Pub Date : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00046
Dhruv Kumar, Sohaib Ahmad, A. Chandra, R. Sitaraman
Geo-distributed analytics (GDA) involves processing of data stored across geographically distributed sites. Such analytics involves data transfer over the wide area network (WAN) links. WAN links are highly constrained and heterogeneous in nature, making the data transfer over the WAN slow and costly. To tackle this issue, recent approaches have proposed WAN-aware scheduling and placement of geo-distributed analytics tasks. However, computing joins in a geo-distributed setting remains a challenging problem. In this work, we propose AggFirstJoin, an approach to minimize the cost of geo-distributed joins using a theoretically sound query transformation technique. Our optimization approach takes a combined view of the join and aggregation operations which are often part of the same query and pushes (a transformed) aggregation before join in a manner to produce the same results as the original query. We augment our query transformation technique with a WAN-aware task placement and a Bloom filtering approach to further reduce query execution time and WAN usage respectively. We implement our proposed technique on top of Apache Spark, a popular engine for big data analytics. We extensively evaluate our proposed technique using synthetic, TPC-H and Amplab Big Data benchmark datasets on a real geo-distributed testbed on AWS as well as an emulated testbed. Our evaluations show our proposed technique achieves up to 300x reduction in query execution time and 200x reduction in WAN usage as compared to state-of-the-art GDA techniques.
{"title":"AggFirstJoin: Optimizing Geo-Distributed Joins using Aggregation-Based Transformations","authors":"Dhruv Kumar, Sohaib Ahmad, A. Chandra, R. Sitaraman","doi":"10.1109/CCGrid57682.2023.00046","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00046","url":null,"abstract":"Geo-distributed analytics (GDA) involves processing of data stored across geographically distributed sites. Such analytics involves data transfer over the wide area network (WAN) links. WAN links are highly constrained and heterogeneous in nature, making the data transfer over the WAN slow and costly. To tackle this issue, recent approaches have proposed WAN-aware scheduling and placement of geo-distributed analytics tasks. However, computing joins in a geo-distributed setting remains a challenging problem. In this work, we propose AggFirstJoin, an approach to minimize the cost of geo-distributed joins using a theoretically sound query transformation technique. Our optimization approach takes a combined view of the join and aggregation operations which are often part of the same query and pushes (a transformed) aggregation before join in a manner to produce the same results as the original query. We augment our query transformation technique with a WAN-aware task placement and a Bloom filtering approach to further reduce query execution time and WAN usage respectively. We implement our proposed technique on top of Apache Spark, a popular engine for big data analytics. We extensively evaluate our proposed technique using synthetic, TPC-H and Amplab Big Data benchmark datasets on a real geo-distributed testbed on AWS as well as an emulated testbed. Our evaluations show our proposed technique achieves up to 300x reduction in query execution time and 200x reduction in WAN usage as compared to state-of-the-art GDA techniques.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114970180","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00059
Lokendra Vishwakarma, Amritesh Kumar, D. Das
With the advent of cross-chain, moving assets across blockchain is now possible on a decentralized network. In a decentralized environment, asset transfer to a random blockchain would eliminate committing to a single blockchain. Many domains, such as banking, smart healthcare, smart homes, and the industrial internet of things (IIoT), benefit from cross-chain applications of blockchain. Cross-chain implementation is still in its infancy and confronts issues in preserving many of the asset's features when transferred across the network. Using cross-chain for asset transfers necessitates the presence of five essential characteristics: non-repudiation, unlinkability, confidentiality, atomicity, and interoperability. We proposed CrossLedger, a new Cross-chain based technique for asset transfer that includes all of the features mentioned above. To keep the qualities described above, the CrossLedger uses a novel Asset Forwarder Selection (AFS), Trust Establishment (TE), and Asset Transfer and Confirmation (ATC) algorithms. The proof of characteristics demonstrates that CrossLedger supports all the aforementioned features for asset transfer. The security analysis proved that CrossLedger is protected from double-spending, liveness, and Sybil attacks.
