On-demand Resource Allocation Research Articles

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.

Host load prediction in cloud computing using Long Short-Term Memory Encoder–Decoder

Cloud computing has been developed as a means to allocate resources efficiently while maintaining service-level agreements by providing on-demand resource allocation. As reactive strategies cause delays in the allocation of resources, proactive approaches that use predictions are necessary. However, due to high variance of cloud host load compared to that of grid computing, providing accurate predictions is still a challenge. Thus, in this paper we have proposed a prediction method based on Long Short-Term Memory Encoder–Decoder (LSTM-ED) to predict both mean load over consecutive intervals and actual load multi-step ahead. Our LSTM-ED-based approach improves the memory capability of LSTM, which is used in the recent previous work, by building an internal representation of time series data. In order to evaluate our approach, we have conducted experiments using a 1-month trace of a Google data centre with more than twelve thousand machines. Our experimental results show that while multi-layer LSTM causes overfitting and decrease in accuracy compared to single-layer LSTM, which was used in the previous work, our LSTM-ED-based approach successfully achieves higher accuracy than other previous models, including the recent LSTM one.

Design of Admissions Examination System Based on Cloud Architecture

With the enrichment of admission scale and exam types,traditional hardware architecture of information system can’t meet the requirements of existing system. Different systems and processes have different requirements for hardware facilities. If allocating a set of hardware for each type of system, not only a large amount of hardware resources will be wasted,but also a huge burden will be set on the system operation and maintenance. This paper redesign the infrastructure service layer of the admission examination system with cloud architecture. We use cloud computing[1] technology torealize the dynamic expansion and on-demand allocation of hardware resources, and provides an effective solution to meet the diverse requirements of the examination system.

SDN-based and multitenant-aware resource provisioning mechanism for cloud-based big data streaming

Cloud computing provides elastic on-demand resource allocation, enabling big data systems to process large amounts of streaming data in real time. However, a shared cloud infrastructure (multitenant at the hypervisor level) may reduce system performance or even resource availability, particularly when big data processing demands significantly increase through concurrent task allocations on the same physical hardware. Such situations are not easily detectable from the tenant's perspective, because the tenant may suffer from poor performance without knowing why, as the infrastructure is not under the tenant's control. Moreover, as task processing demand changes over time, the available infrastructure may be insufficient owing to increased processing load or multitenant interference. This paper presents a multitenant-aware resource provisioning mechanism that is independent of any hypervisor and can perform task scheduling and dynamic ongoing task rescheduling for big data streaming while considering the state of each virtual machine (VM). Moreover, the proposed mechanism ensures load balancing through several cloud-based clusters of VMs using a software-defined network (SDN). The prototype was implemented using Apache Storm (big data), Helion Eucalyptus (cloud computing), and Floodlight (SDN). The evaluation shows that when the resources are under multitenant interference, our proposal results in an improvement of 50.1% for CPU-bound tasks, 62.3% for disk-bound tasks, and 43.8% for network-bound tasks. In addition, the load balancer forwarded 72.04% of the load to a fully available cluster, meaning that our mechanism can realize a 22.04% improvement in effectiveness over traditional approaches.

Bandwidth Slicing in Software-Defined 5G: A Stackelberg Game Approach

With the explosive development of mobile Internet and the emergence of multimedia-rich applications such as augmented reality, virtual reality, video streaming, online gaming, and social networks, the traffic demands in the incoming fifth-generation (5G) era could grow significantly. To accommodate this massive amount of data traffic, Internet service providers (ISPs) must deploy new infrastructures to expand the capacity of current networks, which results in high capital and maintenance costs. Furthermore, mobile Internet applications in 5G will have diverse quality-of-service (QoS) requirements in terms of latency, burst size, throughput, and packet arrival rate, and reliable QoS provisioning depends on efficient and on-demand resource allocation [1], [2]. However, it is difficult to realize intelligent resource allocation due to the coupling between service provisioning and the taskoriented hardware.

Energy-Efficient Mapping of LTE-A PHY Signal Processing Tasks on Microservers

Centralized radio access network (C-RAN) is a network architecture that is emerging as a key technology enabler for 5G mobile networks as capacity demands for mobile traffic continue to proliferate. Essentially, C-RAN involves separating the remote radio heads from baseband units to be processed in the cloud. A systematic design of C-RAN involves mapping of individual baseband signal processing tasks to general purpose cloud infrastructures, such as the microserver to reduce the energy footprint. In this paper, we start with mapping the lowest protocol stack, i.e., the physical layer (PHY), which is characterized by strict latency and dynamic data rate. To achieve this, we explore the use of machine intelligence for energy-efficient mapping of PHY signal processing on microservers. Fundamental to this approach is: 1) the use of principal component analysis to represent workload from multi-dimensional hardware performance statistics, demonstrating 99.88% correlation with the critical PHY processing latency and 2) the use of deep learning to model latency and predict dynamic workload for on-demand resource allocation, resulting in up to 36% reduction in hardware usage. These principles are built into a cross-layer run-time framework, which adapts resource allocation in response to time-varying data rate, guaranteeing latency, and improving energy efficiency by up to 48% (average 28%).

