@inproceedings{MTMT:34217400,
	title = {An Auto-Scaling Framework for Predictable Open Source FaaS Function Chains},
	url = {https://m2.mtmt.hu/api/publication/34217400},
	author = {Balla, Dávid and Maliosz, Markosz and Simon, Csaba},
	booktitle = {2023 IEEE 16th International Conference on Cloud Computing (CLOUD)},
	doi = {10.1109/CLOUD60044.2023.00034},
	volume = {2023-July},
	unique-id = {34217400},
	year = {2023},
	pages = {229-237}
}

@inproceedings{MTMT:34048348,
	title = {Deterministic Local Cloud for Industrial Applications},
	url = {https://m2.mtmt.hu/api/publication/34048348},
	author = {Maliosz, Markosz and Moldován, István and Máté, Miklós and Simon, Csaba and Harmatos, J.},
	booktitle = {2023 IEEE 19th International Conference on Factory Communication Systems (WFCS)},
	doi = {10.1109/WFCS57264.2023.10144237},
	volume = {2023-April},
	unique-id = {34048348},
	year = {2023},
	pages = {1-8}
}

@inproceedings{MTMT:33364749,
	title = {Predictable Open Source FaaS Function Chains},
	url = {https://m2.mtmt.hu/api/publication/33364749},
	author = {Balla, Dávid and Maliosz, Markosz and Simon, Csaba},
	booktitle = {2022 IEEE 11th International Conference on Cloud Networking (CloudNet)},
	doi = {10.1109/CloudNet55617.2022.9978820},
	unique-id = {33364749},
	year = {2022},
	pages = {186-194}
}

@inproceedings{MTMT:33115816,
	title = {Towards a Predictable Open Source FaaS},
	url = {https://m2.mtmt.hu/api/publication/33115816},
	author = {Balla, Dávid and Maliosz, Markosz and Simon, Csaba},
	booktitle = {NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium},
	doi = {10.1109/NOMS54207.2022.9789777},
	unique-id = {33115816},
	abstract = {Auto-scaling is the capability of Function as a Service systems, that supports dynamic scaling of the function instances according to the incoming load. Auto-scalers fire scaling events when a certain threshold is exceeded. However, if this threshold is not set properly, the function can suffer from under or over-provisioning. In this paper we introduce an autoscaling solution for compute intensive functions that calculates the scaling threshold according to the user needs and keeps the completion times predictable even when the function is scaled out. The scaling threshold is given by a simulator that determines the completion time distribution of the function for a given load. We also show which load-balancing algorithm is recommended to use for our auto-scaler. We compare our auto-scaler to existing ones implemented in open source serverless projects.},
	keywords = {Computer Science, Information Systems; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; FaaS; simulator; Auto-scaling},
	year = {2022}
}

@article{MTMT:33108393,
	title = {Asynchronous Time-Aware Shaper for Time-Sensitive Networking},
	url = {https://m2.mtmt.hu/api/publication/33108393},
	author = {Máté, Miklós and Simon, Csaba and Maliosz, Markosz},
	doi = {10.1007/s10922-022-09688-y},
	journal-iso = {J NETW SYST MANAG},
	journal = {JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT},
	volume = {30},
	unique-id = {33108393},
	issn = {1064-7570},
	abstract = {The Time Sensitive Networking Task Group of the IEEE 802.1 Working Group has developed a number of enhancements to the priority queueing architecture used by Ethernet. Perhaps the most notable one is the time-aware shaper (TAS), which can perfectly isolate priority classes in time with periodically scheduled traffic gates, thereby enabling ultra low latency applications to coexist with lower priority traffic on the same network. It requires near-perfect clock synchronization across the entire network, though, and a central network controller to calculate the timings for the traffic gates, based on known propagation delays on the network links. As a cheaper alternative, we have developed the asynchronous TAS that needs no clock synchronization, network planning, or a central controller. It uses local processes at each switch port to track the high priority streams, predict the arrival times of their next frames, and control the traffic gates such that the low priority streams are not interfering. We show how a simple predictor with minimal computation requirements can be used to track the high priority streams, and we demonstrate via simulations that this gating mechanism can protect the high priority traffic from interference.},
	year = {2022},
	eissn = {1573-7705}
}

@inproceedings{MTMT:32761546,
	title = {Resource Reservation in DetNet with AVB},
	url = {https://m2.mtmt.hu/api/publication/32761546},
	author = {Simon, Csaba and Máté, Miklós and Maliosz, Markosz},
	booktitle = {2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)},
	doi = {10.23919/EECSI53397.2021.9624286},
	unique-id = {32761546},
	abstract = {The Deterministic Networking (DetNet) working group of the Internet Engineering Task Force (IETF) is developing methods for building large networks with bounded latency, zero packet loss, and high reliability out of existing networking technologies. To provide strong end-to-end Quality of Service guarantees across multiple network domains, DetNet has to perform joint layer 3 and layer 2 resource reservation. The prime candidate for layer 2 technology in DetNet is Ethernet with Time-Sensitive Networking (TSN) extensions, but it is too new, and not yet fully standardized. In this paper we explore the possibilities of using Audio-Video Bridging (AVB), the precursor of TSN, as the layer 2 medium in DetNet by integrating the AVB resource reservation protocol with layer 3 reservations across multi-domain networks. We show how to match the flow identifiers and the QoS descriptors across the domains, and review the signaling steps needed to establish the connection.},
	year = {2021},
	pages = {107-112}
}

