Dynamic Resource Provisioning Research Articles

Dynamic Resource Provisioning for Sustainable Cloud Computing Systems in the Presence of Correlated Failures

Dependence of computing resources on each other in cloud computing systems (CCS) makes them prone to fail in correlated manner which significantly impacts their service reliability and energy efficiency. Focusing on these two metrics of CCS while considering correlated failures remained an open question, which is the focus of this work. This paper proposes mechanisms for improving reliability and energy efficiency jointly under correlated failures in CCS. In order to model failure correlation, statistical cluster analysis techniques are applied to real failure traces. Then, mathematical models are built to calculate reliability and energy consumption of failure prone CCS. These mathematical models are used to design fault-tolerant and energy-aware resource provisioning mechanisms/policies. In order to further reduce the energy consumption, a correlated failure-aware VM consolidation policy is also proposed in this paper. A simulation based study of the proposed resource management policies and fault tolerance mechanisms is conducted by using real failure traces and Bag-of-Tasks workload. The results demonstrate that by exploiting failure correlation with the proposed resource management policies, we reduce the occurrence of failures on tasks by 34 percent and increase the energy efficiency of the system by 20 percent, approximately in comparison to the environments where failures are handled independently.

ANN Based Execution Time Prediction Model and Assessment of Input Parameters through ISM

Cloud computing is on-demand network access model which provides dynamic resource provisioning, selection and scheduling. The performance of these techniques extensively depends on the prediction of various factors e.g., task execution time, resource trust value etc., As the accuracy of prediction model absolutely depends on the input data that are fed into the network, Selection of suitable inputs also plays vital role in predicting the appropriate value. Based on predicted value, Scheduler can choose the suitable resource and perform scheduling for efficient resource utilization and reduced makespan estimates. However, precise prediction of execution time is difficult in cloud environment due to heterogeneous nature of resources and varying input data. As each task has different characteristic and execution criteria, the environment must be intelligent enough to select the suitable resource. To solve these issues, an Artificial Neural Network (ANN) based prediction model is proposed to predict the execution time of tasks. First, input parameters are identified and selected through Interpretive Structural Modeling (ISM) approach. Second, a prediction model is proposed for predicting the task execution time for varying number of inputs. Third, the proposed model is validated and provides 21.72% reduction in mean relative error compared to other state-of-the-art methods

The Control Unit of the KM3NeT Data Acquisition System

The KM3NeT Collaboration runs a multi-site neutrino observatory in the Mediterranean Sea. Water Cherenkov particle detectors, deep in the sea and far off the coasts of France and Italy, are already taking data while incremental construction progresses. Data Acquisition Control software is operating off-shore detectors as well as testing and qualification stations for their components. The software, named Control Unit, is highly modular. It can undergo upgrades and reconfiguration with the acquisition running. Interplay with the central database of the Collaboration is obtained in a way that allows for data taking even if Internet links fail. In order to simplify the management of computing resources in the long term, and to cope with possible hardware failures of one or more computers, the KM3NeT Control Unit software features a custom dynamic resource provisioning and failover technology, which is especially important for ensuring continuity in case of rare transient events in multi-messenger astronomy. The software architecture relies on ubiquitous tools and broadly adopted technologies and has been successfully tested on several operating systems.

BC-Mobile Device Cloud: A Blockchain-Based Decentralized Truthful Framework for Mobile Device Cloud

