Software: Practice and Experience最新文献

{"title":"SCARS: Suturing wounds due to conflicts between non-functional requirements in autonomous and robotic systems","authors":"Mandira Roy, Raunak Bag, Novarun Deb, Agostino Cortesi, Rituparna Chaki, Nabendu Chaki","doi":"10.1002/spe.3297","DOIUrl":"https://doi.org/10.1002/spe.3297","url":null,"abstract":"In autonomous and robotic systems, the functional requirements (FRs) and non-functional requirements (NFRs) are gathered from multiple stakeholders. The different stakeholder requirements are associated with different components of the robotic system and with the contexts in which the system may operate. This aggregation of requirements from different sources (multiple stakeholders) often results in inconsistent or conflicting sets of requirements. Conflicts among NFRs for robotic systems heavily depend on features of actual execution contexts. It is essential to analyze the inconsistencies and conflicts among the requirements in the early planning phase to design the robotic systems in a systematic manner. In this work, we design and experimentally evaluate a framework, called SCARS, providing: (a) a domain-specific language extending the ROS2 Domain Specific Language (DSL) concepts by considering the different environmental contexts in which the system has to operate, (b) support to analyze their impact on NFRs, and (c) the computation of the optimal degree of NFR satisfaction that can be achieved within different system configurations. The effectiveness of SCARS has been validated on the iRobot<math altimg=\"urn:x-wiley:spe:media:spe3297:spe3297-math-0001\" display=\"inline\" location=\"graphic/spe3297-math-0001.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<msup>\u0000<mrow></mrow>\u0000<mrow>\u0000<mi>®</mi>\u0000</mrow>\u0000</msup>\u0000</mrow>\u0000$$ {}^{circledR } $$</annotation>\u0000</semantics></math> Create<math altimg=\"urn:x-wiley:spe:media:spe3297:spe3297-math-0002\" display=\"inline\" location=\"graphic/spe3297-math-0002.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<msup>\u0000<mrow></mrow>\u0000<mrow>\u0000<mi>®</mi>\u0000</mrow>\u0000</msup>\u0000</mrow>\u0000$$ {}^{circledR } $$</annotation>\u0000</semantics></math>3 robot using Gazebo simulation.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138688452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An elastic framework construction method based on task migration in edge computing","authors":"Yonglin Pu, Ziyang Li, Jiong Yu, Liang Lu, Binglei Guo","doi":"10.1002/spe.3302","DOIUrl":"https://doi.org/10.1002/spe.3302","url":null,"abstract":"Edge computing (EC) serves as an effective technology, empowering end-users to attain high bandwidth and low latency by offloading tasks with high computational demands from mobile devices to edge servers. However, a major challenge arises when the processing load fluctuates continuously, leading to a performance bottleneck due to the inability to rescale edge node (EN) resources. To address this problem, the approach of task migration is introduced, and EN load prediction model, the resource constrained model, optimal communication overhead model, optimal task migration model, and energy consumption model are built to form a theoretical foundation from which to propose a task migration based resilient framework construction method in EC. With the aid of the domino effect and the combined effect of task migration, a dynamic node-growing algorithm (DNGA) and a dynamic node-shrinking algorithm (DNSA), both based on the task migration strategy, are proposed. Specifically, the DNGA smoothly expands the EN scale when the processing load increases, while the DNSA shrinks the EN scale when the processing load decreases. The experimental results show that for standard benchmarks deployed on an elastic framework, the proposed method realizes a smooth scaling mechanism in the EC, which reduces the latency and improves the reliability of data processing.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138631528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FLight: A lightweight federated learning framework in edge and fog computing","authors":"Wuji Zhu, Mohammad Goudarzi, Rajkumar Buyya","doi":"10.1002/spe.3300","DOIUrl":"https://doi.org/10.1002/spe.3300","url":null,"abstract":"The number of Internet of Things (IoT) applications, especially latency-sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The edge/fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo-distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well-suited to edge/fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on edge/fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this article, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource-limited edge/fog devices to powerful cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic-based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight. The worker selection technique reduces the training time of reaching 80% accuracy by 34% compared to sequential training, while asynchronous one helps to improve synchronous FL training time by 64%.