Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00022
Agathe Blaise, Filippo Rebecchi
In recent years, there has been an explosion of attacks directed at microservice-based platforms – a trend that follows closely the massive shift of the digital industries towards these environments. Management and operation of container-based microservices is automation-heavy, leveraging on container orchestration engines such as Kubernetes (K8s). Helm is the package manager of choice for K8s and provides Charts, i.e., configuration files that define a programmatic model for application deployments. In this paper, we propose a novel methodology for extracting and evaluating the security model of Helm Charts. Our proposal extracts a topological graph of the Chart, whose nodes and edges are then characterised by security features. We carry out risk assessments that refer to the attack tactics of the MITRE ATT&CK framework. Furthermore, starting from these scores, we extract the riskiest attack paths. We adopt an experimental validation approach by analysing a dataset created from multiple publicly accessible Helm Chart repositories. Our methodology reveals that, in most cases, they have vulnerabilities that can be exploited through complex attack paths.
{"title":"Stay at the Helm: secure Kubernetes deployments via graph generation and attack reconstruction","authors":"Agathe Blaise, Filippo Rebecchi","doi":"10.1109/CLOUD55607.2022.00022","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00022","url":null,"abstract":"In recent years, there has been an explosion of attacks directed at microservice-based platforms – a trend that follows closely the massive shift of the digital industries towards these environments. Management and operation of container-based microservices is automation-heavy, leveraging on container orchestration engines such as Kubernetes (K8s). Helm is the package manager of choice for K8s and provides Charts, i.e., configuration files that define a programmatic model for application deployments. In this paper, we propose a novel methodology for extracting and evaluating the security model of Helm Charts. Our proposal extracts a topological graph of the Chart, whose nodes and edges are then characterised by security features. We carry out risk assessments that refer to the attack tactics of the MITRE ATT&CK framework. Furthermore, starting from these scores, we extract the riskiest attack paths. We adopt an experimental validation approach by analysing a dataset created from multiple publicly accessible Helm Chart repositories. Our methodology reveals that, in most cases, they have vulnerabilities that can be exploited through complex attack paths.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"51 1","pages":"59-69"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85720362","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 : 2022-06-06DOI: 10.1109/CLOUD55607.2022.00059
Pinglan Liu, Wensheng Zhang
When neural network model and data are outsourced to a cloud server for inference, it is desired to preserve the privacy of the model/data as the involved parties (i.e., cloud server, and model/data providing clients) may not trust mutually. Solutions have been proposed based on multi-party computation, trusted execution environment (TEE) and leveled or fully homomorphic encryption (LHE or FHE), but they all have limitations that hamper practical application. We propose a new framework based on integration of LHE and TEE, which enables collaboration among mutually-untrusted three parties, while minimizing the involvement of resource-constrained TEE but fully utilizing the untrusted but resource-rich part of server. We also propose a generic and efficient LHE-based inference scheme, along with optimizations, as an important performance-determining component of the framework. We implemented and evaluated the proposed scheme on a moderate platform, and the evaluations show that, our proposed system is applicable and scalable to various settings, and it has better or comparable performance when compared with the state-of-the-art solutions which are more restrictive in applicability and scalability.
{"title":"Towards Practical Privacy-Preserving Solution for Outsourced Neural Network Inference","authors":"Pinglan Liu, Wensheng Zhang","doi":"10.1109/CLOUD55607.2022.00059","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00059","url":null,"abstract":"When neural network model and data are outsourced to a cloud server for inference, it is desired to preserve the privacy of the model/data as the involved parties (i.e., cloud server, and model/data providing clients) may not trust mutually. Solutions have been proposed based on multi-party computation, trusted execution environment (TEE) and leveled or fully homomorphic encryption (LHE or FHE), but they all have limitations that hamper practical application. We propose a new framework based on integration of LHE and TEE, which enables collaboration among mutually-untrusted three parties, while minimizing the involvement of resource-constrained TEE but fully utilizing the untrusted but resource-rich part of server. We also propose a generic and efficient LHE-based inference scheme, along with optimizations, as an important performance-determining component of the framework. We implemented and evaluated the proposed scheme on a moderate platform, and the evaluations show that, our proposed system is applicable and scalable to various settings, and it has better or comparable performance when compared with the state-of-the-art solutions which are more restrictive in applicability and scalability.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"12 1","pages":"357-362"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74149935","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 : 2022-06-06DOI: 10.1109/CLOUD55607.2022.00046
Lorson Blair, Carlos A. Varela, S. Patterson
Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable intelligence. We consider a compute platform consisting of a limited number of highly-resourced UAVs that act as mobile edge computing (MEC) servers to process the workload on premises. We propose a novel distributed solution to the collaborative processing problem that adaptively positions the MEC UAVs in response to the changing workload that arises both from the sensing UAVs’ mobility and the task generation. Our solution consists of two key building blocks: (1) an efficient workload estimation process by which the UAVs estimate the task field—a continuous approximation of the number of tasks to be processed at each location in the airspace, and (2) a distributed optimization method by which the UAVs partition the task field so as to maximize the system throughput. We evaluate our proposed solution using realistic models of surveillance UAV mobility and show that our method achieves up to 28% improvement in throughput over a non-adaptive baseline approach.
