Habib Ben Abdallah, Afeez Adewale Sanni, Krunal Thummar, Talal Halabi
{"title":"Online Energy-efficient Resource Allocation in Cloud Computing Data Centers","authors":"Habib Ben Abdallah, Afeez Adewale Sanni, Krunal Thummar, Talal Halabi","doi":"10.1109/ICIN51074.2021.9385557","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385557","url":null,"abstract":"Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for a smaller cost. Data centers are no exception to this rule as their energy use represents a large portion of the global consumption, and it is needless to say that they ought to perform optimally while being eco-friendly in order to preserve natural resources as much as possible while providing a high quality service for the users. In this paper, we propose an efficient algorithm for allocating users to a pool of servers in an energy-efficient way. Our allocation model emphasizes the critical importance of nondominant resource types such as memory, which usually tend to be wasted by homogeneous allocation approaches. We show that the performance of the algorithm makes it worthy of being used in real-time environments where split-second decisions must be made. We compare our algorithm to the most well-known metaheuristics used in operations research and we show that they do not provide a significant improvement in a reasonable time.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"67 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132237469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Liscano, Samridhi, Akramul Azim, Nahid Hasan Khan, A. Abedin, Brian Pulito, Yee-Kang Chang
{"title":"Supporting SIP Port Mapping and RTP Affinity Constraints in Container Orchestration Environments","authors":"R. Liscano, Samridhi, Akramul Azim, Nahid Hasan Khan, A. Abedin, Brian Pulito, Yee-Kang Chang","doi":"10.1109/ICIN51074.2021.9385534","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385534","url":null,"abstract":"The use of container-oriented orchestration platforms such as Kubernetes, is becoming more popular due to their ability to hide from developers the deployment details of the application and the capability of these platforms to support autoscaling and failure recovery. Most container orchestration environments have focused on supporting the HTTP protocol and are challenging to use in protocols such as SIP and RTP. When SIP/RTP services are deployed in the Kubernetes environment, failures related to port mapping and affinity occur. These failures are reflected as packets sent to the non-existent ports in either the overlay or underlay network in Kubernetes. This paper details these failures and presents solutions for resolving the SIP/RTP port mapping and the RTP affinity constraint failures. The SIP/RTP port mapping failure is solved by correctly mapping the internal SIP/RTP ports to those exposed externally by Kubernetes while the RTP affinity constraint solution leverages the concept of the creation of unique endpoints and labels for each RTP service port opened for a SIP call. The paper also offers some insight into the challenges faced in replicating the SIP constraint affinity failure. These solutions have been implemented in a Kubernetes aware SIP Proxy (SIP-K8S-Proxy) that supports port mapping and managing of the RTP affinity as well. The overhead associated with implementing this solution has been computed to be about 2 seconds in the establishment of a SIP call.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115848903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Traffic Analysis in Support of Hybrid SDN Campus Architectures for Enhanced Cybersecurity","authors":"William Brockelsby, R. Dutta","doi":"10.1109/ICIN51074.2021.9385530","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385530","url":null,"abstract":"The scale and complexity of campus networks continues to accelerate due to recent paradigms such as the Internet of Things (IoT) resulting in a heightened awareness of the need for enhanced cybersecurity. Traditional cybersecurity approaches such as the placement of firewalls and other policy enforcement mechanisms at strategic choke points effectively divide the network into zones and are unable to regulate intrazone host-to-host communication. This traditional approach introduces significant risk as there is little in place to prevent the horizontal propagation of malware or other unwanted traffic within a given zone. In this paper we explore approaches for improving cybersecurity in campus networks by analyzing contemporary campus traffic patterns and propose several architectural enhancements in light of these patterns which introduce strategically placed hardware or hardware-accelerated software data planes which are evaluated from performance and effectiveness perspectives.