StarCube: An On-Demand and Cost-Effective Framework for Cloud Data Center Networks with Performance Guarantee

In this paper, we propose a resource management framework called StarCube , which guarantees non-blocking resource allocation and topology-preserving reallocation for fat-tree based multi-tenant cloud data centers. With StarCube , each cloud service is allocated an isolated non-blocking virtual network topology, and the topology provisioned to each service is guaranteed logically unchanged during and after virtual machine reallocation. This resource management problem is formulated and proved to be NP-complete. To achieve high resource efficiency in acceptable time, we propose a cost-effective algorithm with polynomial-time complexity based on StarCube for on-demand resource allocation and reallocation. We demonstrate via extensive simulations that the server resources in StarCube -based cloud data centers can be nearly fully utilized with negligible reallocation cost. The results also show that StarCube supports a large variety of service provisioning feasibly and efficiently for cloud data centers of various scales and with dynamic demands. To the best of our knowledge, StarCube is the first solution to allocating and reallocating cloud services for fat-tree networks with guarantee on non-blocking properties.

On-demand physical resource allocation method for cloud virtual machine to support random service requests

On-demand physical resource allocation method for cloud virtual machine to support random service requests

A Conceptual Framework for Realizing Energy Efficient Resource Allocation in Cloud Data Centre

Objectives: To present the state of the art on energy efficient frameworks in cloud computing environments Methods/ Statistical Analysis: To propose a conceptual framework for energy efficient IaaS (Infrastructure as a Service) of single and multi-cloud data centre. Findings: The approach is based on virtualization and consolidation technique that enables on-demand and dynamic resource allocation while minimizing energy consumption and carbon emission of the data centre with different energy sources. Applications/Improvements: The proposed framework unlike the previous approaches support intra and inter-data centre resource provisioning and also deals with dynamic resource allocation of single and multi-cloud data centre.

Optimal planning of virtual content delivery networks under uncertain traffic demands

Content Delivery Networks (CDNs) have been identified as one of the relevant use cases where the emerging paradigm of Network Functions Virtualization (NFV) will likely be beneficial. In fact, virtualization fosters flexibility, since on-demand resource allocation of virtual CDN nodes can accommodate sudden traffic demand changes. However, there are cases where physical appliances should still be preferred, therefore we envision a mixed architecture in between these two solutions, capable to exploit the advantages of both of them. Motivated by these reasons, in this paper we formulate a two-stage stochastic planning model that can be used by CDN operators to compute the optimal long-term network planning decision, deploying physical CDN appliances in the network and/or leasing resources for virtual CDN nodes in data centers. Key findings demonstrate that for a large range of pricing options and traffic profiles, NFV can significantly save network costs spent by the operator to provide the content distribution service.

Dynamic Resource Allocation Scheme in Cloud Computing

Cloud Computing environment provisions the supply of computing resources on the basis of demand, as and when needed. It builds upon advances of virtualisation and distributed computing to support cost efficient usage of computing resources, emphasizing on resource scalability and on-demand services. It allows business outcomes to scale up and down their resources based on needs. Managing the customer demand creates the challenges of on-demand resource allocation. Virtual Machine (VM) technology has been employed for resource provisioning. It is expected that using virtualized environment will reduce the average job response time as well as executes the task according to the availability of resources. Hence VMs are allocated to the user based on characteristics of the job. Effective and dynamic utilization of the resources in cloud can help to balance the load and avoid situations like slow run of systems. This paper mainly focuses on allocation of VM to the user, based on analyzing the characteristics of the job. Main principle of this work is that low priority jobs (deadline of the job is high) should not delay the execution of high priority jobs (deadline of the job is low) and to dynamically allocate VM resources for a user job within deadline

English

Cloud computing is a phenomenon which bestows new model of computing. It has various service modes. The service model which provides on-demand access to computing resources is known as infrastructure-as- a-service (IaaS). The IaaS allows cloud users to access resources in pay per use fashion. The problem with this service model in the existing solutions is that the cloud provider may either overprovision the resources or not in a position to serve large number of requests. At any given time, all users might not need on-demand access. Keeping this in mind a strategy is required in order to optimize resource allocation. Many applications exists that take care of high-throughput computing (HTC) where more resources are expected to be allocated. For instance Condor is such application that assigns available resource to jobs and then the resources are released once job is completed. Recently Marshall et al. proposed cloud infrastructure that has provision for on-demand allocation of resources using backfill virtual machines. For this purpose Nimbus is used. In this paper we have implemented the concept of optimal resource allocation using the notion of HTC users and on-demand users. We built a prototype application that demonstrates the proof of concept. The empirical results are encouraging.

