| 1. |
- Al-Dulaimy, Auday, et al.
(författare)
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MultiScaler : A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications
- 2022
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Ingår i: IEEE Transactions on Cloud Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-7161. ; 10:4, s. 2769-2786
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Tidskriftsartikel (refereegranskat)abstract
- Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called MultiScaler , to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. MultiScaler is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the MultiScaler approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, MultiScaler produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.
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| 2. |
- HoseinyFarahabady, M. Reza, et al.
(författare)
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Low Latency Execution Guarantee Under Uncertainty in Serverless Platforms
- 2022
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Ingår i: Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. - Cham : Springer. - 9783030967727 - 9783030967710 ; , s. 324-335
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Konferensbidrag (refereegranskat)abstract
- Serverless computing recently emerged as a new run-time paradigm to disentangle the client from the burden of provisioning physical computing resources, leaving such difficulty on the service provider's side. However, an unsolved problem in such an environment is how to cope with the challenges of executing several co-running applications while fulfilling the requested Quality of Service (QoS) level requested by all application owners. In practice, developing an efficient mechanism to reach the requested performance level (such as p-99 latency and throughput) is limited to the awareness (resource availability, performance interference among consolidation workloads, etc.) of the controller about the dynamics of the underlying platforms. In this paper, we develop an adaptive feedback controller for coping with the buffer instability of serverless platforms when several collocated applications are run in a shared environment. The goal is to support a low-latency execution by managing the arrival event rate of each application when shared resource contention causes a significant throughput degradation among workloads with different priorities. The key component of the proposed architecture is a continues management of server-side internal buffers for each application to provide a low-latency feedback control mechanism based on the requested QoS level of each application (e.g., buffer information) and the worker nodes throughput. The empirical results confirm the response stability for high priority workloads when a dynamic condition is caused by low priority applications. We evaluate the performance of the proposed solution with respect to the response time and the QoS violation rate for high priority applications in a serverless platform with four worker nodes set up in our in-house virtualized cluster. We compare the proposed architecture against the default resource management policy in Apache OpenWhisk which is extensively used in commercial serverless platforms. The results show that our approach achieves a very low overhead (less than 0.7%) while it can improve the p-99 latency of high priority applications by 64%, on average, in the presence of dynamic high traffic conditions.
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| 3. |
- HoseinyFarahabady, M. Reza, et al.
(författare)
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QSpark : Distributed Execution of Batch & Streaming Analytics in Spark Platform
- 2021
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Ingår i: 2021 IEEE 20TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA). - : IEEE. - 9781665495509
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Konferensbidrag (refereegranskat)abstract
- A significant portion of research work in the past decade has been devoted on developing resource allocation and task scheduling solutions for large-scale data processing platforms. Such algorithms are designed to facilitate deployment of data analytic applications across either conventional cluster computing systems or modern virtualized data-centers. The main reason for such a huge research effort stems from the fact that even a slight improvement in the performance of such platforms can bring a considerable monetary savings for vendors, especially for modern data processing engines that are designed solely to perform high throughput or/and low-latency computations over massive-scale batch or streaming data. A challenging question to be yet answered in such a context is to design an effective resource allocation solution that can prevent low resource utilization while meeting the enforced performance level (such as 99-th latency percentile) in circumstances where contention among applications to obtain the capacity of shared resources is a non negligible performance-limiting parameter. This paper proposes a resource controller system, called QSpark, to cope with the problem of (i) low performance (i.e., resource utilization in the batch mode and p-99 response time in the streaming mode), and (ii) the shared resource interference among collocated applications in a multi-tenancy modern Spark platform. The proposed solution leverages a set of controlling mechanisms for dynamic partitioning of the allocation of computing resources, in a way that it can fulfill the QoS requirements of latency-critical data processing applications, while enhancing the throughput for all working nodes without reaching their saturation points. Through extensive experiments in our in-house Spark cluster, we compared the achieved performance of proposed solution against the default Spark resource allocation policy for a variety of Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) applications. Experimental results show the effectiveness of the proposed solution by reducing the p-99 latency of high priority applications by 32% during the burst traffic periods (for both batch and stream modes), while it can enhance the QoS satisfaction level by 65% for applications with the highest priority (compared with the results of default Spark resource allocation strategy).
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| 4. |
- Hoseinyfarahabady, M. Reza, et al.
(författare)
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Toward designing a dynamic CPU cap manager for timely dataflow platforms
- 2018
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Ingår i: HPC '18 Proceedings of the High Performance Computing Symposium. - : Association for Computing Machinery (ACM). - 9781510860162 ; , s. 60-70
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Konferensbidrag (refereegranskat)abstract
- In this work, we propose a control-based solution for the problem of CPU resource allocation in data-flow platform that considers the degradation of performance caused by running concurrent data-flow processes. Our aim is to cut the QoS violation incidents for applications belonging to the highest QoS class. The performance of the proposed solution is bench-marked with the famous round robin algorithm. The experimental results confirms that the proposed algorithm can decrease the latency of processing data records for applications by 48% compared to the round robin policy.
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| 5. |
- Reza Hoseinyfarahabady, M., et al.
(författare)
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Q-Flink : A QoS-Aware Controller for Apache Flink
- 2020
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Ingår i: Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728160955 ; , s. 629-638
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Konferensbidrag (refereegranskat)abstract
- Modern stream-data processing platforms are required to execute processing pipelines over high-volume, yet high-velocity, datasets under tight latency constraints. Apache Flink has emerged as an important new technology of large-scale platform that can distribute processing over a large number of computing nodes in a cluster (i.e., scale-out processing). Flink allows application developers to design and execute queries over continuous raw-inputs to analyze a large amount of streaming data in a parallel and distributed fashion. To increase the throughput of computing resources in stream processing platforms, a service provider might be tempted to use a consolidation strategy to pack as many processing applications as possible on the working nodes, with the hope of increasing the total revenue by improving the overall resource utilization. However, there is a hidden trap for achieving such a higher throughput solely by relying on an interference-oblivious consolidation strategy. In practice, collocated applications in a shared platform can fiercely compete with each others for obtaining the capacity of shared resources (e.g., cache and memory bandwidth) which in turn can lead to a severe performance degradation for all consolidated workloads.This paper addresses the shared resource contention problem associated with the auto-resource controlling mechanism of Apache Flink engine running across a distributed cluster. A controlling strategy is proposed to handle scenarios in which stream processing applications may have different quality of service (QoS) requirements while the resource interference is considered as the key performance-limiting parameter. The performance evaluation is carried out by comparing the proposed controller with the default Flink resource allocation strategy in a testbed cluster with total 32 Intel Xeon cores under different workload traffic with up to 4000 streaming applications chosen from various benchmarking tools. Experimental results demonstrate that the proposed controller can successfully decrease the average latency of high priority applications by 223% during the burst traffic while maintaining the requested QoS enforcement levels.
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