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126024757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comparing Feature Extraction techniques using SVM for Early Fault Classification in NFV context","authors":"Arij Elmajed, Frédéric Faucheux","doi":"10.1109/ICIN51074.2021.9385526","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385526","url":null,"abstract":"Networks are adopting virtualization techniques and thus, become large distributed software-driven systems. Ensuring Quality of Service (QoS) in such complex environments is critical and arduous especially now. We need to detect and correct expeditiously the issues as well as to understand systems behavior i.e. need for Root Cause Analysis. In this paper, we propose a comparative study of two Feature Extraction (FE) approaches for Early Fault Classification combined with two Support Vector Machine (SVM) algorithms while having preliminary symptoms in a Network Function Virtualization (NFV) based environment. We use data generated with a stimulus-based approach in such a context, and we compare two existing FE techniques in combination with SVM. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are applied to features for early fault classification. LDA in combination with SVM leads to an accuracy of 90%.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124606660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Fernández, L. Contreras, Ricardo Flores Moyano, Sandra García
{"title":"NFV/SDN Based Multiple Upstream Interfaces Multicast Proxy Service","authors":"D. Fernández, L. Contreras, Ricardo Flores Moyano, Sandra García","doi":"10.1109/ICIN51074.2021.9385529","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385529","url":null,"abstract":"Traditional IP multicast proxies only consider a single upstream interface per proxy, thus limiting its applicability to some presently demanded network scenarios. Recently, extensions have been proposed to add support for multiple upstream interfaces, enabling new multicast distribution architectures that improve the multicast distribution reliability and are suitable to be applied to demanded use cases like provider migration or merging scenarios. This paper describes the work-in-progress activities around the development of a proof-of-concept SDN based implementation of a multiple upstream interfaces multicast proxy, capable of being controlled through a centralized SDN controller and deployable as a VNF.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127305592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"VNF Placement Problem: A Multi-Tenant Intent-Based Networking Approach","authors":"Aris Leivadeas, M. Falkner","doi":"10.1109/ICIN51074.2021.9385553","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385553","url":null,"abstract":"Network Function Virtualization (NFV) has revolutionized the way networking services are offered and deployed. Moving away from a rigid and hardware-centric approach, where expensive and dedicated network components are used, NFV is now leveraging standard x86 servers, where softwarized images of network functions (NFs) can be hosted as Virtual Machines (VNFs) or containers (CNFs). However, in terms of deploying, configuring, and interconnecting these softwarized images, a lot of manual intervention is required. To this end, the Intent-Based Networking (IBN) paradigm has emerged, which has as a goal to automate the network configuration by translating a high-level and abstract request of a network service into a detailed policy description. Usually, IBN and NFV are studied separately, even though in reality they are highly correlated and can benefit from each other. In particular, network services can be expressed as abstract service requirements from the users, where through an IBN System (IBNS) will be translated into specific network policies and a VNF/CNF deployment solution, called VNF Placement solution. Accordingly, in this paper, we aim to combine these two technologies together in order to automate the deployment of the VNFs in a Cloud-based infrastructure, while supporting multitenancy and intent refinement. Our results reveal that an IBN-based VNF placement solution can successfully offer network services, expressed as user intents, in such a way that the network services are automatically configured according to the quality of service and security requirements included in the intent.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130619622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ashima Chawla, P. Babu, Trushnesh Gawande, Erik Aumayr, P. Jacob, Sheila Fallon
{"title":"Intelligent Monitoring of IoT Devices using Neural Networks","authors":"Ashima Chawla, P. Babu, Trushnesh Gawande, Erik Aumayr, P. Jacob, Sheila Fallon","doi":"10.1109/ICIN51074.2021.9385543","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385543","url":null,"abstract":"The Internet of Things (IoT) has seen expeditious growth in recent times with 7 billion connected devices in 2020, thus leading to the vital importance of real-time monitoring of IoT devices. Through this paper, we demonstrate the idea of building a cloud-native application to monitor smart home devices. The application intends to provide valuable performance metrics from the perspective of end-users and react to anomalies in real-time. In this demo paper, we conduct the demonstration using Autoencoder (an unsupervised technique) based Deep Neural Networks (DNNs) to learn the normal operating conditions of power consumption of smart devices. When an anomaly is detected, the DNNs take proactive action and send appropriate commands back to the device. In addition, the users are provided with a real-time graphical representation of power consumption. This will help to save electricity on a domestic as well as industrial level. Finally, we discuss the future prospects of monitoring IoT devices.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114581779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Farinaz Rasouli, Amin Ebrahimzadeh, Somayeh Kianpisheh, Nattakorn Promwongsa, F. Belqasmi, R. Glitho
