An energy consumption model and analysis tool for Cloud computing environments
- Authors: Chen, Feifei , Schneider, Jean-Guy , Yang, Yun , Grundy, John , He, Qiang
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 First International Workshop on Green and Sustainable Software (GREENS) : Part of the 34th International Conference on Software Engineering (ICSE) p. 45-50
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- Description: Cloud computing delivers computing as a utility to users worldwide. A consequence of this model is that cloud data centres have high deployment and operational costs, as well as significant carbon footprints for the environment. We need to develop Green Cloud Computing (GCC) solutions that reduce these deployment and operational costs and thus save energy and reduce adverse environmental impacts. In order to achieve this objective, a thorough understanding of the energy consumption patterns in complex Cloud environments is needed. We present a new energy consumption model and associated analysis tool for Cloud computing environments. We measure energy consumption in Cloud environments based on different runtime tasks. Empirical analysis of the correlation of energy consumption and Cloud data and computational tasks, as well as system performance, will be investigated based on our energy consumption model and analysis tool. Our research results can be integrated into Cloud systems to monitor energy consumption and support static or dynamic system-level optimisation.
Automated analysis of performance and energy consumption for cloud applications
- Authors: Chen, Feifei , Grundy, John , Schneider, Jean-Guy , Yang, Yun , He, Qiang
- Date: 2014
- Type: Text , Conference paper
- Relation: Proceedings of the 5th ACM/SPEC international conference on Performance engineering p. 39-50
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- Description: In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
Automating Performance and Energy Consumption Analysis for Cloud Applications
- Authors: Chen, Feifei , Grundy, John , Schneider, Jean-Guy , Yang, Yun , He, Qiang
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE World Congress on Services
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- Description: In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.
Discovering regularities from traditional chinese medicine prescriptions via bipartite embedding model
- Authors: Ruan, Chunyang , Ma, Jiangang , Wang, Ye , Zhang, Yanchun , Yang, Yun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: International Joint Conferences on Artificial Intelligence (IJCAI-49); Macao, China; 10th-16th August 2019; published in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) p. 3346-3352
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- Description: Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.
Experimental analysis of task-based energy consumption in cloud computing systems
- Authors: Chen, Feifei , Grundy, John , Yang, Yun , Schneider, Jean-Guy , He, Qiang
- Date: 2013
- Type: Text , Conference paper
- Relation: 4th ACM/SPEC International Conference on Performance Engineering p. 295-306
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- Description: Cloud computing delivers IT solutions as a utility to users. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A common objective of cloud providers is to develop resource provisioning and management solutions that minimise energy consumption while guaranteeing Service Level Agreements (SLAs). In order to achieve this objective, a thorough understanding of energy consumption patterns in complex cloud systems is imperative. We have developed an energy consumption model for cloud computing systems. To operationalise this model, we have conducted extensive experiments to profile the energy consumption in cloud computing systems based on three types of tasks: computation-intensive, data-intensive and communication-intensive tasks. We collected fine-grained energy consumption and performance data with varying system configurations and workloads. Our experimental results show the correlation coefficients of energy consumption, system configuration and workload, as well as system performance in cloud systems. These results can be used for designing energy consumption monitors, and static or dynamic system-level energy consumption optimisation strategies for green cloud computing systems.
Keyword search for building service-based systems
- Authors: He, Qiang , Zhou, Rui , Zhang, Xuyun , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Grundy, John , Yang, Yun
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Transactions on Software Engineering Vol. 43, no. 7 (2017), p. 658-674
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- Description: With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3. © 1976-2012 IEEE.
Localising runtime Anomalies in Service-Oriented Systems
- Authors: He, Qiang , Xie, Xiaoyuan , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Jin, Hai , Yang, Yun
- Date: 2016
- Type: Text , Conference proceedings
- Relation: IEEE Transactions on Services Computing ( Volume: 10, Issue: 1, Jan.-Feb. 1 2017 ) Vol. 10, p. 94-106
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- Description: In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.
