-based rule. For different realistic factors, three priority rules are developed to allocate tasks to appropriate available time slots, from which a weighted rule is constructed for task scheduling. The weights are calibrated by random instances. Experiments are conducted using a benchmark realistic workflow. Experimental results show that the proposal is effective and efficient for realistic workflows.

Elastic Resource Provisioning Approach for Container in Micro-Service Architecture

Elastic Resource Provisioning Approach for Container in Micro-Service Architecture

Mobile Cloud Computing for Big Data Management in Future Smart Phone App Development

Mobile devices are becoming the universal interface to online services and cloud computing applications. Since mobile phones have limited computing power and battery life, there is a potential to migrate computation intensive application components to external computing resources. The Cloud is an attractive platform for offloading due to elastic resource provisioning and the ability to support large scale service deployment. Combination of these two gives some advance functionalities with high speed responding and computing. This paper summarizes the current implementation details, results of the application and the future application areas of mobile cloud computing for big data management in the future smart phone application development.