{"title":"CrossLedger: A Pioneer Cross-chain Asset Transfer Protocol","authors":"Lokendra Vishwakarma, Amritesh Kumar, D. Das","doi":"10.1109/CCGrid57682.2023.00059","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00059","url":null,"abstract":"With the advent of cross-chain, moving assets across blockchain is now possible on a decentralized network. In a decentralized environment, asset transfer to a random blockchain would eliminate committing to a single blockchain. Many domains, such as banking, smart healthcare, smart homes, and the industrial internet of things (IIoT), benefit from cross-chain applications of blockchain. Cross-chain implementation is still in its infancy and confronts issues in preserving many of the asset's features when transferred across the network. Using cross-chain for asset transfers necessitates the presence of five essential characteristics: non-repudiation, unlinkability, confidentiality, atomicity, and interoperability. We proposed CrossLedger, a new Cross-chain based technique for asset transfer that includes all of the features mentioned above. To keep the qualities described above, the CrossLedger uses a novel Asset Forwarder Selection (AFS), Trust Establishment (TE), and Asset Transfer and Confirmation (ATC) algorithms. The proof of characteristics demonstrates that CrossLedger supports all the aforementioned features for asset transfer. The security analysis proved that CrossLedger is protected from double-spending, liveness, and Sybil attacks.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129723570","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00052
Luc Angelelli, A. Silva, Yiannis Georgiou, Michael Mercier, G. Mounié, D. Trystram
The cloud is extended towards the edge to form a computing continuum while managing resources' heterogeneity. The serverless technology simplified how to build cloud applications and use resources, becoming a driving force in consolidating the continuum with the deployment of small functions with short execution. However, the adaptation of serverless to the edge-cloud continuum brings new challenges mainly related to resource management and scheduling. Standard cloud scheduling policies are based on greedy algorithms that do not efficiently handle platforms' heterogeneity nor deal with problems such as cold start delays. This work introduces a new scheduling policy that tries to address these issues. It is based on multi-objective optimization for data transfers and makespan while considering heterogeneity. Using simulations that vary workloads, platforms, and heterogeneity levels, we study the system utilization, the trade-offs between the targets, and the impacts of considering platforms' heterogeneity. We perform comparisons with a baseline inspired by a Kubernetes-based policy, representing greedy algorithms. Our experiments show considerable gaps between the efficiency of a greedy-based scheduling policy and a multi-objective-based one. The last outperforms the baseline by reducing makespan, data transfers, and system utilization by up to two orders of magnitudes in relevant cases for the edge-cloud continuum.
{"title":"Towards a Multi-objective Scheduling Policy for Serverless-based Edge-Cloud Continuum","authors":"Luc Angelelli, A. Silva, Yiannis Georgiou, Michael Mercier, G. Mounié, D. Trystram","doi":"10.1109/CCGrid57682.2023.00052","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00052","url":null,"abstract":"The cloud is extended towards the edge to form a computing continuum while managing resources' heterogeneity. The serverless technology simplified how to build cloud applications and use resources, becoming a driving force in consolidating the continuum with the deployment of small functions with short execution. However, the adaptation of serverless to the edge-cloud continuum brings new challenges mainly related to resource management and scheduling. Standard cloud scheduling policies are based on greedy algorithms that do not efficiently handle platforms' heterogeneity nor deal with problems such as cold start delays. This work introduces a new scheduling policy that tries to address these issues. It is based on multi-objective optimization for data transfers and makespan while considering heterogeneity. Using simulations that vary workloads, platforms, and heterogeneity levels, we study the system utilization, the trade-offs between the targets, and the impacts of considering platforms' heterogeneity. We perform comparisons with a baseline inspired by a Kubernetes-based policy, representing greedy algorithms. Our experiments show considerable gaps between the efficiency of a greedy-based scheduling policy and a multi-objective-based one. The last outperforms the baseline by reducing makespan, data transfers, and system utilization by up to two orders of magnitudes in relevant cases for the edge-cloud continuum.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128257168","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00060
M. K. B. Ashan, S. Puri, S. Prasad
Polygonal geometric operations are fundamental in domains such as Computer Graphics, Computer-Aided Design, and Geographic Information Systems. Handling degenerate cases in such operations is important when real-world spatial data are used. The popular Greiner-Hormann (GH) clipping algorithm does not handle such cases properly without perturbing vertices leading to inaccuracies and ambiguities. In this work, we parallelize the $O$(n2)-time general polygon clipping algorithm by Foster et al., which can handle degenerate cases without perturbation. Our CREW PRAM algorithm can perform clipping in O (log n) time using $n$ + $k$ number of processors with simple polygons, where $n$ is the number of input edges and $k$ is the number of edge intersections. For efficient GPU implementation, we employ three effective filters which have not been used in prior work on polygon clipping: 1) Common-minimum-bounding-rectangle filter, 2) Count-based filter, and 3) Line-segment-minimum-bounding-rectangle filter. They drastically reduce O($n$2) candidate edge pairs comparisons by 80% - 99%, leading to significantly faster parallel execution. In our experiments, C++ CUDA-based implementation yields up to 40X speedup over real-world datasets, processing two polygons with a total of 174K vertices on an Nvidia Quadro RTX 5000 GPU compared to the sequential Foster's algorithm running on an Intel Xeon Silver 4210R CPU.