By exploiting the massive data generated from the numerous interconnected machines and control systems, industrial Internet-of-Things (IIoT) provides unprecedented opportunities for facilitating the intelligence and smartness of manufacturing. Timely processing the large-scaled IIoT data by the conventional computation framework, such as Cloud computing, however, is nontrivial due to its costly resource usage, intolerable delay, and unbearable backbone pressures. By leveraging the idle resources of smart objects at the edge, mobile device cloud (MDC) becomes promising for the IIoT data analysis, thanks to the flexible resource provision and nearby task offloading. However, MDC workers are mostly human-carried devices with large scale, high dynamic resource provision, and untruthful behaviors, which pose significant challenges on MDC task allocation. In this article, we propose a blockchain-based decentralized and truthful framework for MDC (BC-MDC). BC-MDC enables the decentralization and prevents dishonesty by incorporating a plasma-based blockchain into the MDC. We design four smart contracts for distributedly managing the worker registration, task posting/allocation, rewarding, and penalizing. Furthermore, MDC task allocation is formulated as a stochastic optimization problem that jointly minimizes the long-term processing cost and risk of task failing. We also design a truthful reward/penalty algorithm that stimulates workers to provide resources and enforce them to keep the promise as well. Collaborated by the extensive simulation tests, we show how our proposed scheme achieves low cost on usage and high truthfulness and outperforms state-of-the-art solutions.

Live Migration of Virtual Machines using Mirroring Technique

Virtualization is a key concept in empowering the “Infrastructure as- a-Service (IaaS)” of cloud-based services. Live migration of virtual machines is the process of moving virtual machine from one physical host to another without interrupting other VMs. Migration of VMs supports the improved efficiency of resource usage and dynamic resource provision capabilities. The performance metrics of live migration are down time and total migration time. In this paper, a technique to reduce the total migration time of standard memory migration using the mirroring of VMs at destination host is proposed. It mainly concentrates on the total migration time and maximum profit of providers. The proposed technique avoids the iterative transmission of memory pages from the source host to destination host. Performance results depict that the proposed algorithm can reduce 36% of the data transmitted during migration and 26% of the total migration time when compared with pre-copy migration technique.

VMS Mean and FFD VM Allocation Algorithm for Cloud Datacenter

The datacenter power consumption is increasing speedily, because of the scalable and dynamic provisioning of resources to the remote consumers. Not efficiently utilizing the resource of host machine is the main reason for increase in power consumption. By efficiently placing the number of virtual machine in fewer active host and shutdown the inactive host could reduce the consumption of power in datacenter. In this paper VMs Mean and FFD Bin Packing Virtual Machine Placement Algorithm is proposed.FFD preprocess VM’s and Host’s by sorting all the VM’s by MIPS and Host’s by their power. To place the VM’s in host machine VMs Mean method is used which finds the lowest CPU utilizing VM. The main objective is to increase the host machine utilization by efficiently placing the VM’s into few active Host’s and also reduced the power consumption. To test this algorithm CloudSim Toolkit is been used. The parameter such as EC, SLAV, PDM, ESV, VM migration and SLATAH is used as a metric to evaluate the Algorithm efficiency.

Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

SummaryEdge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software‐defined network (SDN)‐based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm‐III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time.

Shared data-aware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using Aneka

In the recent years, data-intensive applications have been growing at an increasing rate and there is a critical need to solve the high-performance and scalability issues. Hybrid Cloud Computing paradigm provides a promising solution to harness local infrastructure and remote resources and provide high Quality of Service (QoS) for time sensitive and data-intensive applications. Generally, hybrid cloud deployments have a heterogeneous pool of resources and it becomes a challenging task to efficiently utilize resources to provide optimum results. In modern data hungry applications, it is crucial to optimize bandwidth consumption, latency and networking overheads. Moreover, most of them have large extent of file sharing capability. The existing algorithms do not explicitly consider file sharing scenarios that leads large data transmission times and has severe effects on latency. In this direction, this paper focuses on building upon existing dynamic resource provisioning and task scheduling algorithms to provide better QoS in hybrid cloud environments for data intensive applications in a shared file task environment. The efficiency of proposed algorithms is demonstrated by deploying them on Microsoft Azure using Aneka, a platform for developing scalable applications on the Cloud. Experiments using real-world applications and datasets show that proposed algorithms are able to allocate tasks and extend to public cloud resources more efficiently, reducing deadline violations and improving response times to give response time reduction of upto 40.12% for a sample local alignment search application on genome sequences.