Design and Analysis Unified Resource Management Platform of Grid Dispatching System Based on Virtualization Technology

As the command center of Strong Smart Grid, the safe and stable operation of the power grid dispatching automation system is the firm guarantee of electric power supply. And dispatching platform is the foundation platform of the power grid dispatching automation system. To make full use of the computing resources and enhance the availability and flexibility of system, this paper puts forward the construction of uniform resource management platform for power grid dispatching automation system based on virtualization technology, thus to provide required computation, storage, network and other resources. With the introduction of virtualization technology including server virtualization, storage virtualization, network virtualization and other technologies, and the construction of corresponding resource pool, the paper designs uniform resources management platform based on virtualization technology and provides new architecture for power grid dispatching automation system. The new system can allocate and dispatch the resources as a whole, thus to enhance the availability and flexibility of system and realize on-demand allocation and high efficient utilization of resources. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4569 Full Text: PDF

Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses

Due to the upcoming data deluge of genome data, the need for storing and processing large-scale genome data, easy access to biomedical analyses tools, efficient data sharing and retrieval has presented significant challenges. The variability in data volume results in variable computing and storage requirements, therefore biomedical researchers are pursuing more reliable, dynamic and convenient methods for conducting sequencing analyses. This paper proposes a Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses, which enables reliable and highly scalable execution of sequencing analyses workflows in a fully automated manner. Our platform extends the existing Galaxy workflow system by adding data management capabilities for transferring large quantities of data efficiently and reliably (via Globus Transfer), domain-specific analyses tools preconfigured for immediate use by researchers (via user-specific tools integration), automatic deployment on Cloud for on-demand resource allocation and pay-as-you-go pricing (via Globus Provision), a Cloud provisioning tool for auto-scaling (via HTCondor scheduler), and the support for validating the correctness of workflows (via semantic verification tools). Two bioinformatics workflow use cases as well as performance evaluation are presented to validate the feasibility of the proposed approach.

Cloud security auditing based on behavioural modelling

Multi-tenancy is one of the most attractive features of cloud computing, which provides significant benefits to both clients and service providers by supporting elastic, efficient and on-demand resource provisioning and allocation. Multi-tenancy also introduces additional security auditing opportunities. Security auditing can be consolidated and offloaded onto a dedicated and well-protected service. The timely prevention of intrusive behaviour and malicious processes using signature-based intrusion detection technologies or system call level anomaly analysis is a very challenging task due to a high rate of false alarms. In this work, a behavioural modelling scheme is proposed to audit the behaviours of client virtual machine and detect suspicious processes on the level of functionality. The proposed scheme can be used as a community security auditing service. The scheme can also be used by cloud providers to offer automatic identification and auditing of the tenant’s services. Our preliminary results have...

CRESS: A platform of infrastructure resource sharing for educational Cloud computing

Cloud computing is very attractive for schools, research institutions and enterprises which need reducing IT costs, improving computing platform sharing and meeting license constraints. Sharing, management and on-demand allocation of network resources are particularly important in Cloud computing. However, nearly all-current available cloud computing platforms are either proprietary or their software infrastructure is invisible to the research community except for a few open-source platforms. For universities and research institutes, more open and testable experimental platforms are needed in a lab-level with PCs. In this paper, a platform of infrastructure resource sharing system (Platform as a Service (PaaS)) is developed in virtual Cloud computing environment. Its architecture, core modules, main functions, design and operational environment and applications are introduced in detail. It has good expandability and can improve resource sharing and utilization and is applied to regular computer science teaching and research process.

QBARM: A Queue Theory-Based Adaptive Resource Usage Model

Workflow is becoming a more and more important tool for business operations, scientific research and engineering. Cloud computing provides an elastic, on-demand and high cost-efficient resource allocation model for workflow executions. During workflow execution, the load will change from time to time and therefore, it becomes an interesting topic to optimize resource utilization of workflows in the cloud computing environment. In this paper, a workflow framework is proposed that can adaptively use cloud resources. In detail, after users specify the desired goal to achieve, the proposed workflow framework then monitors the workflow execution, and utilizes different pricing models to acquire cloud resources according to the change of workflow load. In this way, the cost of workflow execution is reduced. .

A Two-Tiered On-Demand Resource Allocation Mechanism for VM-Based Data Centers

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

A Two-Tiered On-Demand Resource Allocation Mechanism for VM-Based Data Centers

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which ...

Practical and analytical approaches for designing scalable on-demand policy-based resource allocation in stateless IP networks

Today the policy-based management (PBM) approach is recognized as an efficient solution to simplify the complex task of managing and controlling networks. To this end, the Internet Engineering Task Force (IETF) has introduced a reference framework to build PBM systems. However, this framework only addresses the provisioning of relatively long validity period services based on predefined service-level agreements. Furthermore, very little work addresses the scalability properties of the instantiation of this framework in a real network. This work aims to extend the IETF PBM framework in order to support dynamic provisioning of short-term services (end-system signalling) as well as an instantiation scheme that is scalable (distributed provisioning of edge routers). This instantiation scheme is based on the distribution of the provisioning process while keeping centralized only the parts that involve critical resources, namely bandwidth brokerage. The performance properties of the proposed scheme are then demonstrated throughout both extensive experimentation and an analytical study. The extension of this performance analysis to the case where multiple bandwidth brokers are used is also discussed. The results of this work are intended to be used as a guideline to help network operators to design a scalable PBM system in order to offer to their customers services with quality of service assurance on an on-demand basis. Copyright © 2011 John Wiley & Sons, Ltd.