Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00020
H. Kimm, Dennis Flynn
In this paper we present an Android mobile platform application for tracking pedestrian movements in realtime using the Android Fused Location Provider API and the Firebase Realtime Database. Two significant barriers in developing accurate and efficient realtime path tracking systems are the limitations inherent to utilizing raw GPS data and the overhead involved in developing backend components. Data from satellite based systems is subject to a number of factors limiting their effective use in generating movement track paths. Previous work to mitigate these concerns involved using additional sensors to supplement and modify the erroneous raw GPS teleme-try. Google has incorporated and expanded upon these methods, making them available through the Fused Location Provider API. Firebase, a set of Backend-as-a-Service (BaaS) tools developed by google, streamlines the development process. Together, the Fused Location Provider and the Firebase Realtime Database make the foundation of a system that accurately and efficiently maps user movements in realtime.
{"title":"Android Application for Tracking Pedestrian Movements in Realtime with Firebase","authors":"H. Kimm, Dennis Flynn","doi":"10.1109/CloudSummit54781.2022.00020","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00020","url":null,"abstract":"In this paper we present an Android mobile platform application for tracking pedestrian movements in realtime using the Android Fused Location Provider API and the Firebase Realtime Database. Two significant barriers in developing accurate and efficient realtime path tracking systems are the limitations inherent to utilizing raw GPS data and the overhead involved in developing backend components. Data from satellite based systems is subject to a number of factors limiting their effective use in generating movement track paths. Previous work to mitigate these concerns involved using additional sensors to supplement and modify the erroneous raw GPS teleme-try. Google has incorporated and expanded upon these methods, making them available through the Fused Location Provider API. Firebase, a set of Backend-as-a-Service (BaaS) tools developed by google, streamlines the development process. Together, the Fused Location Provider and the Firebase Realtime Database make the foundation of a system that accurately and efficiently maps user movements in realtime.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126531949","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00012
S. Ristov, Simon Brandacher, M. Felderer, R. Breu
Federated Function-as-a-Service (FaaS) offers higher scalability, better resilience and cost-performance trade-off than running serverless applications in a single cloud region. However, existing Infrastructure-as-Code (IaC) tools are mainly focused on the FaaS provider, rather than on applications, which increases developer effort to code multiple times the same data in order to deploy a serverless function on various cloud regions in federated FaaS. To bridge this gap, this paper introduces GoDeploy, a framework that simplifies coding the deployment of serverless functions in Federated FaaS. Using the design principle “code once, deploy everywhere”, GoDeploy offers developers a domain-specific language, which introduces a three-levels hierarchy serverless function → FaaS providers →cloud regions of FaaS provider, rather than existing either the two-levels hierarchy FaaS provider → serverless functions or flat horizontal structure. Moreover, GoDeploy hides the complexity and requirements of each FaaS provider to store deployment packages (zip) of serverless functions on their storages. With this approach, GoDeploy reduces deployment script length measured in lines of code (LoC) compared to the recent FaaSifier M2FaaS by up to 33.33% for deployment on three cloud regions of AWS. When deploying a single function on three cloud regions of each of three FaaS providers AWS, IBM, Google, LoC are reduced by up to 72.34% compared to the state-of-the-art IaC tool Terraform. The improvement is higher when a serverless function needs to be deployed on multiple cloud regions because GoDeploy's three-level hierarchy requires a single LoC per cloud region, compared to multiple LoC in Terraform's and M2FaaS DSLs.