@inproceedings{MTMT:32761545,
	title = {Asynchronous Time-Aware Shaper for Time-Sensitive Networking},
	url = {https://m2.mtmt.hu/api/publication/32761545},
	author = {Máté, Miklós and Simon, Csaba and Maliosz, Markosz},
	booktitle = {2021 17th International Conference on Network and Service Management (CNSM)},
	doi = {10.23919/CNSM52442.2021.9615545},
	unique-id = {32761545},
	abstract = {The Time Sensitive Networking task group of IEEE 802.1 has developed a number of enhancements to the priority queueing architecture used by Ethernet. Perhaps the most notable one is the Time-Aware Shaper, which can perfectly isolate priority classes in time with periodically scheduled traffic gates. It requires near-perfect clock synchronization across the entire network, though, and a central network controller to calculate the timings for the traffic gates. As a cheaper alternative, we developed the Asynchronous Time-Aware Shaper that needs no clock synchronization, or a central controller. It uses local processes at each switch port to track the high priority streams, predict the arrival times of their next frames, and control the traffic gates such that the low priority streams are not interfering.},
	keywords = {Engineering, Electrical & Electronic; Computer Science, Information Systems; Computer Science, Hardware & Architecture},
	year = {2021},
	pages = {565-571}
}

@article{MTMT:32584660,
	title = {Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and Vineyard Protection},
	url = {https://m2.mtmt.hu/api/publication/32584660},
	author = {Cinkler, Tibor and Nagy, Kristof and Simon, Csaba and Vida, Rolland and Rajab, Husam},
	doi = {10.1109/JSEN.2021.3134817},
	journal-iso = {IEEE SENS J},
	journal = {IEEE SENSORS JOURNAL},
	volume = {22},
	unique-id = {32584660},
	issn = {1530-437X},
	abstract = {For a wireless sensor network consisting of numerous sensors, spread over a large area with no direct power supply, energy efficiency is of paramount importance. As most power is consumed by the communication module, we should pay special attention to reduce communication needs as much as possible. The more data we send, the larger the power requirement of the sensor module. Preprocessing can help in reducing the amount of data to send. However, it also consumes energy. This paper focuses on this tradeoff between preprocessing, pre-filtering and preselecting of sensor data on one hand, and uploading of unprocessed and unfiltered raw data on the other hand, for the special case of protecting vineyards from starlings. We propose a two-phase decision mechanism based on machine learning: the less complex first phase is executed on the microcontroller of the sensor module, while the more complex, more accurate second phase is performed in the cloud. Individual noise sensors monitor the environment, and try to detect starling songs, using a simple, SVM-based classification. These sensors are grouped into clusters, through a mechanism similar to the well-known LEACH protocol, and signal to the current cluster-head the likelihood of starling presence. If several alerts are received to justify further investigation, the cluster-head asks the node with highest starling detection likelihood to upload a 1 s sound sample to the cloud. There, the more complex and more accurate second phase sound matching is performed, and the actuators deployed in the field are remotely triggered, if needed.},
	year = {2021},
	eissn = {1558-1748},
	pages = {11393-11404}
}

@{MTMT:32559369,
	title = {Estimating Function Completion Time Distribution in Open Source FaaS},
	url = {https://m2.mtmt.hu/api/publication/32559369},
	author = {Balla, Dávid and Maliosz, Markosz and Simon, Csaba},
	booktitle = {2021 IEEE 10th International Conference on Cloud Networking (CloudNet)},
	doi = {10.1109/CloudNet53349.2021.9657119},
	unique-id = {32559369},
	abstract = {Function as a Service (FaaS) is the newest stage of application virtualization. Several public cloud providers offer FaaS solutions, however, the open source community also embraced this technology. In this paper we introduce a Python based function run-time, applicable in open source FaaS platforms for latency sensitive compute intensive applications that reduces the maximum completion times by taking into account the number of CPU cores. We also present our simulator that estimates the distribution of the completion times for compute intensive functions, when our proposed function run-time is in use. We present the results of our simulator by using two compute intensive functions. We also show a scenario when the user function is not purely compute intensive.},
	year = {2021},
	pages = {65-71}
}

@{MTMT:32559015,
	title = {Performance Evaluation of Asynchronous FaaS},
	url = {https://m2.mtmt.hu/api/publication/32559015},
	author = {Balla, Dávid and Maliosz, Markosz and Simon, Csaba},
	booktitle = {2021 IEEE 14th International Conference on Cloud Computing (CLOUD)},
	doi = {10.1109/CLOUD53861.2021.00028},
	unique-id = {32559015},
	abstract = {Function as a Service (FaaS) is a novel but dynamically emerging field of cloud computing. The majority of the leading cloud service providers have their own FaaS platforms, however, the open source community has embraced this technology, therefore an increasing number of FaaS alternatives can be deployed for on-premise use-cases. FaaS systems support both synchronous and asynchronous function invocations. In this paper we examine the differences in performance and billing between the two invocation types in OpenFaaS, Kubeless, Fission and Knative by using a simple function chain containing echo functions and a more complex image and natural language processing chain, implemented in Python3. We also present our solution, the implementation of asynchronous function invocations by using the Redis key-value store. Finally, we show how asynchronous function invocations avoid the negative effects on the billing when functions are cold-started.},
	year = {2021},
	pages = {147-156}
}