{"title":"Efficient PRAM and Practical GPU Algorithms for Large Polygon Clipping with Degenerate Cases","authors":"M. K. B. Ashan, S. Puri, S. Prasad","doi":"10.1109/CCGrid57682.2023.00060","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00060","url":null,"abstract":"Polygonal geometric operations are fundamental in domains such as Computer Graphics, Computer-Aided Design, and Geographic Information Systems. Handling degenerate cases in such operations is important when real-world spatial data are used. The popular Greiner-Hormann (GH) clipping algorithm does not handle such cases properly without perturbing vertices leading to inaccuracies and ambiguities. In this work, we parallelize the $O$(n2)-time general polygon clipping algorithm by Foster et al., which can handle degenerate cases without perturbation. Our CREW PRAM algorithm can perform clipping in O (log n) time using $n$ + $k$ number of processors with simple polygons, where $n$ is the number of input edges and $k$ is the number of edge intersections. For efficient GPU implementation, we employ three effective filters which have not been used in prior work on polygon clipping: 1) Common-minimum-bounding-rectangle filter, 2) Count-based filter, and 3) Line-segment-minimum-bounding-rectangle filter. They drastically reduce O($n$2) candidate edge pairs comparisons by 80% - 99%, leading to significantly faster parallel execution. In our experiments, C++ CUDA-based implementation yields up to 40X speedup over real-world datasets, processing two polygons with a total of 174K vertices on an Nvidia Quadro RTX 5000 GPU compared to the sequential Foster's algorithm running on an Intel Xeon Silver 4210R CPU.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704316","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00030
J. Ravi, S. Byna, M. Becchi
To alleviate bottlenecks in storing and accessing data on high-performance computing (HPC) systems, I/O libraries are enabling computation while data is in-transit, such as HDFS filters. For scientific applications that commonly use floating-point data, error-bounded lossy compression methods are a critical technique to significantly reduce the storage and bandwidth requirements. Thus far, deciding when and where to schedule in-transit data transformations, such as compression, has been outside the scope of I/O libraries. In this paper, we introduce Runway, a runtime framework that enables computation on in-transit data with an object storage abstraction. Runway is designed to be extensible to execute user-defined functions at runtime. In this effort, we focus on studying methods to offload data compression operations to available processing units based on latency and throughput. We compare the performance of running compression on multi-core CPUs, as well as offloading it to a GPU and a Data Processing Unit (DPU). We implement a state-of-the-art error-bounded lossy compression algorithm, SZ3, as a Runway function with a variant optimized for DPUs. We propose dynamic modeling to guide scheduling decisions for in-transit data compression. We evaluate Runway using four scientific datasets from the SDRBench benchmark suite on a the Perlmutter supercomputer at NERSC.