{"title":"GoDeploy: Portable Deployment of Serverless Functions in Federated FaaS","authors":"S. Ristov, Simon Brandacher, M. Felderer, R. Breu","doi":"10.1109/CloudSummit54781.2022.00012","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00012","url":null,"abstract":"Federated Function-as-a-Service (FaaS) offers higher scalability, better resilience and cost-performance trade-off than running serverless applications in a single cloud region. However, existing Infrastructure-as-Code (IaC) tools are mainly focused on the FaaS provider, rather than on applications, which increases developer effort to code multiple times the same data in order to deploy a serverless function on various cloud regions in federated FaaS. To bridge this gap, this paper introduces GoDeploy, a framework that simplifies coding the deployment of serverless functions in Federated FaaS. Using the design principle “code once, deploy everywhere”, GoDeploy offers developers a domain-specific language, which introduces a three-levels hierarchy serverless function → FaaS providers →cloud regions of FaaS provider, rather than existing either the two-levels hierarchy FaaS provider → serverless functions or flat horizontal structure. Moreover, GoDeploy hides the complexity and requirements of each FaaS provider to store deployment packages (zip) of serverless functions on their storages. With this approach, GoDeploy reduces deployment script length measured in lines of code (LoC) compared to the recent FaaSifier M2FaaS by up to 33.33% for deployment on three cloud regions of AWS. When deploying a single function on three cloud regions of each of three FaaS providers AWS, IBM, Google, LoC are reduced by up to 72.34% compared to the state-of-the-art IaC tool Terraform. The improvement is higher when a serverless function needs to be deployed on multiple cloud regions because GoDeploy's three-level hierarchy requires a single LoC per cloud region, compared to multiple LoC in Terraform's and M2FaaS DSLs.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127696349","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00015
Ahamed Aljuhani
The Internet of Medical Things (IoMT) has been integrated and deployed in the health care domain as it significantly facilitates medical services for both health care providers and consumers. The IoMT allows various medical devices to exchange data over different wireless communication technologies without the need for human intervention. The widespread adoption of such a technology increases the demand for designing efficient, reliable, and secure IoMT environments. Although IoMT significantly improves quality of medical services, maintaining security and privacy remain major challenges. This article proposes a collaborative intrusion detection framework empowered blockchain technology for protecting IoMT networks, called IDS-Chain. The IDS-Chain framework leverages fog computing to deploy security functions through distributed fog nodes to efficiently detect cyberattacks near data source. Blockchain is used to ensure data trust, integrity, and privacy, as well as to enable secure data exchange among distributed IDS entities.
{"title":"IDS-Chain: A Collaborative Intrusion Detection Framework Empowered Blockchain for Internet of Medical Things","authors":"Ahamed Aljuhani","doi":"10.1109/CloudSummit54781.2022.00015","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00015","url":null,"abstract":"The Internet of Medical Things (IoMT) has been integrated and deployed in the health care domain as it significantly facilitates medical services for both health care providers and consumers. The IoMT allows various medical devices to exchange data over different wireless communication technologies without the need for human intervention. The widespread adoption of such a technology increases the demand for designing efficient, reliable, and secure IoMT environments. Although IoMT significantly improves quality of medical services, maintaining security and privacy remain major challenges. This article proposes a collaborative intrusion detection framework empowered blockchain technology for protecting IoMT networks, called IDS-Chain. The IDS-Chain framework leverages fog computing to deploy security functions through distributed fog nodes to efficiently detect cyberattacks near data source. Blockchain is used to ensure data trust, integrity, and privacy, as well as to enable secure data exchange among distributed IDS entities.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117287274","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00009
Bita Fatemipour, Wei Shi, M. St-Hilaire
Geographically distributed data centers have been de-ployed for different purposes, such as minimizing the transmission and response time and the amount of data exchanges throughout the networks. Beyond fault tolerance purposes, data replication has been a popular solution to increase data availability by bringing data closer to the end users. Most existing studies migrate replicas to the desired destinations from a single source, and few of such solutions are cost-aware. By having a multi-source and cost-aware approach, we can accelerate the transmission time resulting in a better quality of service for the end users. Towards that end, this paper introduces a cost-effective and deadline-aware replica migration approach for geo-distributed data centers. The proposed model discovers the appropriate source(s) and paths to transmit the replicas to a desired destination cost-effectively. This problem, which jointly considers cost and deadline, is formulated into a mixed-integer linear programming optimization model. Extensive evaluation against two of the most recent approaches shows significant improvement in meeting the deadlines and reducing the cost incurred to the customers.
