A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increase the provider’s profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to make HPC applications consuming less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO₂ emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson’s PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO₂ emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications.     

A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing.     

Cost-aware job scheduling for cloud instances using deep reinforcement learning

As the services provided by cloud vendors are providing better performance, achieving auto-scaling, load-balancing, and optimized performance along with low infrastructure maintenance, more and more companies migrate their services to the cloud. Since the cloud workload is dynamic and complex, scheduling the jobs submitted by users in an effective way is proving to be a challenging task. Although a lot of advanced job scheduling approaches have been proposed in the past years, almost all of them are designed to handle batch jobs rather than real-time workloads, such as that user requests are submitted at any time with any amount of numbers. In this work, we have proposed a Deep Reinforcement Learning (DRL) based job scheduler that dispatches the jobs in real time to tackle this problem. Specifically, we focus on scheduling user requests in such a way as to provide the quality of service (QoS) to the end-user along with a significant reduction of the cost spent on the execution of jobs on the virtual instances. We have implemented our method by Deep Q-learning Network (DQN) model, and our experimental results demonstrate that our approach can significantly outperform the commonly used real-time scheduling algorithms.

     

Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network

These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications.

     

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.     

A cost-efficient scheduling algorithm for streaming processing applications on cloud

Cluster Computing - Stream processing is a new memory computing paradigm that deals with dynamic data streams efficiently. Storm is one of the stream processing frameworks, but the default stream...     

Janus: a framework to boost HPC applications in the cloud based on SDN path provisioning

Data centers, clusters, and grids have historically supported High-Performance Computing (HPC) applications. Due to the high capital and operational expenditures associated with such infrastructures, we have witnessed consistent efforts to run HPC applications in the cloud in the recent past. The potential advantages of this shift include higher scalability and lower costs. If, on the one hand, app instantiation—through customized Virtual Machines (VMs)—is a well-solved issue, on the other, the network still represents a significant bottleneck. When switching HPC applications to be executed on the cloud, we lose control of where VMs will be positioned and of the paths that will be traversed for processes to communicate with one another. To bridge this gap, we present Janus, a framework for dynamic, just-in-time path provisioning in cloud infrastructures. By leveraging emerging software-defined networking principles, the framework allows for an HPC application, once deployed, to have interprocess communication paths configured upon usage based on least-used network links (instead of resorting to shortest, pre-computed paths). Janus is fully configurable to cope with different operating parameters and communication strategies, providing a rich ecosystem for application execution speed up. Through an extensive experimental evaluation, we provide evidence that the proposed framework can lead to significant gains regarding runtime. Moreover, we show what one can expect in terms of system overheads, providing essential insights on how better benefiting from Janus.

     

Energy efficient job scheduling with workload prediction on cloud data center

Data centers are the backbone of cloud infrastructure platform to support large-scale data processing and storage. More and more business-to-consumer and enterprise applications are based on cloud data center. However, the amount of data center energy consumption is inevitably lead to high operation costs. The aim of this paper is to comprehensive reduce energy consumption of cloud data center servers, network, and cooling systems. We first build an energy efficient cloud data center system including its architecture, job and power consumption model. Then, we combine the linear regression and wavelet neural network techniques into a prediction method, which we call MLWNN, to forecast the cloud data center short-term workload. Third, we propose a heuristic energy efficient job scheduling with workload prediction solution, which is divided into resource management strategy and online energy efficient job scheduling algorithm. Our extensive simulation performance evaluation results clearly demonstrate that our proposed solution has good performance and is very suitable for low workload cloud data center.     

A strategy for scheduling reduce task based on intermediate data locality of the MapReduce

In this paper, researching on task scheduling is a way from the perspective of resource allocation and management to improve performance of Hadoop system. In order to save the network bandwidth resources in Hadoop cluster environment and improve the performance of Hadoop system, a ReduceTask scheduling strategy that based on data-locality is improved. In MapReduce stage, there are two main data streams in cluster network, they are slow task migration and remote copies of data. The two overlapping burst data transfer can easily become bottlenecks of the cluster network. To reduce the amount of remote copies of data, combining with data-locality, we establish a minimum network resource consumption model (MNRC). MNRC is used to calculate the network resources consumption of ReduceTask. Based on this model, we design a delay priority scheduling policy for the ReduceTask which is based on the cost of network resource consumption. Finally, MNRC is verified by simulation experiments. Evaluation results show that MNRC outperforms the saving cluster network resource by an average of 7.5% in heterogeneous.     

Task scheduling on computational Grids using Gravitational Search Algorithm

Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, Gravitational Search Algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules.     

Studies on the cloud point and micellar phases of triton X-100 using 220 MHz proton magnetic resonance techniques

High-resolution proton magnetic resonance techniques at 220 MHz were employed to follow the transformation of Triton X-100 between its micellar and cloud point phases as a function of temperature. The results obtained suggest that while a phase separation occurs rather sharply above the cloud point, the increase in temperature below the cloud point is accompanied by the gradual formation of very large structures suspended in the aqueous phase. The proton magnetic resonance studies show that the separation of phases, which occurs above the cloud point, appears to be accompanied by a fractionation of the polydisperse detergent. In addition, a lowering of the cloud point of Triton X-100 by dipalmitoyl phosphatidylcholine was observed by visual means and the results are reported here.     

COMT Val158Met多态型青年在数字工作记忆任务中的视觉事件相关电位的研究

目的:针对不同COMT基因型健康青年被试,进行连续3-back任务1h共12Block,探讨健康成人数字工作记忆能力变化情况。方法:将112名健康青年分组抽取出18名不同基因型作为被试,利用视觉事件相关电位P3来观测被试连续工作记忆任务中COMT基因多态型与脑皮层电生理的关系。结果:Val/Val基因型的被试P3波幅显著高于Val/Met基因型(P<0.01),但和Met/Met基因型被试的波幅无差异。结论:Val/Met基因型被试关联着最差的工作记忆任务的成绩,被试者的P3波幅和3-back任务成绩成正相关。     

Influence of mutual arrangement of the electric dipole and the spatial nonuniformity of brain electrical conductivity on the solution of the direct task of electroencephalography using the method of finite elements

The results of comparing the solutions of the direct task of electroencephalography on a spherical model and a spherical model with one nonuniformity are discussed. The nonuniformity was simulated by two parabolas situated on the same axis of symmetry and crossing the boundary of the gray and the white matter. The region between the larger and the smaller parabolas had the physical characteristics of the gray matter, and the region inside the smaller parabola had the characteristics of the cerebrospinal fluid. The task was to find a combination of parameters (distance between the dipole and the nonuniformity, angle of rotation of the dipole relative to the nonuniformity, sizes of the dipole and the nonuniformity, etc.) that provides the maximum effect of the difference of potentials on the outer surface of the scalp in the spherical model with one nonuniformity and the spherical model. The influence of the points of grounding on the value of the effect was analyzed (ground only at the right ear and ground at both ears). The data obtained show that a maximum difference of potentials is reached at the positions of dipoles close to tangential relative to the scalp surface.