{"title":"A Continuum Approach for Collaborative Task Processing in UAV MEC Networks","authors":"Lorson Blair, Carlos A. Varela, S. Patterson","doi":"10.1109/CLOUD55607.2022.00046","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00046","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable intelligence. We consider a compute platform consisting of a limited number of highly-resourced UAVs that act as mobile edge computing (MEC) servers to process the workload on premises. We propose a novel distributed solution to the collaborative processing problem that adaptively positions the MEC UAVs in response to the changing workload that arises both from the sensing UAVs’ mobility and the task generation. Our solution consists of two key building blocks: (1) an efficient workload estimation process by which the UAVs estimate the task field—a continuous approximation of the number of tasks to be processed at each location in the airspace, and (2) a distributed optimization method by which the UAVs partition the task field so as to maximize the system throughput. We evaluate our proposed solution using realistic models of surveillance UAV mobility and show that our method achieves up to 28% improvement in throughput over a non-adaptive baseline approach.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"195 1","pages":"247-256"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74437291","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 : 2022-06-01DOI: 10.1109/CLOUD55607.2022.00060
Christoph Auer, Michele Dolfi, A. Carvalho, Cesar Berrospi Ramis, P. W. J. S. I. Research, SoftINSA Lda.
Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce. In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure. Our key objective is to achieve high scalability and responsiveness for different workload profiles in a well-defined resource budget. We outline the requirements, design, and implementation choices of our document conversion service and reflect on the challenges we faced. Evidence for the scaling behavior and resource efficiency is provided for two alternative workload distribution strategies and deployment configurations. Our best-performing method achieves sustained throughput of over one million PDF pages per hour on 3072 CPU cores across 192 nodes.
{"title":"Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness","authors":"Christoph Auer, Michele Dolfi, A. Carvalho, Cesar Berrospi Ramis, P. W. J. S. I. Research, SoftINSA Lda.","doi":"10.1109/CLOUD55607.2022.00060","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00060","url":null,"abstract":"Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce. In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure. Our key objective is to achieve high scalability and responsiveness for different workload profiles in a well-defined resource budget. We outline the requirements, design, and implementation choices of our document conversion service and reflect on the challenges we faced. Evidence for the scaling behavior and resource efficiency is provided for two alternative workload distribution strategies and deployment configurations. Our best-performing method achieves sustained throughput of over one million PDF pages per hour on 3072 CPU cores across 192 nodes.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"110 1","pages":"363-373"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87703561","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 : 2022-05-31DOI: 10.1109/CLOUD55607.2022.00069
Ali Mokhtari, Md. Abir Hossen, Pooyan Jamshidi, M. Salehi
Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.
{"title":"FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems","authors":"Ali Mokhtari, Md. Abir Hossen, Pooyan Jamshidi, M. Salehi","doi":"10.1109/CLOUD55607.2022.00069","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00069","url":null,"abstract":"Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider \"fairness\" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"48 1","pages":"459-468"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78240473","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 : 2022-05-28DOI: 10.1109/CLOUD55607.2022.00038
F. Minna, F. Massacci, Katja Tuma
Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on "trial and error" experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a knowledge, AND/OR, graphs approach to model cloud deployment security objects and vulnerabilities. In this way, we can capture relationships between configurations, permissions (e.g., CAP_SYS_ADMIN), and security profiles (e.g., AppArmor and SecComp). Such an approach allows us to suggest alternative and safer configurations, support administrators in the study of what-if scenarios, and scale the analysis to large scale deployments. We present an initial validation and illustrate the approach with three real vulnerabilities from known sources.