{"title":"A Cost-Effective and Multi-Source-Aware Replica Migration Approach for Geo-Distributed Data Centers","authors":"Bita Fatemipour, Wei Shi, M. St-Hilaire","doi":"10.1109/CloudSummit54781.2022.00009","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00009","url":null,"abstract":"Geographically distributed data centers have been de-ployed for different purposes, such as minimizing the transmission and response time and the amount of data exchanges throughout the networks. Beyond fault tolerance purposes, data replication has been a popular solution to increase data availability by bringing data closer to the end users. Most existing studies migrate replicas to the desired destinations from a single source, and few of such solutions are cost-aware. By having a multi-source and cost-aware approach, we can accelerate the transmission time resulting in a better quality of service for the end users. Towards that end, this paper introduces a cost-effective and deadline-aware replica migration approach for geo-distributed data centers. The proposed model discovers the appropriate source(s) and paths to transmit the replicas to a desired destination cost-effectively. This problem, which jointly considers cost and deadline, is formulated into a mixed-integer linear programming optimization model. Extensive evaluation against two of the most recent approaches shows significant improvement in meeting the deadlines and reducing the cost incurred to the customers.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123499063","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00018
Jerico Moeyersons, Brecht Stamper, B. Volckaert, F. de Turck
A recent trend observed in the Kubernetes world is trying to deploy a Kubernetes cluster entirely or partially to the edge. When further looking into these edge environments, it is often noted that wireless technologies are used. Kubernetes is not designed to support these wireless network setups and will have to be extended to run smoothly on these networks. One of the most used network setups in edge environments is a wireless mesh network. Therefore the main focus will be on running Kubernetes on top of a wireless mesh network. Other mesh networks will also be supported with a minimal set of changes needed. The main goal of this paper is to list all the problems encountered when running Kubernetes on top of a mesh network and provide a framework of components and extensions to solve these problems. The majority of these components will be implemented, and demonstrations show that these components are able to solve most of the listed problems when used in a setup with some predefined restrictions. When deploying a demo application with the proposed solution, the required bitrate is observed 97 percent of the time compared to 42 percent with the native Kubernetes scheduler.
{"title":"Implementing a Network-Aware Kubernetes Scheduler on top of a Mesh Network","authors":"Jerico Moeyersons, Brecht Stamper, B. Volckaert, F. de Turck","doi":"10.1109/CloudSummit54781.2022.00018","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00018","url":null,"abstract":"A recent trend observed in the Kubernetes world is trying to deploy a Kubernetes cluster entirely or partially to the edge. When further looking into these edge environments, it is often noted that wireless technologies are used. Kubernetes is not designed to support these wireless network setups and will have to be extended to run smoothly on these networks. One of the most used network setups in edge environments is a wireless mesh network. Therefore the main focus will be on running Kubernetes on top of a wireless mesh network. Other mesh networks will also be supported with a minimal set of changes needed. The main goal of this paper is to list all the problems encountered when running Kubernetes on top of a mesh network and provide a framework of components and extensions to solve these problems. The majority of these components will be implemented, and demonstrations show that these components are able to solve most of the listed problems when used in a setup with some predefined restrictions. When deploying a demo application with the proposed solution, the required bitrate is observed 97 percent of the time compared to 42 percent with the native Kubernetes scheduler.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125674796","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00008
Yi Liu, Qiaoling Li, Qingping Cao, Zhilan Huang, Yangchun Li, Yongbing Fan
There are differences in the way of public cloud and private cloud use network performance acceleration technologies (such as DPDK and SR-IOV), which will affect the performance of the 5GC network. This paper evaluates the difference in forwarding performance between AWS (public cloud) and the operator's private cloud by deploying Free5GC for testing. Under similar conditions, the 5GC forwarding performance of the private cloud using SR-IOV is higher than that of the public cloud, which is higher than that of the private cloud using DPDK.