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138576699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parsing millions of URLs per second","authors":"Yagiz Nizipli, Daniel Lemire","doi":"10.1002/spe.3296","DOIUrl":"https://doi.org/10.1002/spe.3296","url":null,"abstract":"URLs are fundamental elements of web applications. By applying vector algorithms, we built a fast standard-compliant C++ implementation. Our parser uses three times fewer instructions than competing parsers following the WHATWG standard (e.g., Servo's rust-url) and up to eight times fewer instructions than the popular curl parser. The Node.js environment adopted our C++ library. In our tests on realistic data, a recent Node.js version (20.0) with our parser is four to five times faster than the last version with the legacy URL parser.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cloud-edge service offloading method for the metaverse in smart manufacturing","authors":"Haolong Xiang, Xuyun Zhang, Muhammad Bilal","doi":"10.1002/spe.3301","DOIUrl":"https://doi.org/10.1002/spe.3301","url":null,"abstract":"With the development of artificial intelligence, cloud-edge computing and virtual reality, the industrial design that originally depends on human imagination and computing power can be transitioned to metaverse applications in smart manufacturing, which offloads the services of metaverse to cloud and edge platforms for enhancing quality of service (QoS), considering inadequate computing power of terminal devices like industrial sensors and access points (APs). However, large overhead and privacy exposure occur during data transmission to cloud, while edge computing devices (ECDs) are at risk of overloading with redundant service requests and difficult central control. To address these challenges, this paper proposes a minority game (MG) based cloud-edge service offloading method named COM for metaverse manufacturing. Technically, MG possesses a distribution mechanism that can minimize reliance on centralized control, and gains its effectiveness in resource allocation. Besides, a dynamic control of cut-off value is supplemented on the basis of MG for better adaptability to network variations. Then, agents in COM (i.e., APs) leverage reinforcement learning (RL) to work on MG history, offloading decision, QoS mapping to state, action and reward, for further optimizing distributed offloading decision-making. Finally, COM is evaluated using a variety of real-world datasets of manufacturing. The results indicate that COM has 5.38% higher QoS and 8.58% higher privacy level comparing to benchmark method.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyScribe–Learning to describe python code","authors":"Juncai Guo, Jin Liu, Xiao Liu, Yao Wan, Yanjie Zhao, Li Li, Kui Liu, Jacques Klein, Tegawendé F. Bissyandé","doi":"10.1002/spe.3291","DOIUrl":"https://doi.org/10.1002/spe.3291","url":null,"abstract":"Code comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a prototype tool titled <span>PyScribe</span>, which extends the Transformer model to a new encoder-decoder-based framework. Particularly, the triplet position is designed and integrated into the node-level and edge-level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two-stage decoding process to yield higher qualified comments. To evaluate the effectiveness of <span>PyScribe</span>, we resort to a large dataset of code-comment pairs by mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that <span>PyScribe</span> is indeed effective, outperforming the state-ofthe-art by achieving an average BLEU score (i.e., av-BLEU) of <math altimg=\"urn:x-wiley:spe:media:spe3291:spe3291-math-0001\" display=\"inline\" location=\"graphic/spe3291-math-0001.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mo form=\"prefix\">≈</mo>\u0000</mrow>\u0000$$ approx $$</annotation>\u0000</semantics></math>0.28.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing power-efficient SRAM cells with SGFinFETs using LECTOR technique","authors":"Sivaiah Sankranti, S. Roji Marjorie","doi":"10.1002/spe.3293","DOIUrl":"https://doi.org/10.1002/spe.3293","url":null,"abstract":"Static random-access memory (SRAM) plays a vital component of digital systems. The main issue of SRAM cells is power leakage, which results in an increase in chip area. Therefore this manuscript proposes a shorted-gate fin-type field-effect transistor based SRAM cell utilizing leakage control transistor technique (SGFinFETs-SRAM-LECTOR) for decreasing the leakage power delay by improving the static noise margins (SNMs) together with power delay product (PDP). Here, the SGFinFETs-SRAM-LECTOR is primarily applied to stacking enhancement for lessening the leakage power dissipation (LPD). Two more transistors are used in LECTOR for reducing the leakage current with delay, which is based on transistor stacking. LECTOR employs two more transistors that are connected in series between pull-up and pull-down networks that means additional SG FinFETs PMOS transistor insertions amongst the pull-up network and output terminal, additional SG FinFETs NMOS transistor insertions amidst the pull down network and output terminal. These additional transistors can decrease the leakage current. The simulation of the proposed approach is implemented in HSPICE simulation tool. Some metrics are computed to validate the efficacy of the proposed approach. Finally, the proposed technique reaches 11.31%, 51.47%, 45.46% less read delay, 44.44%, 26.33%, 33.45% less write delay, 36.12%, 45.28%, 26.45% less read power, 34.5%, 33.56%, 22.41% less write power, 37.4%, 15.3%, 26.54% high read SNM, 33.67%, 35.8%,12.09% high write SNM when analyzed to the existing models.