{"title":"Runway: In-transit Data Compression on Heterogeneous HPC Systems","authors":"J. Ravi, S. Byna, M. Becchi","doi":"10.1109/CCGrid57682.2023.00030","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00030","url":null,"abstract":"To alleviate bottlenecks in storing and accessing data on high-performance computing (HPC) systems, I/O libraries are enabling computation while data is in-transit, such as HDFS filters. For scientific applications that commonly use floating-point data, error-bounded lossy compression methods are a critical technique to significantly reduce the storage and bandwidth requirements. Thus far, deciding when and where to schedule in-transit data transformations, such as compression, has been outside the scope of I/O libraries. In this paper, we introduce Runway, a runtime framework that enables computation on in-transit data with an object storage abstraction. Runway is designed to be extensible to execute user-defined functions at runtime. In this effort, we focus on studying methods to offload data compression operations to available processing units based on latency and throughput. We compare the performance of running compression on multi-core CPUs, as well as offloading it to a GPU and a Data Processing Unit (DPU). We implement a state-of-the-art error-bounded lossy compression algorithm, SZ3, as a Runway function with a variant optimized for DPUs. We propose dynamic modeling to guide scheduling decisions for in-transit data compression. We evaluate Runway using four scientific datasets from the SDRBench benchmark suite on a the Perlmutter supercomputer at NERSC.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121447863","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00050
Manojit Ghose, Krishna Prabin Pandey, Niyati Chaudhari, A. Sahu
Nowadays the cloud system receives requests from a wide horizon of users. In order to execute a large number of modern resource-intensive, latency-sensitive applications with deadline requests from the users, the cloud systems are equipped with powerful machines, and the machines run for a significant amount of time. This leads to an increase in the probability of failures of these machines. Hence, the reliability of the cloud system is to be duly considered while designing a scheduling strategy for executing resource-intensive, latency-sensitive applications on it. This paper proposes an efficient scheduling strategy for executing real-time applications (scientific applications) maintaining the reliability constraints of both the cloud system and applications and the deadline constraints of these applications. The proposed policy assigns recoveries for an optimal number of tasks of the application while scheduling them on the cloud considering the reliability constraints of both the cloud system and the application. The experimental evaluation proves that the proposed policy outperforms the state-of-the-art policy both for the synthetic task set and scientific workflows.
{"title":"Soft Reliability Aware Scheduling of Real-time Applications on Cloud with MTTF constraints","authors":"Manojit Ghose, Krishna Prabin Pandey, Niyati Chaudhari, A. Sahu","doi":"10.1109/CCGrid57682.2023.00050","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00050","url":null,"abstract":"Nowadays the cloud system receives requests from a wide horizon of users. In order to execute a large number of modern resource-intensive, latency-sensitive applications with deadline requests from the users, the cloud systems are equipped with powerful machines, and the machines run for a significant amount of time. This leads to an increase in the probability of failures of these machines. Hence, the reliability of the cloud system is to be duly considered while designing a scheduling strategy for executing resource-intensive, latency-sensitive applications on it. This paper proposes an efficient scheduling strategy for executing real-time applications (scientific applications) maintaining the reliability constraints of both the cloud system and applications and the deadline constraints of these applications. The proposed policy assigns recoveries for an optimal number of tasks of the application while scheduling them on the cloud considering the reliability constraints of both the cloud system and the application. The experimental evaluation proves that the proposed policy outperforms the state-of-the-art policy both for the synthetic task set and scientific workflows.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131882604","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00066
Yafan Huang, Kai Zhao, S. Di, Guanpeng Li, M. Dmitriev, T. Tonellot, F. Cappello
Seismic imaging is an exploration method for estimating the seismic characteristics of the earth's sub-surface for geologists and geophysicists. Reverse time migration (RTM) is a critical method in seismic imaging analysis. It can produce huge volumes of data that need to be stored for later use during its execution. The traditional solution transfers the vast amount of data to peripheral devices and loads them back to memory whenever needed, which may cause a substantial burden to I/O and storage space. As such, an efficient data compressor turns out to be a very critical solution. In order to get the best overall RTM analysis performance, we develop a novel hybrid lossy compression method (called HyZ), which is not only fairly fast in both compression and decompression but also has a good compression ratio with satisfactory reconstructed data quality for post hoc analysis. We evaluate several state-of-the-art error-controlled lossy compression algorithms (including HyZ, BR, SZx, SZ, SZ-Interp, ZFP, etc.) in a supercomputer. Experiments show that HyZ not only significantly improves the overall performance for RTM by 6.29∼6.60× but also obtains fairly good qualities for both RTM single snapshots and the final stacking image.
