| 1. |
- Obeso Duque, Aleksandra, et al.
(författare)
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A Qualitative Evaluation of Service Mesh-based Traffic Management for Mobile Edge Cloud
- 2022
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Ingår i: 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022. - : IEEE. - 9781665499576 - 9781665499569 ; , s. 210-219
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Konferensbidrag (refereegranskat)abstract
- Service mesh is getting widely adopted as the cloud-native mechanism for traffic management in microservice-based applications, in particular for generic IT workloads hosted in more centralized cloud environments. Performance-demanding applications continue to drive the decentralization of modern application execution environments, as in the case of mobile edge cloud. This paper presents a systematic and qualitative analysis of state-of-the-art service mesh to evaluate how suitable its design is for addressing the traffic management needs of performance-demanding application workloads hosted in a mobile edge cloud environment. With this analysis, we argue that today's dependability-centric service mesh design fails at addressing the needs of the different types of emerging mobile edge cloud workloads and motivate further research in the directions of performance-efficient architectures, stronger QoS guarantees and higher complexity abstractions of cloud-native traffic manage-ment frameworks.
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| 2. |
- Obeso Duque, Aleksandra, et al.
(författare)
-
Evaluating Service Mesh-based Traffic Management for Mobile Edge Cloud
- 2022
-
Konferensbidrag (refereegranskat)abstract
- Service mesh is getting more widely adopted as the cloud-native mechanism for traffic management in microservice-based applications, in particular for generic IT workloads hosted in more centralized cloud environments. Performance-demanding applications continue to drive the decentralization of modern application execution environments as in the case of mobile edge cloud.This paper presents a systematic analysis of state-of-the-art service mesh to evaluate how suitable this approach is at addressing the traffic management needs of performance-demanding application workloads hosted in a mobile edge cloud environment. With this analysis, we argue that today’s dependability-centric service mesh fails at addressing the needs of the different types of emerging workloads in mobile edge cloud and motivate further research in the directions of performance-efficient architectures, stronger QoS guarantees and higher complexity abstractions of cloud-native traffic management frameworks.
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| 3. |
- Rahmanian, Ali, et al.
(författare)
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Microsplit : efficient splitting of microservices on edge clouds
- 2022
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Ingår i: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC). - : IEEE. - 9781665486118 - 9781665486125 ; , s. 252-264
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Konferensbidrag (refereegranskat)abstract
- Edge cloud systems reduce the latency between users and applications by offloading computations to a set of small-scale computing resources deployed at the edge of the network. However, since edge resources are constrained, they can become saturated and bottlenecked due to increased load, resulting in an exponential increase in response times or failures. In this paper, we argue that an application can be split between the edge and the cloud, allowing for better performance compared to full migration to the cloud, releasing precious resources at the edge. We model an application's internal call-Graph as a Directed-Acyclic-Graph. We use this model to develop MicroSplit, a tool for efficient splitting of microservices between constrained edge resources and large-scale distant backend clouds. MicroSplit analyzes the dependencies between the microservices of an application, and using the Louvain method for community detection---a popular algorithm from Network Science---decides how to split the microservices between the constrained edge and distant data centers. We test MicroSplit with four microservice based applications in various realistic cloud-edge settings. Our results show that Microsplit migrates up to 60% of the microservices of an application with a slight increase in the mean-response time compared to running on the edge, and a latency reduction of up to 800% compared to migrating the entire application to the cloud. Compared to other methods from the State-of-the-Art, MicroSplit reduces the total number of services on the edge by up to five times, with minimal reduction in response times.
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| 4. |
- Rahmanian, Ali, et al.
(författare)
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RAVAS: interference-aware model selection and resource allocation for live edge video analytics
- 2023
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Ingår i: 2023 IEEE/ACM Symposium on Edge Computing (SEC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798400701238 ; , s. 27-39, s. 27-39
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Konferensbidrag (refereegranskat)abstract
- Numerous edge applications that rely on video analytics demand precise, low-latency processing of multiple video streams from cameras. When these cameras are mobile, such as when mounted on a car or a robot, the processing load on the shared edge GPU can vary considerably. Provisioning the edge with GPUs for the worst-case load can be expensive and, for many applications, not feasible. In this paper, we introduce RAVAS, a Real-time Adaptive stream Video Analytics System that enables efficient edge GPU sharing for processing streams from various mobile cameras. RAVAS uses Q-Learning to choose between a set of Deep Neural Network (DNN) models with varying accuracy and processing requirements based on the current GPU utilization and workload. RAVAS employs an innovative resource allocation strategy to mitigate interference during concurrent GPU execution. Compared to state-of-the-art approaches, our results show that RAVAS incurs 57% less compute overhead, achieves 41% improvement in latency, and 43% savings in total GPU usage for a single video stream. Processing multiple concurrent video streams results in up to 99% and 40% reductions in latency and overall GPU usage, respectively, while meeting the accuracy constraints.
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| 5. |
- Saleh Sedghpour, Mohammad Reza, 1989-, et al.
(författare)
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Hydragen : a microservice benchmark generator
- 2023
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Ingår i: 2023 IEEE 16th international conference on cloud computing (CLOUD). - : IEEE. - 9798350304817 - 9798350304824 ; , s. 189-200
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Konferensbidrag (refereegranskat)abstract
- Microservice-based architectures have become ubiq-uitous in large-scale software systems. Experimental cloud re-searchers constantly propose enhanced resource management mechanisms for such systems. These mechanisms need to be eval-uated using both realistic and flexible microservice benchmarks to study in which ways diverse application characteristics can affect their performance and scalability. However, current mi-croservice benchmarks have limitations including static compu-tational complexity, limited architectural scale, and fixed topology (i.e., number of tiers, fan-in, and fan-out characteristics).We therefore propose HydraGen, a tool that enables re-searchers to systematically generate benchmarks with different computational complexities and topologies, to tackle experimental evaluation of performance at scale for web-serving applications, with a focus on inter-service communication. To illustrate the potential of our open-source tool, we demonstrate how it can reproduce an existing microservice benchmark with preserved architectural properties. We also demonstrate how HydraGen can enrich the evaluation of cloud management systems based on a case study related to traffic engineering.
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