Personalized quality centric service recommendation
- Authors: Zhang, Yiwen , Ai, Xiaofei , He, Qiang , Zhang, Xuyun , Dou, Wanchun , Chen, Feifei , Chen, Liang , Yang, Yun
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 15th International Conference on Service-Oriented Computing, ICSOC 2017; Malaga, Spain; 13th-16th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10601 LNCS, p. 528-544
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- Description: The broad application of service-oriented architecture (SOA) has fueled the rapid growth of web and cloud services and service-based systems (SBSs). Tremendous web and cloud services have been deployed all over the world. Finding the right services becomes difficult and critical. Thus, service recommendation has become of paramount research and practical importance. Existing web service recommendation approaches employ utility functions or skyline techniques. However, those approaches have not addressed a critical and fundamental problem: how to recommend services according to a system engineer’s quality constraints, e.g., response time, failure rate, etc. To address this issue, we first propose two basic personalized quality centric approaches for service recommendation, which employ the k-nearest neighbours and the dynamic skyline techniques respectively. To overcome the respective limitations of the two basic approaches, we propose two hybrid approaches, namely KNN-DSL and DSL-KNN. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches. © Springer International Publishing AG 2017.
- Description: The broad application of service-oriented architecture (SOA) has fueled the rapid growth of web and cloud services and service-based systems (SBSs). Tremendous web and cloud services have been deployed all over the world. Finding the right services becomes difficult and critical. Thus, service recommendation has become of paramount research and practical importance. Existing web service recommendation approaches employ utility functions or skyline techniques. However, those approaches have not addressed a critical and fundamental problem: how to recommend services according to a system engineer’s quality constraints, e.g., response time, failure rate, etc. To address this issue, we first propose two basic personalized quality centric approaches for service recommendation, which employ the k-nearest neighbors and the dynamic skyline techniques respectively. To overcome the respective limitations of the two basic approaches, we propose two hybrid approaches, namely KNN-DSL and DSL-KNN. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches. © Springer International Publishing AG 2017.
QoS-aware service selection for customisable multi-tenant service-based systems : Maturity and approaches
- Authors: He, Qiang , Han, Jun , Chen, Feifei , Wang, Yanchun , Vasa, Rajesh , Yang, Yun , Jin, Hai
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD) p. 237-244
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- Description: Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.
Sign language digits and alphabets recognition by capsule networks
- Authors: Xiao, Hongwang , Yang, Yun , Yu, Ke , Tian, Jiao , Cai, Xinyi , Muhammad, Usman , Chen, Jinjun
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Ambient Intelligence and Humanized Computing Vol. 13, no. 4 (2022), p. 2131-2141
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- Description: There exist communication barriers between the deaf people and the listeners. Sign language translation is a reasonable and effective way to break these barriers. Recognition of sign language symbols is an essential part of sign language translation. Sign language digits of (0–9) and alphabetic letters of (A–Z) are elementary but important symbols of sign languages of different countries or regions. Capsule networks (CapsNet) are promising alternative to convolutional neural networks (CNN), which take into account of the spatial relationships and orientations of the features of an entity. For sign language digits and alphabets recognition tasks, the proposed SLR-CapsNet architecture achieves a start-of-the-art test accuracy of 99.52% with 100*100 RGB input size and 99.94% with 32*32 RGB input size on Sign Language Digits Dataset and 99.60% with 28*28 Gray-scale input on Sign Language MNIST Dataset. The experimental results also prove that CapsNet has higher generalization and expressiveness capacity on unseen data than CNN dose. Another important finding in our work is that SLR-CapsNet is robust to routing iterations, i.e., its performance will not be affected by various routing iterations. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
Spectrum-Based Runtime Anomaly Localisation in Service-Based Systems
- Authors: He, Qiang , Xie, Xiaoyuan , Chen, Feifei , Vasa, Rajesh , Yang, Yun , Jin, Hai
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE International Conference on Services Computing (SCC)
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- Description: Runtime anomalies occurring to service-based systems (SBSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly is impractical due to excessive resource consumption. Inspecting all component services upon anomalies is time-consuming and thus also impractical. In this work, we propose a novel approach that employs spectrum-based fault localisation techniques to locate runtime anomalies in SBSs. Large-scale experiments are conducted and experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach.
StressCloud : A tool for analysing performance and energy consumption of cloud applications
- Authors: Chen, Feifei , Grundy, John , Schneider, Jean-Guy , Yang, Yun , He, Qiang
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE)
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- Description: Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption.