Cross-Layer Scheduling in Cloud Systems

Alkaff, Hilfi; Gupta, Indranil; Leslie, Luke M.

doi:10.1109/ic2e.2015.36

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…111-122 116 два наиболее важных параметра для высокопроизводительных вычислительных приложений -пропускная способность и задержка [14]. Другие работы в основном были направлены на оптимизацию пропускной способности (BASS [15], PYTHIA [16], CLS [17], ASETS [18]). Несмотря на то, что даже простой учет этих сетевых параметров при планировании задач позволил повысить производительность приложений, некоторые результаты показывают, что в отдельных случаях производительность приложений в SDN ухудшалась.…”

Section: Sdn и приложения Hpc-big Dataunclassified

“…QoS считается одним из существенных свойств, влияющих на производительность приложения, которое интенсивно использует коммуникационную сеть [17]. В контексте сети QoS рассматривается как способность обеспечения сервиса.…”

Section: качество обслуживания в Sdnunclassified

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Quality of Service in Software Defined Networks for Scientific Applications: Opportunities and Challenges

Лосано-Риcк¹,

Ривера-Родригес²,

Ньето-Иполито³

et al. 2021

Proceedings of ISP RAS

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Scientific applications require to process, analyze and transfer large volumes of data in the shortest possible time from distributed data sources. In order to improve their performance, it is necessary to provide them with specific QoS parameters. On the other hand, SDN is presented as a new paradigm of communications networks that facilitates the management of the communications infrastructure and consequently allows to dynamically incorporate QoS parameters to the applications running in this type of network. With both these paradigms in mind, we conducted this research to answer the following questions: Do scientific applications that are running in an SDN-Enabled distributed data centers improve their performance? Do they consider network QoS parameters for job scheduling? The methodology used was to consult articles in specialized databases containing the keywords SDN and for scientific applications: HPC and Big Data. Then, we analyzed the articles where these keywords intersect with some of the parameters related to QoS in communications networks. Also, we reviewed QoS proposals in SDN to identify the advances in this research area. The results of this paper are: i) QoS is an open issue to incorporate in scientific applications that are running in an SDN ii) we identified the challenges to join both these paradigms, and iii) we present a strategy to provide QoS to scientific applications that are being executed among SDN-Enabled distributed data centers.

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Section: Sdn и приложения Hpc-big Dataunclassified

Section: качество обслуживания в Sdnunclassified

Quality of Service in Software Defined Networks for Scientific Applications: Opportunities and Challenges

Лосано-Риcк¹,

Ривера-Родригес²,

Ньето-Иполито³

et al. 2021

Proceedings of ISP RAS

View full text Add to dashboard Cite

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“…In CLS (Cross-Layer scheduling) [17], this proposal includes the application-level job scheduler interacting with the SDN scheduler which is responsible for assigning network links. The objective of this work is to allocate the tasks and a selection of links that can achieve high performance for the application.…”

Section: Related Workmentioning

confidence: 99%

“…As a summary of the reviewed proposals, GSBT [13] gets available routes in the SDN and calculates the shortest path for use in the reduction tree for MPI communications process without considering traffic congestion, delay, and packet loss that can exist in some routes. BASS [15], ASETS [16], CLS [17], and BWARE [18] consider the network bandwidth parameter in their application job scheduling process.…”

Section: Related Workmentioning

confidence: 99%

QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics

et al. 2020

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When Internet of Things (IoT) big data analytics (BDA) require to transfer data streams among software defined network (SDN)-based distributed data centers, the data flow forwarding in the communication network is typically done by an SDN controller using a traditional shortest path algorithm or just considering bandwidth requirements by the applications. In BDA, this scheme could affect their performance resulting in a longer job completion time because additional metrics were not considered, such as end-to-end delay, jitter, and packet loss rate in the data transfer path. These metrics are quality of service (QoS) parameters in the communication network. This research proposes a solution called QoSComm, an SDN strategy to allocate QoS-based data flows for BDA running across distributed data centers to minimize their job completion time. QoSComm operates in two phases: (i) based on the current communication network conditions, it calculates the feasible paths for each data center using a multi-objective optimization method; (ii) it distributes the resultant paths among data centers configuring their openflow Switches (OFS) dynamically. Simulation results show that QoSComm can improve BDA job completion time by an average of 18%.

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“…Hedera [34] and MicroTE [35] manage network flows using centralized network-wide scheduler in order to increase network throughput. Recently, Alkaff et al [36] proposed to adopt SDN-based traffic management for optimizing cloud applications. They focused on the coordination between the application layer's task placement with network layer's route/path selection strategies.…”

Section: B Sdn-based Traffic Managementmentioning

confidence: 99%

On SDN-Enabled Online and Dynamic Bandwidth Allocation for Stream Analytics

Aljoby

Wang

et al. 2019

IEEE J. Select. Areas Commun.

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Data communication in cloud-based distributed stream data analytics often involves a collection of parallel and pipelined TCP flows. As the standard TCP congestion control mechanism and its variants are designed for achieving "fairness" among competing flows and are agnostic to the application layer contexts, the bandwidth allocation among a set of TCP flows traversing bottleneck links often leads to sub-optimal application-layer performance measures, e.g., stream processing throughput or average tuple complete latency. Motivated by this and enabled by the rapid development of the software-defined networking (SDN) techniques, in this paper, we re-investigate the design space of the bandwidth allocation problem and propose a cross-layer framework which utilizes the instantaneous information obtained from the application layer and provides on-thefly and dynamic bandwidth adjustment algorithms for assisting the stream analytics applications achieving better performance during the runtime. We implement a prototype cross-layer bandwidth allocation framework based on a popular open-source distributed stream processing platform, Apache Storm, together with the OpenDaylight controller, and carry out extensive experiments with real-world analytical workloads on top of a local cluster consisting of ten workstations interconnected by a SDN-enabled fat-tree like testbed. The experiment results clearly validate the effectiveness and efficiency of our proposed framework and algorithms. Finally, we leverage the proposed cross-layer SDN framework and introduce an exemplary mechanism for bandwidth sharing and performance reasoning among multiple active applications and show a case of a point solution on how to approximate application-level fairness. optimizing network activity is important to improving and delivering real-time responses in these applications. In this context, there has been flurry of research attempts toward optimizing streaming applications. While in large part successful, however, their focus mainly has centered to schedule and provision computation resources of the applications or limited to minimizing traffic across the network. Hence, these solutions have largely overlooked allocation and provision of network bandwidth. As a result, they are either suboptimal in optimizing network transfer [9], [11], [12], or assuming the network with sufficient bandwidth resource [13].In current stream processing frameworks, the share of network bandwidth has left to the mercy of the underlying transport mechanisms (e.g., TCP [14], DCTCP [15]).Nonetheless, such mechanisms are designed mainly for end-to-end data delivery in an application agnostic manner,

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Cross-Layer Scheduling in Cloud Systems

Cited by 10 publications

References 47 publications

Quality of Service in Software Defined Networks for Scientific Applications: Opportunities and Challenges

Quality of Service in Software Defined Networks for Scientific Applications: Opportunities and Challenges

QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics

On SDN-Enabled Online and Dynamic Bandwidth Allocation for Stream Analytics

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