Performance Modeling of Softwarized Network Services Based on Queuing Theory With Experimental Validation

Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson's networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10 percent. Finally, we show the usefulness of the model for performing the dynamic resource provisioning of the vMME experimentally.

A Robust QSCTA-EDRA routing protocol for Cognitive Radio Sensor Networks

The exponential rise in wireless communication demands have alarmed academia-industries to develop certain robust routing protocol to achieve quality-of-service (QoS) communication over constrained network conditions. Amongst major wireless communication technologies wireless sensor network (WSN) has been found as the dominant paradigm to meet major communication demands such as surveillance systems, healthcare sector, intelligent transport system, industrial monitoring and control, defence systems, etc. Being a constrained networking technologies WSN demands certain resource efficient (i.e., energy-efficient as well as spectrum efficient) routing protocol to meet exponential up surging demands. Unlike major classical routing protocols, in this paper the prime focus is made on augmenting major functional paradigms of the clustered-CRSN systems by augmenting joint power and resource access/scheduling in intra-cluster as well as inter-cluster data communication, QoS-sensitive cognitive terminal activation (QS-CTA) etc. to achieve energy-efficient dynamic resource access (EDRA) or provision. We have proposed QSCTA-EDRA routing protocol that exploited efficacy of joint-power and resource scheduling and data channel quality (DCQ) and reporting channel quality (RCQ) assisted cognitive terminal activation to accomplish energy-efficient and QoS centric communication over CRSN. Performance comparison for the proposed QSCTA-EDRA routing protocol exhibited that it outperforms existing state-of-art dynamic resource access protocols.

A robust QSCTA-EDRA routing protocol for cognitive radio sensor networks

The exponential rise in wireless communication demands have alarmed academia-industries to develop certain robust routing protocol to achieve quality-of-service (QoS) communication over constrained network conditions. Amongst major wireless communication technologies wireless sensor network (WSN) has been found as the dominant paradigm to meet major communication demands such as surveillance systems, healthcare sector, intelligent transport system, industrial monitoring and control, defence systems, etc. Being a constrained networking technologies WSN demands certain resource efficient (i.e., energy-efficient as well as spectrum efficient) routing protocol to meet exponential up surging demands. Unlike major classical routing protocols, in this paper the prime focus is made on augmenting major functional paradigms of the clustered-CRSN systems by augmenting joint power and resource access/scheduling in intra-cluster as well as inter-cluster data communication, QoS-sensitive cognitive terminal activation (QS-CTA) etc. to achieve energy-efficient dynamic resource access (EDRA) or provision. We have proposed QSCTA-EDRA routing protocol that exploited efficacy of joint-power and resource scheduling and data channel quality (DCQ) and reporting channel quality (RCQ) assisted cognitive terminal activation to accomplish energy-efficient and QoS centric communication over CRSN. Performance comparison for the proposed QSCTA-EDRA routing protocol exhibited that it outperforms existing state-of-art dynamic resource access protocols.

Learning-Based Dynamic Resource Provisioning for Network Slicing with Ensured End-to-End Performance Bound

To accommodate different sets of network functions with different quality-of-service requirements for different types of applications in 5G networks, network slicing, which dynamically creates virtual networks, was proposed in the literature and IETF. A critical issue for network slicing is to determine the amount of resources for a network slice to ensure the quality-of-service requirement, and as such we need to determine the relationship among traffic demand, amount of resources, and end-to-end delay. This problem is non-trivial in a dynamic, virtualized environment. In this paper, we first use stochastic network calculus (SNC) to study the end-to-end delay bound with given traffic demand and resources. Then, we propose a solution to find the amount of resources that should be allocated with given traffic distribution and end-to-end delay bound. Beyond that, we investigate the range of traffic demands that a network slice can support and design a learning-based dynamic network slice resizing strategy, which can significantly reduce overall resizing cost with quality-of-service guarantee. Our work provides a set of useful tools for network slice tenants to (1) decide the amount of resources to request from physical network providers and (2) cost-effectively adjust the resource amounts that align with the dynamic traffic demand.

Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks

<span lang="EN-US">The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the cloud overlay network environment. Hence, it does not fulfill the monitoring requirement of cloud overlay traffic. As the growth of cloud network traffic causes difficulties for the service providers and end-users to manage the traffic efficiently, an enhanced IPFIX flow monitoring mechanism for cloud overlay networks was proposed to address this problem. The monitoring mechanism provided detail visibility and information of overlay network traffic that traversed the cloud environment, which is not available in the current network monitoring systems. The experimental results showed that the proposed monitoring system able to capture overlay network traffic and segregated the tenant traffic based on virtual machines as compare to the standard monitoring system.</span>

A Research on an Efficient Cloud Scheduling with a Geo Microarray Data Set

Investigations on micro-array organisms for various researches have made a non discrete dealing of thousands of gene expressions achievable. For any applications, the results would be more accurate only when maximum count is analyzed within a predictable time and it is one of the unseen challenges in the field of bio medicine. The purpose of this data analysis is to regulate and control the activities of thousands of genes in our body. This paper develops a scheduling analysis of how effectively gene molecular patterns are taken into experimentation. This motivated our investigation in a new dimension for a cloud environment. This paper is about applying our previous works such as Workflow Shuffling and Hole Filling Algorithm (WSHF) [13], Agent Centric Enhanced Reinforcement learning algorithm (AGERL) [14], Heuristic Flow Equilibrium based Load Balancing (HFEL) [15] and Dynamic Resource Provisioning and Load Balancing (DRBLHS) [16] algorithms collaboratively for a Gene Express Omnibus dataset as a case study. The gene data’s plays an important role in monitoring the human activities and how well, the data has been processed in the cloud with minimum budget, time and minimum virtual machines. Finally, the efficiency of the system is analyzed in terms of resource utilization, completion time, response time, throughput and VM Migration time

CPU–RAM-based energy-efficient resource allocation in clouds

Cloud computing provides online services and solutions for dynamic and on-demand resource provisioning. These resources consume high energy leading to higher operational expenditures and carbon footprints in data centers. There are several research works performed on energy efficiency of data centers, but mostly focus on energy consumption of a single factor, i.e., CPU, leaving the RAM neglected. Recently, the researchers have focused on the impact of RAM’s energy consumption on the data centers. Studies show that RAM consumes 25% of a server’s overall energy. In this paper, we propose two sets of schemes that consider the server capacity for virtual machine consolidation to reduce the overall energy cost. The proposed techniques are implemented in CloudSim, and the results are compared with state-of-the-art solutions. Our proposed techniques reduce energy consumption and maintain a service level agreement to satisfy the customer requirements with a minimum cost.

LARPA: A learning automata‐based resource provisioning approach for massively multiplayer online games in cloud environments

SummaryNowadays, with the development of communication systems, massively multiplayer online games (MMOGs) have become very popular. In these games, the players all over the world dynamically interact with each other by sending play actions such as shootings, movements, or chatting in the form of MMOG sessions in real time through a large‐scale distributed environment. Leveraging affordable cloud computing to host such services is a widely investigated issue. It is because the arrival rate of players to the game environment has to make fluctuations, and the players expect services to be always available with an acceptable quality of service (QoS), especially in terms of the response time. Therefore, the dynamic provisioning of resources in order to deal with fluctuating demands due to variability in the arrival rate of players of the MMOG services is highly recommended. In this paper, we propose a learning‐based resource provisioning approach for MMOG services that is based on the combination of the autonomic computing paradigm and learning automata (LA). The remarkable performance of the proposed approach in terms of response time, cost, and allocated virtual machines (VMs) is assessed through simulation and comparison with the existing approaches.

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.