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on energy-efficient workflow scheduling algorithms in cloud computing","authors":"Prateek Verma, Ashish Kumar Maurya, Rama Shankar Yadav","doi":"10.1002/spe.3292","DOIUrl":"https://doi.org/10.1002/spe.3292","url":null,"abstract":"The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever-increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state-of-the-art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy-efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real-world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An edge-assisted federated contrastive learning method with local intrinsic dimensionality in noisy label environment","authors":"Siyuan Wu, Guoming Zhang, Fei Dai, Bowen Liu, Wanchun Dou","doi":"10.1002/spe.3295","DOIUrl":"https://doi.org/10.1002/spe.3295","url":null,"abstract":"The advent of federated learning (FL) has presented a viable solution for distributed training in edge environment, while simultaneously ensuring the preservation of privacy. In real-world scenarios, edge devices may be subject to label noise caused by environmental differences, automated weakly supervised annotation, malicious tampering, or even human error. However, the potential of the noisy samples have not been fully leveraged by prior studies on FL aimed at addressing label noise. Rather, they have primarily focused on conventional filtering or correction techniques to alleviate the impact of noisy labels. To tackle this challenge, a method, named <b>DETECTION</b>, is proposed in this article. It aims at effectively detecting noisy clients and mitigating the adverse impact of label noise while preserving data privacy. Specially, a confidence scoring mechanism based on local intrinsic dimensionality (LID) is investigated for distinguishing noisy clients from clean clients. Then, a loss function based on prototype contrastive learning is designed to optimize the local model. To address the varying levels of noise across clients, a LID weighted aggregation strategy (LA) is introduced. Experimental results on three datasets demonstrate the effectiveness of DETECTION in addressing the issue of label noise in FL while maintaining data privacy.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"34 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue on efficient management of microservice-based systems and applications","authors":"Minxian Xu, Schahram Dustdar, Massimo Villari, Rajkumar Buyya","doi":"10.1002/spe.3298","DOIUrl":"https://doi.org/10.1002/spe.3298","url":null,"abstract":"<p>The advent of microservice architecture marks a transition from conventional monolithic applications to a landscape of loosely linked, lightweight, and autonomous microservice components. The primary objective is to ensure strong environmental uniformity, portability across various operating systems, and robust resource isolation. Leading cloud service providers such as Amazon, Microsoft, Google, and Alibaba have widely embraced microservices within their infrastructures. This adoption is geared toward automating application management and optimizing system performance. Consequently, addressing the automation of tasks like deployment, maintenance, auto-scaling, and networking of microservices becomes pivotal. This underscores the importance of efficient management of systems and applications built on microservices as a critical research challenge.</p>\u0000<p>Efficient management methods must not only ensure the quality of service (QoS) across multiple microservices units (containers) but also provide greater control over individual components. However, the dynamic and varied nature of microservice applications and environments significantly amplifies the complexity of these management approaches. Each microservice unit can be deployed and operated independently, catering to distinct functionalities and business objectives. Furthermore, microservices can interact and combine through lightweight communication techniques to form a complete application. The expanding scale of microservice-based systems and their intricate interdependencies pose challenges in terms of load distribution and resource management at the infrastructure level. Furthermore, as cloud workloads surge in resource demands, bandwidth consumption, and QoS requirements, the traditional cloud computing environment extends to fog and edge infrastructures that are in close proximity to end users. As a result, current microservice management approaches need further enhancement to address the mounting resource diversity, application distribution, workload profiles, security prerequisites, and scalability demands across hybrid cloud infrastructures.</p>\u0000<p>Keeping this in mind, this special issue addressed some of the aspects related to efficient management of microservice-based systems and applications with the focus on various challenges faced, and promising solutions to address such challenges by using software engineering, machine learning and deep learning techniques. We have received 21 submissions in this issue, and we accepted six high-quality submissions for publication after a rigorous review process with at least three reviewers for each paper. The authors are from diverse countries, including the USA, China, UK, Germany, India, Brazil, etc. Each of the accepted papers is summarized as follows.</p>\u0000<p>In the first article, Batista et al.<span>¹</span> presented two strategies for handling asynchronous workloads associated with tax integration in a multi-tenant micros","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":"34 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513630","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}