{"title":"Evaluation of Free5GC Forwarding Performance on Private and Public Clouds","authors":"Yi Liu, Qiaoling Li, Qingping Cao, Zhilan Huang, Yangchun Li, Yongbing Fan","doi":"10.1109/CloudSummit54781.2022.00008","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00008","url":null,"abstract":"There are differences in the way of public cloud and private cloud use network performance acceleration technologies (such as DPDK and SR-IOV), which will affect the performance of the 5GC network. This paper evaluates the difference in forwarding performance between AWS (public cloud) and the operator's private cloud by deploying Free5GC for testing. Under similar conditions, the 5GC forwarding performance of the private cloud using SR-IOV is higher than that of the public cloud, which is higher than that of the private cloud using DPDK.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128413434","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00024
T. Nguyen, D. Min, Eunmi Choi, Iure Fé, Francisco Airton Silva
In Industry 4.0, the emergence of the Internet of industrial things (IoIT) has been a mainstream computing infrastructure for smart factories. However, IoITs show a multitude of inherent weaknesses which may restrict IoITs from fulfilling implementation expectations due to the con-figuration of the Cloud-Edge continuum. Under the needs of high-level production chain business continuity, an IoIT may be influenced by parametric or structural changes on one hand, but the system may also fail on the other. These possible events may be quantified using two metrics: survivability and resilience. This work proposes to model and evaluate a specific IoIT for survivability and resiliency quantification using a hierarchical model. The system model consists of three layers: (i) reliability block diagram (RBD) at the top level to capture the overall IoIT architecture, (ii) fault tree (FT) at the middle level to capture the configurations of subsystems, and (iii) continuous-time Markov chain (CTMC) models at the bottom level to represent the operational states of the underlying components and devices. The study can assist system managers in ensuring the maximum level of survivability and resiliency of industrial processes in smart factories by preserving operating circumstances and system configurations.
{"title":"Survivability and Resiliency Modeling and Analysis of an Internet of Industrial Things using Hierarchical Models","authors":"T. Nguyen, D. Min, Eunmi Choi, Iure Fé, Francisco Airton Silva","doi":"10.1109/CloudSummit54781.2022.00024","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00024","url":null,"abstract":"In Industry 4.0, the emergence of the Internet of industrial things (IoIT) has been a mainstream computing infrastructure for smart factories. However, IoITs show a multitude of inherent weaknesses which may restrict IoITs from fulfilling implementation expectations due to the con-figuration of the Cloud-Edge continuum. Under the needs of high-level production chain business continuity, an IoIT may be influenced by parametric or structural changes on one hand, but the system may also fail on the other. These possible events may be quantified using two metrics: survivability and resilience. This work proposes to model and evaluate a specific IoIT for survivability and resiliency quantification using a hierarchical model. The system model consists of three layers: (i) reliability block diagram (RBD) at the top level to capture the overall IoIT architecture, (ii) fault tree (FT) at the middle level to capture the configurations of subsystems, and (iii) continuous-time Markov chain (CTMC) models at the bottom level to represent the operational states of the underlying components and devices. The study can assist system managers in ensuring the maximum level of survivability and resiliency of industrial processes in smart factories by preserving operating circumstances and system configurations.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131033199","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00023
Sekou L. Remy, Kugamoorthy Gajananan, Alexei Karve
Edge Computing is a distributed computing paradigm which is closely related to other often conflated terms like Fog Computing, Mist Computing, IoT, and even Cloud Computing. Since it is a computing paradigm, it shapes how devices, protocols and other assets can be incorporated to develop overall solutions. In this paper we briefly describe how Edge Computing is currently defined in the literature and provide our view of looking at the Edge based on dominant data flows which reduce entropy across locations and ensures overall system health.
