| 1. |
- Al-Dulaimy, Auday, et al.
(författare)
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BWSLICER : A bandwidth slicing framework for cloud data centers
- 2020
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Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 112, s. 767-784
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Tidskriftsartikel (refereegranskat)abstract
- Bandwidth allocation is an important and influential factor in enhancing the performance of the data centers' nodes. In this paper we propose bwSlicer, a framework for bandwidth slicing in cloud data centers, that sheds light on the virtues of effective dynamic bandwidth allocation on improving the system performance and energy efficiency. Three algorithms are investigated to deal with this issue. In the first algorithm, called Fair Bandwidth Reallocation (FBR), two virtual machines co-hosted on the same node conditionally exchange bandwidth slices based on their requirements. The second algorithm, called Required Bandwidth Allocation (RBA), periodically monitors the co-hosted virtual machines and adds/removes bandwidth slices for each of them based on their bandwidth utilization. The third algorithm, called Divide Bandwidth Reallocation (DBR), divides the bandwidth of the virtual machine into slices once it finishes its execution, and distributes the slices among the co-hosted running virtual machines according to a specific policy. The proposed bandwidth slicing algorithms are emulated in a virtualized networking environment using the Mininet network emulator. The emulation results demonstrated a promising improvement ratio in execution time and energy consumption reaching up to 30%. These results present a call for action for further research into bandwidth slicing and reallocation as a viable complement to other energy-saving techniques for enhancing the energy consumption in cloud data centers.
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| 2. |
- Al-Dulaimy, Auday, et al.
(författare)
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Introduction to edge computing
- 2020
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Ingår i: Edge Computing. - : Institution of Engineering and Technology. - 9781785619410
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Bokkapitel (refereegranskat)abstract
- Edge computing is the model that extends cloud computing services to the edge of the network. This model aims to move decision-making operations as close as possible to data sources since it acts as an intermediate layer connecting cloud data centres to edge devices/sensors. Transferring all the data from the network edge to the cloud data centres for processing may create a latency problem and outstrip the network's bandwidth capacity. To resolve this issue, it might be best to process data closer to the devices/sensors. This chapter will take a deep dive into edge computing, its applications, and the existing challenges related to this model.
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| 3. |
- Al-Dulaimy, Auday, et al.
(författare)
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LOOPS : A Holistic Control Approach for Resource Management in Cloud Computing
- 2021
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Ingår i: ICPE 2021 - Proceedings of the ACM/SPEC International Conference on Performance Engineering. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450381949 ; , s. 117-124
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Konferensbidrag (refereegranskat)abstract
- In cloud computing model, resource sharing introduces major benefits for improving resource utilization and total cost of ownership, but it can create technical challenges on the running performance. In practice, orchestrators are required to allocate sufficient physical resources to each Virtual Machine (VM) to meet a set of predefined performance goals. To ensure a specific service level objective, the orchestrator needs to be equipped with a dynamic tool for assigning computing resources to each VM, based on the run-Time state of the target environment. To this end, we present LOOPS, a multi-loop control approach, to allocate resources to VMs based on the service level agreement (SLA) requirements and the run-Time conditions. LOOPS is mainly composed of one essential unit to monitor VMs, and three control levels to allocate resources to VMs based on requests from the essential node. A tailor-made controller is proposed with each level to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one host to another. The three levels work together to meet the required SLA. The experimental results have shown that the proposed approach can meet applications' performance goals by assigning the resources required by cloud-based applications.
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| 4. |
- 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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| 5. |
- Alizadeh Noghani, Kyoomars, et al.
(författare)
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Multi-Objective genetic algorithm for fast service function chain reconfiguration
- 2023
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Ingår i: IEEE Transactions on Network and Service Management. - : Institute of Electrical and Electronics Engineers (IEEE). - 1932-4537. ; 20:3, s. 3501-3522
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Tidskriftsartikel (refereegranskat)abstract
- The optimal placement of virtual network functions (VNFs) improves the overall performance of servicefunction chains (SFCs) and decreases the operational costs formobile network operators. To cope with changes in demands,VNF instances may be added or removed dynamically, resourceallocations may be adjusted, and servers may be consolidated.To maintain an optimal placement of SFCs when conditionschange, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, suchreconfigurations may lead to stress on the VNF infrastructure,which may cause service degradation. On the other hand, notchanging the placement may lead to suboptimal operation,and servers and links may become congested or underutilized,leading to high operational costs. In this paper, we investigatethe trade-off between the reconfiguration of SFCs and theoptimality of their new placement and service-flow routing. Wedevelop a multi-objective genetic algorithm that explores thePareto front by balancing the optimality of the new placementand the cost to achieve it. Our numerical evaluations show thata small number of reconfigurations can significantly reduce theoperational cost of the VNF infrastructure. In contrast, toomuch reconfiguration may not pay off due to high costs. Webelieve that our work provides an important tool that helpsnetwork providers to plan a good reconfiguration strategy fortheir service chains.
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| 6. |
- Aslanpour, M. S., et al.
(författare)
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AutoScaleSim : A simulation toolkit for auto-scaling Web applications in clouds
- 2021
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Ingår i: Simulation (San Diego, Calif.). - : Elsevier. - 1569-190X .- 1878-1462. ; 108
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Tidskriftsartikel (refereegranskat)abstract
- Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges on either real cloud platforms or custom test-beds. Challenges include – but not limited to – deployment impediments, the complexity of setting parameters, and most importantly, the cost of hosting and testing Web applications on a massive scale. Hence, simulation is presently one of the most popular evaluation solutions to overcome these obstacles. Existing simulators, however, fail to provide support for hosting, deploying and subsequently auto-scaling of Web applications. In this paper, we introduce AutoScaleSim, which extends the existing CloudSim simulator, to support auto-scaling of Web applications in cloud environments in a customizable, extendable and scalable manner. Using AutoScaleSim, the cloud community can freely implement/evaluate policies for all four phases of auto-scaling mechanisms, that is, Monitoring, Analysis, Planning and Execution. AutoScaleSim can also be used for evaluating load balancing algorithms similarly. We conducted a set of experiments to validate and carefully evaluate the performance of AutoScaleSim in a real cloud platform, with a wide range of performance metrics.
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| 7. |
- Bhamare, Deval, et al.
(författare)
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IntOpt : In-band Network Telemetry optimization framework to monitor network slices using P4
- 2022
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Ingår i: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 216
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.
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| 8. |
- Chahed, Hamza, et al.
(författare)
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AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications
- 2023
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Ingår i: Internet of Things. - : Elsevier. - 2542-6605. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases.
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| 9. |
- Deng, Shuiguang, et al.
(författare)
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Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems : A Reinforcement Learning Method
- 2020
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 16:9, s. 6103-6113
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Tidskriftsartikel (refereegranskat)abstract
- Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.
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| 10. |
- Deng, Shuiguang, et al.
(författare)
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Optimal Application Deployment in Resource Constrained Distributed Edges
- 2021
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 20:5, s. 1907-1923
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Tidskriftsartikel (refereegranskat)abstract
- The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.
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