{"title":"A Predictive Framework for Haptic Enabled VR-based Remote Phobia Treatment in Cloud/Fog Environment","authors":"Farinaz Rasouli, Amin Ebrahimzadeh, Somayeh Kianpisheh, Nattakorn Promwongsa, F. Belqasmi, R. Glitho","doi":"10.1109/ICIN51074.2021.9385536","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385536","url":null,"abstract":"The emerging Tactile Internet aims to transmit the modality of touch in addition to the conventional audiovisual signals, thus converting the content delivery networks into skill-set delivery networks. An interesting example of immersive, low-latency Tactile Internet applications is haptic-enabled virtual reality (VR), where an extremely low latency of less than 50 ms is required. In this paper, we consider a recently proposed fog-based haptic-enabled VR system for remote treatment of animal phobia. Specifically, we address the problem of excessive packet latency as well as packet loss, which may result in quality-of-experience (QoE) degradation. Toward this end, we aim to use machine learning to decouple the impact of excessive latency and extreme packet loss from the user experience by utilizing our proposed edge tactile learner (ETL), which is responsible for predicting the zones touched by the therapist and then delivering it to the patient fog domain immediately, if needed. The simulation results indicate that our proposed predictive method outperforms two benchmark algorithms in terms of accuracy and prediction time.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134059003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Genetic Approach to Continuous Optimization of Virtual Network Embedding","authors":"P. Martinez-Julia, Ved P. Kafle, H. Asaeda","doi":"10.1109/ICIN51074.2021.9385542","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385542","url":null,"abstract":"Obtaining the optimum configuration for a virtual network to be embedded on a substrate network is known to be unfeasible and intractable for large networks. This limitation can be overcome by using evolutionary algorithms guided by heuristics, such as genetic algorithms. Although they are fast to reach a good configuration, it is usually just a local optimum that could be easily improved with more computation time. In this paper we propose an algorithm that, after providing a configuration in a very reduced time boundary, continues its work to get the best configuration possible within some constraints of time, number of iterations, and distance from the ideal solution. We demonstrate that, after some additional iterations, the algorithm obtains a configuration that is 7 times better than the initial configuration. Although the latter can be already enforced in the network, the improved configuration will be enforced when it is ready, so the network efficiency will be continuously improved.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Offline Virtual Network Function Packing Problem","authors":"Y. Carlinet, É. Gourdin, N. Perrot","doi":"10.1109/ICIN51074.2021.9385554","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385554","url":null,"abstract":"This paper presents the offline Virtual Network Function Packing (VNF-P) problem, a new challenging optimization problem that telecom operators face when designing the cloud infrastructure to host virtual network functions, especially in the context of 5G networks deployment. The VNF-P problem is a generalization of the classical Bin Packing Problem, where VNFs are packed onto physical servers, while taking into account capacity constraints, variable bin size and cost, conflicts between items and between items and bins, some unsplittable items, and two-compartment bins. Our contributions to this problem include a formal definition of the VNF-P problem, exact procedures and heuristic algorithms to solve it. An efficient algorithm, providing a good quality lower bound by solving a relaxation of the problem, is also provided. Numerical experiments on realistic instances were conducted in order to compare the different approaches. They show that the classical results of the literature cannot be extended naturally to this variant. The proposed algorithms have been validated on real use-case studies at Orange, and have been embedded in an operational tool to design the future cloud infrastructure.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121172542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}