{"title":"Towards Improving Reverse Time Migration Performance by High-speed Lossy Compression","authors":"Yafan Huang, Kai Zhao, S. Di, Guanpeng Li, M. Dmitriev, T. Tonellot, F. Cappello","doi":"10.1109/CCGrid57682.2023.00066","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00066","url":null,"abstract":"Seismic imaging is an exploration method for estimating the seismic characteristics of the earth's sub-surface for geologists and geophysicists. Reverse time migration (RTM) is a critical method in seismic imaging analysis. It can produce huge volumes of data that need to be stored for later use during its execution. The traditional solution transfers the vast amount of data to peripheral devices and loads them back to memory whenever needed, which may cause a substantial burden to I/O and storage space. As such, an efficient data compressor turns out to be a very critical solution. In order to get the best overall RTM analysis performance, we develop a novel hybrid lossy compression method (called HyZ), which is not only fairly fast in both compression and decompression but also has a good compression ratio with satisfactory reconstructed data quality for post hoc analysis. We evaluate several state-of-the-art error-controlled lossy compression algorithms (including HyZ, BR, SZx, SZ, SZ-Interp, ZFP, etc.) in a supercomputer. Experiments show that HyZ not only significantly improves the overall performance for RTM by 6.29∼6.60× but also obtains fairly good qualities for both RTM single snapshots and the final stacking image.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123076011","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00048
Ankur Nahar, Himani Sikarwar, Sanyam Jain, D. Das
This paper investigates the feasibility of cache content prediction and coherence in the context of secure communication and search. We introduce a distributed multi-tier mobility-assisted edge intelligence based caching framework for the Internet of Vehicles (IoVs), called CacheIn. The proposed framework leverages user preferences, data correlations, and mobility information to prefetch content to the IoV edge. To enable content management based on mobility, we propose a novel Normalized Hidden Markov Model (NM-HMM) that anticipates a vehicle's future position. The framework also utilizes a mobility-aware collaborative filtering-based federated learning (FL) technique to enhance cache hit, reduce latency, and protect user privacy. To ensure secure cross-domain data sharing and mitigate the risk of data breaches, we also propose an extended ciphertext policy attribute-based encryption (ECP-ABE) mechanism. Compared to content popularity-based caching schemes, CacheIn achieves up to 80%, 38%, and 55% improvement in cache hit ratio for different cache sizes, vehicle densities, and cache lookup scenarios. Moreover, our approach reduces key generation, encryption, and decryption times by 35 %.
{"title":"CacheIn: A Secure Distributed Multi-layer Mobility-Assisted Edge Intelligence based Caching for Internet of Vehicles","authors":"Ankur Nahar, Himani Sikarwar, Sanyam Jain, D. Das","doi":"10.1109/CCGrid57682.2023.00048","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00048","url":null,"abstract":"This paper investigates the feasibility of cache content prediction and coherence in the context of secure communication and search. We introduce a distributed multi-tier mobility-assisted edge intelligence based caching framework for the Internet of Vehicles (IoVs), called CacheIn. The proposed framework leverages user preferences, data correlations, and mobility information to prefetch content to the IoV edge. To enable content management based on mobility, we propose a novel Normalized Hidden Markov Model (NM-HMM) that anticipates a vehicle's future position. The framework also utilizes a mobility-aware collaborative filtering-based federated learning (FL) technique to enhance cache hit, reduce latency, and protect user privacy. To ensure secure cross-domain data sharing and mitigate the risk of data breaches, we also propose an extended ciphertext policy attribute-based encryption (ECP-ABE) mechanism. Compared to content popularity-based caching schemes, CacheIn achieves up to 80%, 38%, and 55% improvement in cache hit ratio for different cache sizes, vehicle densities, and cache lookup scenarios. Moreover, our approach reduces key generation, encryption, and decryption times by 35 %.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114619263","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00024
Vincent Lannurien, Laurent d'Orazio, Olivier Barais, Esther Bernard, Olivier Weppe, Laurent Beaulieu, Amine Kacete, S. Paquelet, Jalil Boukhobza
Serverless is a trending service model for cloud computing. It shifts a lot of the complexity from customers to service providers. However, current serverless platforms mostly consider the provider's infrastructure as homogeneous, as well as the users' requests. This limits possibilities for the provider to leverage heterogeneity in their infrastructure to improve function response time and reduce energy consumption. We propose a heterogeneity-aware serverless orchestrator for private clouds that consists of two components: the autoscaler allocates heterogeneous hardware resources (CPUs, GPUs, FPGAs) for function replicas, while the scheduler maps function executions to these replicas. Our objective is to guarantee function response time, while enabling the provider to reduce resource usage and energy consumption. This work considers a case study for a deepfake detection application relying on CNN inference. We devised a simulation environment that implements our model and a baseline Knative orchestrator, and evaluated both policies with regard to consolidation of tasks, energy consumption and SLA penalties. Experimental results show that our platform yields substantial gains for all those metrics, with an average of 35% less energy consumed for function executions while consolidating tasks on less than 40% of the infrastructure's nodes, and more than 60% less SLA violations.