{"title":"Towards a Security Stress-Test for Cloud Configurations","authors":"F. Minna, F. Massacci, Katja Tuma","doi":"10.1109/CLOUD55607.2022.00038","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00038","url":null,"abstract":"Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on \"trial and error\" experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a knowledge, AND/OR, graphs approach to model cloud deployment security objects and vulnerabilities. In this way, we can capture relationships between configurations, permissions (e.g., CAP_SYS_ADMIN), and security profiles (e.g., AppArmor and SecComp). Such an approach allows us to suggest alternative and safer configurations, support administrators in the study of what-if scenarios, and scale the analysis to large scale deployments. We present an initial validation and illustrate the approach with three real vulnerabilities from known sources.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"9 1","pages":"191-196"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82214687","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 : 2022-05-21DOI: 10.1109/CLOUD55607.2022.00056
Shreshth Tuli, G. Casale, N. Jennings
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep neural networks (DNNs) based schedulers have been proposed in recent years to allow scalable and efficient resource management in dynamic workload settings. However, optimal scheduling frequently relies on sophisticated DNNs with high computational needs implying higher execution costs. Further, even in non-stationary environments, sophisticated schedulers might not always be required and we could briefly rely on low-cost schedulers in the interest of cost-efficiency. Therefore, this work aims to solve the non-trivial meta problem of online dynamic selection of a scheduling policy using a surrogate model called MetaNet. Unlike traditional solutions with a fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large set of DNN based methods to optimize task scheduling and execution costs in tandem. Compared to state-of-the-art DNN schedulers, this allows for improvement in execution costs, energy consumption, response time and service level agreement violations by up to 11, 43, 8 and 13 percent, respectively.
{"title":"MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments","authors":"Shreshth Tuli, G. Casale, N. Jennings","doi":"10.1109/CLOUD55607.2022.00056","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00056","url":null,"abstract":"Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep neural networks (DNNs) based schedulers have been proposed in recent years to allow scalable and efficient resource management in dynamic workload settings. However, optimal scheduling frequently relies on sophisticated DNNs with high computational needs implying higher execution costs. Further, even in non-stationary environments, sophisticated schedulers might not always be required and we could briefly rely on low-cost schedulers in the interest of cost-efficiency. Therefore, this work aims to solve the non-trivial meta problem of online dynamic selection of a scheduling policy using a surrogate model called MetaNet. Unlike traditional solutions with a fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large set of DNN based methods to optimize task scheduling and execution costs in tandem. Compared to state-of-the-art DNN schedulers, this allows for improvement in execution costs, energy consumption, response time and service level agreement violations by up to 11, 43, 8 and 13 percent, respectively.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"1 1","pages":"331-341"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89139802","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 : 2022-04-20DOI: 10.1109/CLOUD55607.2022.00067
M. Lazuka, Thomas P. Parnell, Andreea Anghel, Haralambos Pozidis
Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. While most cloud providers offer similar functionality, they may differ significantly in terms of performance and/or cost. A customer looking to benefit from such differences will naturally want to solve the multi-cloud configuration problem: given a workload, which cloud provider should be chosen and how should its nodes be configured in order to minimize runtime or cost? In this work, we consider possible solutions to this multi-cloud optimization problem. We develop and evaluate possible adaptations of state-of-the-art cloud configuration solutions to the multi-cloud domain. Furthermore, we identify an analogy between multi-cloud configuration and the selection-configuration problems that are commonly studied in the automated machine learning (AutoML) field. Inspired by this connection, we utilize popular optimizers from AutoML to solve multi-cloud configuration. Finally, we propose a new algorithm for solving multi-cloud configuration, CloudBandit. It treats the outer problem of cloud provider selection as a best-arm identification problem, in which each arm pull corresponds to running an arbitrary black-box optimizer on the inner problem of node configuration. Our extensive experiments indicate that (a) many state-of-the-art cloud configuration solutions can be adapted to multi-cloud, with best results obtained for adaptations which utilize the hierarchical structure of the multi-cloud configuration domain, (b) hierarchical methods from AutoML can be used for the multi-cloud configuration task and can outperform state-of-the-art cloud configuration solutions and (c) CloudBandit achieves competitive or lower regret relative to other tested algorithms, whilst also identifying configurations that have 65% lower median cost and 20% lower median runtime in production, compared to choosing a random provider and configuration.
