• Title/Summary/Keyword: cloud computing systems

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Kubernetes Scheduler Framework Implementation with Realtime Resource Monitoring (실시간 자원 모니터링을 활용한 쿠버네티스 스케줄러 프레임워크 구현)

  • Kim, Tae-Young;Lee, Jae-Ryun;Kim, Tae-Hyun;Chun, In-Geol;Park, Jeman;Jin, Sunggeun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.15 no.3
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    • pp.129-137
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    • 2020
  • Kubernetes is one of the most widely used tool in cloud computing environments. It m anages POD as a basic operational unit, providing a isolated environments through container tech nology. Basically, its scheduler properly allocates POD, considering the utilizations of CPUs, mem ories and volumes on hosts. In our work, we develop a customized scheduling framework additio nally considering network resources. In the framework, we can monitor the dynamic variations of resources and make it possible to utilize the resources for the scheduler. This framework offers not only observing necessary information but also visualizing the data to scheduler for providing convenience.

Study on Data Processing of the IOT Sensor Network Based on a Hadoop Cloud Platform and a TWLGA Scheduling Algorithm

  • Li, Guoyu;Yang, Kang
    • Journal of Information Processing Systems
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    • v.17 no.6
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    • pp.1035-1043
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    • 2021
  • An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

A Hadoop-based Multimedia Transcoding System for Processing Social Media in the PaaS Platform of SMCCSE

  • Kim, Myoungjin;Han, Seungho;Cui, Yun;Lee, Hanku;Jeong, Changsung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.11
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    • pp.2827-2848
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    • 2012
  • Previously, we described a social media cloud computing service environment (SMCCSE). This SMCCSE supports the development of social networking services (SNSs) that include audio, image, and video formats. A social media cloud computing PaaS platform, a core component in a SMCCSE, processes large amounts of social media in a parallel and distributed manner for supporting a reliable SNS. Here, we propose a Hadoop-based multimedia system for image and video transcoding processing, necessary functions of our PaaS platform. Our system consists of two modules, including an image transcoding module and a video transcoding module. We also design and implement the system by using a MapReduce framework running on a Hadoop Distributed File System (HDFS) and the media processing libraries Xuggler and JAI. In this way, our system exponentially reduces the encoding time for transcoding large amounts of image and video files into specific formats depending on user-requested options (such as resolution, bit rate, and frame rate). In order to evaluate system performance, we measure the total image and video transcoding time for image and video data sets, respectively, under various experimental conditions. In addition, we compare the video transcoding performance of our cloud-based approach with that of the traditional frame-level parallel processing-based approach. Based on experiments performed on a 28-node cluster, the proposed Hadoop-based multimedia transcoding system delivers excellent speed and quality.

Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm

  • Fahim, Youssef;Rahhali, Hamza;Hanine, Mohamed;Benlahmar, El-Habib;Labriji, El-Houssine;Hanoune, Mostafa;Eddaoui, Ahmed
    • Journal of Information Processing Systems
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    • v.14 no.3
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    • pp.569-589
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    • 2018
  • Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

Study on Program Partitioning and Data Protection in Computation Offloading (코드 오프로딩 환경에서 프로그램 분할과 데이터 보호에 대한 연구)

  • Lee, Eunyoung;Pak, Suehee
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.11
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    • pp.377-386
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    • 2020
  • Mobile cloud computing involves mobile or embedded devices as clients, and features small devices with constrained resource and low availability. Due to the fast expansion of smart phones and smart peripheral devices, researches on mobile cloud computing attract academia's interest more than ever. Computation offloading, or code offloading, enhances the performance of computation by migrating a part of computation of a mobile system to nearby cloud servers with more computational resources through wired or wireless networks. Code offloading is considered as one of the best approaches overcoming the limited resources of mobile systems. In this paper, we analyze the factors and the performance of code offloading, especially focusing on static program partitioning and data protection. We survey state-of-the-art researches on analyzed topics. We also describe directions for future research.

Performance Management Technique of Remote VR Service for Multiple Users in Container-Based Cloud Environments Sharing GPU (GPU를 공유하는 컨테이너 기반 클라우드 환경에서 다수의 사용자를 위한 원격 VR 서비스의 성능 관리 기법)

  • Kang, Jihun
    • KIPS Transactions on Computer and Communication Systems
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    • v.11 no.1
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    • pp.9-22
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    • 2022
  • Virtual Reality(VR) technology is an interface technology that is actively used in various audio-visual-based applications by showing users a virtual world composed of computer graphics. Since VR-based applications are graphic processing-based applications, expensive computing devices equipped with Graphics Processing Unit(GPU) are essential for graphic processing. This incurs a cost burden on VR application users for maintaining and managing computing devices, and as one of the solutions to this, a method of operating services in cloud environments is being used. This paper proposes a performance management technique to address the problem of performance interference between containers owing to GPU resource competition in container-based high-performance cloud environments in which multiple containers share a single GPU. The proposed technique reduces performance deviation due to performance interference, helping provide uniform performance-based remote VR services for users. In addition, this paper verifies the efficiency of the proposed technique through experiments.

Content Distribution for 5G Systems Based on Distributed Cloud Service Network Architecture

  • Jiang, Lirong;Feng, Gang;Qin, Shuang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.11
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    • pp.4268-4290
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    • 2015
  • Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.

Virtual Machine Provisioning Scheduling with Conditional Probability Inference for Transport Information Service in Cloud Environment (클라우드 환경의 교통정보 서비스를 위한 조건부 확률 추론을 이용한 가상 머신 프로비저닝 스케줄링)

  • Kim, Jae-Kwon;Lee, Jong-Sik
    • Journal of the Korea Society for Simulation
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    • v.20 no.4
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    • pp.139-147
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    • 2011
  • There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

  • Okafor, Kennedy Chinedu;Longe, Omowunmi Mary
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.7
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    • pp.2257-2285
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    • 2022
  • Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

LDBAS: Location-aware Data Block Allocation Strategy for HDFS-based Applications in the Cloud

  • Xu, Hua;Liu, Weiqing;Shu, Guansheng;Li, Jing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.1
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    • pp.204-226
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    • 2018
  • Big data processing applications have been migrated into cloud gradually, due to the advantages of cloud computing. Hadoop Distributed File System (HDFS) is one of the fundamental support systems for big data processing on MapReduce-like frameworks, such as Hadoop and Spark. Since HDFS is not aware of the co-location of virtual machines in the cloud, the default scheme of block allocation in HDFS does not fit well in the cloud environments behaving in two aspects: data reliability loss and performance degradation. In this paper, we present a novel location-aware data block allocation strategy (LDBAS). LDBAS jointly optimizes data reliability and performance for upper-layer applications by allocating data blocks according to the locations and different processing capacities of virtual nodes in the cloud. We apply LDBAS to two stages of data allocation of HDFS in the cloud (the initial data allocation and data recovery), and design the corresponding algorithms. Finally, we implement LDBAS into an actual Hadoop cluster and evaluate the performance with the benchmark suite BigDataBench. The experimental results show that LDBAS can guarantee the designed data reliability while reducing the job execution time of the I/O-intensive applications in Hadoop by 8.9% on average and up to 11.2% compared with the original Hadoop in the cloud.