{"title":"HeROfake: Heterogeneous Resources Orchestration in a Serverless Cloud – An Application to Deepfake Detection","authors":"Vincent Lannurien, Laurent d'Orazio, Olivier Barais, Esther Bernard, Olivier Weppe, Laurent Beaulieu, Amine Kacete, S. Paquelet, Jalil Boukhobza","doi":"10.1109/CCGrid57682.2023.00024","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00024","url":null,"abstract":"Serverless is a trending service model for cloud computing. It shifts a lot of the complexity from customers to service providers. However, current serverless platforms mostly consider the provider's infrastructure as homogeneous, as well as the users' requests. This limits possibilities for the provider to leverage heterogeneity in their infrastructure to improve function response time and reduce energy consumption. We propose a heterogeneity-aware serverless orchestrator for private clouds that consists of two components: the autoscaler allocates heterogeneous hardware resources (CPUs, GPUs, FPGAs) for function replicas, while the scheduler maps function executions to these replicas. Our objective is to guarantee function response time, while enabling the provider to reduce resource usage and energy consumption. This work considers a case study for a deepfake detection application relying on CNN inference. We devised a simulation environment that implements our model and a baseline Knative orchestrator, and evaluated both policies with regard to consolidation of tasks, energy consumption and SLA penalties. Experimental results show that our platform yields substantial gains for all those metrics, with an average of 35% less energy consumed for function executions while consolidating tasks on less than 40% of the infrastructure's nodes, and more than 60% less SLA violations.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436874","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 : 2023-05-01DOI: 10.1109/CCGrid57682.2023.00020
M. Jay, Vladimir Ostapenco, L. Lefèvre, D. Trystram, Anne-Cécile Orgerie, Benjamin Fichel
The global energy demand for digital activities is constantly growing. Computing nodes and cloud services are at the heart of these activities. Understanding their energy consumption is an important step towards reducing it. On one hand, physical power meters are very accurate in measuring energy but they are expensive, difficult to deploy on a large scale, and are not able to provide measurements at the service level. On the other hand, power models and vendor-specific internal interfaces are already available or can be implemented on existing systems. Plenty of tools, called software-based power meters, have been developed around the concepts of power models and internal interfaces, in order to report the power consumption at levels ranging from the whole computing node to applications and services. However, we have found that it can be difficult to choose the right tool for a specific need. In this work, we qualitatively and experimentally compare several software-based power meters able to deal with CPU or GPU-based infrastructures. For this purpose, we evaluate them against high-precision physical power meters while executing various intensive workloads. We extend this empirical study to highlight the strengths and limitations of each software-based power meter.
{"title":"An experimental comparison of software-based power meters: focus on CPU and GPU","authors":"M. Jay, Vladimir Ostapenco, L. Lefèvre, D. Trystram, Anne-Cécile Orgerie, Benjamin Fichel","doi":"10.1109/CCGrid57682.2023.00020","DOIUrl":"https://doi.org/10.1109/CCGrid57682.2023.00020","url":null,"abstract":"The global energy demand for digital activities is constantly growing. Computing nodes and cloud services are at the heart of these activities. Understanding their energy consumption is an important step towards reducing it. On one hand, physical power meters are very accurate in measuring energy but they are expensive, difficult to deploy on a large scale, and are not able to provide measurements at the service level. On the other hand, power models and vendor-specific internal interfaces are already available or can be implemented on existing systems. Plenty of tools, called software-based power meters, have been developed around the concepts of power models and internal interfaces, in order to report the power consumption at levels ranging from the whole computing node to applications and services. However, we have found that it can be difficult to choose the right tool for a specific need. In this work, we qualitatively and experimentally compare several software-based power meters able to deal with CPU or GPU-based infrastructures. For this purpose, we evaluate them against high-precision physical power meters while executing various intensive workloads. We extend this empirical study to highlight the strengths and limitations of each software-based power meter.","PeriodicalId":363806,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121758224","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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