A cognitive/intelligent resource provisioning for cloud computing services: opportunities and challenges

In cloud computing, resources could be provisioned in a dynamic way on demand for cloud services. Cloud providers seek to realize effective SLA execution mechanisms for avoiding SLA violations by provisioning the resources or applications and timely interacting to environmental changes and failures. Sufficient resource provisioning to cloud’s services relies on the requirements of the workloads to achieve a high performance for quality of service. Therefore, deciding the suitable amount of cloud’s resources for these services to achieve is one of the main works in cloud computing. During the runtime of services, the amount of cloud’s resources can be specified and provisioned based on the actual workloads changes. Determining the correct amount of cloud’s resources needed for running the services on clouds is not easy task, and it depends on the existing workloads of services. Consequently, it is required to predict the future workloads for dynamic provisioning of resources in order to meet the changes in workloads and demands of services in cloud computing environments. In this paper, we study the possibility of using a cognitive/intelligent approach for cloud resource provisioning which is a combination of the autonomic computing concept, deep learning technique and fuzzy logic control. Deep learning technique is a state-of-the-art in the machine learning field. It achieved promising results in many other fields like image classification and speech recognition. For these reasons, deep learning is proposed in this work to tackle the workload prediction in cloud computing. Additionally, we also propose to use a fuzzy logic-based method in order to make a decision in the case of uncertainty of the workload prediction. We study various exiting works on autonomic cloud resource provisioning and show that there is still an opportunity to improve the current methods. We also present the challenges that may exist on this domain.

Dynamic Priority-Based Resource Provisioning for Video Transcoding With Heterogeneous QoS

Video transcoding is widely adopted in online video services to transcode videos into multiple representations for dynamic adaptive bitrate streaming. This solution may consume significant resources and incur intolerable processing delays. Meanwhile, different videos have different quality-of-service (QoS) requirements for transcoding. Delay-sensitive videos must be transcoded within a strict deadline, whereas delay-tolerant videos are not required to be transcoded immediately. Some intelligent policies are required for provisioning the right amount of resources in the transcoding system to meet the heterogeneous QoS requirements, especially under dynamic workloads. To this end, we develop a robust dynamic priority-based resource provisioning scheme for video transcoding. We adopt the preemptive resume priority discipline to design a multiple-priority transcoding mechanism. The system performs the transcoding for delay-tolerant videos by utilizing idle resources for improving resource utilization while not affecting the transcoding for delay-sensitive videos. We adopt the model predictive control framework to design an online algorithm for dynamic resource provisioning to accommodate time-varying workloads by predicting future workloads. To seek performance robustness against prediction noise, we improve the performance of our online algorithm via robust design . The experimental results demonstrate that our proposed method can satisfy the heterogeneous QoS requirements while significantly reducing computing resource consumption.

Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing

Dynamic resource allocation is the key objective of the paper motivated due to a large number of user’s service request and increasing network infrastructure complexity. Load balancing and Service Broker Policy are taken as two main key areas for the dynamic provision of resources to the cloud user in order to meet the QoS requirement. While provisioning the resources, the conventional approaches degrade due to QoS performance limits such as time delay, energy, etc. To overcome those problems, we proposed a new approach to provide dynamic provisioning of resources based on load balancing and service brokering. Initially, the Multi-agent Deep Reinforcement Learning-Dynamic Resource Allocation (MADRL-DRA) is used in the Local User Agent (LUA) to predict the environmental activities of user task and allocate the task to the Virtual Machine (VM) based on priority. Next, a Load balancing (LB) is performed in the VM, which increases the throughput and reduces the response time in the resource allocation task. Secondly, the Dynamic Optimal Load-Aware Service Broker (DOLASB) is used in the Global User Agent (GUA) for scheduling the task and provide the services to the users based on the available cloud brokers (CBs). In the global agent, cloud brokers are the mediators between users and providers. The optimization problem in Global Agent (GA) is formulated by the programming of mixed integers, and Bender decomposition algorithm. The result of our proposed method is better as compared with the conventional techniques in terms of Execution Time, Waiting Time, Energy Efficiency, Throughput, Resource Usage, and Makespan.