{"title":"Redefining Edge Computing","authors":"Sekou L. Remy, Kugamoorthy Gajananan, Alexei Karve","doi":"10.1109/CloudSummit54781.2022.00023","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00023","url":null,"abstract":"Edge Computing is a distributed computing paradigm which is closely related to other often conflated terms like Fog Computing, Mist Computing, IoT, and even Cloud Computing. Since it is a computing paradigm, it shapes how devices, protocols and other assets can be incorporated to develop overall solutions. In this paper we briefly describe how Edge Computing is currently defined in the literature and provide our view of looking at the Edge based on dominant data flows which reduce entropy across locations and ensures overall system health.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132344810","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00017
Resul Serkan Keskin, P. Pileggi
The recent Cloud Monitor invention [1] aims to open actual insights, and give greater agency, to stakeholders in the Cloud ecosystem. But the varied nature and diverse user and provider needs, challenge the practicality of creating custom continuous cloud monitoring solutions using it. To accommodate customized needs at scale for dynamic systems and operating environments, we take a first step towards a flexible adaptation mechanism that automates individual monitoring requests in a custom way. We propose a real-time adaptive continuous monitoring extension to the Cloud Monitor and demonstrate potential value from the Cloud User's perspective. We present a reasonable scenario using a real cloud environment. Our result suggests that, despite the need for more research into robustness, this mechanism enables improved agency of Cloud stakeholders. It enables the manageability of continuous monitoring with the Cloud Monitor under dynamic conditions. Ultimately, stakehold-ers can monitor the actual Quality of Service delivered by their cloud in use better and at their own discretion.
{"title":"Making the Cloud Monitor Real-Time Adaptive","authors":"Resul Serkan Keskin, P. Pileggi","doi":"10.1109/CloudSummit54781.2022.00017","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00017","url":null,"abstract":"The recent Cloud Monitor invention [1] aims to open actual insights, and give greater agency, to stakeholders in the Cloud ecosystem. But the varied nature and diverse user and provider needs, challenge the practicality of creating custom continuous cloud monitoring solutions using it. To accommodate customized needs at scale for dynamic systems and operating environments, we take a first step towards a flexible adaptation mechanism that automates individual monitoring requests in a custom way. We propose a real-time adaptive continuous monitoring extension to the Cloud Monitor and demonstrate potential value from the Cloud User's perspective. We present a reasonable scenario using a real cloud environment. Our result suggests that, despite the need for more research into robustness, this mechanism enables improved agency of Cloud stakeholders. It enables the manageability of continuous monitoring with the Cloud Monitor under dynamic conditions. Ultimately, stakehold-ers can monitor the actual Quality of Service delivered by their cloud in use better and at their own discretion.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129551442","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00011
Saleh Al Shamaa, Nabil Harrabida, Wei Shi, M. St-Hilaire
Due to cloud computing services' dynamic and elastic nature, implementing efficient task scheduling methods becomes primordial for cloud providers to handle the ever-growing demands and meet the Service Level Agreements (SLA) cost-effectively. In this paper, we propose a novel task scheduling approach, named ENS-PSO, that enhances Particle Swarm Op-timization (PSO) with an efficient neighborhood search strategy. We evaluate ENS-PSO using the CloudSim toolkit. Simulation results demonstrate that the proposed task scheduling with en-hanced neighborhood search outperforms other task scheduling algorithms in terms of makespan, energy consumption, and degree of imbalance.
{"title":"Particle Swarm Optimization with Enhanced Neighborhood Search for Task Scheduling in Cloud Computing","authors":"Saleh Al Shamaa, Nabil Harrabida, Wei Shi, M. St-Hilaire","doi":"10.1109/CloudSummit54781.2022.00011","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00011","url":null,"abstract":"Due to cloud computing services' dynamic and elastic nature, implementing efficient task scheduling methods becomes primordial for cloud providers to handle the ever-growing demands and meet the Service Level Agreements (SLA) cost-effectively. In this paper, we propose a novel task scheduling approach, named ENS-PSO, that enhances Particle Swarm Op-timization (PSO) with an efficient neighborhood search strategy. We evaluate ENS-PSO using the CloudSim toolkit. Simulation results demonstrate that the proposed task scheduling with en-hanced neighborhood search outperforms other task scheduling algorithms in terms of makespan, energy consumption, and degree of imbalance.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115868496","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}
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