{"title":"Search-based Methods for Multi-Cloud Configuration","authors":"M. Lazuka, Thomas P. Parnell, Andreea Anghel, Haralambos Pozidis","doi":"10.1109/CLOUD55607.2022.00067","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00067","url":null,"abstract":"Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. While most cloud providers offer similar functionality, they may differ significantly in terms of performance and/or cost. A customer looking to benefit from such differences will naturally want to solve the multi-cloud configuration problem: given a workload, which cloud provider should be chosen and how should its nodes be configured in order to minimize runtime or cost? In this work, we consider possible solutions to this multi-cloud optimization problem. We develop and evaluate possible adaptations of state-of-the-art cloud configuration solutions to the multi-cloud domain. Furthermore, we identify an analogy between multi-cloud configuration and the selection-configuration problems that are commonly studied in the automated machine learning (AutoML) field. Inspired by this connection, we utilize popular optimizers from AutoML to solve multi-cloud configuration. Finally, we propose a new algorithm for solving multi-cloud configuration, CloudBandit. It treats the outer problem of cloud provider selection as a best-arm identification problem, in which each arm pull corresponds to running an arbitrary black-box optimizer on the inner problem of node configuration. Our extensive experiments indicate that (a) many state-of-the-art cloud configuration solutions can be adapted to multi-cloud, with best results obtained for adaptations which utilize the hierarchical structure of the multi-cloud configuration domain, (b) hierarchical methods from AutoML can be used for the multi-cloud configuration task and can outperform state-of-the-art cloud configuration solutions and (c) CloudBandit achieves competitive or lower regret relative to other tested algorithms, whilst also identifying configurations that have 65% lower median cost and 20% lower median runtime in production, compared to choosing a random provider and configuration.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"29 1","pages":"438-448"},"PeriodicalIF":0.0,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81520982","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 : 2022-03-19DOI: 10.1109/CLOUD55607.2022.00071
Parwat Singh Anjana, Adithya Rajesh Chandrassery, Sathya Peri
Replicated tree data structures are extensively used in collaborative applications and distributed file systems, where clients often perform move operations. Local move operations at different replicas may be safe. However, remote move operations may not be safe. When clients perform arbitrary move operations concurrently on different replicas, it could result in various bugs, making this operation challenging to implement. Previous work has revealed bugs such as data duplication and cycling in replicated trees. In this paper, we present an efficient algorithm to perform move operations on the distributed replicated tree while ensuring eventual consistency. The proposed technique is primarily concerned with resolving conflicts efficiently, requires no interaction between replicas, and works well with network partitions. We use the last write win semantics for conflict resolution based on globally unique timestamps of operations. The proposed solution requires only one compensation operation to avoid cycles being formed when move operations are applied. The proposed approach achieves an effective speedup of 14.6× to 68.19× over the state-of-the-art approach in a geo-replicated setting.
{"title":"An Efficient Approach to Move Elements in a Distributed Geo-Replicated Tree","authors":"Parwat Singh Anjana, Adithya Rajesh Chandrassery, Sathya Peri","doi":"10.1109/CLOUD55607.2022.00071","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00071","url":null,"abstract":"Replicated tree data structures are extensively used in collaborative applications and distributed file systems, where clients often perform move operations. Local move operations at different replicas may be safe. However, remote move operations may not be safe. When clients perform arbitrary move operations concurrently on different replicas, it could result in various bugs, making this operation challenging to implement. Previous work has revealed bugs such as data duplication and cycling in replicated trees. In this paper, we present an efficient algorithm to perform move operations on the distributed replicated tree while ensuring eventual consistency. The proposed technique is primarily concerned with resolving conflicts efficiently, requires no interaction between replicas, and works well with network partitions. We use the last write win semantics for conflict resolution based on globally unique timestamps of operations. The proposed solution requires only one compensation operation to avoid cycles being formed when move operations are applied. The proposed approach achieves an effective speedup of 14.6× to 68.19× over the state-of-the-art approach in a geo-replicated setting.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"55 1","pages":"479-488"},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75335080","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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